From a352c021eca1d39c3fc128a6ee65c0527f5c6f3c Mon Sep 17 00:00:00 2001 From: blepping Date: Thu, 8 Feb 2024 02:24:23 -0700 Subject: [PATCH 001/378] Allow custom samplers to request discard penultimate sigma --- comfy/samplers.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/comfy/samplers.py b/comfy/samplers.py index f4c3e268..5dd72f3f 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -652,6 +652,7 @@ def sampler_object(name): class KSampler: SCHEDULERS = SCHEDULER_NAMES SAMPLERS = SAMPLER_NAMES + DISCARD_PENULTIMATE_SIGMA_SAMPLERS = set(('dpm_2', 'dpm_2_ancestral', 'uni_pc', 'uni_pc_bh2')) def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}): self.model = model @@ -670,7 +671,7 @@ class KSampler: sigmas = None discard_penultimate_sigma = False - if self.sampler in ['dpm_2', 'dpm_2_ancestral', 'uni_pc', 'uni_pc_bh2']: + if self.sampler in self.DISCARD_PENULTIMATE_SIGMA_SAMPLERS: steps += 1 discard_penultimate_sigma = True From 2ccc0be28f64474191b46aa9dd5bb27070a9c1df Mon Sep 17 00:00:00 2001 From: Imran Azeez Date: Thu, 8 Feb 2024 22:01:56 +1000 Subject: [PATCH 002/378] Add batch number to filename with %batch_num% Allow configurable addition of batch number to output file name. --- nodes.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/nodes.py b/nodes.py index fe38be9d..0cce5f51 100644 --- a/nodes.py +++ b/nodes.py @@ -1434,7 +1434,7 @@ class SaveImage: filename_prefix += self.prefix_append full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) results = list() - for image in images: + for (batch_number, image) in enumerate(images): i = 255. * image.cpu().numpy() img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) metadata = None @@ -1446,7 +1446,8 @@ class SaveImage: for x in extra_pnginfo: metadata.add_text(x, json.dumps(extra_pnginfo[x])) - file = f"{filename}_{counter:05}_.png" + filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) + file = f"{filename_with_batch_num}_{counter:05}_.png" img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level) results.append({ "filename": file, From f44225fd5f433daf78484b9c21b9b777bea04220 Mon Sep 17 00:00:00 2001 From: Jedrzej Kosinski Date: Fri, 9 Feb 2024 17:11:34 -0600 Subject: [PATCH 003/378] Fix infinite while loop being possible in ddim_scheduler --- comfy/samplers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/samplers.py b/comfy/samplers.py index f4c3e268..f2ac3c5d 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -295,7 +295,7 @@ def simple_scheduler(model, steps): def ddim_scheduler(model, steps): s = model.model_sampling sigs = [] - ss = len(s.sigmas) // steps + ss = max(len(s.sigmas) // steps, 1) x = 1 while x < len(s.sigmas): sigs += [float(s.sigmas[x])] From 20e3da6b313feaac07c34a4cc746e5da931f7c76 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 10 Feb 2024 08:27:05 -0500 Subject: [PATCH 004/378] Add a node to give the controlnet a prompt different from the unet. --- comfy_extras/nodes_cond.py | 25 +++++++++++++++++++++++++ nodes.py | 1 + 2 files changed, 26 insertions(+) create mode 100644 comfy_extras/nodes_cond.py diff --git a/comfy_extras/nodes_cond.py b/comfy_extras/nodes_cond.py new file mode 100644 index 00000000..646fefa1 --- /dev/null +++ b/comfy_extras/nodes_cond.py @@ -0,0 +1,25 @@ + + +class CLIPTextEncodeControlnet: + @classmethod + def INPUT_TYPES(s): + return {"required": {"clip": ("CLIP", ), "conditioning": ("CONDITIONING", ), "text": ("STRING", {"multiline": True})}} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "_for_testing/conditioning" + + def encode(self, clip, conditioning, text): + tokens = clip.tokenize(text) + cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) + c = [] + for t in conditioning: + n = [t[0], t[1].copy()] + n[1]['cross_attn_controlnet'] = cond + n[1]['pooled_output_controlnet'] = pooled + c.append(n) + return (c, ) + +NODE_CLASS_MAPPINGS = { + "CLIPTextEncodeControlnet": CLIPTextEncodeControlnet +} diff --git a/nodes.py b/nodes.py index fe38be9d..d9bc4884 100644 --- a/nodes.py +++ b/nodes.py @@ -1965,6 +1965,7 @@ def init_custom_nodes(): "nodes_stable3d.py", "nodes_sdupscale.py", "nodes_photomaker.py", + "nodes_cond.py", ] for node_file in extras_files: From 02409c30d9ea5314e5103d03f7c9933fa1012659 Mon Sep 17 00:00:00 2001 From: Steven Lu Date: Mon, 12 Feb 2024 01:44:53 +0700 Subject: [PATCH 005/378] Safari: Draws certain elements on CPU. In case of search popup, can cause 10 seconds+ main thread lock due to painting. (#2763) * lets toggle this setting first. * also makes it easier for debug. I'll be honest this is generally preferred behavior as well for me but I ain't no power user shrug. * attempting trick to put the work for filter: brightness on GPU as a first attempt before falling back to not using filter for large lists! * revert litegraph.core.js changes from branch * oops --- web/style.css | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/web/style.css b/web/style.css index 863840b2..cf7a8b9e 100644 --- a/web/style.css +++ b/web/style.css @@ -197,6 +197,7 @@ button.comfy-close-menu-btn { .comfy-modal button:hover, .comfy-menu-actions button:hover { filter: brightness(1.2); + will-change: transform; cursor: pointer; } @@ -462,11 +463,13 @@ dialog::backdrop { z-index: 9999 !important; background-color: var(--comfy-menu-bg) !important; filter: brightness(95%); + will-change: transform; } .litegraph.litecontextmenu .litemenu-entry:hover:not(.disabled):not(.separator) { background-color: var(--comfy-menu-bg) !important; filter: brightness(155%); + will-change: transform; color: var(--input-text); } @@ -527,12 +530,14 @@ dialog::backdrop { color: var(--input-text); background-color: var(--comfy-input-bg); filter: brightness(80%); + will-change: transform; padding-left: 0.2em; } .litegraph.lite-search-item.generic_type { color: var(--input-text); filter: brightness(50%); + will-change: transform; } @media only screen and (max-width: 450px) { @@ -551,4 +556,4 @@ dialog::backdrop { text-align: center; border-top: none; } -} \ No newline at end of file +} From cf4910a3a451ad9e2e5261749a5a44acdcf7bbec Mon Sep 17 00:00:00 2001 From: chrisgoringe Date: Mon, 12 Feb 2024 08:59:25 +1100 Subject: [PATCH 006/378] Prevent hideWidget being called twice for same widget Fix for #2766 --- web/extensions/core/widgetInputs.js | 1 + 1 file changed, 1 insertion(+) diff --git a/web/extensions/core/widgetInputs.js b/web/extensions/core/widgetInputs.js index b12ad968..23f51d81 100644 --- a/web/extensions/core/widgetInputs.js +++ b/web/extensions/core/widgetInputs.js @@ -22,6 +22,7 @@ function isConvertableWidget(widget, config) { } function hideWidget(node, widget, suffix = "") { + if (widget.type?.startsWith(CONVERTED_TYPE)) return; widget.origType = widget.type; widget.origComputeSize = widget.computeSize; widget.origSerializeValue = widget.serializeValue; From 0c9bc19768683c9e2772bd75e7bf823f976ccfba Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 12 Feb 2024 12:46:15 -0500 Subject: [PATCH 007/378] Add ImageFromBatch. --- comfy_extras/nodes_images.py | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/comfy_extras/nodes_images.py b/comfy_extras/nodes_images.py index aa80f526..8f638bf8 100644 --- a/comfy_extras/nodes_images.py +++ b/comfy_extras/nodes_images.py @@ -48,6 +48,25 @@ class RepeatImageBatch: s = image.repeat((amount, 1,1,1)) return (s,) +class ImageFromBatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), + "batch_index": ("INT", {"default": 0, "min": 0, "max": 63}), + "length": ("INT", {"default": 1, "min": 1, "max": 64}), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "frombatch" + + CATEGORY = "image/batch" + + def frombatch(self, image, batch_index, length): + s_in = image + batch_index = min(s_in.shape[0] - 1, batch_index) + length = min(s_in.shape[0] - batch_index, length) + s = s_in[batch_index:batch_index + length].clone() + return (s,) + class SaveAnimatedWEBP: def __init__(self): self.output_dir = folder_paths.get_output_directory() @@ -170,6 +189,7 @@ class SaveAnimatedPNG: NODE_CLASS_MAPPINGS = { "ImageCrop": ImageCrop, "RepeatImageBatch": RepeatImageBatch, + "ImageFromBatch": ImageFromBatch, "SaveAnimatedWEBP": SaveAnimatedWEBP, "SaveAnimatedPNG": SaveAnimatedPNG, } From 38b7ac6e269e6ecc5bdd6fefdfb2fb1185b09c9d Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 13 Feb 2024 00:01:08 -0500 Subject: [PATCH 008/378] Don't init the CLIP model when the checkpoint has no CLIP weights. --- comfy/sd.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/comfy/sd.py b/comfy/sd.py index c15d73fe..5b22d117 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -470,10 +470,13 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o w = WeightsLoader() clip_target = model_config.clip_target() if clip_target is not None: - clip = CLIP(clip_target, embedding_directory=embedding_directory) - w.cond_stage_model = clip.cond_stage_model sd = model_config.process_clip_state_dict(sd) - load_model_weights(w, sd) + if any(k.startswith('cond_stage_model.') for k in sd): + clip = CLIP(clip_target, embedding_directory=embedding_directory) + w.cond_stage_model = clip.cond_stage_model + load_model_weights(w, sd) + else: + print("no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.") left_over = sd.keys() if len(left_over) > 0: From 7f89cb48bf8200254cde1306ba60d10ca019264d Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 14 Feb 2024 02:59:53 -0500 Subject: [PATCH 009/378] Add a disabled SaveImageWebsocket custom node. This node can be used to efficiently get images without saving them to disk when using ComfyUI as a backend. --- custom_nodes/websocket_image_save.py.disabled | 49 +++++++++++++++++++ 1 file changed, 49 insertions(+) create mode 100644 custom_nodes/websocket_image_save.py.disabled diff --git a/custom_nodes/websocket_image_save.py.disabled b/custom_nodes/websocket_image_save.py.disabled new file mode 100644 index 00000000..b85a5de8 --- /dev/null +++ b/custom_nodes/websocket_image_save.py.disabled @@ -0,0 +1,49 @@ +from PIL import Image, ImageOps +from io import BytesIO +import numpy as np +import struct +import comfy.utils +import time + +#You can use this node to save full size images through the websocket, the +#images will be sent in exactly the same format as the image previews: as +#binary images on the websocket with a 8 byte header indicating the type +#of binary message (first 4 bytes) and the image format (next 4 bytes). + +#The reason this node is disabled by default is because there is a small +#issue when using it with the default ComfyUI web interface: When generating +#batches only the last image will be shown in the UI. + +#Note that no metadata will be put in the images saved with this node. + +class SaveImageWebsocket: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"images": ("IMAGE", ),} + } + + RETURN_TYPES = () + FUNCTION = "save_images" + + OUTPUT_NODE = True + + CATEGORY = "image" + + def save_images(self, images): + pbar = comfy.utils.ProgressBar(images.shape[0]) + step = 0 + for image in images: + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + pbar.update_absolute(step, images.shape[0], ("PNG", img, None)) + step += 1 + + return {} + + def IS_CHANGED(s, images): + return time.time() + +NODE_CLASS_MAPPINGS = { + "SaveImageWebsocket": SaveImageWebsocket, +} From aeaeca10bd7cf6e40d6e71f1089c594e0fab5a99 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 15 Feb 2024 21:10:10 -0500 Subject: [PATCH 010/378] Small refactor of is_device_* functions. --- comfy/model_management.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index a8dc91b9..0b3f6ead 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -684,17 +684,20 @@ def mps_mode(): global cpu_state return cpu_state == CPUState.MPS -def is_device_cpu(device): +def is_device_type(device, type): if hasattr(device, 'type'): - if (device.type == 'cpu'): + if (device.type == type): return True return False +def is_device_cpu(device): + return is_device_type(device, 'cpu') + def is_device_mps(device): - if hasattr(device, 'type'): - if (device.type == 'mps'): - return True - return False + return is_device_type(device, 'mps') + +def is_device_cuda(device): + return is_device_type(device, 'cuda') def should_use_fp16(device=None, model_params=0, prioritize_performance=True, manual_cast=False): global directml_enabled From 5e06baf112c0ccccbf51f0249abb3e121147f2d3 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 16 Feb 2024 06:30:39 -0500 Subject: [PATCH 011/378] Stable Cascade Stage A. --- comfy/ldm/cascade/stage_a.py | 254 +++++++++++++++++++++++++++++++++++ comfy/sd.py | 24 +++- 2 files changed, 272 insertions(+), 6 deletions(-) create mode 100644 comfy/ldm/cascade/stage_a.py diff --git a/comfy/ldm/cascade/stage_a.py b/comfy/ldm/cascade/stage_a.py new file mode 100644 index 00000000..55fdbf17 --- /dev/null +++ b/comfy/ldm/cascade/stage_a.py @@ -0,0 +1,254 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Stability AI + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import torch +from torch import nn +from torch.autograd import Function + +class vector_quantize(Function): + @staticmethod + def forward(ctx, x, codebook): + with torch.no_grad(): + codebook_sqr = torch.sum(codebook ** 2, dim=1) + x_sqr = torch.sum(x ** 2, dim=1, keepdim=True) + + dist = torch.addmm(codebook_sqr + x_sqr, x, codebook.t(), alpha=-2.0, beta=1.0) + _, indices = dist.min(dim=1) + + ctx.save_for_backward(indices, codebook) + ctx.mark_non_differentiable(indices) + + nn = torch.index_select(codebook, 0, indices) + return nn, indices + + @staticmethod + def backward(ctx, grad_output, grad_indices): + grad_inputs, grad_codebook = None, None + + if ctx.needs_input_grad[0]: + grad_inputs = grad_output.clone() + if ctx.needs_input_grad[1]: + # Gradient wrt. the codebook + indices, codebook = ctx.saved_tensors + + grad_codebook = torch.zeros_like(codebook) + grad_codebook.index_add_(0, indices, grad_output) + + return (grad_inputs, grad_codebook) + + +class VectorQuantize(nn.Module): + def __init__(self, embedding_size, k, ema_decay=0.99, ema_loss=False): + """ + Takes an input of variable size (as long as the last dimension matches the embedding size). + Returns one tensor containing the nearest neigbour embeddings to each of the inputs, + with the same size as the input, vq and commitment components for the loss as a touple + in the second output and the indices of the quantized vectors in the third: + quantized, (vq_loss, commit_loss), indices + """ + super(VectorQuantize, self).__init__() + + self.codebook = nn.Embedding(k, embedding_size) + self.codebook.weight.data.uniform_(-1./k, 1./k) + self.vq = vector_quantize.apply + + self.ema_decay = ema_decay + self.ema_loss = ema_loss + if ema_loss: + self.register_buffer('ema_element_count', torch.ones(k)) + self.register_buffer('ema_weight_sum', torch.zeros_like(self.codebook.weight)) + + def _laplace_smoothing(self, x, epsilon): + n = torch.sum(x) + return ((x + epsilon) / (n + x.size(0) * epsilon) * n) + + def _updateEMA(self, z_e_x, indices): + mask = nn.functional.one_hot(indices, self.ema_element_count.size(0)).float() + elem_count = mask.sum(dim=0) + weight_sum = torch.mm(mask.t(), z_e_x) + + self.ema_element_count = (self.ema_decay * self.ema_element_count) + ((1-self.ema_decay) * elem_count) + self.ema_element_count = self._laplace_smoothing(self.ema_element_count, 1e-5) + self.ema_weight_sum = (self.ema_decay * self.ema_weight_sum) + ((1-self.ema_decay) * weight_sum) + + self.codebook.weight.data = self.ema_weight_sum / self.ema_element_count.unsqueeze(-1) + + def idx2vq(self, idx, dim=-1): + q_idx = self.codebook(idx) + if dim != -1: + q_idx = q_idx.movedim(-1, dim) + return q_idx + + def forward(self, x, get_losses=True, dim=-1): + if dim != -1: + x = x.movedim(dim, -1) + z_e_x = x.contiguous().view(-1, x.size(-1)) if len(x.shape) > 2 else x + z_q_x, indices = self.vq(z_e_x, self.codebook.weight.detach()) + vq_loss, commit_loss = None, None + if self.ema_loss and self.training: + self._updateEMA(z_e_x.detach(), indices.detach()) + # pick the graded embeddings after updating the codebook in order to have a more accurate commitment loss + z_q_x_grd = torch.index_select(self.codebook.weight, dim=0, index=indices) + if get_losses: + vq_loss = (z_q_x_grd - z_e_x.detach()).pow(2).mean() + commit_loss = (z_e_x - z_q_x_grd.detach()).pow(2).mean() + + z_q_x = z_q_x.view(x.shape) + if dim != -1: + z_q_x = z_q_x.movedim(-1, dim) + return z_q_x, (vq_loss, commit_loss), indices.view(x.shape[:-1]) + + +class ResBlock(nn.Module): + def __init__(self, c, c_hidden): + super().__init__() + # depthwise/attention + self.norm1 = nn.LayerNorm(c, elementwise_affine=False, eps=1e-6) + self.depthwise = nn.Sequential( + nn.ReplicationPad2d(1), + nn.Conv2d(c, c, kernel_size=3, groups=c) + ) + + # channelwise + self.norm2 = nn.LayerNorm(c, elementwise_affine=False, eps=1e-6) + self.channelwise = nn.Sequential( + nn.Linear(c, c_hidden), + nn.GELU(), + nn.Linear(c_hidden, c), + ) + + self.gammas = nn.Parameter(torch.zeros(6), requires_grad=True) + + # Init weights + def _basic_init(module): + if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): + torch.nn.init.xavier_uniform_(module.weight) + if module.bias is not None: + nn.init.constant_(module.bias, 0) + + self.apply(_basic_init) + + def _norm(self, x, norm): + return norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) + + def forward(self, x): + mods = self.gammas + + x_temp = self._norm(x, self.norm1) * (1 + mods[0]) + mods[1] + x = x + self.depthwise(x_temp) * mods[2] + + x_temp = self._norm(x, self.norm2) * (1 + mods[3]) + mods[4] + x = x + self.channelwise(x_temp.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) * mods[5] + + return x + + +class StageA(nn.Module): + def __init__(self, levels=2, bottleneck_blocks=12, c_hidden=384, c_latent=4, codebook_size=8192, + scale_factor=0.43): # 0.3764 + super().__init__() + self.c_latent = c_latent + self.scale_factor = scale_factor + c_levels = [c_hidden // (2 ** i) for i in reversed(range(levels))] + + # Encoder blocks + self.in_block = nn.Sequential( + nn.PixelUnshuffle(2), + nn.Conv2d(3 * 4, c_levels[0], kernel_size=1) + ) + down_blocks = [] + for i in range(levels): + if i > 0: + down_blocks.append(nn.Conv2d(c_levels[i - 1], c_levels[i], kernel_size=4, stride=2, padding=1)) + block = ResBlock(c_levels[i], c_levels[i] * 4) + down_blocks.append(block) + down_blocks.append(nn.Sequential( + nn.Conv2d(c_levels[-1], c_latent, kernel_size=1, bias=False), + nn.BatchNorm2d(c_latent), # then normalize them to have mean 0 and std 1 + )) + self.down_blocks = nn.Sequential(*down_blocks) + self.down_blocks[0] + + self.codebook_size = codebook_size + self.vquantizer = VectorQuantize(c_latent, k=codebook_size) + + # Decoder blocks + up_blocks = [nn.Sequential( + nn.Conv2d(c_latent, c_levels[-1], kernel_size=1) + )] + for i in range(levels): + for j in range(bottleneck_blocks if i == 0 else 1): + block = ResBlock(c_levels[levels - 1 - i], c_levels[levels - 1 - i] * 4) + up_blocks.append(block) + if i < levels - 1: + up_blocks.append( + nn.ConvTranspose2d(c_levels[levels - 1 - i], c_levels[levels - 2 - i], kernel_size=4, stride=2, + padding=1)) + self.up_blocks = nn.Sequential(*up_blocks) + self.out_block = nn.Sequential( + nn.Conv2d(c_levels[0], 3 * 4, kernel_size=1), + nn.PixelShuffle(2), + ) + + def encode(self, x, quantize=False): + x = self.in_block(x) + x = self.down_blocks(x) + if quantize: + qe, (vq_loss, commit_loss), indices = self.vquantizer.forward(x, dim=1) + return qe / self.scale_factor, x / self.scale_factor, indices, vq_loss + commit_loss * 0.25 + else: + return x / self.scale_factor + + def decode(self, x): + x = x * self.scale_factor + x = self.up_blocks(x) + x = self.out_block(x) + return x + + def forward(self, x, quantize=False): + qe, x, _, vq_loss = self.encode(x, quantize) + x = self.decode(qe) + return x, vq_loss + + +class Discriminator(nn.Module): + def __init__(self, c_in=3, c_cond=0, c_hidden=512, depth=6): + super().__init__() + d = max(depth - 3, 3) + layers = [ + nn.utils.spectral_norm(nn.Conv2d(c_in, c_hidden // (2 ** d), kernel_size=3, stride=2, padding=1)), + nn.LeakyReLU(0.2), + ] + for i in range(depth - 1): + c_in = c_hidden // (2 ** max((d - i), 0)) + c_out = c_hidden // (2 ** max((d - 1 - i), 0)) + layers.append(nn.utils.spectral_norm(nn.Conv2d(c_in, c_out, kernel_size=3, stride=2, padding=1))) + layers.append(nn.InstanceNorm2d(c_out)) + layers.append(nn.LeakyReLU(0.2)) + self.encoder = nn.Sequential(*layers) + self.shuffle = nn.Conv2d((c_hidden + c_cond) if c_cond > 0 else c_hidden, 1, kernel_size=1) + self.logits = nn.Sigmoid() + + def forward(self, x, cond=None): + x = self.encoder(x) + if cond is not None: + cond = cond.view(cond.size(0), cond.size(1), 1, 1, ).expand(-1, -1, x.size(-2), x.size(-1)) + x = torch.cat([x, cond], dim=1) + x = self.shuffle(x) + x = self.logits(x) + return x diff --git a/comfy/sd.py b/comfy/sd.py index 5b22d117..5e37cff9 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -2,6 +2,8 @@ import torch from comfy import model_management from .ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine +from .ldm.cascade.stage_a import StageA + import yaml import comfy.utils @@ -156,6 +158,8 @@ class VAE: self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype) self.downscale_ratio = 8 self.latent_channels = 4 + self.process_input = lambda image: image * 2.0 - 1.0 + self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0) if config is None: if "decoder.mid.block_1.mix_factor" in sd: @@ -168,6 +172,14 @@ class VAE: decoder_config={'target': "comfy.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config}) elif "taesd_decoder.1.weight" in sd: self.first_stage_model = comfy.taesd.taesd.TAESD() + elif "vquantizer.codebook.weight" in sd: #VQGan: stage a of stable cascade + self.first_stage_model = StageA() + self.downscale_ratio = 4 + #TODO + #self.memory_used_encode + #self.memory_used_decode + self.process_input = lambda image: image + self.process_output = lambda image: image else: #default SD1.x/SD2.x VAE parameters ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} @@ -206,12 +218,12 @@ class VAE: steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap) pbar = comfy.utils.ProgressBar(steps) - decode_fn = lambda a: (self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)) + 1.0).float() - output = torch.clamp(( + decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float() + output = self.process_output( (comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, pbar = pbar) + comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, pbar = pbar) + comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, pbar = pbar)) - / 3.0) / 2.0, min=0.0, max=1.0) + / 3.0) return output def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): @@ -220,7 +232,7 @@ class VAE: steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap) pbar = comfy.utils.ProgressBar(steps) - encode_fn = lambda a: self.first_stage_model.encode((2. * a - 1.).to(self.vae_dtype).to(self.device)).float() + encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float() samples = comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) @@ -238,7 +250,7 @@ class VAE: pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * self.downscale_ratio), round(samples_in.shape[3] * self.downscale_ratio)), device=self.output_device) for x in range(0, samples_in.shape[0], batch_number): samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device) - pixel_samples[x:x+batch_number] = torch.clamp((self.first_stage_model.decode(samples).to(self.output_device).float() + 1.0) / 2.0, min=0.0, max=1.0) + pixel_samples[x:x+batch_number] = self.process_output(self.first_stage_model.decode(samples).to(self.output_device).float()) except model_management.OOM_EXCEPTION as e: print("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.") pixel_samples = self.decode_tiled_(samples_in) @@ -261,7 +273,7 @@ class VAE: batch_number = max(1, batch_number) samples = torch.empty((pixel_samples.shape[0], self.latent_channels, round(pixel_samples.shape[2] // self.downscale_ratio), round(pixel_samples.shape[3] // self.downscale_ratio)), device=self.output_device) for x in range(0, pixel_samples.shape[0], batch_number): - pixels_in = (2. * pixel_samples[x:x+batch_number] - 1.).to(self.vae_dtype).to(self.device) + pixels_in = self.process_input(pixel_samples[x:x+batch_number]).to(self.vae_dtype).to(self.device) samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).to(self.output_device).float() except model_management.OOM_EXCEPTION as e: From f83109f09bec04f39f028c275b4eb1231adba00a Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 16 Feb 2024 10:55:08 -0500 Subject: [PATCH 012/378] Stable Cascade Stage C. --- comfy/controlnet.py | 17 ++- comfy/latent_formats.py | 6 + comfy/ldm/cascade/common.py | 161 ++++++++++++++++++++ comfy/ldm/cascade/stage_c.py | 271 +++++++++++++++++++++++++++++++++ comfy/model_base.py | 40 ++++- comfy/model_detection.py | 30 +++- comfy/model_management.py | 34 ++++- comfy/model_sampling.py | 30 ++++ comfy/sd.py | 20 +-- comfy/supported_models.py | 35 ++++- comfy/supported_models_base.py | 6 +- 11 files changed, 619 insertions(+), 31 deletions(-) create mode 100644 comfy/ldm/cascade/common.py create mode 100644 comfy/ldm/cascade/stage_c.py diff --git a/comfy/controlnet.py b/comfy/controlnet.py index d9d990a7..41619758 100644 --- a/comfy/controlnet.py +++ b/comfy/controlnet.py @@ -318,9 +318,10 @@ def load_controlnet(ckpt_path, model=None): return ControlLora(controlnet_data) controlnet_config = None + supported_inference_dtypes = None + if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format - unet_dtype = comfy.model_management.unet_dtype() - controlnet_config = comfy.model_detection.unet_config_from_diffusers_unet(controlnet_data, unet_dtype) + controlnet_config = comfy.model_detection.unet_config_from_diffusers_unet(controlnet_data) diffusers_keys = comfy.utils.unet_to_diffusers(controlnet_config) diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight" diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias" @@ -380,12 +381,20 @@ def load_controlnet(ckpt_path, model=None): return net if controlnet_config is None: - unet_dtype = comfy.model_management.unet_dtype() - controlnet_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, unet_dtype, True).unet_config + model_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, True) + supported_inference_dtypes = model_config.supported_inference_dtypes + controlnet_config = model_config.unet_config + load_device = comfy.model_management.get_torch_device() + if supported_inference_dtypes is None: + unet_dtype = comfy.model_management.unet_dtype() + else: + unet_dtype = comfy.model_management.unet_dtype(supported_dtypes=supported_inference_dtypes) + manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device) if manual_cast_dtype is not None: controlnet_config["operations"] = comfy.ops.manual_cast + controlnet_config["dtype"] = unet_dtype controlnet_config.pop("out_channels") controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1] control_model = comfy.cldm.cldm.ControlNet(**controlnet_config) diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py index 2252a075..8ba76737 100644 --- a/comfy/latent_formats.py +++ b/comfy/latent_formats.py @@ -37,3 +37,9 @@ class SDXL(LatentFormat): class SD_X4(LatentFormat): def __init__(self): self.scale_factor = 0.08333 + +class SC_Prior(LatentFormat): + def __init__(self): + self.scale_factor = 1.0 + + diff --git a/comfy/ldm/cascade/common.py b/comfy/ldm/cascade/common.py new file mode 100644 index 00000000..3c5bf997 --- /dev/null +++ b/comfy/ldm/cascade/common.py @@ -0,0 +1,161 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Stability AI + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import torch +import torch.nn as nn +from comfy.ldm.modules.attention import optimized_attention + +class Linear(torch.nn.Linear): + def reset_parameters(self): + return None + +class Conv2d(torch.nn.Conv2d): + def reset_parameters(self): + return None + +class OptimizedAttention(nn.Module): + def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None): + super().__init__() + self.heads = nhead + + self.to_q = operations.Linear(c, c, bias=True, dtype=dtype, device=device) + self.to_k = operations.Linear(c, c, bias=True, dtype=dtype, device=device) + self.to_v = operations.Linear(c, c, bias=True, dtype=dtype, device=device) + + self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device) + + def forward(self, q, k, v): + q = self.to_q(q) + k = self.to_k(k) + v = self.to_v(v) + + out = optimized_attention(q, k, v, self.heads) + + return self.out_proj(out) + +class Attention2D(nn.Module): + def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None): + super().__init__() + self.attn = OptimizedAttention(c, nhead, dtype=dtype, device=device, operations=operations) + # self.attn = nn.MultiheadAttention(c, nhead, dropout=dropout, bias=True, batch_first=True, dtype=dtype, device=device) + + def forward(self, x, kv, self_attn=False): + orig_shape = x.shape + x = x.view(x.size(0), x.size(1), -1).permute(0, 2, 1) # Bx4xHxW -> Bx(HxW)x4 + if self_attn: + kv = torch.cat([x, kv], dim=1) + # x = self.attn(x, kv, kv, need_weights=False)[0] + x = self.attn(x, kv, kv) + x = x.permute(0, 2, 1).view(*orig_shape) + return x + + +def LayerNorm2d_op(operations): + class LayerNorm2d(operations.LayerNorm): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, x): + return super().forward(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) + return LayerNorm2d + +class GlobalResponseNorm(nn.Module): + "from https://github.com/facebookresearch/ConvNeXt-V2/blob/3608f67cc1dae164790c5d0aead7bf2d73d9719b/models/utils.py#L105" + def __init__(self, dim, dtype=None, device=None): + super().__init__() + self.gamma = nn.Parameter(torch.zeros(1, 1, 1, dim, dtype=dtype, device=device)) + self.beta = nn.Parameter(torch.zeros(1, 1, 1, dim, dtype=dtype, device=device)) + + def forward(self, x): + Gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True) + Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6) + return self.gamma * (x * Nx) + self.beta + x + + +class ResBlock(nn.Module): + def __init__(self, c, c_skip=0, kernel_size=3, dropout=0.0, dtype=None, device=None, operations=None): # , num_heads=4, expansion=2): + super().__init__() + self.depthwise = operations.Conv2d(c, c, kernel_size=kernel_size, padding=kernel_size // 2, groups=c, dtype=dtype, device=device) + # self.depthwise = SAMBlock(c, num_heads, expansion) + self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.channelwise = nn.Sequential( + operations.Linear(c + c_skip, c * 4, dtype=dtype, device=device), + nn.GELU(), + GlobalResponseNorm(c * 4, dtype=dtype, device=device), + nn.Dropout(dropout), + operations.Linear(c * 4, c, dtype=dtype, device=device) + ) + + def forward(self, x, x_skip=None): + x_res = x + x = self.norm(self.depthwise(x)) + if x_skip is not None: + x = torch.cat([x, x_skip], dim=1) + x = self.channelwise(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) + return x + x_res + + +class AttnBlock(nn.Module): + def __init__(self, c, c_cond, nhead, self_attn=True, dropout=0.0, dtype=None, device=None, operations=None): + super().__init__() + self.self_attn = self_attn + self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.attention = Attention2D(c, nhead, dropout, dtype=dtype, device=device, operations=operations) + self.kv_mapper = nn.Sequential( + nn.SiLU(), + operations.Linear(c_cond, c, dtype=dtype, device=device) + ) + + def forward(self, x, kv): + kv = self.kv_mapper(kv) + x = x + self.attention(self.norm(x), kv, self_attn=self.self_attn) + return x + + +class FeedForwardBlock(nn.Module): + def __init__(self, c, dropout=0.0, dtype=None, device=None, operations=None): + super().__init__() + self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.channelwise = nn.Sequential( + operations.Linear(c, c * 4, dtype=dtype, device=device), + nn.GELU(), + GlobalResponseNorm(c * 4, dtype=dtype, device=device), + nn.Dropout(dropout), + operations.Linear(c * 4, c, dtype=dtype, device=device) + ) + + def forward(self, x): + x = x + self.channelwise(self.norm(x).permute(0, 2, 3, 1)).permute(0, 3, 1, 2) + return x + + +class TimestepBlock(nn.Module): + def __init__(self, c, c_timestep, conds=['sca'], dtype=None, device=None, operations=None): + super().__init__() + self.mapper = operations.Linear(c_timestep, c * 2, dtype=dtype, device=device) + self.conds = conds + for cname in conds: + setattr(self, f"mapper_{cname}", operations.Linear(c_timestep, c * 2, dtype=dtype, device=device)) + + def forward(self, x, t): + t = t.chunk(len(self.conds) + 1, dim=1) + a, b = self.mapper(t[0])[:, :, None, None].chunk(2, dim=1) + for i, c in enumerate(self.conds): + ac, bc = getattr(self, f"mapper_{c}")(t[i + 1])[:, :, None, None].chunk(2, dim=1) + a, b = a + ac, b + bc + return x * (1 + a) + b diff --git a/comfy/ldm/cascade/stage_c.py b/comfy/ldm/cascade/stage_c.py new file mode 100644 index 00000000..2e0f4706 --- /dev/null +++ b/comfy/ldm/cascade/stage_c.py @@ -0,0 +1,271 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Stability AI + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import torch +from torch import nn +import numpy as np +import math +from .common import AttnBlock, LayerNorm2d_op, ResBlock, FeedForwardBlock, TimestepBlock +# from .controlnet import ControlNetDeliverer + +class UpDownBlock2d(nn.Module): + def __init__(self, c_in, c_out, mode, enabled=True, dtype=None, device=None, operations=None): + super().__init__() + assert mode in ['up', 'down'] + interpolation = nn.Upsample(scale_factor=2 if mode == 'up' else 0.5, mode='bilinear', + align_corners=True) if enabled else nn.Identity() + mapping = operations.Conv2d(c_in, c_out, kernel_size=1, dtype=dtype, device=device) + self.blocks = nn.ModuleList([interpolation, mapping] if mode == 'up' else [mapping, interpolation]) + + def forward(self, x): + for block in self.blocks: + x = block(x) + return x + + +class StageC(nn.Module): + def __init__(self, c_in=16, c_out=16, c_r=64, patch_size=1, c_cond=2048, c_hidden=[2048, 2048], nhead=[32, 32], + blocks=[[8, 24], [24, 8]], block_repeat=[[1, 1], [1, 1]], level_config=['CTA', 'CTA'], + c_clip_text=1280, c_clip_text_pooled=1280, c_clip_img=768, c_clip_seq=4, kernel_size=3, + dropout=[0.1, 0.1], self_attn=True, t_conds=['sca', 'crp'], switch_level=[False], stable_cascade_stage=None, + dtype=None, device=None, operations=None): + super().__init__() + self.dtype = dtype + self.c_r = c_r + self.t_conds = t_conds + self.c_clip_seq = c_clip_seq + if not isinstance(dropout, list): + dropout = [dropout] * len(c_hidden) + if not isinstance(self_attn, list): + self_attn = [self_attn] * len(c_hidden) + + # CONDITIONING + self.clip_txt_mapper = operations.Linear(c_clip_text, c_cond, dtype=dtype, device=device) + self.clip_txt_pooled_mapper = operations.Linear(c_clip_text_pooled, c_cond * c_clip_seq, dtype=dtype, device=device) + self.clip_img_mapper = operations.Linear(c_clip_img, c_cond * c_clip_seq, dtype=dtype, device=device) + self.clip_norm = operations.LayerNorm(c_cond, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + + self.embedding = nn.Sequential( + nn.PixelUnshuffle(patch_size), + operations.Conv2d(c_in * (patch_size ** 2), c_hidden[0], kernel_size=1, dtype=dtype, device=device), + LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6) + ) + + def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0, self_attn=True): + if block_type == 'C': + return ResBlock(c_hidden, c_skip, kernel_size=kernel_size, dropout=dropout, dtype=dtype, device=device, operations=operations) + elif block_type == 'A': + return AttnBlock(c_hidden, c_cond, nhead, self_attn=self_attn, dropout=dropout, dtype=dtype, device=device, operations=operations) + elif block_type == 'F': + return FeedForwardBlock(c_hidden, dropout=dropout, dtype=dtype, device=device, operations=operations) + elif block_type == 'T': + return TimestepBlock(c_hidden, c_r, conds=t_conds, dtype=dtype, device=device, operations=operations) + else: + raise Exception(f'Block type {block_type} not supported') + + # BLOCKS + # -- down blocks + self.down_blocks = nn.ModuleList() + self.down_downscalers = nn.ModuleList() + self.down_repeat_mappers = nn.ModuleList() + for i in range(len(c_hidden)): + if i > 0: + self.down_downscalers.append(nn.Sequential( + LayerNorm2d_op(operations)(c_hidden[i - 1], elementwise_affine=False, eps=1e-6), + UpDownBlock2d(c_hidden[i - 1], c_hidden[i], mode='down', enabled=switch_level[i - 1], dtype=dtype, device=device, operations=operations) + )) + else: + self.down_downscalers.append(nn.Identity()) + down_block = nn.ModuleList() + for _ in range(blocks[0][i]): + for block_type in level_config[i]: + block = get_block(block_type, c_hidden[i], nhead[i], dropout=dropout[i], self_attn=self_attn[i]) + down_block.append(block) + self.down_blocks.append(down_block) + if block_repeat is not None: + block_repeat_mappers = nn.ModuleList() + for _ in range(block_repeat[0][i] - 1): + block_repeat_mappers.append(nn.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1)) + self.down_repeat_mappers.append(block_repeat_mappers) + + # -- up blocks + self.up_blocks = nn.ModuleList() + self.up_upscalers = nn.ModuleList() + self.up_repeat_mappers = nn.ModuleList() + for i in reversed(range(len(c_hidden))): + if i > 0: + self.up_upscalers.append(nn.Sequential( + LayerNorm2d_op(operations)(c_hidden[i], elementwise_affine=False, eps=1e-6), + UpDownBlock2d(c_hidden[i], c_hidden[i - 1], mode='up', enabled=switch_level[i - 1], dtype=dtype, device=device, operations=operations) + )) + else: + self.up_upscalers.append(nn.Identity()) + up_block = nn.ModuleList() + for j in range(blocks[1][::-1][i]): + for k, block_type in enumerate(level_config[i]): + c_skip = c_hidden[i] if i < len(c_hidden) - 1 and j == k == 0 else 0 + block = get_block(block_type, c_hidden[i], nhead[i], c_skip=c_skip, dropout=dropout[i], + self_attn=self_attn[i]) + up_block.append(block) + self.up_blocks.append(up_block) + if block_repeat is not None: + block_repeat_mappers = nn.ModuleList() + for _ in range(block_repeat[1][::-1][i] - 1): + block_repeat_mappers.append(nn.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1)) + self.up_repeat_mappers.append(block_repeat_mappers) + + # OUTPUT + self.clf = nn.Sequential( + LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6), + operations.Conv2d(c_hidden[0], c_out * (patch_size ** 2), kernel_size=1, dtype=dtype, device=device), + nn.PixelShuffle(patch_size), + ) + + # --- WEIGHT INIT --- + # self.apply(self._init_weights) # General init + # nn.init.normal_(self.clip_txt_mapper.weight, std=0.02) # conditionings + # nn.init.normal_(self.clip_txt_pooled_mapper.weight, std=0.02) # conditionings + # nn.init.normal_(self.clip_img_mapper.weight, std=0.02) # conditionings + # torch.nn.init.xavier_uniform_(self.embedding[1].weight, 0.02) # inputs + # nn.init.constant_(self.clf[1].weight, 0) # outputs + # + # # blocks + # for level_block in self.down_blocks + self.up_blocks: + # for block in level_block: + # if isinstance(block, ResBlock) or isinstance(block, FeedForwardBlock): + # block.channelwise[-1].weight.data *= np.sqrt(1 / sum(blocks[0])) + # elif isinstance(block, TimestepBlock): + # for layer in block.modules(): + # if isinstance(layer, nn.Linear): + # nn.init.constant_(layer.weight, 0) + # + # def _init_weights(self, m): + # if isinstance(m, (nn.Conv2d, nn.Linear)): + # torch.nn.init.xavier_uniform_(m.weight) + # if m.bias is not None: + # nn.init.constant_(m.bias, 0) + + def gen_r_embedding(self, r, max_positions=10000): + r = r * max_positions + half_dim = self.c_r // 2 + emb = math.log(max_positions) / (half_dim - 1) + emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp() + emb = r[:, None] * emb[None, :] + emb = torch.cat([emb.sin(), emb.cos()], dim=1) + if self.c_r % 2 == 1: # zero pad + emb = nn.functional.pad(emb, (0, 1), mode='constant') + return emb + + def gen_c_embeddings(self, clip_txt, clip_txt_pooled, clip_img): + clip_txt = self.clip_txt_mapper(clip_txt) + if len(clip_txt_pooled.shape) == 2: + clip_txt_pooled = clip_txt_pooled.unsqueeze(1) + if len(clip_img.shape) == 2: + clip_img = clip_img.unsqueeze(1) + clip_txt_pool = self.clip_txt_pooled_mapper(clip_txt_pooled).view(clip_txt_pooled.size(0), clip_txt_pooled.size(1) * self.c_clip_seq, -1) + clip_img = self.clip_img_mapper(clip_img).view(clip_img.size(0), clip_img.size(1) * self.c_clip_seq, -1) + clip = torch.cat([clip_txt, clip_txt_pool, clip_img], dim=1) + clip = self.clip_norm(clip) + return clip + + def _down_encode(self, x, r_embed, clip, cnet=None): + level_outputs = [] + block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers) + for down_block, downscaler, repmap in block_group: + x = downscaler(x) + for i in range(len(repmap) + 1): + for block in down_block: + if isinstance(block, ResBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + ResBlock)): + if cnet is not None: + next_cnet = cnet() + if next_cnet is not None: + x = x + nn.functional.interpolate(next_cnet, size=x.shape[-2:], mode='bilinear', + align_corners=True) + x = block(x) + elif isinstance(block, AttnBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + AttnBlock)): + x = block(x, clip) + elif isinstance(block, TimestepBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + TimestepBlock)): + x = block(x, r_embed) + else: + x = block(x) + if i < len(repmap): + x = repmap[i](x) + level_outputs.insert(0, x) + return level_outputs + + def _up_decode(self, level_outputs, r_embed, clip, cnet=None): + x = level_outputs[0] + block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers) + for i, (up_block, upscaler, repmap) in enumerate(block_group): + for j in range(len(repmap) + 1): + for k, block in enumerate(up_block): + if isinstance(block, ResBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + ResBlock)): + skip = level_outputs[i] if k == 0 and i > 0 else None + if skip is not None and (x.size(-1) != skip.size(-1) or x.size(-2) != skip.size(-2)): + x = torch.nn.functional.interpolate(x, skip.shape[-2:], mode='bilinear', + align_corners=True) + if cnet is not None: + next_cnet = cnet() + if next_cnet is not None: + x = x + nn.functional.interpolate(next_cnet, size=x.shape[-2:], mode='bilinear', + align_corners=True) + x = block(x, skip) + elif isinstance(block, AttnBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + AttnBlock)): + x = block(x, clip) + elif isinstance(block, TimestepBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + TimestepBlock)): + x = block(x, r_embed) + else: + x = block(x) + if j < len(repmap): + x = repmap[j](x) + x = upscaler(x) + return x + + def forward(self, x, r, clip_text, clip_text_pooled, clip_img, cnet=None, **kwargs): + # Process the conditioning embeddings + r_embed = self.gen_r_embedding(r).to(dtype=x.dtype) + for c in self.t_conds: + t_cond = kwargs.get(c, torch.zeros_like(r)) + r_embed = torch.cat([r_embed, self.gen_r_embedding(t_cond).to(dtype=x.dtype)], dim=1) + clip = self.gen_c_embeddings(clip_text, clip_text_pooled, clip_img) + + # Model Blocks + x = self.embedding(x) + if cnet is not None: + cnet = ControlNetDeliverer(cnet) + level_outputs = self._down_encode(x, r_embed, clip, cnet) + x = self._up_decode(level_outputs, r_embed, clip, cnet) + return self.clf(x) + + def update_weights_ema(self, src_model, beta=0.999): + for self_params, src_params in zip(self.parameters(), src_model.parameters()): + self_params.data = self_params.data * beta + src_params.data.clone().to(self_params.device) * (1 - beta) + for self_buffers, src_buffers in zip(self.buffers(), src_model.buffers()): + self_buffers.data = self_buffers.data * beta + src_buffers.data.clone().to(self_buffers.device) * (1 - beta) diff --git a/comfy/model_base.py b/comfy/model_base.py index aafb88e0..dde2a2f3 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -1,5 +1,6 @@ import torch from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep +from comfy.ldm.cascade.stage_c import StageC from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation from comfy.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation import comfy.model_management @@ -12,9 +13,10 @@ class ModelType(Enum): EPS = 1 V_PREDICTION = 2 V_PREDICTION_EDM = 3 + STABLE_CASCADE = 4 -from comfy.model_sampling import EPS, V_PREDICTION, ModelSamplingDiscrete, ModelSamplingContinuousEDM +from comfy.model_sampling import EPS, V_PREDICTION, ModelSamplingDiscrete, ModelSamplingContinuousEDM, StableCascadeSampling def model_sampling(model_config, model_type): @@ -27,6 +29,9 @@ def model_sampling(model_config, model_type): elif model_type == ModelType.V_PREDICTION_EDM: c = V_PREDICTION s = ModelSamplingContinuousEDM + elif model_type == ModelType.STABLE_CASCADE: + c = EPS + s = StableCascadeSampling class ModelSampling(s, c): pass @@ -35,7 +40,7 @@ def model_sampling(model_config, model_type): class BaseModel(torch.nn.Module): - def __init__(self, model_config, model_type=ModelType.EPS, device=None): + def __init__(self, model_config, model_type=ModelType.EPS, device=None, unet_model=UNetModel): super().__init__() unet_config = model_config.unet_config @@ -48,7 +53,7 @@ class BaseModel(torch.nn.Module): operations = comfy.ops.manual_cast else: operations = comfy.ops.disable_weight_init - self.diffusion_model = UNetModel(**unet_config, device=device, operations=operations) + self.diffusion_model = unet_model(**unet_config, device=device, operations=operations) self.model_type = model_type self.model_sampling = model_sampling(model_config, model_type) @@ -427,3 +432,32 @@ class SD_X4Upscaler(BaseModel): out['c_concat'] = comfy.conds.CONDNoiseShape(image) out['y'] = comfy.conds.CONDRegular(noise_level) return out + +class StableCascade_C(BaseModel): + def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None): + super().__init__(model_config, model_type, device=device, unet_model=StageC) + self.diffusion_model.eval().requires_grad_(False) + + def extra_conds(self, **kwargs): + out = {} + clip_text_pooled = kwargs["pooled_output"] + if clip_text_pooled is not None: + out['clip_text_pooled'] = comfy.conds.CONDRegular(clip_text_pooled) + + if "unclip_conditioning" in kwargs: + embeds = [] + for unclip_cond in kwargs["unclip_conditioning"]: + weight = unclip_cond["strength"] + embeds.append(unclip_cond["clip_vision_output"].image_embeds.unsqueeze(0) * weight) + clip_img = torch.cat(embeds, dim=1) + else: + clip_img = torch.zeros((1, 1, 768)) + out["clip_img"] = comfy.conds.CONDRegular(clip_img) + out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,))) + out["crp"] = comfy.conds.CONDRegular(torch.zeros((1,))) + + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['clip_text'] = comfy.conds.CONDCrossAttn(cross_attn) + return out + diff --git a/comfy/model_detection.py b/comfy/model_detection.py index ea824c44..8d4fb7b6 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -28,9 +28,26 @@ def calculate_transformer_depth(prefix, state_dict_keys, state_dict): return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack return None -def detect_unet_config(state_dict, key_prefix, dtype): +def detect_unet_config(state_dict, key_prefix): state_dict_keys = list(state_dict.keys()) + if '{}clf.1.weight'.format(key_prefix) in state_dict_keys: #stable cascade + unet_config = {} + text_mapper_name = '{}clip_txt_mapper.weight'.format(key_prefix) + if text_mapper_name in state_dict_keys: + unet_config['stable_cascade_stage'] = 'c' + w = state_dict[text_mapper_name] + if w.shape[0] == 1536: #stage c lite + unet_config['c_cond'] = 1536 + unet_config['c_hidden'] = [1536, 1536] + unet_config['nhead'] = [24, 24] + unet_config['blocks'] = [[4, 12], [12, 4]] + elif w.shape[0] == 2048: #stage c full + unet_config['c_cond'] = 2048 + elif '{}clip_mapper.weight'.format(key_prefix) in state_dict_keys: + unet_config['stable_cascade_stage'] = 'b' + return unet_config + unet_config = { "use_checkpoint": False, "image_size": 32, @@ -45,7 +62,6 @@ def detect_unet_config(state_dict, key_prefix, dtype): else: unet_config["adm_in_channels"] = None - unet_config["dtype"] = dtype model_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[0] in_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[1] @@ -159,8 +175,8 @@ def model_config_from_unet_config(unet_config): print("no match", unet_config) return None -def model_config_from_unet(state_dict, unet_key_prefix, dtype, use_base_if_no_match=False): - unet_config = detect_unet_config(state_dict, unet_key_prefix, dtype) +def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=False): + unet_config = detect_unet_config(state_dict, unet_key_prefix) model_config = model_config_from_unet_config(unet_config) if model_config is None and use_base_if_no_match: return comfy.supported_models_base.BASE(unet_config) @@ -206,7 +222,7 @@ def convert_config(unet_config): return new_config -def unet_config_from_diffusers_unet(state_dict, dtype): +def unet_config_from_diffusers_unet(state_dict, dtype=None): match = {} transformer_depth = [] @@ -313,8 +329,8 @@ def unet_config_from_diffusers_unet(state_dict, dtype): return convert_config(unet_config) return None -def model_config_from_diffusers_unet(state_dict, dtype): - unet_config = unet_config_from_diffusers_unet(state_dict, dtype) +def model_config_from_diffusers_unet(state_dict): + unet_config = unet_config_from_diffusers_unet(state_dict) if unet_config is not None: return model_config_from_unet_config(unet_config) return None diff --git a/comfy/model_management.py b/comfy/model_management.py index 0b3f6ead..eb7178b4 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -487,7 +487,7 @@ def unet_inital_load_device(parameters, dtype): else: return cpu_dev -def unet_dtype(device=None, model_params=0): +def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]): if args.bf16_unet: return torch.bfloat16 if args.fp16_unet: @@ -497,20 +497,31 @@ def unet_dtype(device=None, model_params=0): if args.fp8_e5m2_unet: return torch.float8_e5m2 if should_use_fp16(device=device, model_params=model_params, manual_cast=True): - return torch.float16 + if torch.float16 in supported_dtypes: + return torch.float16 + if should_use_bf16(device): + if torch.bfloat16 in supported_dtypes: + return torch.bfloat16 return torch.float32 # None means no manual cast -def unet_manual_cast(weight_dtype, inference_device): +def unet_manual_cast(weight_dtype, inference_device, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]): if weight_dtype == torch.float32: return None - fp16_supported = comfy.model_management.should_use_fp16(inference_device, prioritize_performance=False) + fp16_supported = should_use_fp16(inference_device, prioritize_performance=False) if fp16_supported and weight_dtype == torch.float16: return None - if fp16_supported: + bf16_supported = should_use_bf16(inference_device) + if bf16_supported and weight_dtype == torch.bfloat16: + return None + + if fp16_supported and torch.float16 in supported_dtypes: return torch.float16 + + elif bf16_supported and torch.bfloat16 in supported_dtypes: + return torch.bfloat16 else: return torch.float32 @@ -760,6 +771,19 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma return True +def should_use_bf16(device=None): + if is_intel_xpu(): + return True + + if device is None: + device = torch.device("cuda") + + props = torch.cuda.get_device_properties(device) + if props.major >= 8: + return True + + return False + def soft_empty_cache(force=False): global cpu_state if cpu_state == CPUState.MPS: diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py index d5870027..b1fbf3e2 100644 --- a/comfy/model_sampling.py +++ b/comfy/model_sampling.py @@ -132,3 +132,33 @@ class ModelSamplingContinuousEDM(torch.nn.Module): log_sigma_min = math.log(self.sigma_min) return math.exp((math.log(self.sigma_max) - log_sigma_min) * percent + log_sigma_min) + +class StableCascadeSampling(ModelSamplingDiscrete): + def __init__(self, model_config=None): + super().__init__() + self.num_timesteps = 1000 + cosine_s=8e-3 + self.cosine_s = torch.tensor([cosine_s]) + sigmas = torch.empty((self.num_timesteps), dtype=torch.float32) + self._init_alpha_cumprod = torch.cos(self.cosine_s / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 + for x in range(self.num_timesteps): + t = x / self.num_timesteps + sigmas[x] = self.sigma(t) + + self.set_sigmas(sigmas) + + def sigma(self, timestep): + alpha_cumprod = (torch.cos((timestep + self.cosine_s) / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 / self._init_alpha_cumprod).clamp(0.0001, 0.9999) + return ((1 - alpha_cumprod) / alpha_cumprod) ** 0.5 + + def timestep(self, sigma): + return super().timestep(sigma) / 1000.0 + + def percent_to_sigma(self, percent): + if percent <= 0.0: + return 999999999.9 + if percent >= 1.0: + return 0.0 + + percent = 1.0 - percent + return self.sigma(torch.tensor(percent)) diff --git a/comfy/sd.py b/comfy/sd.py index 5e37cff9..f3ec62b3 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -450,15 +450,15 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o clip_target = None parameters = comfy.utils.calculate_parameters(sd, "model.diffusion_model.") - unet_dtype = model_management.unet_dtype(model_params=parameters) load_device = model_management.get_torch_device() - manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device) class WeightsLoader(torch.nn.Module): pass - model_config = model_detection.model_config_from_unet(sd, "model.diffusion_model.", unet_dtype) - model_config.set_manual_cast(manual_cast_dtype) + model_config = model_detection.model_config_from_unet(sd, "model.diffusion_model.") + unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes) + manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes) + model_config.set_inference_dtype(unet_dtype, manual_cast_dtype) if model_config is None: raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path)) @@ -507,16 +507,15 @@ def load_unet_state_dict(sd): #load unet in diffusers format parameters = comfy.utils.calculate_parameters(sd) unet_dtype = model_management.unet_dtype(model_params=parameters) load_device = model_management.get_torch_device() - manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device) - if "input_blocks.0.0.weight" in sd: #ldm - model_config = model_detection.model_config_from_unet(sd, "", unet_dtype) + if "input_blocks.0.0.weight" in sd or 'clf.1.weight' in sd: #ldm or stable cascade + model_config = model_detection.model_config_from_unet(sd, "") if model_config is None: return None new_sd = sd else: #diffusers - model_config = model_detection.model_config_from_diffusers_unet(sd, unet_dtype) + model_config = model_detection.model_config_from_diffusers_unet(sd) if model_config is None: return None @@ -528,8 +527,11 @@ def load_unet_state_dict(sd): #load unet in diffusers format new_sd[diffusers_keys[k]] = sd.pop(k) else: print(diffusers_keys[k], k) + offload_device = model_management.unet_offload_device() - model_config.set_manual_cast(manual_cast_dtype) + unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes) + manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes) + model_config.set_inference_dtype(unet_dtype, manual_cast_dtype) model = model_config.get_model(new_sd, "") model = model.to(offload_device) model.load_model_weights(new_sd, "") diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 1d442d4d..a8863e72 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -306,5 +306,38 @@ class SD_X4Upscaler(SD20): out = model_base.SD_X4Upscaler(self, device=device) return out -models = [Stable_Zero123, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B, Segmind_Vega, SD_X4Upscaler] +class Stable_Cascade_C(supported_models_base.BASE): + unet_config = { + "stable_cascade_stage": 'c', + } + + unet_extra_config = {} + + latent_format = latent_formats.SC_Prior + supported_inference_dtypes = [torch.bfloat16, torch.float32] + + def process_unet_state_dict(self, state_dict): + key_list = list(state_dict.keys()) + for y in ["weight", "bias"]: + suffix = "in_proj_{}".format(y) + keys = filter(lambda a: a.endswith(suffix), key_list) + for k_from in keys: + weights = state_dict.pop(k_from) + prefix = k_from[:-(len(suffix) + 1)] + shape_from = weights.shape[0] // 3 + for x in range(3): + p = ["to_q", "to_k", "to_v"] + k_to = "{}.{}.{}".format(prefix, p[x], y) + state_dict[k_to] = weights[shape_from*x:shape_from*(x + 1)] + return state_dict + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.StableCascade_C(self, device=device) + return out + + def clip_target(self): + return None + + +models = [Stable_Zero123, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C] models += [SVD_img2vid] diff --git a/comfy/supported_models_base.py b/comfy/supported_models_base.py index 58535a9f..3bd4f9c6 100644 --- a/comfy/supported_models_base.py +++ b/comfy/supported_models_base.py @@ -22,13 +22,14 @@ class BASE: sampling_settings = {} latent_format = latent_formats.LatentFormat vae_key_prefix = ["first_stage_model."] + supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] manual_cast_dtype = None @classmethod def matches(s, unet_config): for k in s.unet_config: - if s.unet_config[k] != unet_config[k]: + if k not in unet_config or s.unet_config[k] != unet_config[k]: return False return True @@ -80,5 +81,6 @@ class BASE: replace_prefix = {"": "first_stage_model."} return utils.state_dict_prefix_replace(state_dict, replace_prefix) - def set_manual_cast(self, manual_cast_dtype): + def set_inference_dtype(self, dtype, manual_cast_dtype): + self.unet_config['dtype'] = dtype self.manual_cast_dtype = manual_cast_dtype From 667c92814e46e60eba82b86bb23b664f3157c9b9 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 16 Feb 2024 12:56:11 -0500 Subject: [PATCH 013/378] Stable Cascade Stage B. --- comfy/latent_formats.py | 4 +- comfy/ldm/cascade/common.py | 2 +- comfy/ldm/cascade/stage_b.py | 257 +++++++++++++++++++++++++++ comfy/ldm/cascade/stage_c.py | 8 +- comfy/model_base.py | 25 +++ comfy/ops.py | 49 ++++- comfy/supported_models.py | 16 +- comfy/utils.py | 2 + comfy_extras/nodes_stable_cascade.py | 74 ++++++++ nodes.py | 1 + 10 files changed, 430 insertions(+), 8 deletions(-) create mode 100644 comfy/ldm/cascade/stage_b.py create mode 100644 comfy_extras/nodes_stable_cascade.py diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py index 8ba76737..68fd73d0 100644 --- a/comfy/latent_formats.py +++ b/comfy/latent_formats.py @@ -42,4 +42,6 @@ class SC_Prior(LatentFormat): def __init__(self): self.scale_factor = 1.0 - +class SC_B(LatentFormat): + def __init__(self): + self.scale_factor = 1.0 diff --git a/comfy/ldm/cascade/common.py b/comfy/ldm/cascade/common.py index 3c5bf997..c2ef3ec4 100644 --- a/comfy/ldm/cascade/common.py +++ b/comfy/ldm/cascade/common.py @@ -84,7 +84,7 @@ class GlobalResponseNorm(nn.Module): def forward(self, x): Gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True) Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6) - return self.gamma * (x * Nx) + self.beta + x + return self.gamma.to(x.device) * (x * Nx) + self.beta.to(x.device) + x class ResBlock(nn.Module): diff --git a/comfy/ldm/cascade/stage_b.py b/comfy/ldm/cascade/stage_b.py new file mode 100644 index 00000000..6d2c2223 --- /dev/null +++ b/comfy/ldm/cascade/stage_b.py @@ -0,0 +1,257 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Stability AI + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import math +import numpy as np +import torch +from torch import nn +from .common import AttnBlock, LayerNorm2d_op, ResBlock, FeedForwardBlock, TimestepBlock + +class StageB(nn.Module): + def __init__(self, c_in=4, c_out=4, c_r=64, patch_size=2, c_cond=1280, c_hidden=[320, 640, 1280, 1280], + nhead=[-1, -1, 20, 20], blocks=[[2, 6, 28, 6], [6, 28, 6, 2]], + block_repeat=[[1, 1, 1, 1], [3, 3, 2, 2]], level_config=['CT', 'CT', 'CTA', 'CTA'], c_clip=1280, + c_clip_seq=4, c_effnet=16, c_pixels=3, kernel_size=3, dropout=[0, 0, 0.0, 0.0], self_attn=True, + t_conds=['sca'], stable_cascade_stage=None, dtype=None, device=None, operations=None): + super().__init__() + self.dtype = dtype + self.c_r = c_r + self.t_conds = t_conds + self.c_clip_seq = c_clip_seq + if not isinstance(dropout, list): + dropout = [dropout] * len(c_hidden) + if not isinstance(self_attn, list): + self_attn = [self_attn] * len(c_hidden) + + # CONDITIONING + self.effnet_mapper = nn.Sequential( + operations.Conv2d(c_effnet, c_hidden[0] * 4, kernel_size=1, dtype=dtype, device=device), + nn.GELU(), + operations.Conv2d(c_hidden[0] * 4, c_hidden[0], kernel_size=1, dtype=dtype, device=device), + LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + ) + self.pixels_mapper = nn.Sequential( + operations.Conv2d(c_pixels, c_hidden[0] * 4, kernel_size=1, dtype=dtype, device=device), + nn.GELU(), + operations.Conv2d(c_hidden[0] * 4, c_hidden[0], kernel_size=1, dtype=dtype, device=device), + LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + ) + self.clip_mapper = operations.Linear(c_clip, c_cond * c_clip_seq, dtype=dtype, device=device) + self.clip_norm = operations.LayerNorm(c_cond, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + + self.embedding = nn.Sequential( + nn.PixelUnshuffle(patch_size), + operations.Conv2d(c_in * (patch_size ** 2), c_hidden[0], kernel_size=1, dtype=dtype, device=device), + LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + ) + + def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0, self_attn=True): + if block_type == 'C': + return ResBlock(c_hidden, c_skip, kernel_size=kernel_size, dropout=dropout, dtype=dtype, device=device, operations=operations) + elif block_type == 'A': + return AttnBlock(c_hidden, c_cond, nhead, self_attn=self_attn, dropout=dropout, dtype=dtype, device=device, operations=operations) + elif block_type == 'F': + return FeedForwardBlock(c_hidden, dropout=dropout, dtype=dtype, device=device, operations=operations) + elif block_type == 'T': + return TimestepBlock(c_hidden, c_r, conds=t_conds, dtype=dtype, device=device, operations=operations) + else: + raise Exception(f'Block type {block_type} not supported') + + # BLOCKS + # -- down blocks + self.down_blocks = nn.ModuleList() + self.down_downscalers = nn.ModuleList() + self.down_repeat_mappers = nn.ModuleList() + for i in range(len(c_hidden)): + if i > 0: + self.down_downscalers.append(nn.Sequential( + LayerNorm2d_op(operations)(c_hidden[i - 1], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device), + operations.Conv2d(c_hidden[i - 1], c_hidden[i], kernel_size=2, stride=2, dtype=dtype, device=device), + )) + else: + self.down_downscalers.append(nn.Identity()) + down_block = nn.ModuleList() + for _ in range(blocks[0][i]): + for block_type in level_config[i]: + block = get_block(block_type, c_hidden[i], nhead[i], dropout=dropout[i], self_attn=self_attn[i]) + down_block.append(block) + self.down_blocks.append(down_block) + if block_repeat is not None: + block_repeat_mappers = nn.ModuleList() + for _ in range(block_repeat[0][i] - 1): + block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device)) + self.down_repeat_mappers.append(block_repeat_mappers) + + # -- up blocks + self.up_blocks = nn.ModuleList() + self.up_upscalers = nn.ModuleList() + self.up_repeat_mappers = nn.ModuleList() + for i in reversed(range(len(c_hidden))): + if i > 0: + self.up_upscalers.append(nn.Sequential( + LayerNorm2d_op(operations)(c_hidden[i], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device), + operations.ConvTranspose2d(c_hidden[i], c_hidden[i - 1], kernel_size=2, stride=2, dtype=dtype, device=device), + )) + else: + self.up_upscalers.append(nn.Identity()) + up_block = nn.ModuleList() + for j in range(blocks[1][::-1][i]): + for k, block_type in enumerate(level_config[i]): + c_skip = c_hidden[i] if i < len(c_hidden) - 1 and j == k == 0 else 0 + block = get_block(block_type, c_hidden[i], nhead[i], c_skip=c_skip, dropout=dropout[i], + self_attn=self_attn[i]) + up_block.append(block) + self.up_blocks.append(up_block) + if block_repeat is not None: + block_repeat_mappers = nn.ModuleList() + for _ in range(block_repeat[1][::-1][i] - 1): + block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device)) + self.up_repeat_mappers.append(block_repeat_mappers) + + # OUTPUT + self.clf = nn.Sequential( + LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device), + operations.Conv2d(c_hidden[0], c_out * (patch_size ** 2), kernel_size=1, dtype=dtype, device=device), + nn.PixelShuffle(patch_size), + ) + + # --- WEIGHT INIT --- + # self.apply(self._init_weights) # General init + # nn.init.normal_(self.clip_mapper.weight, std=0.02) # conditionings + # nn.init.normal_(self.effnet_mapper[0].weight, std=0.02) # conditionings + # nn.init.normal_(self.effnet_mapper[2].weight, std=0.02) # conditionings + # nn.init.normal_(self.pixels_mapper[0].weight, std=0.02) # conditionings + # nn.init.normal_(self.pixels_mapper[2].weight, std=0.02) # conditionings + # torch.nn.init.xavier_uniform_(self.embedding[1].weight, 0.02) # inputs + # nn.init.constant_(self.clf[1].weight, 0) # outputs + # + # # blocks + # for level_block in self.down_blocks + self.up_blocks: + # for block in level_block: + # if isinstance(block, ResBlock) or isinstance(block, FeedForwardBlock): + # block.channelwise[-1].weight.data *= np.sqrt(1 / sum(blocks[0])) + # elif isinstance(block, TimestepBlock): + # for layer in block.modules(): + # if isinstance(layer, nn.Linear): + # nn.init.constant_(layer.weight, 0) + # + # def _init_weights(self, m): + # if isinstance(m, (nn.Conv2d, nn.Linear)): + # torch.nn.init.xavier_uniform_(m.weight) + # if m.bias is not None: + # nn.init.constant_(m.bias, 0) + + def gen_r_embedding(self, r, max_positions=10000): + r = r * max_positions + half_dim = self.c_r // 2 + emb = math.log(max_positions) / (half_dim - 1) + emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp() + emb = r[:, None] * emb[None, :] + emb = torch.cat([emb.sin(), emb.cos()], dim=1) + if self.c_r % 2 == 1: # zero pad + emb = nn.functional.pad(emb, (0, 1), mode='constant') + return emb + + def gen_c_embeddings(self, clip): + if len(clip.shape) == 2: + clip = clip.unsqueeze(1) + clip = self.clip_mapper(clip).view(clip.size(0), clip.size(1) * self.c_clip_seq, -1) + clip = self.clip_norm(clip) + return clip + + def _down_encode(self, x, r_embed, clip): + level_outputs = [] + block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers) + for down_block, downscaler, repmap in block_group: + x = downscaler(x) + for i in range(len(repmap) + 1): + for block in down_block: + if isinstance(block, ResBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + ResBlock)): + x = block(x) + elif isinstance(block, AttnBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + AttnBlock)): + x = block(x, clip) + elif isinstance(block, TimestepBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + TimestepBlock)): + x = block(x, r_embed) + else: + x = block(x) + if i < len(repmap): + x = repmap[i](x) + level_outputs.insert(0, x) + return level_outputs + + def _up_decode(self, level_outputs, r_embed, clip): + x = level_outputs[0] + block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers) + for i, (up_block, upscaler, repmap) in enumerate(block_group): + for j in range(len(repmap) + 1): + for k, block in enumerate(up_block): + if isinstance(block, ResBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + ResBlock)): + skip = level_outputs[i] if k == 0 and i > 0 else None + if skip is not None and (x.size(-1) != skip.size(-1) or x.size(-2) != skip.size(-2)): + x = torch.nn.functional.interpolate(x, skip.shape[-2:], mode='bilinear', + align_corners=True) + x = block(x, skip) + elif isinstance(block, AttnBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + AttnBlock)): + x = block(x, clip) + elif isinstance(block, TimestepBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + TimestepBlock)): + x = block(x, r_embed) + else: + x = block(x) + if j < len(repmap): + x = repmap[j](x) + x = upscaler(x) + return x + + def forward(self, x, r, effnet, clip, pixels=None, **kwargs): + if pixels is None: + pixels = x.new_zeros(x.size(0), 3, 8, 8) + + # Process the conditioning embeddings + r_embed = self.gen_r_embedding(r).to(dtype=x.dtype) + for c in self.t_conds: + t_cond = kwargs.get(c, torch.zeros_like(r)) + r_embed = torch.cat([r_embed, self.gen_r_embedding(t_cond).to(dtype=x.dtype)], dim=1) + clip = self.gen_c_embeddings(clip) + + # Model Blocks + x = self.embedding(x) + x = x + self.effnet_mapper( + nn.functional.interpolate(effnet, size=x.shape[-2:], mode='bilinear', align_corners=True)) + x = x + nn.functional.interpolate(self.pixels_mapper(pixels), size=x.shape[-2:], mode='bilinear', + align_corners=True) + level_outputs = self._down_encode(x, r_embed, clip) + x = self._up_decode(level_outputs, r_embed, clip) + return self.clf(x) + + def update_weights_ema(self, src_model, beta=0.999): + for self_params, src_params in zip(self.parameters(), src_model.parameters()): + self_params.data = self_params.data * beta + src_params.data.clone().to(self_params.device) * (1 - beta) + for self_buffers, src_buffers in zip(self.buffers(), src_model.buffers()): + self_buffers.data = self_buffers.data * beta + src_buffers.data.clone().to(self_buffers.device) * (1 - beta) diff --git a/comfy/ldm/cascade/stage_c.py b/comfy/ldm/cascade/stage_c.py index 2e0f4706..08e33ade 100644 --- a/comfy/ldm/cascade/stage_c.py +++ b/comfy/ldm/cascade/stage_c.py @@ -42,7 +42,7 @@ class StageC(nn.Module): def __init__(self, c_in=16, c_out=16, c_r=64, patch_size=1, c_cond=2048, c_hidden=[2048, 2048], nhead=[32, 32], blocks=[[8, 24], [24, 8]], block_repeat=[[1, 1], [1, 1]], level_config=['CTA', 'CTA'], c_clip_text=1280, c_clip_text_pooled=1280, c_clip_img=768, c_clip_seq=4, kernel_size=3, - dropout=[0.1, 0.1], self_attn=True, t_conds=['sca', 'crp'], switch_level=[False], stable_cascade_stage=None, + dropout=[0.0, 0.0], self_attn=True, t_conds=['sca', 'crp'], switch_level=[False], stable_cascade_stage=None, dtype=None, device=None, operations=None): super().__init__() self.dtype = dtype @@ -100,7 +100,7 @@ class StageC(nn.Module): if block_repeat is not None: block_repeat_mappers = nn.ModuleList() for _ in range(block_repeat[0][i] - 1): - block_repeat_mappers.append(nn.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1)) + block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device)) self.down_repeat_mappers.append(block_repeat_mappers) # -- up blocks @@ -126,12 +126,12 @@ class StageC(nn.Module): if block_repeat is not None: block_repeat_mappers = nn.ModuleList() for _ in range(block_repeat[1][::-1][i] - 1): - block_repeat_mappers.append(nn.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1)) + block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device)) self.up_repeat_mappers.append(block_repeat_mappers) # OUTPUT self.clf = nn.Sequential( - LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6), + LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device), operations.Conv2d(c_hidden[0], c_out * (patch_size ** 2), kernel_size=1, dtype=dtype, device=device), nn.PixelShuffle(patch_size), ) diff --git a/comfy/model_base.py b/comfy/model_base.py index dde2a2f3..fefce763 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -1,6 +1,7 @@ import torch from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep from comfy.ldm.cascade.stage_c import StageC +from comfy.ldm.cascade.stage_b import StageB from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation from comfy.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation import comfy.model_management @@ -461,3 +462,27 @@ class StableCascade_C(BaseModel): out['clip_text'] = comfy.conds.CONDCrossAttn(cross_attn) return out + +class StableCascade_B(BaseModel): + def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None): + super().__init__(model_config, model_type, device=device, unet_model=StageB) + self.diffusion_model.eval().requires_grad_(False) + + def extra_conds(self, **kwargs): + out = {} + noise = kwargs.get("noise", None) + + clip_text_pooled = kwargs["pooled_output"] + if clip_text_pooled is not None: + out['clip_text_pooled'] = comfy.conds.CONDRegular(clip_text_pooled) + + #size of prior doesn't really matter if zeros because it gets resized but I still want it to get batched + prior = kwargs.get("stable_cascade_prior", torch.zeros((1, 16, (noise.shape[2] * 4) // 42, (noise.shape[3] * 4) // 42), dtype=noise.dtype, layout=noise.layout, device=noise.device)) + + out["effnet"] = comfy.conds.CONDRegular(prior) + out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,))) + + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['clip'] = comfy.conds.CONDCrossAttn(cross_attn) + return out diff --git a/comfy/ops.py b/comfy/ops.py index f674b47f..517688e8 100644 --- a/comfy/ops.py +++ b/comfy/ops.py @@ -1,3 +1,21 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Stability AI + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + import torch import comfy.model_management @@ -78,7 +96,11 @@ class disable_weight_init: return None def forward_comfy_cast_weights(self, input): - weight, bias = cast_bias_weight(self, input) + if self.weight is not None: + weight, bias = cast_bias_weight(self, input) + else: + weight = None + bias = None return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps) def forward(self, *args, **kwargs): @@ -87,6 +109,28 @@ class disable_weight_init: else: return super().forward(*args, **kwargs) + class ConvTranspose2d(torch.nn.ConvTranspose2d): + comfy_cast_weights = False + def reset_parameters(self): + return None + + def forward_comfy_cast_weights(self, input, output_size=None): + num_spatial_dims = 2 + output_padding = self._output_padding( + input, output_size, self.stride, self.padding, self.kernel_size, + num_spatial_dims, self.dilation) + + weight, bias = cast_bias_weight(self, input) + return torch.nn.functional.conv_transpose2d( + input, weight, bias, self.stride, self.padding, + output_padding, self.groups, self.dilation) + + def forward(self, *args, **kwargs): + if self.comfy_cast_weights: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + @classmethod def conv_nd(s, dims, *args, **kwargs): if dims == 2: @@ -112,3 +156,6 @@ class manual_cast(disable_weight_init): class LayerNorm(disable_weight_init.LayerNorm): comfy_cast_weights = True + + class ConvTranspose2d(disable_weight_init.ConvTranspose2d): + comfy_cast_weights = True diff --git a/comfy/supported_models.py b/comfy/supported_models.py index a8863e72..7859bac9 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -338,6 +338,20 @@ class Stable_Cascade_C(supported_models_base.BASE): def clip_target(self): return None +class Stable_Cascade_B(Stable_Cascade_C): + unet_config = { + "stable_cascade_stage": 'b', + } -models = [Stable_Zero123, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C] + unet_extra_config = {} + + latent_format = latent_formats.SC_B + supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.StableCascade_B(self, device=device) + return out + + +models = [Stable_Zero123, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B] models += [SVD_img2vid] diff --git a/comfy/utils.py b/comfy/utils.py index 1113bf0f..04cf76ed 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -169,6 +169,8 @@ UNET_MAP_BASIC = { } def unet_to_diffusers(unet_config): + if "num_res_blocks" not in unet_config: + return {} num_res_blocks = unet_config["num_res_blocks"] channel_mult = unet_config["channel_mult"] transformer_depth = unet_config["transformer_depth"][:] diff --git a/comfy_extras/nodes_stable_cascade.py b/comfy_extras/nodes_stable_cascade.py new file mode 100644 index 00000000..5d31c1e5 --- /dev/null +++ b/comfy_extras/nodes_stable_cascade.py @@ -0,0 +1,74 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Stability AI + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import torch +import nodes + + +class StableCascade_EmptyLatentImage: + def __init__(self, device="cpu"): + self.device = device + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "width": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}), + "compression": ("INT", {"default": 42, "min": 32, "max": 64, "step": 1}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}) + }} + RETURN_TYPES = ("LATENT", "LATENT") + RETURN_NAMES = ("stage_c", "stage_b") + FUNCTION = "generate" + + CATEGORY = "_for_testing/stable_cascade" + + def generate(self, width, height, compression, batch_size=1): + c_latent = torch.zeros([batch_size, 16, height // compression, width // compression]) + b_latent = torch.zeros([batch_size, 4, height // 4, width // 4]) + return ({ + "samples": c_latent, + }, { + "samples": b_latent, + }) + +class StableCascade_StageB_Conditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": { "conditioning": ("CONDITIONING",), + "stage_c": ("LATENT",), + }} + RETURN_TYPES = ("CONDITIONING",) + + FUNCTION = "set_prior" + + CATEGORY = "_for_testing/stable_cascade" + + def set_prior(self, conditioning, stage_c): + c = [] + for t in conditioning: + d = t[1].copy() + d['stable_cascade_prior'] = stage_c['samples'] + n = [t[0], d] + c.append(n) + return (c, ) + +NODE_CLASS_MAPPINGS = { + "StableCascade_EmptyLatentImage": StableCascade_EmptyLatentImage, + "StableCascade_StageB_Conditioning": StableCascade_StageB_Conditioning, +} diff --git a/nodes.py b/nodes.py index d4f6f3ab..7b8f09de 100644 --- a/nodes.py +++ b/nodes.py @@ -1967,6 +1967,7 @@ def init_custom_nodes(): "nodes_sdupscale.py", "nodes_photomaker.py", "nodes_cond.py", + "nodes_stable_cascade.py", ] for node_file in extras_files: From 97d03ae04a228d9a51581106eb9f4e90009ac4f6 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 16 Feb 2024 13:29:04 -0500 Subject: [PATCH 014/378] StableCascade CLIP model support. --- comfy/sd.py | 14 +++++++++++--- comfy/sd1_clip.py | 4 ++-- comfy/sdxl_clip.py | 22 ++++++++++++++++++++++ comfy/supported_models.py | 2 +- nodes.py | 9 +++++++-- 5 files changed, 43 insertions(+), 8 deletions(-) diff --git a/comfy/sd.py b/comfy/sd.py index f3ec62b3..d8c0bfa7 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -1,4 +1,5 @@ import torch +from enum import Enum from comfy import model_management from .ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine @@ -309,8 +310,11 @@ def load_style_model(ckpt_path): model.load_state_dict(model_data) return StyleModel(model) +class CLIPType(Enum): + STABLE_DIFFUSION = 1 + STABLE_CASCADE = 2 -def load_clip(ckpt_paths, embedding_directory=None): +def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION): clip_data = [] for p in ckpt_paths: clip_data.append(comfy.utils.load_torch_file(p, safe_load=True)) @@ -326,8 +330,12 @@ def load_clip(ckpt_paths, embedding_directory=None): clip_target.params = {} if len(clip_data) == 1: if "text_model.encoder.layers.30.mlp.fc1.weight" in clip_data[0]: - clip_target.clip = sdxl_clip.SDXLRefinerClipModel - clip_target.tokenizer = sdxl_clip.SDXLTokenizer + if clip_type == CLIPType.STABLE_CASCADE: + clip_target.clip = sdxl_clip.StableCascadeClipModel + clip_target.tokenizer = sdxl_clip.StableCascadeTokenizer + else: + clip_target.clip = sdxl_clip.SDXLRefinerClipModel + clip_target.tokenizer = sdxl_clip.SDXLTokenizer elif "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data[0]: clip_target.clip = sd2_clip.SD2ClipModel clip_target.tokenizer = sd2_clip.SD2Tokenizer diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py index 65ea909f..8287ad2e 100644 --- a/comfy/sd1_clip.py +++ b/comfy/sd1_clip.py @@ -67,7 +67,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): ] def __init__(self, version="openai/clip-vit-large-patch14", device="cpu", max_length=77, freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=comfy.clip_model.CLIPTextModel, - special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True): # clip-vit-base-patch32 + special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False): # clip-vit-base-patch32 super().__init__() assert layer in self.LAYERS @@ -88,7 +88,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): self.special_tokens = special_tokens self.text_projection = torch.nn.Parameter(torch.eye(self.transformer.get_input_embeddings().weight.shape[1])) self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055)) - self.enable_attention_masks = False + self.enable_attention_masks = enable_attention_masks self.layer_norm_hidden_state = layer_norm_hidden_state if layer == "hidden": diff --git a/comfy/sdxl_clip.py b/comfy/sdxl_clip.py index b35056bb..3ce5c7e0 100644 --- a/comfy/sdxl_clip.py +++ b/comfy/sdxl_clip.py @@ -64,3 +64,25 @@ class SDXLClipModel(torch.nn.Module): class SDXLRefinerClipModel(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None): super().__init__(device=device, dtype=dtype, clip_name="g", clip_model=SDXLClipG) + + +class StableCascadeClipGTokenizer(sd1_clip.SDTokenizer): + def __init__(self, tokenizer_path=None, embedding_directory=None): + super().__init__(tokenizer_path, pad_with_end=True, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g') + +class StableCascadeTokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None): + super().__init__(embedding_directory=embedding_directory, clip_name="g", tokenizer=StableCascadeClipGTokenizer) + +class StableCascadeClipG(sd1_clip.SDClipModel): + def __init__(self, device="cpu", max_length=77, freeze=True, layer="hidden", layer_idx=-1, dtype=None): + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json") + super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, + special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=False, enable_attention_masks=True) + + def load_sd(self, sd): + return super().load_sd(sd) + +class StableCascadeClipModel(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None): + super().__init__(device=device, dtype=dtype, clip_name="g", clip_model=StableCascadeClipG) diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 7859bac9..3a317edc 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -336,7 +336,7 @@ class Stable_Cascade_C(supported_models_base.BASE): return out def clip_target(self): - return None + return supported_models_base.ClipTarget(sdxl_clip.StableCascadeTokenizer, sdxl_clip.StableCascadeClipModel) class Stable_Cascade_B(Stable_Cascade_C): unet_config = { diff --git a/nodes.py b/nodes.py index 7b8f09de..47203f41 100644 --- a/nodes.py +++ b/nodes.py @@ -854,15 +854,20 @@ class CLIPLoader: @classmethod def INPUT_TYPES(s): return {"required": { "clip_name": (folder_paths.get_filename_list("clip"), ), + "type": (["stable_diffusion", "stable_cascade"], ), }} RETURN_TYPES = ("CLIP",) FUNCTION = "load_clip" CATEGORY = "advanced/loaders" - def load_clip(self, clip_name): + def load_clip(self, clip_name, type="stable_diffusion"): + clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION + if type == "stable_cascade": + clip_type = comfy.sd.CLIPType.STABLE_CASCADE + clip_path = folder_paths.get_full_path("clip", clip_name) - clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings")) + clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type) return (clip,) class DualCLIPLoader: From 0b3c50480c8cfcfe22c2a4059f91cec337114a78 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 16 Feb 2024 23:01:54 -0500 Subject: [PATCH 015/378] Make --force-fp32 disable loading models in bf16. --- comfy/model_management.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/comfy/model_management.py b/comfy/model_management.py index eb7178b4..f0f4ebf5 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -772,6 +772,9 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma return True def should_use_bf16(device=None): + if FORCE_FP32: + return False + if is_intel_xpu(): return True From f2d1d16f4f7c4221af3d6d121817e4bd8f6ddc88 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 16 Feb 2024 23:41:23 -0500 Subject: [PATCH 016/378] Support Stable Cascade Stage B lite. --- comfy/model_detection.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/comfy/model_detection.py b/comfy/model_detection.py index 8d4fb7b6..8fca6d8c 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -46,6 +46,18 @@ def detect_unet_config(state_dict, key_prefix): unet_config['c_cond'] = 2048 elif '{}clip_mapper.weight'.format(key_prefix) in state_dict_keys: unet_config['stable_cascade_stage'] = 'b' + w = state_dict['{}down_blocks.1.0.channelwise.0.weight'.format(key_prefix)] + if w.shape[-1] == 640: + unet_config['c_hidden'] = [320, 640, 1280, 1280] + unet_config['nhead'] = [-1, -1, 20, 20] + unet_config['blocks'] = [[2, 6, 28, 6], [6, 28, 6, 2]] + unet_config['block_repeat'] = [[1, 1, 1, 1], [3, 3, 2, 2]] + elif w.shape[-1] == 576: #stage b lite + unet_config['c_hidden'] = [320, 576, 1152, 1152] + unet_config['nhead'] = [-1, 9, 18, 18] + unet_config['blocks'] = [[2, 4, 14, 4], [4, 14, 4, 2]] + unet_config['block_repeat'] = [[1, 1, 1, 1], [2, 2, 2, 2]] + return unet_config unet_config = { From 805c36ac9c0611a483b1f494e2dfe6b67f09fe36 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 17 Feb 2024 00:42:22 -0500 Subject: [PATCH 017/378] Make Stable Cascade work on old pytorch 2.0 --- comfy/ldm/cascade/stage_a.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/comfy/ldm/cascade/stage_a.py b/comfy/ldm/cascade/stage_a.py index 55fdbf17..260ccfc0 100644 --- a/comfy/ldm/cascade/stage_a.py +++ b/comfy/ldm/cascade/stage_a.py @@ -150,7 +150,11 @@ class ResBlock(nn.Module): mods = self.gammas x_temp = self._norm(x, self.norm1) * (1 + mods[0]) + mods[1] - x = x + self.depthwise(x_temp) * mods[2] + try: + x = x + self.depthwise(x_temp) * mods[2] + except: #operation not implemented for bf16 + x_temp = self.depthwise[0](x_temp.float()).to(x.dtype) + x = x + self.depthwise[1](x_temp) * mods[2] x_temp = self._norm(x, self.norm2) * (1 + mods[3]) + mods[4] x = x + self.channelwise(x_temp.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) * mods[5] From 6c875d846b54ee34eb032fdb790a2fd1621d18f4 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 17 Feb 2024 07:46:17 -0500 Subject: [PATCH 018/378] Fix clip attention mask issues on some hardware. --- comfy/clip_model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/clip_model.py b/comfy/clip_model.py index 09e7bbca..9ba4e039 100644 --- a/comfy/clip_model.py +++ b/comfy/clip_model.py @@ -97,7 +97,7 @@ class CLIPTextModel_(torch.nn.Module): x = self.embeddings(input_tokens) mask = None if attention_mask is not None: - mask = 1.0 - attention_mask.to(x.dtype).unsqueeze(1).unsqueeze(1).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) + mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], attention_mask.shape[-1], attention_mask.shape[-1]) mask = mask.masked_fill(mask.to(torch.bool), float("-inf")) causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1) From 929e266f3e298478a1433fcff8b0209e52790068 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 17 Feb 2024 08:13:17 -0500 Subject: [PATCH 019/378] Manual cast for bf16 on older GPUs. --- comfy/model_management.py | 25 +++++++++++++++++++++++-- 1 file changed, 23 insertions(+), 2 deletions(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index f0f4ebf5..681208ea 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -499,7 +499,7 @@ def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, tor if should_use_fp16(device=device, model_params=model_params, manual_cast=True): if torch.float16 in supported_dtypes: return torch.float16 - if should_use_bf16(device): + if should_use_bf16(device, model_params=model_params, manual_cast=True): if torch.bfloat16 in supported_dtypes: return torch.bfloat16 return torch.float32 @@ -771,10 +771,24 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma return True -def should_use_bf16(device=None): +def should_use_bf16(device=None, model_params=0, prioritize_performance=True, manual_cast=False): + if device is not None: + if is_device_cpu(device): #TODO ? bf16 works on CPU but is extremely slow + return False + + if device is not None: #TODO not sure about mps bf16 support + if is_device_mps(device): + return False + if FORCE_FP32: return False + if directml_enabled: + return False + + if cpu_mode() or mps_mode(): + return False + if is_intel_xpu(): return True @@ -785,6 +799,13 @@ def should_use_bf16(device=None): if props.major >= 8: return True + bf16_works = torch.cuda.is_bf16_supported() + + if bf16_works or manual_cast: + free_model_memory = (get_free_memory() * 0.9 - minimum_inference_memory()) + if (not prioritize_performance) or model_params * 4 > free_model_memory: + return True + return False def soft_empty_cache(force=False): From 5b40e7a5ed192e217575c55e061c17a52cf9a15d Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 17 Feb 2024 11:38:47 -0500 Subject: [PATCH 020/378] Implement shift schedule for cascade stage C. --- comfy/model_sampling.py | 25 ++++++++++++++++++++++--- comfy/supported_models.py | 8 ++++++++ 2 files changed, 30 insertions(+), 3 deletions(-) diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py index b1fbf3e2..f42f3015 100644 --- a/comfy/model_sampling.py +++ b/comfy/model_sampling.py @@ -136,9 +136,16 @@ class ModelSamplingContinuousEDM(torch.nn.Module): class StableCascadeSampling(ModelSamplingDiscrete): def __init__(self, model_config=None): super().__init__() + + if model_config is not None: + sampling_settings = model_config.sampling_settings + else: + sampling_settings = {} + self.num_timesteps = 1000 + self.shift = sampling_settings.get("shift", 1.0) cosine_s=8e-3 - self.cosine_s = torch.tensor([cosine_s]) + self.cosine_s = torch.tensor(cosine_s) sigmas = torch.empty((self.num_timesteps), dtype=torch.float32) self._init_alpha_cumprod = torch.cos(self.cosine_s / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 for x in range(self.num_timesteps): @@ -148,11 +155,23 @@ class StableCascadeSampling(ModelSamplingDiscrete): self.set_sigmas(sigmas) def sigma(self, timestep): - alpha_cumprod = (torch.cos((timestep + self.cosine_s) / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 / self._init_alpha_cumprod).clamp(0.0001, 0.9999) + alpha_cumprod = (torch.cos((timestep + self.cosine_s) / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 / self._init_alpha_cumprod) + + if self.shift != 1.0: + var = alpha_cumprod + logSNR = (var/(1-var)).log() + logSNR += 2 * torch.log(1.0 / torch.tensor(self.shift)) + alpha_cumprod = logSNR.sigmoid() + + alpha_cumprod = alpha_cumprod.clamp(0.0001, 0.9999) return ((1 - alpha_cumprod) / alpha_cumprod) ** 0.5 def timestep(self, sigma): - return super().timestep(sigma) / 1000.0 + var = 1 / ((sigma * sigma) + 1) + var = var.clamp(0, 1.0) + s, min_var = self.cosine_s.to(var.device), self._init_alpha_cumprod.to(var.device) + t = (((var * min_var) ** 0.5).acos() / (torch.pi * 0.5)) * (1 + s) - s + return t def percent_to_sigma(self, percent): if percent <= 0.0: diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 3a317edc..1a673646 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -316,6 +316,10 @@ class Stable_Cascade_C(supported_models_base.BASE): latent_format = latent_formats.SC_Prior supported_inference_dtypes = [torch.bfloat16, torch.float32] + sampling_settings = { + "shift": 2.0, + } + def process_unet_state_dict(self, state_dict): key_list = list(state_dict.keys()) for y in ["weight", "bias"]: @@ -348,6 +352,10 @@ class Stable_Cascade_B(Stable_Cascade_C): latent_format = latent_formats.SC_B supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] + sampling_settings = { + "shift": 1.0, + } + def get_model(self, state_dict, prefix="", device=None): out = model_base.StableCascade_B(self, device=device) return out From 3b9969c1c5a0428fc1d8be79129a9e97cfcc5e7d Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 17 Feb 2024 12:13:13 -0500 Subject: [PATCH 021/378] Properly fix attention masks in CLIP with batches. --- comfy/clip_model.py | 2 +- comfy/ldm/modules/attention.py | 9 ++++++++- 2 files changed, 9 insertions(+), 2 deletions(-) diff --git a/comfy/clip_model.py b/comfy/clip_model.py index 9ba4e039..9b82a246 100644 --- a/comfy/clip_model.py +++ b/comfy/clip_model.py @@ -97,7 +97,7 @@ class CLIPTextModel_(torch.nn.Module): x = self.embeddings(input_tokens) mask = None if attention_mask is not None: - mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], attention_mask.shape[-1], attention_mask.shape[-1]) + mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) mask = mask.masked_fill(mask.to(torch.bool), float("-inf")) causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1) diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py index 9c9cb761..bb539def 100644 --- a/comfy/ldm/modules/attention.py +++ b/comfy/ldm/modules/attention.py @@ -114,7 +114,8 @@ def attention_basic(q, k, v, heads, mask=None): mask = repeat(mask, 'b j -> (b h) () j', h=h) sim.masked_fill_(~mask, max_neg_value) else: - sim += mask + mask = mask.reshape(mask.shape[0], -1, mask.shape[-2], mask.shape[-1]).expand(-1, heads, -1, -1).reshape(sim.shape) + sim.add_(mask) # attention, what we cannot get enough of sim = sim.softmax(dim=-1) @@ -165,6 +166,9 @@ def attention_sub_quad(query, key, value, heads, mask=None): if query_chunk_size is None: query_chunk_size = 512 + if mask is not None: + mask = mask.reshape(mask.shape[0], -1, mask.shape[-2], mask.shape[-1]).expand(-1, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) + hidden_states = efficient_dot_product_attention( query, key, @@ -223,6 +227,9 @@ def attention_split(q, k, v, heads, mask=None): raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). ' f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free') + if mask is not None: + mask = mask.reshape(mask.shape[0], -1, mask.shape[-2], mask.shape[-1]).expand(-1, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) + # print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size) first_op_done = False cleared_cache = False From f8706546f3842fdc160c7ab831c2100701d5456e Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 17 Feb 2024 15:22:21 -0500 Subject: [PATCH 022/378] Fix attention mask batch size in some attention functions. --- comfy/ldm/modules/attention.py | 18 +++++++++++++++--- 1 file changed, 15 insertions(+), 3 deletions(-) diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py index bb539def..f1dca2c2 100644 --- a/comfy/ldm/modules/attention.py +++ b/comfy/ldm/modules/attention.py @@ -114,7 +114,11 @@ def attention_basic(q, k, v, heads, mask=None): mask = repeat(mask, 'b j -> (b h) () j', h=h) sim.masked_fill_(~mask, max_neg_value) else: - mask = mask.reshape(mask.shape[0], -1, mask.shape[-2], mask.shape[-1]).expand(-1, heads, -1, -1).reshape(sim.shape) + if len(mask.shape) == 2: + bs = 1 + else: + bs = mask.shape[0] + mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(-1, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) sim.add_(mask) # attention, what we cannot get enough of @@ -167,7 +171,11 @@ def attention_sub_quad(query, key, value, heads, mask=None): query_chunk_size = 512 if mask is not None: - mask = mask.reshape(mask.shape[0], -1, mask.shape[-2], mask.shape[-1]).expand(-1, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) + if len(mask.shape) == 2: + bs = 1 + else: + bs = mask.shape[0] + mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(-1, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) hidden_states = efficient_dot_product_attention( query, @@ -228,7 +236,11 @@ def attention_split(q, k, v, heads, mask=None): f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free') if mask is not None: - mask = mask.reshape(mask.shape[0], -1, mask.shape[-2], mask.shape[-1]).expand(-1, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) + if len(mask.shape) == 2: + bs = 1 + else: + bs = mask.shape[0] + mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(-1, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) # print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size) first_op_done = False From 11e3221f1fd05d49b261ccec7dd99b704a86a89f Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 17 Feb 2024 15:27:31 -0500 Subject: [PATCH 023/378] fp8 weight support for Stable Cascade. --- comfy/ldm/cascade/common.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/ldm/cascade/common.py b/comfy/ldm/cascade/common.py index c2ef3ec4..124902c0 100644 --- a/comfy/ldm/cascade/common.py +++ b/comfy/ldm/cascade/common.py @@ -84,7 +84,7 @@ class GlobalResponseNorm(nn.Module): def forward(self, x): Gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True) Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6) - return self.gamma.to(x.device) * (x * Nx) + self.beta.to(x.device) + x + return self.gamma.to(device=x.device, dtype=x.dtype) * (x * Nx) + self.beta.to(device=x.device, dtype=x.dtype) + x class ResBlock(nn.Module): From 6bcf57ff10f4488f7dfc6e3c47ac516967567c22 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 17 Feb 2024 16:15:18 -0500 Subject: [PATCH 024/378] Fix attention masks properly for multiple batches. --- comfy/ldm/modules/attention.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py index f1dca2c2..48399bc0 100644 --- a/comfy/ldm/modules/attention.py +++ b/comfy/ldm/modules/attention.py @@ -118,7 +118,7 @@ def attention_basic(q, k, v, heads, mask=None): bs = 1 else: bs = mask.shape[0] - mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(-1, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) + mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) sim.add_(mask) # attention, what we cannot get enough of @@ -175,7 +175,7 @@ def attention_sub_quad(query, key, value, heads, mask=None): bs = 1 else: bs = mask.shape[0] - mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(-1, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) + mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) hidden_states = efficient_dot_product_attention( query, @@ -240,7 +240,7 @@ def attention_split(q, k, v, heads, mask=None): bs = 1 else: bs = mask.shape[0] - mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(-1, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) + mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) # print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size) first_op_done = False From 8b60d33bb7ce969a53fc5e25bfa0e2dca7a17b23 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 18 Feb 2024 00:55:23 -0500 Subject: [PATCH 025/378] Add ModelSamplingStableCascade to control the shift sampling parameter. shift is 2.0 by default on Stage C and 1.0 by default on Stage B. --- comfy/model_sampling.py | 12 ++++++++---- comfy_extras/nodes_model_advanced.py | 27 +++++++++++++++++++++++++++ 2 files changed, 35 insertions(+), 4 deletions(-) diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py index f42f3015..ae42d81f 100644 --- a/comfy/model_sampling.py +++ b/comfy/model_sampling.py @@ -142,12 +142,16 @@ class StableCascadeSampling(ModelSamplingDiscrete): else: sampling_settings = {} - self.num_timesteps = 1000 - self.shift = sampling_settings.get("shift", 1.0) - cosine_s=8e-3 + self.set_parameters(sampling_settings.get("shift", 1.0)) + + def set_parameters(self, shift=1.0, cosine_s=8e-3): + self.shift = shift self.cosine_s = torch.tensor(cosine_s) - sigmas = torch.empty((self.num_timesteps), dtype=torch.float32) self._init_alpha_cumprod = torch.cos(self.cosine_s / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 + + #This part is just for compatibility with some schedulers in the codebase + self.num_timesteps = 1000 + sigmas = torch.empty((self.num_timesteps), dtype=torch.float32) for x in range(self.num_timesteps): t = x / self.num_timesteps sigmas[x] = self.sigma(t) diff --git a/comfy_extras/nodes_model_advanced.py b/comfy_extras/nodes_model_advanced.py index 541ce8fa..ac7c1c17 100644 --- a/comfy_extras/nodes_model_advanced.py +++ b/comfy_extras/nodes_model_advanced.py @@ -99,6 +99,32 @@ class ModelSamplingDiscrete: m.add_object_patch("model_sampling", model_sampling) return (m, ) +class ModelSamplingStableCascade: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "shift": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step":0.01}), + }} + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "advanced/model" + + def patch(self, model, shift): + m = model.clone() + + sampling_base = comfy.model_sampling.StableCascadeSampling + sampling_type = comfy.model_sampling.EPS + + class ModelSamplingAdvanced(sampling_base, sampling_type): + pass + + model_sampling = ModelSamplingAdvanced(model.model.model_config) + model_sampling.set_parameters(shift) + m.add_object_patch("model_sampling", model_sampling) + return (m, ) + class ModelSamplingContinuousEDM: @classmethod def INPUT_TYPES(s): @@ -171,5 +197,6 @@ class RescaleCFG: NODE_CLASS_MAPPINGS = { "ModelSamplingDiscrete": ModelSamplingDiscrete, "ModelSamplingContinuousEDM": ModelSamplingContinuousEDM, + "ModelSamplingStableCascade": ModelSamplingStableCascade, "RescaleCFG": RescaleCFG, } From dccca1daa5af1954d55918f365e83a3331019549 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 18 Feb 2024 02:20:23 -0500 Subject: [PATCH 026/378] Fix gligen lowvram mode. --- comfy/gligen.py | 52 +++++++++++++++++++++++++------------------------ 1 file changed, 27 insertions(+), 25 deletions(-) diff --git a/comfy/gligen.py b/comfy/gligen.py index 71892dfb..59252276 100644 --- a/comfy/gligen.py +++ b/comfy/gligen.py @@ -2,7 +2,8 @@ import torch from torch import nn from .ldm.modules.attention import CrossAttention from inspect import isfunction - +import comfy.ops +ops = comfy.ops.manual_cast def exists(val): return val is not None @@ -22,7 +23,7 @@ def default(val, d): class GEGLU(nn.Module): def __init__(self, dim_in, dim_out): super().__init__() - self.proj = nn.Linear(dim_in, dim_out * 2) + self.proj = ops.Linear(dim_in, dim_out * 2) def forward(self, x): x, gate = self.proj(x).chunk(2, dim=-1) @@ -35,14 +36,14 @@ class FeedForward(nn.Module): inner_dim = int(dim * mult) dim_out = default(dim_out, dim) project_in = nn.Sequential( - nn.Linear(dim, inner_dim), + ops.Linear(dim, inner_dim), nn.GELU() ) if not glu else GEGLU(dim, inner_dim) self.net = nn.Sequential( project_in, nn.Dropout(dropout), - nn.Linear(inner_dim, dim_out) + ops.Linear(inner_dim, dim_out) ) def forward(self, x): @@ -57,11 +58,12 @@ class GatedCrossAttentionDense(nn.Module): query_dim=query_dim, context_dim=context_dim, heads=n_heads, - dim_head=d_head) + dim_head=d_head, + operations=ops) self.ff = FeedForward(query_dim, glu=True) - self.norm1 = nn.LayerNorm(query_dim) - self.norm2 = nn.LayerNorm(query_dim) + self.norm1 = ops.LayerNorm(query_dim) + self.norm2 = ops.LayerNorm(query_dim) self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) @@ -87,17 +89,18 @@ class GatedSelfAttentionDense(nn.Module): # we need a linear projection since we need cat visual feature and obj # feature - self.linear = nn.Linear(context_dim, query_dim) + self.linear = ops.Linear(context_dim, query_dim) self.attn = CrossAttention( query_dim=query_dim, context_dim=query_dim, heads=n_heads, - dim_head=d_head) + dim_head=d_head, + operations=ops) self.ff = FeedForward(query_dim, glu=True) - self.norm1 = nn.LayerNorm(query_dim) - self.norm2 = nn.LayerNorm(query_dim) + self.norm1 = ops.LayerNorm(query_dim) + self.norm2 = ops.LayerNorm(query_dim) self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) @@ -126,14 +129,14 @@ class GatedSelfAttentionDense2(nn.Module): # we need a linear projection since we need cat visual feature and obj # feature - self.linear = nn.Linear(context_dim, query_dim) + self.linear = ops.Linear(context_dim, query_dim) self.attn = CrossAttention( - query_dim=query_dim, context_dim=query_dim, dim_head=d_head) + query_dim=query_dim, context_dim=query_dim, dim_head=d_head, operations=ops) self.ff = FeedForward(query_dim, glu=True) - self.norm1 = nn.LayerNorm(query_dim) - self.norm2 = nn.LayerNorm(query_dim) + self.norm1 = ops.LayerNorm(query_dim) + self.norm2 = ops.LayerNorm(query_dim) self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) @@ -201,11 +204,11 @@ class PositionNet(nn.Module): self.position_dim = fourier_freqs * 2 * 4 # 2 is sin&cos, 4 is xyxy self.linears = nn.Sequential( - nn.Linear(self.in_dim + self.position_dim, 512), + ops.Linear(self.in_dim + self.position_dim, 512), nn.SiLU(), - nn.Linear(512, 512), + ops.Linear(512, 512), nn.SiLU(), - nn.Linear(512, out_dim), + ops.Linear(512, out_dim), ) self.null_positive_feature = torch.nn.Parameter( @@ -215,16 +218,15 @@ class PositionNet(nn.Module): def forward(self, boxes, masks, positive_embeddings): B, N, _ = boxes.shape - dtype = self.linears[0].weight.dtype - masks = masks.unsqueeze(-1).to(dtype) - positive_embeddings = positive_embeddings.to(dtype) + masks = masks.unsqueeze(-1) + positive_embeddings = positive_embeddings # embedding position (it may includes padding as placeholder) - xyxy_embedding = self.fourier_embedder(boxes.to(dtype)) # B*N*4 --> B*N*C + xyxy_embedding = self.fourier_embedder(boxes) # B*N*4 --> B*N*C # learnable null embedding - positive_null = self.null_positive_feature.view(1, 1, -1) - xyxy_null = self.null_position_feature.view(1, 1, -1) + positive_null = self.null_positive_feature.to(device=boxes.device, dtype=boxes.dtype).view(1, 1, -1) + xyxy_null = self.null_position_feature.to(device=boxes.device, dtype=boxes.dtype).view(1, 1, -1) # replace padding with learnable null embedding positive_embeddings = positive_embeddings * \ @@ -251,7 +253,7 @@ class Gligen(nn.Module): def func(x, extra_options): key = extra_options["transformer_index"] module = self.module_list[key] - return module(x, objs) + return module(x, objs.to(device=x.device, dtype=x.dtype)) return func def set_position(self, latent_image_shape, position_params, device): From 51714141435d050c255bba0cf588349f77bbc634 Mon Sep 17 00:00:00 2001 From: shiimizu Date: Sat, 17 Feb 2024 22:03:34 -0800 Subject: [PATCH 027/378] Support additional PNG info. --- web/scripts/pnginfo.js | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/web/scripts/pnginfo.js b/web/scripts/pnginfo.js index 83a4ebc8..16960920 100644 --- a/web/scripts/pnginfo.js +++ b/web/scripts/pnginfo.js @@ -24,7 +24,7 @@ export function getPngMetadata(file) { const length = dataView.getUint32(offset); // Get the chunk type const type = String.fromCharCode(...pngData.slice(offset + 4, offset + 8)); - if (type === "tEXt" || type == "comf") { + if (type === "tEXt" || type == "comf" || type === "iTXt") { // Get the keyword let keyword_end = offset + 8; while (pngData[keyword_end] !== 0) { @@ -33,7 +33,7 @@ export function getPngMetadata(file) { const keyword = String.fromCharCode(...pngData.slice(offset + 8, keyword_end)); // Get the text const contentArraySegment = pngData.slice(keyword_end + 1, offset + 8 + length); - const contentJson = Array.from(contentArraySegment).map(s=>String.fromCharCode(s)).join('') + const contentJson = new TextDecoder("utf-8").decode(contentArraySegment); txt_chunks[keyword] = contentJson; } From 3b2e579926d5cf8231de0e68e79096d1ee8091f2 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 19 Feb 2024 04:06:49 -0500 Subject: [PATCH 028/378] Support loading the Stable Cascade effnet and previewer as a VAE. The effnet can be used to encode images for img2img with Stage C. --- comfy/sd.py | 42 ++++++++++++++++++++++++++++++++++++++---- nodes.py | 20 ++++---------------- 2 files changed, 42 insertions(+), 20 deletions(-) diff --git a/comfy/sd.py b/comfy/sd.py index d8c0bfa7..00633e10 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -4,6 +4,7 @@ from enum import Enum from comfy import model_management from .ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine from .ldm.cascade.stage_a import StageA +from .ldm.cascade.stage_c_coder import StageC_coder import yaml @@ -158,6 +159,7 @@ class VAE: self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) #These are for AutoencoderKL and need tweaking (should be lower) self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype) self.downscale_ratio = 8 + self.upscale_ratio = 8 self.latent_channels = 4 self.process_input = lambda image: image * 2.0 - 1.0 self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0) @@ -176,11 +178,31 @@ class VAE: elif "vquantizer.codebook.weight" in sd: #VQGan: stage a of stable cascade self.first_stage_model = StageA() self.downscale_ratio = 4 + self.upscale_ratio = 4 #TODO #self.memory_used_encode #self.memory_used_decode self.process_input = lambda image: image self.process_output = lambda image: image + elif "backbone.1.0.block.0.1.num_batches_tracked" in sd: #effnet: encoder for stage c latent of stable cascade + self.first_stage_model = StageC_coder() + self.downscale_ratio = 32 + self.latent_channels = 16 + new_sd = {} + for k in sd: + new_sd["encoder.{}".format(k)] = sd[k] + sd = new_sd + elif "blocks.11.num_batches_tracked" in sd: #previewer: decoder for stage c latent of stable cascade + self.first_stage_model = StageC_coder() + self.latent_channels = 16 + new_sd = {} + for k in sd: + new_sd["previewer.{}".format(k)] = sd[k] + sd = new_sd + elif "encoder.backbone.1.0.block.0.1.num_batches_tracked" in sd: #combined effnet and previewer for stable cascade + self.first_stage_model = StageC_coder() + self.downscale_ratio = 32 + self.latent_channels = 16 else: #default SD1.x/SD2.x VAE parameters ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} @@ -188,6 +210,7 @@ class VAE: if 'encoder.down.2.downsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE ddconfig['ch_mult'] = [1, 2, 4] self.downscale_ratio = 4 + self.upscale_ratio = 4 self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=4) else: @@ -213,6 +236,15 @@ class VAE: self.patcher = comfy.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device) + def vae_encode_crop_pixels(self, pixels): + x = (pixels.shape[1] // self.downscale_ratio) * self.downscale_ratio + y = (pixels.shape[2] // self.downscale_ratio) * self.downscale_ratio + if pixels.shape[1] != x or pixels.shape[2] != y: + x_offset = (pixels.shape[1] % self.downscale_ratio) // 2 + y_offset = (pixels.shape[2] % self.downscale_ratio) // 2 + pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :] + return pixels + def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16): steps = samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap) steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap) @@ -221,9 +253,9 @@ class VAE: decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float() output = self.process_output( - (comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, pbar = pbar) + - comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, pbar = pbar) + - comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, pbar = pbar)) + (comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) + + comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) + + comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar)) / 3.0) return output @@ -248,7 +280,7 @@ class VAE: batch_number = int(free_memory / memory_used) batch_number = max(1, batch_number) - pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * self.downscale_ratio), round(samples_in.shape[3] * self.downscale_ratio)), device=self.output_device) + pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * self.upscale_ratio), round(samples_in.shape[3] * self.upscale_ratio)), device=self.output_device) for x in range(0, samples_in.shape[0], batch_number): samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device) pixel_samples[x:x+batch_number] = self.process_output(self.first_stage_model.decode(samples).to(self.output_device).float()) @@ -265,6 +297,7 @@ class VAE: return output.movedim(1,-1) def encode(self, pixel_samples): + pixel_samples = self.vae_encode_crop_pixels(pixel_samples) pixel_samples = pixel_samples.movedim(-1,1) try: memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype) @@ -284,6 +317,7 @@ class VAE: return samples def encode_tiled(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): + pixel_samples = self.vae_encode_crop_pixels(pixel_samples) model_management.load_model_gpu(self.patcher) pixel_samples = pixel_samples.movedim(-1,1) samples = self.encode_tiled_(pixel_samples, tile_x=tile_x, tile_y=tile_y, overlap=overlap) diff --git a/nodes.py b/nodes.py index 47203f41..a577c212 100644 --- a/nodes.py +++ b/nodes.py @@ -309,18 +309,7 @@ class VAEEncode: CATEGORY = "latent" - @staticmethod - def vae_encode_crop_pixels(pixels): - x = (pixels.shape[1] // 8) * 8 - y = (pixels.shape[2] // 8) * 8 - if pixels.shape[1] != x or pixels.shape[2] != y: - x_offset = (pixels.shape[1] % 8) // 2 - y_offset = (pixels.shape[2] % 8) // 2 - pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :] - return pixels - def encode(self, vae, pixels): - pixels = self.vae_encode_crop_pixels(pixels) t = vae.encode(pixels[:,:,:,:3]) return ({"samples":t}, ) @@ -336,7 +325,6 @@ class VAEEncodeTiled: CATEGORY = "_for_testing" def encode(self, vae, pixels, tile_size): - pixels = VAEEncode.vae_encode_crop_pixels(pixels) t = vae.encode_tiled(pixels[:,:,:,:3], tile_x=tile_size, tile_y=tile_size, ) return ({"samples":t}, ) @@ -350,14 +338,14 @@ class VAEEncodeForInpaint: CATEGORY = "latent/inpaint" def encode(self, vae, pixels, mask, grow_mask_by=6): - x = (pixels.shape[1] // 8) * 8 - y = (pixels.shape[2] // 8) * 8 + x = (pixels.shape[1] // vae.downscale_ratio) * vae.downscale_ratio + y = (pixels.shape[2] // vae.downscale_ratio) * vae.downscale_ratio mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear") pixels = pixels.clone() if pixels.shape[1] != x or pixels.shape[2] != y: - x_offset = (pixels.shape[1] % 8) // 2 - y_offset = (pixels.shape[2] % 8) // 2 + x_offset = (pixels.shape[1] % vae.downscale_ratio) // 2 + y_offset = (pixels.shape[2] % vae.downscale_ratio) // 2 pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:] mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset] From a7b5eaa7e3d5ecc1ae1d395e50d781124bb4e611 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 19 Feb 2024 04:25:46 -0500 Subject: [PATCH 029/378] Forgot to commit this. --- comfy/ldm/cascade/stage_c_coder.py | 96 ++++++++++++++++++++++++++++++ 1 file changed, 96 insertions(+) create mode 100644 comfy/ldm/cascade/stage_c_coder.py diff --git a/comfy/ldm/cascade/stage_c_coder.py b/comfy/ldm/cascade/stage_c_coder.py new file mode 100644 index 00000000..98c9a0b6 --- /dev/null +++ b/comfy/ldm/cascade/stage_c_coder.py @@ -0,0 +1,96 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Stability AI + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" +import torch +import torchvision +from torch import nn + + +# EfficientNet +class EfficientNetEncoder(nn.Module): + def __init__(self, c_latent=16): + super().__init__() + self.backbone = torchvision.models.efficientnet_v2_s().features.eval() + self.mapper = nn.Sequential( + nn.Conv2d(1280, c_latent, kernel_size=1, bias=False), + nn.BatchNorm2d(c_latent, affine=False), # then normalize them to have mean 0 and std 1 + ) + self.mean = nn.Parameter(torch.tensor([0.485, 0.456, 0.406])) + self.std = nn.Parameter(torch.tensor([0.229, 0.224, 0.225])) + + def forward(self, x): + x = x * 0.5 + 0.5 + x = (x - self.mean.view([3,1,1])) / self.std.view([3,1,1]) + o = self.mapper(self.backbone(x)) + print(o.shape) + return o + + +# Fast Decoder for Stage C latents. E.g. 16 x 24 x 24 -> 3 x 192 x 192 +class Previewer(nn.Module): + def __init__(self, c_in=16, c_hidden=512, c_out=3): + super().__init__() + self.blocks = nn.Sequential( + nn.Conv2d(c_in, c_hidden, kernel_size=1), # 16 channels to 512 channels + nn.GELU(), + nn.BatchNorm2d(c_hidden), + + nn.Conv2d(c_hidden, c_hidden, kernel_size=3, padding=1), + nn.GELU(), + nn.BatchNorm2d(c_hidden), + + nn.ConvTranspose2d(c_hidden, c_hidden // 2, kernel_size=2, stride=2), # 16 -> 32 + nn.GELU(), + nn.BatchNorm2d(c_hidden // 2), + + nn.Conv2d(c_hidden // 2, c_hidden // 2, kernel_size=3, padding=1), + nn.GELU(), + nn.BatchNorm2d(c_hidden // 2), + + nn.ConvTranspose2d(c_hidden // 2, c_hidden // 4, kernel_size=2, stride=2), # 32 -> 64 + nn.GELU(), + nn.BatchNorm2d(c_hidden // 4), + + nn.Conv2d(c_hidden // 4, c_hidden // 4, kernel_size=3, padding=1), + nn.GELU(), + nn.BatchNorm2d(c_hidden // 4), + + nn.ConvTranspose2d(c_hidden // 4, c_hidden // 4, kernel_size=2, stride=2), # 64 -> 128 + nn.GELU(), + nn.BatchNorm2d(c_hidden // 4), + + nn.Conv2d(c_hidden // 4, c_hidden // 4, kernel_size=3, padding=1), + nn.GELU(), + nn.BatchNorm2d(c_hidden // 4), + + nn.Conv2d(c_hidden // 4, c_out, kernel_size=1), + ) + + def forward(self, x): + return (self.blocks(x) - 0.5) * 2.0 + +class StageC_coder(nn.Module): + def __init__(self): + super().__init__() + self.previewer = Previewer() + self.encoder = EfficientNetEncoder() + + def encode(self, x): + return self.encoder(x) + + def decode(self, x): + return self.previewer(x) From dbe0979b3f8ee4215e55012700f6d0afb0fec5b2 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 19 Feb 2024 08:59:34 -0500 Subject: [PATCH 030/378] Larger range for min/max compression for StableCascade_EmptyLatentImage. --- comfy_extras/nodes_stable_cascade.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comfy_extras/nodes_stable_cascade.py b/comfy_extras/nodes_stable_cascade.py index 5d31c1e5..efe3586d 100644 --- a/comfy_extras/nodes_stable_cascade.py +++ b/comfy_extras/nodes_stable_cascade.py @@ -29,8 +29,8 @@ class StableCascade_EmptyLatentImage: return {"required": { "width": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}), - "compression": ("INT", {"default": 42, "min": 32, "max": 64, "step": 1}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}) + "compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}) }} RETURN_TYPES = ("LATENT", "LATENT") RETURN_NAMES = ("stage_c", "stage_b") From d91f45ef280a5acbdc22f3cc757f8fdbb254261b Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 19 Feb 2024 10:29:18 -0500 Subject: [PATCH 031/378] Some cleanups to how the text encoders are loaded. --- comfy/sd.py | 23 +++++++++++++---------- comfy/supported_models.py | 31 +++++++++++++++---------------- comfy/supported_models_base.py | 6 ++++-- 3 files changed, 32 insertions(+), 28 deletions(-) diff --git a/comfy/sd.py b/comfy/sd.py index 00633e10..7a77bb17 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -138,8 +138,11 @@ class CLIP: tokens = self.tokenize(text) return self.encode_from_tokens(tokens) - def load_sd(self, sd): - return self.cond_stage_model.load_sd(sd) + def load_sd(self, sd, full_model=False): + if full_model: + return self.cond_stage_model.load_state_dict(sd, strict=False) + else: + return self.cond_stage_model.load_sd(sd) def get_sd(self): return self.cond_stage_model.state_dict() @@ -494,9 +497,6 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o parameters = comfy.utils.calculate_parameters(sd, "model.diffusion_model.") load_device = model_management.get_torch_device() - class WeightsLoader(torch.nn.Module): - pass - model_config = model_detection.model_config_from_unet(sd, "model.diffusion_model.") unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes) manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes) @@ -521,14 +521,17 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o vae = VAE(sd=vae_sd) if output_clip: - w = WeightsLoader() clip_target = model_config.clip_target() if clip_target is not None: - sd = model_config.process_clip_state_dict(sd) - if any(k.startswith('cond_stage_model.') for k in sd): + clip_sd = model_config.process_clip_state_dict(sd) + if len(clip_sd) > 0: clip = CLIP(clip_target, embedding_directory=embedding_directory) - w.cond_stage_model = clip.cond_stage_model - load_model_weights(w, sd) + m, u = clip.load_sd(clip_sd, full_model=True) + if len(m) > 0: + print("clip missing:", m) + + if len(u) > 0: + print("clip unexpected:", u) else: print("no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.") diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 1a673646..f29f7f3d 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -40,8 +40,8 @@ class SD15(supported_models_base.BASE): state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round() replace_prefix = {} - replace_prefix["cond_stage_model."] = "cond_stage_model.clip_l." - state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) + replace_prefix["cond_stage_model."] = "clip_l." + state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) return state_dict def process_clip_state_dict_for_saving(self, state_dict): @@ -72,10 +72,10 @@ class SD20(supported_models_base.BASE): def process_clip_state_dict(self, state_dict): replace_prefix = {} - replace_prefix["conditioner.embedders.0.model."] = "cond_stage_model.model." #SD2 in sgm format - state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) - - state_dict = utils.transformers_convert(state_dict, "cond_stage_model.model.", "cond_stage_model.clip_h.transformer.text_model.", 24) + replace_prefix["conditioner.embedders.0.model."] = "clip_h." #SD2 in sgm format + replace_prefix["cond_stage_model.model."] = "clip_h." + state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) + state_dict = utils.transformers_convert(state_dict, "clip_h.", "clip_h.transformer.text_model.", 24) return state_dict def process_clip_state_dict_for_saving(self, state_dict): @@ -131,11 +131,10 @@ class SDXLRefiner(supported_models_base.BASE): def process_clip_state_dict(self, state_dict): keys_to_replace = {} replace_prefix = {} + replace_prefix["conditioner.embedders.0.model."] = "clip_g." + state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) - state_dict = utils.transformers_convert(state_dict, "conditioner.embedders.0.model.", "cond_stage_model.clip_g.transformer.text_model.", 32) - keys_to_replace["conditioner.embedders.0.model.text_projection"] = "cond_stage_model.clip_g.text_projection" - keys_to_replace["conditioner.embedders.0.model.logit_scale"] = "cond_stage_model.clip_g.logit_scale" - + state_dict = utils.transformers_convert(state_dict, "clip_g.", "clip_g.transformer.text_model.", 32) state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace) return state_dict @@ -179,13 +178,13 @@ class SDXL(supported_models_base.BASE): keys_to_replace = {} replace_prefix = {} - replace_prefix["conditioner.embedders.0.transformer.text_model"] = "cond_stage_model.clip_l.transformer.text_model" - state_dict = utils.transformers_convert(state_dict, "conditioner.embedders.1.model.", "cond_stage_model.clip_g.transformer.text_model.", 32) - keys_to_replace["conditioner.embedders.1.model.text_projection"] = "cond_stage_model.clip_g.text_projection" - keys_to_replace["conditioner.embedders.1.model.text_projection.weight"] = "cond_stage_model.clip_g.text_projection" - keys_to_replace["conditioner.embedders.1.model.logit_scale"] = "cond_stage_model.clip_g.logit_scale" + replace_prefix["conditioner.embedders.0.transformer.text_model"] = "clip_l.transformer.text_model" + replace_prefix["conditioner.embedders.1.model."] = "clip_g." + state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) + + state_dict = utils.transformers_convert(state_dict, "clip_g.", "clip_g.transformer.text_model.", 32) + keys_to_replace["clip_g.text_projection.weight"] = "clip_g.text_projection" - state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace) return state_dict diff --git a/comfy/supported_models_base.py b/comfy/supported_models_base.py index 3bd4f9c6..4d7e2593 100644 --- a/comfy/supported_models_base.py +++ b/comfy/supported_models_base.py @@ -22,6 +22,7 @@ class BASE: sampling_settings = {} latent_format = latent_formats.LatentFormat vae_key_prefix = ["first_stage_model."] + text_encoder_key_prefix = ["cond_stage_model."] supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] manual_cast_dtype = None @@ -55,6 +56,7 @@ class BASE: return out def process_clip_state_dict(self, state_dict): + state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True) return state_dict def process_unet_state_dict(self, state_dict): @@ -64,7 +66,7 @@ class BASE: return state_dict def process_clip_state_dict_for_saving(self, state_dict): - replace_prefix = {"": "cond_stage_model."} + replace_prefix = {"": self.text_encoder_key_prefix[0]} return utils.state_dict_prefix_replace(state_dict, replace_prefix) def process_clip_vision_state_dict_for_saving(self, state_dict): @@ -78,7 +80,7 @@ class BASE: return utils.state_dict_prefix_replace(state_dict, replace_prefix) def process_vae_state_dict_for_saving(self, state_dict): - replace_prefix = {"": "first_stage_model."} + replace_prefix = {"": self.vae_key_prefix[0]} return utils.state_dict_prefix_replace(state_dict, replace_prefix) def set_inference_dtype(self, dtype, manual_cast_dtype): From 3711b31dff3530ba584e9a30f1bb32feaf2b5886 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 19 Feb 2024 11:20:48 -0500 Subject: [PATCH 032/378] Support Stable Cascade in checkpoint format. --- comfy/supported_models.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/comfy/supported_models.py b/comfy/supported_models.py index f29f7f3d..5bb98d88 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -319,6 +319,10 @@ class Stable_Cascade_C(supported_models_base.BASE): "shift": 2.0, } + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoder."] + clip_vision_prefix = "clip_l_vision." + def process_unet_state_dict(self, state_dict): key_list = list(state_dict.keys()) for y in ["weight", "bias"]: @@ -355,6 +359,8 @@ class Stable_Cascade_B(Stable_Cascade_C): "shift": 1.0, } + clip_vision_prefix = None + def get_model(self, state_dict, prefix="", device=None): out = model_base.StableCascade_B(self, device=device) return out From e93cdd0ad043a637a81c9d398498b1e8a8eca3b0 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 19 Feb 2024 11:47:26 -0500 Subject: [PATCH 033/378] Remove print. --- comfy/ldm/cascade/stage_c_coder.py | 1 - 1 file changed, 1 deletion(-) diff --git a/comfy/ldm/cascade/stage_c_coder.py b/comfy/ldm/cascade/stage_c_coder.py index 98c9a0b6..0cb7c49f 100644 --- a/comfy/ldm/cascade/stage_c_coder.py +++ b/comfy/ldm/cascade/stage_c_coder.py @@ -36,7 +36,6 @@ class EfficientNetEncoder(nn.Module): x = x * 0.5 + 0.5 x = (x - self.mean.view([3,1,1])) / self.std.view([3,1,1]) o = self.mapper(self.backbone(x)) - print(o.shape) return o From 88f300401c0815eb5185683cb69ecf8b52cb6e7b Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 19 Feb 2024 12:00:48 -0500 Subject: [PATCH 034/378] Enable fp16 by default on mps. --- comfy/model_management.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index 681208ea..adcc0e8a 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -720,9 +720,9 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma if FORCE_FP16: return True - if device is not None: #TODO + if device is not None: if is_device_mps(device): - return False + return True if FORCE_FP32: return False @@ -730,8 +730,11 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma if directml_enabled: return False - if cpu_mode() or mps_mode(): - return False #TODO ? + if mps_mode(): + return True + + if cpu_mode(): + return False if is_intel_xpu(): return True From a31152496990913211c6deb3267144bd3095c1ee Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 19 Feb 2024 13:36:20 -0500 Subject: [PATCH 035/378] Node to make stable cascade image to image easier. --- comfy_extras/nodes_stable_cascade.py | 35 ++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) diff --git a/comfy_extras/nodes_stable_cascade.py b/comfy_extras/nodes_stable_cascade.py index efe3586d..b795d008 100644 --- a/comfy_extras/nodes_stable_cascade.py +++ b/comfy_extras/nodes_stable_cascade.py @@ -18,6 +18,7 @@ import torch import nodes +import comfy.utils class StableCascade_EmptyLatentImage: @@ -47,6 +48,39 @@ class StableCascade_EmptyLatentImage: "samples": b_latent, }) +class StableCascade_StageC_VAEEncode: + def __init__(self, device="cpu"): + self.device = device + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image": ("IMAGE",), + "vae": ("VAE", ), + "compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}), + }} + RETURN_TYPES = ("LATENT", "LATENT") + RETURN_NAMES = ("stage_c", "stage_b") + FUNCTION = "generate" + + CATEGORY = "_for_testing/stable_cascade" + + def generate(self, image, vae, compression): + width = image.shape[-2] + height = image.shape[-3] + out_width = (width // compression) * vae.downscale_ratio + out_height = (height // compression) * vae.downscale_ratio + + s = comfy.utils.common_upscale(image.movedim(-1,1), out_width, out_height, "bicubic", "center").movedim(1,-1) + + c_latent = vae.encode(s[:,:,:,:3]) + b_latent = torch.zeros([c_latent.shape[0], 4, height // 4, width // 4]) + return ({ + "samples": c_latent, + }, { + "samples": b_latent, + }) + class StableCascade_StageB_Conditioning: @classmethod def INPUT_TYPES(s): @@ -71,4 +105,5 @@ class StableCascade_StageB_Conditioning: NODE_CLASS_MAPPINGS = { "StableCascade_EmptyLatentImage": StableCascade_EmptyLatentImage, "StableCascade_StageB_Conditioning": StableCascade_StageB_Conditioning, + "StableCascade_StageC_VAEEncode": StableCascade_StageC_VAEEncode, } From ec4d89cee946faacff60681a7f16443305350260 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 19 Feb 2024 13:41:55 -0500 Subject: [PATCH 036/378] Add to Readme that stable cascade is supported. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index ff3ab642..a94a212a 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ This ui will let you design and execute advanced stable diffusion pipelines usin ## Features - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything. -- Fully supports SD1.x, SD2.x, [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/) and [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/) +- Fully supports SD1.x, SD2.x, [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/) and [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/) - Asynchronous Queue system - Many optimizations: Only re-executes the parts of the workflow that changes between executions. - Command line option: ```--lowvram``` to make it work on GPUs with less than 3GB vram (enabled automatically on GPUs with low vram) From c6b7a157ed30eb2dd59891ba465b7b5be97a687a Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 20 Feb 2024 04:05:39 -0500 Subject: [PATCH 037/378] Align simple scheduling closer to official stable cascade scheduler. --- comfy/model_sampling.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py index ae42d81f..97e91a01 100644 --- a/comfy/model_sampling.py +++ b/comfy/model_sampling.py @@ -150,10 +150,10 @@ class StableCascadeSampling(ModelSamplingDiscrete): self._init_alpha_cumprod = torch.cos(self.cosine_s / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 #This part is just for compatibility with some schedulers in the codebase - self.num_timesteps = 1000 + self.num_timesteps = 10000 sigmas = torch.empty((self.num_timesteps), dtype=torch.float32) for x in range(self.num_timesteps): - t = x / self.num_timesteps + t = (x + 1) / self.num_timesteps sigmas[x] = self.sigma(t) self.set_sigmas(sigmas) From 0d0fbabd1d153611a1c21aea3515d16339abc84f Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 20 Feb 2024 04:23:25 -0500 Subject: [PATCH 038/378] Pass pooled CLIP to stage b. --- comfy/model_base.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/comfy/model_base.py b/comfy/model_base.py index fefce763..421f271b 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -474,15 +474,11 @@ class StableCascade_B(BaseModel): clip_text_pooled = kwargs["pooled_output"] if clip_text_pooled is not None: - out['clip_text_pooled'] = comfy.conds.CONDRegular(clip_text_pooled) + out['clip'] = comfy.conds.CONDRegular(clip_text_pooled) #size of prior doesn't really matter if zeros because it gets resized but I still want it to get batched prior = kwargs.get("stable_cascade_prior", torch.zeros((1, 16, (noise.shape[2] * 4) // 42, (noise.shape[3] * 4) // 42), dtype=noise.dtype, layout=noise.layout, device=noise.device)) out["effnet"] = comfy.conds.CONDRegular(prior) out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,))) - - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - out['clip'] = comfy.conds.CONDCrossAttn(cross_attn) return out From 18c151b3e3f6838fab4028e7a8ba526e30e610d3 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 20 Feb 2024 10:57:24 -0500 Subject: [PATCH 039/378] Add some latent2rgb matrices for previews. --- comfy/latent_formats.py | 30 ++++++++++++++++++++++++++++++ 1 file changed, 30 insertions(+) diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py index 68fd73d0..03fd59e3 100644 --- a/comfy/latent_formats.py +++ b/comfy/latent_formats.py @@ -37,11 +37,41 @@ class SDXL(LatentFormat): class SD_X4(LatentFormat): def __init__(self): self.scale_factor = 0.08333 + self.latent_rgb_factors = [ + [-0.2340, -0.3863, -0.3257], + [ 0.0994, 0.0885, -0.0908], + [-0.2833, -0.2349, -0.3741], + [ 0.2523, -0.0055, -0.1651] + ] class SC_Prior(LatentFormat): def __init__(self): self.scale_factor = 1.0 + self.latent_rgb_factors = [ + [-0.0326, -0.0204, -0.0127], + [-0.1592, -0.0427, 0.0216], + [ 0.0873, 0.0638, -0.0020], + [-0.0602, 0.0442, 0.1304], + [ 0.0800, -0.0313, -0.1796], + [-0.0810, -0.0638, -0.1581], + [ 0.1791, 0.1180, 0.0967], + [ 0.0740, 0.1416, 0.0432], + [-0.1745, -0.1888, -0.1373], + [ 0.2412, 0.1577, 0.0928], + [ 0.1908, 0.0998, 0.0682], + [ 0.0209, 0.0365, -0.0092], + [ 0.0448, -0.0650, -0.1728], + [-0.1658, -0.1045, -0.1308], + [ 0.0542, 0.1545, 0.1325], + [-0.0352, -0.1672, -0.2541] + ] class SC_B(LatentFormat): def __init__(self): self.scale_factor = 1.0 + self.latent_rgb_factors = [ + [ 0.1121, 0.2006, 0.1023], + [-0.2093, -0.0222, -0.0195], + [-0.3087, -0.1535, 0.0366], + [ 0.0290, -0.1574, -0.4078] + ] From 820807c8ed2c2057a64539eccb4474563684254d Mon Sep 17 00:00:00 2001 From: feffy380 <114889020+feffy380@users.noreply.github.com> Date: Wed, 21 Feb 2024 10:33:03 +0100 Subject: [PATCH 040/378] Fix Perp-Neg math adjust perp-neg implementation to match the paper --- comfy_extras/nodes_perpneg.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy_extras/nodes_perpneg.py b/comfy_extras/nodes_perpneg.py index 45e4d418..64bbc1dc 100644 --- a/comfy_extras/nodes_perpneg.py +++ b/comfy_extras/nodes_perpneg.py @@ -35,7 +35,7 @@ class PerpNeg: pos = noise_pred_pos - noise_pred_nocond neg = noise_pred_neg - noise_pred_nocond - perp = ((torch.mul(pos, neg).sum())/(torch.norm(neg)**2)) * neg + perp = neg - ((torch.mul(neg, pos).sum())/(torch.norm(pos)**2)) * pos perp_neg = perp * neg_scale cfg_result = noise_pred_nocond + cond_scale*(pos - perp_neg) cfg_result = x - cfg_result From 7faa4507ecbd2ad67afcdea44b46ecdceec75232 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 21 Feb 2024 08:05:43 -0500 Subject: [PATCH 041/378] ModelSamplingDiscrete: x0 model support that predict a denoised image. --- comfy_extras/nodes_model_advanced.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/comfy_extras/nodes_model_advanced.py b/comfy_extras/nodes_model_advanced.py index ac7c1c17..1b3f3945 100644 --- a/comfy_extras/nodes_model_advanced.py +++ b/comfy_extras/nodes_model_advanced.py @@ -17,6 +17,10 @@ class LCM(comfy.model_sampling.EPS): return c_out * x0 + c_skip * model_input +class X0(comfy.model_sampling.EPS): + def calculate_denoised(self, sigma, model_output, model_input): + return model_output + class ModelSamplingDiscreteDistilled(comfy.model_sampling.ModelSamplingDiscrete): original_timesteps = 50 @@ -68,7 +72,7 @@ class ModelSamplingDiscrete: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), - "sampling": (["eps", "v_prediction", "lcm"],), + "sampling": (["eps", "v_prediction", "lcm", "x0"],), "zsnr": ("BOOLEAN", {"default": False}), }} @@ -88,6 +92,8 @@ class ModelSamplingDiscrete: elif sampling == "lcm": sampling_type = LCM sampling_base = ModelSamplingDiscreteDistilled + elif sampling == "x0": + sampling_type = X0 class ModelSamplingAdvanced(sampling_base, sampling_type): pass From f81dbe26e2e363c28ad043db67b59c11bb33f446 Mon Sep 17 00:00:00 2001 From: Rick Love Date: Wed, 21 Feb 2024 19:21:24 -0600 Subject: [PATCH 042/378] FIX recursive_will_execute performance (simple ~300x performance increase} (#2852) * FIX recursive_will_execute performance * Minimize code changes * memo must be created outside lambda --- execution.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/execution.py b/execution.py index 00908ead..3e9d53b0 100644 --- a/execution.py +++ b/execution.py @@ -194,8 +194,12 @@ def recursive_execute(server, prompt, outputs, current_item, extra_data, execute return (True, None, None) -def recursive_will_execute(prompt, outputs, current_item): +def recursive_will_execute(prompt, outputs, current_item, memo={}): unique_id = current_item + + if unique_id in memo: + return memo[unique_id] + inputs = prompt[unique_id]['inputs'] will_execute = [] if unique_id in outputs: @@ -207,9 +211,10 @@ def recursive_will_execute(prompt, outputs, current_item): input_unique_id = input_data[0] output_index = input_data[1] if input_unique_id not in outputs: - will_execute += recursive_will_execute(prompt, outputs, input_unique_id) + will_execute += recursive_will_execute(prompt, outputs, input_unique_id, memo) - return will_execute + [unique_id] + memo[unique_id] = will_execute + [unique_id] + return memo[unique_id] def recursive_output_delete_if_changed(prompt, old_prompt, outputs, current_item): unique_id = current_item @@ -377,7 +382,8 @@ class PromptExecutor: while len(to_execute) > 0: #always execute the output that depends on the least amount of unexecuted nodes first - to_execute = sorted(list(map(lambda a: (len(recursive_will_execute(prompt, self.outputs, a[-1])), a[-1]), to_execute))) + memo = {} + to_execute = sorted(list(map(lambda a: (len(recursive_will_execute(prompt, self.outputs, a[-1], memo)), a[-1]), to_execute))) output_node_id = to_execute.pop(0)[-1] # This call shouldn't raise anything if there's an error deep in From 10847dfafeeb05152e72a9517beaa327ea510b8e Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 23 Feb 2024 02:39:35 -0500 Subject: [PATCH 043/378] Cleanup uni_pc inpainting. This causes some small changes to the uni pc inpainting behavior but it seems to improve results slightly. --- comfy/extra_samplers/uni_pc.py | 37 +++++++++------------------------- comfy/samplers.py | 12 ++--------- 2 files changed, 11 insertions(+), 38 deletions(-) diff --git a/comfy/extra_samplers/uni_pc.py b/comfy/extra_samplers/uni_pc.py index 08bf0fc9..a30d1d03 100644 --- a/comfy/extra_samplers/uni_pc.py +++ b/comfy/extra_samplers/uni_pc.py @@ -358,9 +358,6 @@ class UniPC: thresholding=False, max_val=1., variant='bh1', - noise_mask=None, - masked_image=None, - noise=None, ): """Construct a UniPC. @@ -372,9 +369,6 @@ class UniPC: self.predict_x0 = predict_x0 self.thresholding = thresholding self.max_val = max_val - self.noise_mask = noise_mask - self.masked_image = masked_image - self.noise = noise def dynamic_thresholding_fn(self, x0, t=None): """ @@ -391,10 +385,7 @@ class UniPC: """ Return the noise prediction model. """ - if self.noise_mask is not None: - return self.model(x, t) * self.noise_mask - else: - return self.model(x, t) + return self.model(x, t) def data_prediction_fn(self, x, t): """ @@ -409,8 +400,6 @@ class UniPC: s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims) x0 = torch.clamp(x0, -s, s) / s - if self.noise_mask is not None: - x0 = x0 * self.noise_mask + (1. - self.noise_mask) * self.masked_image return x0 def model_fn(self, x, t): @@ -723,8 +712,6 @@ class UniPC: assert timesteps.shape[0] - 1 == steps # with torch.no_grad(): for step_index in trange(steps, disable=disable_pbar): - if self.noise_mask is not None: - x = x * self.noise_mask + (1. - self.noise_mask) * (self.masked_image * self.noise_schedule.marginal_alpha(timesteps[step_index]) + self.noise * self.noise_schedule.marginal_std(timesteps[step_index])) if step_index == 0: vec_t = timesteps[0].expand((x.shape[0])) model_prev_list = [self.model_fn(x, vec_t)] @@ -766,7 +753,7 @@ class UniPC: model_x = self.model_fn(x, vec_t) model_prev_list[-1] = model_x if callback is not None: - callback(step_index, model_prev_list[-1], x, steps) + callback({'x': x, 'i': step_index, 'denoised': model_prev_list[-1]}) else: raise NotImplementedError() # if denoise_to_zero: @@ -858,7 +845,7 @@ def predict_eps_sigma(model, input, sigma_in, **kwargs): return (input - model(input, sigma_in, **kwargs)) / sigma -def sample_unipc(model, noise, image, sigmas, max_denoise, extra_args=None, callback=None, disable=False, noise_mask=None, variant='bh1'): +def sample_unipc(model, noise, sigmas, extra_args=None, callback=None, disable=False, variant='bh1'): timesteps = sigmas.clone() if sigmas[-1] == 0: timesteps = sigmas[:] @@ -867,16 +854,7 @@ def sample_unipc(model, noise, image, sigmas, max_denoise, extra_args=None, call timesteps = sigmas.clone() ns = SigmaConvert() - if image is not None: - img = image * ns.marginal_alpha(timesteps[0]) - if max_denoise: - noise_mult = 1.0 - else: - noise_mult = ns.marginal_std(timesteps[0]) - img += noise * noise_mult - else: - img = noise - + noise = noise / torch.sqrt(1.0 + timesteps[0] ** 2.0) model_type = "noise" model_fn = model_wrapper( @@ -888,7 +866,10 @@ def sample_unipc(model, noise, image, sigmas, max_denoise, extra_args=None, call ) order = min(3, len(timesteps) - 2) - uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, noise_mask=noise_mask, masked_image=image, noise=noise, variant=variant) - x = uni_pc.sample(img, timesteps=timesteps, skip_type="time_uniform", method="multistep", order=order, lower_order_final=True, callback=callback, disable_pbar=disable) + uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, variant=variant) + x = uni_pc.sample(noise, timesteps=timesteps, skip_type="time_uniform", method="multistep", order=order, lower_order_final=True, callback=callback, disable_pbar=disable) x /= ns.marginal_alpha(timesteps[-1]) return x + +def sample_unipc_bh2(model, noise, sigmas, extra_args=None, callback=None, disable=False): + return sample_unipc(model, noise, sigmas, extra_args, callback, disable, variant='bh2') \ No newline at end of file diff --git a/comfy/samplers.py b/comfy/samplers.py index c795f208..9c45eb60 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -513,14 +513,6 @@ class Sampler: sigma = float(sigmas[0]) return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma -class UNIPC(Sampler): - def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False): - return uni_pc.sample_unipc(model_wrap, noise, latent_image, sigmas, max_denoise=self.max_denoise(model_wrap, sigmas), extra_args=extra_args, noise_mask=denoise_mask, callback=callback, disable=disable_pbar) - -class UNIPCBH2(Sampler): - def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False): - return uni_pc.sample_unipc(model_wrap, noise, latent_image, sigmas, max_denoise=self.max_denoise(model_wrap, sigmas), extra_args=extra_args, noise_mask=denoise_mask, callback=callback, variant='bh2', disable=disable_pbar) - KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral", "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu", "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm"] @@ -640,9 +632,9 @@ def calculate_sigmas_scheduler(model, scheduler_name, steps): def sampler_object(name): if name == "uni_pc": - sampler = UNIPC() + sampler = KSAMPLER(uni_pc.sample_unipc) elif name == "uni_pc_bh2": - sampler = UNIPCBH2() + sampler = KSAMPLER(uni_pc.sample_unipc_bh2) elif name == "ddim": sampler = ksampler("euler", inpaint_options={"random": True}) else: From e1cb93c3835a4c1e975914b5ce898abf509b21fe Mon Sep 17 00:00:00 2001 From: logtd Date: Fri, 23 Feb 2024 01:19:43 -0700 Subject: [PATCH 044/378] Fix model and cond transformer options merge --- comfy/samplers.py | 1 + 1 file changed, 1 insertion(+) diff --git a/comfy/samplers.py b/comfy/samplers.py index 9c45eb60..491c95d3 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -208,6 +208,7 @@ def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): cur_patches[p] = cur_patches[p] + patches[p] else: cur_patches[p] = patches[p] + transformer_options["patches"] = cur_patches else: transformer_options["patches"] = patches From 1e5f0f66be93182f30d5d453dc8df50f3f7c1826 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 23 Feb 2024 12:21:20 -0500 Subject: [PATCH 045/378] Support lora keys with lora_prior_unet_ and lora_prior_te_ --- comfy/lora.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/comfy/lora.py b/comfy/lora.py index 5e4009b4..82d57fe1 100644 --- a/comfy/lora.py +++ b/comfy/lora.py @@ -197,6 +197,8 @@ def model_lora_keys_clip(model, key_map={}): key_map[lora_key] = k lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora key_map[lora_key] = k + lora_key = "lora_prior_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #cascade lora: TODO put lora key prefix in the model config + key_map[lora_key] = k return key_map @@ -207,6 +209,7 @@ def model_lora_keys_unet(model, key_map={}): if k.startswith("diffusion_model.") and k.endswith(".weight"): key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_") key_map["lora_unet_{}".format(key_lora)] = k + key_map["lora_prior_unet_{}".format(key_lora)] = k #cascade lora: TODO put lora key prefix in the model config diffusers_keys = comfy.utils.unet_to_diffusers(model.model_config.unet_config) for k in diffusers_keys: From 6533b172c1eb6e01164672a2e8f6f8d42d16f755 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 24 Feb 2024 23:50:46 -0500 Subject: [PATCH 046/378] Support text encoder text_projection in lora. --- comfy/lora.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/comfy/lora.py b/comfy/lora.py index 82d57fe1..715e0842 100644 --- a/comfy/lora.py +++ b/comfy/lora.py @@ -200,6 +200,13 @@ def model_lora_keys_clip(model, key_map={}): lora_key = "lora_prior_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #cascade lora: TODO put lora key prefix in the model config key_map[lora_key] = k + + k = "clip_g.text_projection" + if k in sdk: + key_map["lora_prior_te_text_projection"] = k #cascade lora + # key_map["text_encoder.text_projection"] = k #TODO: check if other lora have the text_projection too + # key_map["lora_te_text_projection"] = k + return key_map def model_lora_keys_unet(model, key_map={}): From 1cb3f6a83b06f3100cfe9d198a76711809ab4234 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 25 Feb 2024 01:41:08 -0500 Subject: [PATCH 047/378] Move text projection into the CLIP model code. Fix issue with not loading the SSD1B clip correctly. --- comfy/clip_model.py | 8 +++++++- comfy/sd.py | 4 ++-- comfy/sd1_clip.py | 8 +------- comfy/supported_models.py | 14 +++++++++----- comfy/utils.py | 14 ++++++++++++++ 5 files changed, 33 insertions(+), 15 deletions(-) diff --git a/comfy/clip_model.py b/comfy/clip_model.py index 9b82a246..de52a118 100644 --- a/comfy/clip_model.py +++ b/comfy/clip_model.py @@ -119,6 +119,9 @@ class CLIPTextModel(torch.nn.Module): super().__init__() self.num_layers = config_dict["num_hidden_layers"] self.text_model = CLIPTextModel_(config_dict, dtype, device, operations) + embed_dim = config_dict["hidden_size"] + self.text_projection = operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device) + self.text_projection.weight.copy_(torch.eye(embed_dim)) self.dtype = dtype def get_input_embeddings(self): @@ -128,7 +131,10 @@ class CLIPTextModel(torch.nn.Module): self.text_model.embeddings.token_embedding = embeddings def forward(self, *args, **kwargs): - return self.text_model(*args, **kwargs) + x = self.text_model(*args, **kwargs) + out = self.text_projection(x[2]) + return (x[0], x[1], out) + class CLIPVisionEmbeddings(torch.nn.Module): def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, dtype=None, device=None, operations=None): diff --git a/comfy/sd.py b/comfy/sd.py index 7a77bb17..1be34ea3 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -52,7 +52,7 @@ def load_clip_weights(model, sd): if ids.dtype == torch.float32: sd['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round() - sd = comfy.utils.transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24) + sd = comfy.utils.clip_text_transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.") return load_model_weights(model, sd) @@ -361,7 +361,7 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI for i in range(len(clip_data)): if "transformer.resblocks.0.ln_1.weight" in clip_data[i]: - clip_data[i] = comfy.utils.transformers_convert(clip_data[i], "", "text_model.", 32) + clip_data[i] = comfy.utils.clip_text_transformers_convert(clip_data[i], "", "") clip_target = EmptyClass() clip_target.params = {} diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py index 8287ad2e..6f0ed300 100644 --- a/comfy/sd1_clip.py +++ b/comfy/sd1_clip.py @@ -86,7 +86,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): self.layer = layer self.layer_idx = None self.special_tokens = special_tokens - self.text_projection = torch.nn.Parameter(torch.eye(self.transformer.get_input_embeddings().weight.shape[1])) + self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055)) self.enable_attention_masks = enable_attention_masks @@ -182,18 +182,12 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): else: pooled_output = None - if self.text_projection is not None and pooled_output is not None: - pooled_output = pooled_output.float().to(self.text_projection.device) @ self.text_projection.float() return z.float(), pooled_output def encode(self, tokens): return self(tokens) def load_sd(self, sd): - if "text_projection" in sd: - self.text_projection[:] = sd.pop("text_projection") - if "text_projection.weight" in sd: - self.text_projection[:] = sd.pop("text_projection.weight").transpose(0, 1) return self.transformer.load_state_dict(sd, strict=False) def parse_parentheses(string): diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 5bb98d88..dbc3cf26 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -75,7 +75,7 @@ class SD20(supported_models_base.BASE): replace_prefix["conditioner.embedders.0.model."] = "clip_h." #SD2 in sgm format replace_prefix["cond_stage_model.model."] = "clip_h." state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) - state_dict = utils.transformers_convert(state_dict, "clip_h.", "clip_h.transformer.text_model.", 24) + state_dict = utils.clip_text_transformers_convert(state_dict, "clip_h.", "clip_h.transformer.") return state_dict def process_clip_state_dict_for_saving(self, state_dict): @@ -134,7 +134,7 @@ class SDXLRefiner(supported_models_base.BASE): replace_prefix["conditioner.embedders.0.model."] = "clip_g." state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) - state_dict = utils.transformers_convert(state_dict, "clip_g.", "clip_g.transformer.text_model.", 32) + state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.") state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace) return state_dict @@ -182,10 +182,8 @@ class SDXL(supported_models_base.BASE): replace_prefix["conditioner.embedders.1.model."] = "clip_g." state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) - state_dict = utils.transformers_convert(state_dict, "clip_g.", "clip_g.transformer.text_model.", 32) - keys_to_replace["clip_g.text_projection.weight"] = "clip_g.text_projection" - state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace) + state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.") return state_dict def process_clip_state_dict_for_saving(self, state_dict): @@ -338,6 +336,12 @@ class Stable_Cascade_C(supported_models_base.BASE): state_dict[k_to] = weights[shape_from*x:shape_from*(x + 1)] return state_dict + def process_clip_state_dict(self, state_dict): + state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True) + if "clip_g.text_projection" in state_dict: + state_dict["clip_g.transformer.text_projection.weight"] = state_dict.pop("clip_g.text_projection").transpose(0, 1) + return state_dict + def get_model(self, state_dict, prefix="", device=None): out = model_base.StableCascade_C(self, device=device) return out diff --git a/comfy/utils.py b/comfy/utils.py index 04cf76ed..c471024d 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -98,8 +98,22 @@ def transformers_convert(sd, prefix_from, prefix_to, number): p = ["self_attn.q_proj", "self_attn.k_proj", "self_attn.v_proj"] k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, p[x], y) sd[k_to] = weights[shape_from*x:shape_from*(x + 1)] + return sd +def clip_text_transformers_convert(sd, prefix_from, prefix_to): + sd = transformers_convert(sd, prefix_from, "{}text_model.".format(prefix_to), 32) + + tp = "{}text_projection.weight".format(prefix_from) + if tp in sd: + sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp) + + tp = "{}text_projection".format(prefix_from) + if tp in sd: + sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp).transpose(0, 1) + return sd + + UNET_MAP_ATTENTIONS = { "proj_in.weight", "proj_in.bias", From c2cb8e889b6a6d45eba26a534bf57aff86b320eb Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 25 Feb 2024 07:20:31 -0500 Subject: [PATCH 048/378] Always return unprojected pooled output for gligen. --- comfy/clip_model.py | 2 +- comfy/lora.py | 4 ++-- comfy/sd.py | 9 ++++++--- comfy/sd1_clip.py | 37 ++++++++++++++++++++++--------------- comfy/sdxl_clip.py | 12 ++++++------ nodes.py | 2 +- 6 files changed, 38 insertions(+), 28 deletions(-) diff --git a/comfy/clip_model.py b/comfy/clip_model.py index de52a118..14f43c56 100644 --- a/comfy/clip_model.py +++ b/comfy/clip_model.py @@ -133,7 +133,7 @@ class CLIPTextModel(torch.nn.Module): def forward(self, *args, **kwargs): x = self.text_model(*args, **kwargs) out = self.text_projection(x[2]) - return (x[0], x[1], out) + return (x[0], x[1], out, x[2]) class CLIPVisionEmbeddings(torch.nn.Module): diff --git a/comfy/lora.py b/comfy/lora.py index 715e0842..21b9897a 100644 --- a/comfy/lora.py +++ b/comfy/lora.py @@ -201,9 +201,9 @@ def model_lora_keys_clip(model, key_map={}): key_map[lora_key] = k - k = "clip_g.text_projection" + k = "clip_g.transformer.text_projection.weight" if k in sdk: - key_map["lora_prior_te_text_projection"] = k #cascade lora + key_map["lora_prior_te_text_projection"] = k #cascade lora? # key_map["text_encoder.text_projection"] = k #TODO: check if other lora have the text_projection too # key_map["lora_te_text_projection"] = k diff --git a/comfy/sd.py b/comfy/sd.py index 1be34ea3..d208abe6 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -123,10 +123,13 @@ class CLIP: return self.tokenizer.tokenize_with_weights(text, return_word_ids) def encode_from_tokens(self, tokens, return_pooled=False): + self.cond_stage_model.reset_clip_options() + if self.layer_idx is not None: - self.cond_stage_model.clip_layer(self.layer_idx) - else: - self.cond_stage_model.reset_clip_layer() + self.cond_stage_model.set_clip_options({"layer": self.layer_idx}) + + if return_pooled == "unprojected": + self.cond_stage_model.set_clip_options({"projected_pooled": False}) self.load_model() cond, pooled = self.cond_stage_model.encode_token_weights(tokens) diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py index 6f0ed300..04f3dfd3 100644 --- a/comfy/sd1_clip.py +++ b/comfy/sd1_clip.py @@ -91,11 +91,13 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): self.enable_attention_masks = enable_attention_masks self.layer_norm_hidden_state = layer_norm_hidden_state + self.return_projected_pooled = True + if layer == "hidden": assert layer_idx is not None assert abs(layer_idx) < self.num_layers - self.clip_layer(layer_idx) - self.layer_default = (self.layer, self.layer_idx) + self.set_clip_options({"layer": layer_idx}) + self.options_default = (self.layer, self.layer_idx, self.return_projected_pooled) def freeze(self): self.transformer = self.transformer.eval() @@ -103,16 +105,19 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): for param in self.parameters(): param.requires_grad = False - def clip_layer(self, layer_idx): - if abs(layer_idx) > self.num_layers: + def set_clip_options(self, options): + layer_idx = options.get("layer", self.layer_idx) + self.return_projected_pooled = options.get("projected_pooled", self.return_projected_pooled) + if layer_idx is None or abs(layer_idx) > self.num_layers: self.layer = "last" else: self.layer = "hidden" self.layer_idx = layer_idx - def reset_clip_layer(self): - self.layer = self.layer_default[0] - self.layer_idx = self.layer_default[1] + def reset_clip_options(self): + self.layer = self.options_default[0] + self.layer_idx = self.options_default[1] + self.return_projected_pooled = self.options_default[2] def set_up_textual_embeddings(self, tokens, current_embeds): out_tokens = [] @@ -177,10 +182,12 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): else: z = outputs[1] - if outputs[2] is not None: - pooled_output = outputs[2].float() - else: - pooled_output = None + pooled_output = None + if len(outputs) >= 3: + if not self.return_projected_pooled and len(outputs) >= 4 and outputs[3] is not None: + pooled_output = outputs[3].float() + elif outputs[2] is not None: + pooled_output = outputs[2].float() return z.float(), pooled_output @@ -497,11 +504,11 @@ class SD1ClipModel(torch.nn.Module): self.clip = "clip_{}".format(self.clip_name) setattr(self, self.clip, clip_model(device=device, dtype=dtype, **kwargs)) - def clip_layer(self, layer_idx): - getattr(self, self.clip).clip_layer(layer_idx) + def set_clip_options(self, options): + getattr(self, self.clip).set_clip_options(options) - def reset_clip_layer(self): - getattr(self, self.clip).reset_clip_layer() + def reset_clip_options(self): + getattr(self, self.clip).reset_clip_options() def encode_token_weights(self, token_weight_pairs): token_weight_pairs = token_weight_pairs[self.clip_name] diff --git a/comfy/sdxl_clip.py b/comfy/sdxl_clip.py index 3ce5c7e0..e62d1ed8 100644 --- a/comfy/sdxl_clip.py +++ b/comfy/sdxl_clip.py @@ -40,13 +40,13 @@ class SDXLClipModel(torch.nn.Module): self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False) self.clip_g = SDXLClipG(device=device, dtype=dtype) - def clip_layer(self, layer_idx): - self.clip_l.clip_layer(layer_idx) - self.clip_g.clip_layer(layer_idx) + def set_clip_options(self, options): + self.clip_l.set_clip_options(options) + self.clip_g.set_clip_options(options) - def reset_clip_layer(self): - self.clip_g.reset_clip_layer() - self.clip_l.reset_clip_layer() + def reset_clip_options(self): + self.clip_g.reset_clip_options() + self.clip_l.reset_clip_options() def encode_token_weights(self, token_weight_pairs): token_weight_pairs_g = token_weight_pairs["g"] diff --git a/nodes.py b/nodes.py index a577c212..1846797b 100644 --- a/nodes.py +++ b/nodes.py @@ -1003,7 +1003,7 @@ class GLIGENTextBoxApply: def append(self, conditioning_to, clip, gligen_textbox_model, text, width, height, x, y): c = [] - cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled=True) + cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled="unprojected") for t in conditioning_to: n = [t[0], t[1].copy()] position_params = [(cond_pooled, height // 8, width // 8, y // 8, x // 8)] From 4a7e751ce667562661135132c1237b5bb82e40d4 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 25 Feb 2024 07:34:22 -0500 Subject: [PATCH 049/378] Add example for how to use WEB_DIRECTORY to add frontend extensions. --- custom_nodes/example_node.py.example | 3 +++ 1 file changed, 3 insertions(+) diff --git a/custom_nodes/example_node.py.example b/custom_nodes/example_node.py.example index 7ce271ec..f0663259 100644 --- a/custom_nodes/example_node.py.example +++ b/custom_nodes/example_node.py.example @@ -103,6 +103,9 @@ class Example: #def IS_CHANGED(s, image, string_field, int_field, float_field, print_to_screen): # return "" +# Set the web directory, any .js file in that directory will be loaded by the frontend as a frontend extension +# WEB_DIRECTORY = "./somejs" + # A dictionary that contains all nodes you want to export with their names # NOTE: names should be globally unique NODE_CLASS_MAPPINGS = { From 8d7910cee93e28ec4f222775d86c0a328479630e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=83=B5=E5=B0=B8=E6=B5=A9?= <348063288@qq.com> Date: Sun, 25 Feb 2024 20:43:26 +0800 Subject: [PATCH 050/378] disable follow_symlinks in static serving for security reason (#2902) --- server.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/server.py b/server.py index dca06f6f..c6132cdf 100644 --- a/server.py +++ b/server.py @@ -539,11 +539,11 @@ class PromptServer(): for name, dir in nodes.EXTENSION_WEB_DIRS.items(): self.app.add_routes([ - web.static('/extensions/' + urllib.parse.quote(name), dir, follow_symlinks=True), + web.static('/extensions/' + urllib.parse.quote(name), dir), ]) self.app.add_routes([ - web.static('/', self.web_root, follow_symlinks=True), + web.static('/', self.web_root), ]) def get_queue_info(self): From ca7c310a0e8474c992b88fcf0f87040372f9c2c4 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 25 Feb 2024 08:29:12 -0500 Subject: [PATCH 051/378] Support loading old CLIP models saved with CLIPSave. --- comfy/sd.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/comfy/sd.py b/comfy/sd.py index d208abe6..fd5d604e 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -365,6 +365,9 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI for i in range(len(clip_data)): if "transformer.resblocks.0.ln_1.weight" in clip_data[i]: clip_data[i] = comfy.utils.clip_text_transformers_convert(clip_data[i], "", "") + else: + if "text_projection" in clip_data[i]: + clip_data[i]["text_projection.weight"] = clip_data[i]["text_projection"].transpose(0, 1) #old models saved with the CLIPSave node clip_target = EmptyClass() clip_target.params = {} From 8ac69f62e56a01aa781026714b4f08c6bcd7a78f Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 25 Feb 2024 14:49:13 -0500 Subject: [PATCH 052/378] Make return_projected_pooled setable from the __init__ --- comfy/sd1_clip.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py index 04f3dfd3..7bf11ea6 100644 --- a/comfy/sd1_clip.py +++ b/comfy/sd1_clip.py @@ -67,7 +67,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): ] def __init__(self, version="openai/clip-vit-large-patch14", device="cpu", max_length=77, freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=comfy.clip_model.CLIPTextModel, - special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False): # clip-vit-base-patch32 + special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, return_projected_pooled=True): # clip-vit-base-patch32 super().__init__() assert layer in self.LAYERS @@ -91,7 +91,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): self.enable_attention_masks = enable_attention_masks self.layer_norm_hidden_state = layer_norm_hidden_state - self.return_projected_pooled = True + self.return_projected_pooled = return_projected_pooled if layer == "hidden": assert layer_idx is not None From 36f7face37320ec3c6ac85ec3c625738241deaa9 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 26 Feb 2024 08:33:01 -0500 Subject: [PATCH 053/378] Update the standalone package updater so it can self update. --- .ci/update_windows/update.py | 49 +++++++++++- .ci/update_windows/update_comfyui.bat | 8 +- ...update_comfyui_and_python_dependencies.bat | 3 - ...update_comfyui_and_python_dependencies.bat | 11 --- .../windows_release_cu118_dependencies.yml | 71 ----------------- .../windows_release_cu118_dependencies_2.yml | 37 --------- .../windows_release_cu118_package.yml | 79 ------------------- .../windows_release_dependencies.yml | 5 +- .../windows_release_nightly_pytorch.yml | 2 +- 9 files changed, 57 insertions(+), 208 deletions(-) delete mode 100755 .ci/update_windows/update_comfyui_and_python_dependencies.bat delete mode 100755 .ci/update_windows_cu118/update_comfyui_and_python_dependencies.bat delete mode 100644 .github/workflows/windows_release_cu118_dependencies.yml delete mode 100644 .github/workflows/windows_release_cu118_dependencies_2.yml delete mode 100644 .github/workflows/windows_release_cu118_package.yml diff --git a/.ci/update_windows/update.py b/.ci/update_windows/update.py index ef9374c4..6067d1a1 100755 --- a/.ci/update_windows/update.py +++ b/.ci/update_windows/update.py @@ -1,6 +1,9 @@ import pygit2 from datetime import datetime import sys +import os +import shutil +import filecmp def pull(repo, remote_name='origin', branch='master'): for remote in repo.remotes: @@ -42,7 +45,8 @@ def pull(repo, remote_name='origin', branch='master'): raise AssertionError('Unknown merge analysis result') pygit2.option(pygit2.GIT_OPT_SET_OWNER_VALIDATION, 0) -repo = pygit2.Repository(str(sys.argv[1])) +repo_path = str(sys.argv[1]) +repo = pygit2.Repository(repo_path) ident = pygit2.Signature('comfyui', 'comfy@ui') try: print("stashing current changes") @@ -51,7 +55,10 @@ except KeyError: print("nothing to stash") backup_branch_name = 'backup_branch_{}'.format(datetime.today().strftime('%Y-%m-%d_%H_%M_%S')) print("creating backup branch: {}".format(backup_branch_name)) -repo.branches.local.create(backup_branch_name, repo.head.peel()) +try: + repo.branches.local.create(backup_branch_name, repo.head.peel()) +except: + pass print("checking out master branch") branch = repo.lookup_branch('master') @@ -63,3 +70,41 @@ pull(repo) print("Done!") +self_update = True +if len(sys.argv) > 2: + self_update = '--skip_self_update' not in sys.argv + +update_py_path = os.path.realpath(__file__) +repo_update_py_path = os.path.join(repo_path, ".ci/update_windows/update.py") + +cur_path = os.path.dirname(update_py_path) + + +req_path = os.path.join(cur_path, "current_requirements.txt") +repo_req_path = os.path.join(repo_path, "requirements.txt") + + +def files_equal(file1, file2): + try: + return filecmp.cmp(file1, file2, shallow=False) + except: + return False + +def file_size(f): + try: + return os.path.getsize(f) + except: + return 0 + + +if self_update and not files_equal(update_py_path, repo_update_py_path) and file_size(repo_update_py_path) > 10: + shutil.copy(repo_update_py_path, os.path.join(cur_path, "update_new.py")) + exit() + +if not os.path.exists(req_path) or not files_equal(repo_req_path, req_path): + import subprocess + try: + subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-r', repo_req_path]) + shutil.copy(repo_req_path, req_path) + except: + pass diff --git a/.ci/update_windows/update_comfyui.bat b/.ci/update_windows/update_comfyui.bat index 60d1e694..bb08c0de 100755 --- a/.ci/update_windows/update_comfyui.bat +++ b/.ci/update_windows/update_comfyui.bat @@ -1,2 +1,8 @@ +@echo off ..\python_embeded\python.exe .\update.py ..\ComfyUI\ -pause +if exist update_new.py ( + move /y update_new.py update.py + echo Running updater again since it got updated. + ..\python_embeded\python.exe .\update.py ..\ComfyUI\ --skip_self_update +) +if "%~1"=="" pause diff --git a/.ci/update_windows/update_comfyui_and_python_dependencies.bat b/.ci/update_windows/update_comfyui_and_python_dependencies.bat deleted file mode 100755 index b7308550..00000000 --- a/.ci/update_windows/update_comfyui_and_python_dependencies.bat +++ /dev/null @@ -1,3 +0,0 @@ -..\python_embeded\python.exe .\update.py ..\ComfyUI\ -..\python_embeded\python.exe -s -m pip install --upgrade torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117 xformers -r ../ComfyUI/requirements.txt pygit2 -pause diff --git a/.ci/update_windows_cu118/update_comfyui_and_python_dependencies.bat b/.ci/update_windows_cu118/update_comfyui_and_python_dependencies.bat deleted file mode 100755 index c33adc0a..00000000 --- a/.ci/update_windows_cu118/update_comfyui_and_python_dependencies.bat +++ /dev/null @@ -1,11 +0,0 @@ -@echo off -..\python_embeded\python.exe .\update.py ..\ComfyUI\ -echo -echo This will try to update pytorch and all python dependencies, if you get an error wait for pytorch/xformers to fix their stuff -echo You should not be running this anyways unless you really have to -echo -echo If you just want to update normally, close this and run update_comfyui.bat instead. -echo -pause -..\python_embeded\python.exe -s -m pip install --upgrade torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu118 xformers -r ../ComfyUI/requirements.txt pygit2 -pause diff --git a/.github/workflows/windows_release_cu118_dependencies.yml b/.github/workflows/windows_release_cu118_dependencies.yml deleted file mode 100644 index 75c42b62..00000000 --- a/.github/workflows/windows_release_cu118_dependencies.yml +++ /dev/null @@ -1,71 +0,0 @@ -name: "Windows Release cu118 dependencies" - -on: - workflow_dispatch: -# push: -# branches: -# - master - -jobs: - build_dependencies: - env: - # you need at least cuda 5.0 for some of the stuff compiled here. - TORCH_CUDA_ARCH_LIST: "5.0+PTX 6.0 6.1 7.0 7.5 8.0 8.6 8.9" - FORCE_CUDA: 1 - MAX_JOBS: 1 # will crash otherwise - DISTUTILS_USE_SDK: 1 # otherwise distutils will complain on windows about multiple versions of msvc - XFORMERS_BUILD_TYPE: "Release" - runs-on: windows-latest - steps: - - name: Cache Built Dependencies - uses: actions/cache@v3 - id: cache-cu118_python_stuff - with: - path: cu118_python_deps.tar - key: ${{ runner.os }}-build-cu118 - - - if: steps.cache-cu118_python_stuff.outputs.cache-hit != 'true' - uses: actions/checkout@v3 - - - if: steps.cache-cu118_python_stuff.outputs.cache-hit != 'true' - uses: actions/setup-python@v4 - with: - python-version: '3.10.9' - - - if: steps.cache-cu118_python_stuff.outputs.cache-hit != 'true' - uses: comfyanonymous/cuda-toolkit@test - id: cuda-toolkit - with: - cuda: '11.8.0' - # copied from xformers github - - name: Setup MSVC - uses: ilammy/msvc-dev-cmd@v1 - - name: Configure Pagefile - # windows runners will OOM with many CUDA architectures - # we cheat here with a page file - uses: al-cheb/configure-pagefile-action@v1.3 - with: - minimum-size: 2GB - # really unfortunate: https://github.com/ilammy/msvc-dev-cmd#name-conflicts-with-shell-bash - - name: Remove link.exe - shell: bash - run: rm /usr/bin/link - - - if: steps.cache-cu118_python_stuff.outputs.cache-hit != 'true' - shell: bash - run: | - python -m pip wheel --no-cache-dir torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu118 -r requirements.txt pygit2 -w ./temp_wheel_dir - python -m pip install --no-cache-dir ./temp_wheel_dir/* - echo installed basic - git clone --recurse-submodules https://github.com/facebookresearch/xformers.git - cd xformers - python -m pip install --no-cache-dir wheel setuptools twine - echo building xformers - python setup.py bdist_wheel -d ../temp_wheel_dir/ - cd .. - rm -rf xformers - ls -lah temp_wheel_dir - mv temp_wheel_dir cu118_python_deps - tar cf cu118_python_deps.tar cu118_python_deps - - diff --git a/.github/workflows/windows_release_cu118_dependencies_2.yml b/.github/workflows/windows_release_cu118_dependencies_2.yml deleted file mode 100644 index a7760b21..00000000 --- a/.github/workflows/windows_release_cu118_dependencies_2.yml +++ /dev/null @@ -1,37 +0,0 @@ -name: "Windows Release cu118 dependencies 2" - -on: - workflow_dispatch: - inputs: - xformers: - description: 'xformers version' - required: true - type: string - default: "xformers" - -# push: -# branches: -# - master - -jobs: - build_dependencies: - runs-on: windows-latest - steps: - - uses: actions/checkout@v3 - - uses: actions/setup-python@v4 - with: - python-version: '3.10.9' - - - shell: bash - run: | - python -m pip wheel --no-cache-dir torch torchvision torchaudio ${{ inputs.xformers }} --extra-index-url https://download.pytorch.org/whl/cu118 -r requirements.txt pygit2 -w ./temp_wheel_dir - python -m pip install --no-cache-dir ./temp_wheel_dir/* - echo installed basic - ls -lah temp_wheel_dir - mv temp_wheel_dir cu118_python_deps - tar cf cu118_python_deps.tar cu118_python_deps - - - uses: actions/cache/save@v3 - with: - path: cu118_python_deps.tar - key: ${{ runner.os }}-build-cu118 diff --git a/.github/workflows/windows_release_cu118_package.yml b/.github/workflows/windows_release_cu118_package.yml deleted file mode 100644 index 0f0fbf28..00000000 --- a/.github/workflows/windows_release_cu118_package.yml +++ /dev/null @@ -1,79 +0,0 @@ -name: "Windows Release cu118 packaging" - -on: - workflow_dispatch: -# push: -# branches: -# - master - -jobs: - package_comfyui: - permissions: - contents: "write" - packages: "write" - pull-requests: "read" - runs-on: windows-latest - steps: - - uses: actions/cache/restore@v3 - id: cache - with: - path: cu118_python_deps.tar - key: ${{ runner.os }}-build-cu118 - - shell: bash - run: | - mv cu118_python_deps.tar ../ - cd .. - tar xf cu118_python_deps.tar - pwd - ls - - - uses: actions/checkout@v3 - with: - fetch-depth: 0 - persist-credentials: false - - shell: bash - run: | - cd .. - cp -r ComfyUI ComfyUI_copy - curl https://www.python.org/ftp/python/3.10.9/python-3.10.9-embed-amd64.zip -o python_embeded.zip - unzip python_embeded.zip -d python_embeded - cd python_embeded - echo 'import site' >> ./python310._pth - curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py - ./python.exe get-pip.py - ./python.exe -s -m pip install ../cu118_python_deps/* - sed -i '1i../ComfyUI' ./python310._pth - cd .. - - git clone https://github.com/comfyanonymous/taesd - cp taesd/*.pth ./ComfyUI_copy/models/vae_approx/ - - mkdir ComfyUI_windows_portable - mv python_embeded ComfyUI_windows_portable - mv ComfyUI_copy ComfyUI_windows_portable/ComfyUI - - cd ComfyUI_windows_portable - - mkdir update - cp -r ComfyUI/.ci/update_windows/* ./update/ - cp -r ComfyUI/.ci/update_windows_cu118/* ./update/ - cp -r ComfyUI/.ci/windows_base_files/* ./ - - cd .. - - "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable - mv ComfyUI_windows_portable.7z ComfyUI/new_ComfyUI_windows_portable_nvidia_cu118_or_cpu.7z - - cd ComfyUI_windows_portable - python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu - - ls - - - name: Upload binaries to release - uses: svenstaro/upload-release-action@v2 - with: - repo_token: ${{ secrets.GITHUB_TOKEN }} - file: new_ComfyUI_windows_portable_nvidia_cu118_or_cpu.7z - tag: "latest" - overwrite: true - diff --git a/.github/workflows/windows_release_dependencies.yml b/.github/workflows/windows_release_dependencies.yml index aafe8a21..e0841fdf 100644 --- a/.github/workflows/windows_release_dependencies.yml +++ b/.github/workflows/windows_release_dependencies.yml @@ -41,10 +41,9 @@ jobs: - shell: bash run: | echo "@echo off - ..\python_embeded\python.exe .\update.py ..\ComfyUI\\ + call update_comfyui.bat nopause echo - - echo This will try to update pytorch and all python dependencies, if you get an error wait for pytorch/xformers to fix their stuff - echo You should not be running this anyways unless you really have to + echo This will try to update pytorch and all python dependencies. echo - echo If you just want to update normally, close this and run update_comfyui.bat instead. echo - diff --git a/.github/workflows/windows_release_nightly_pytorch.yml b/.github/workflows/windows_release_nightly_pytorch.yml index 90e09d27..56830935 100644 --- a/.github/workflows/windows_release_nightly_pytorch.yml +++ b/.github/workflows/windows_release_nightly_pytorch.yml @@ -68,7 +68,7 @@ jobs: cp -r ComfyUI/.ci/update_windows/* ./update/ cp -r ComfyUI/.ci/windows_base_files/* ./ - echo "..\python_embeded\python.exe .\update.py ..\ComfyUI\\ + echo "call update_comfyui.bat nopause ..\python_embeded\python.exe -s -m pip install --upgrade --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2 pause" > ./update/update_comfyui_and_python_dependencies.bat cd .. From e61755ead09d1db8655b7315667ffc9fc70ab540 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 26 Feb 2024 13:32:14 -0500 Subject: [PATCH 054/378] Update the old updater if present when running on the windows standalone. --- main.py | 7 +++++++ new_updater.py | 35 +++++++++++++++++++++++++++++++++++ 2 files changed, 42 insertions(+) create mode 100644 new_updater.py diff --git a/main.py b/main.py index 69d9bce6..5d07ce2d 100644 --- a/main.py +++ b/main.py @@ -193,6 +193,13 @@ if __name__ == "__main__": folder_paths.set_temp_directory(temp_dir) cleanup_temp() + if args.windows_standalone_build: + try: + import new_updater + new_updater.update_windows_updater() + except: + pass + loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) server = server.PromptServer(loop) diff --git a/new_updater.py b/new_updater.py new file mode 100644 index 00000000..a49e0877 --- /dev/null +++ b/new_updater.py @@ -0,0 +1,35 @@ +import os +import shutil + +base_path = os.path.dirname(os.path.realpath(__file__)) + + +def update_windows_updater(): + top_path = os.path.dirname(base_path) + updater_path = os.path.join(base_path, ".ci/update_windows/update.py") + bat_path = os.path.join(base_path, ".ci/update_windows/update_comfyui.bat") + + dest_updater_path = os.path.join(top_path, "update/update.py") + dest_bat_path = os.path.join(top_path, "update/update_comfyui.bat") + dest_bat_deps_path = os.path.join(top_path, "update/update_comfyui_and_python_dependencies.bat") + + try: + with open(dest_bat_path, 'rb') as f: + contents = f.read() + except: + return + + if not contents.startswith(b"..\\python_embeded\\python.exe .\\update.py"): + return + + shutil.copy(updater_path, dest_updater_path) + try: + with open(dest_bat_deps_path, 'rb') as f: + contents = f.read() + contents = contents.replace(b'..\\python_embeded\\python.exe .\\update.py ..\\ComfyUI\\', b'call update_comfyui.bat nopause') + with open(dest_bat_deps_path, 'wb') as f: + f.write(contents) + except: + pass + shutil.copy(bat_path, dest_bat_path) + print("Updated the windows standalone package updater.") From 03c47fc0f23874b6b884028a5d3f678882b2cb49 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 26 Feb 2024 21:36:37 -0500 Subject: [PATCH 055/378] Add a min_length property to tokenizer class. --- comfy/sd1_clip.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py index 7bf11ea6..87e3eaa4 100644 --- a/comfy/sd1_clip.py +++ b/comfy/sd1_clip.py @@ -355,11 +355,12 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No return embed_out class SDTokenizer: - def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, pad_to_max_length=True): + def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, pad_to_max_length=True, min_length=None): if tokenizer_path is None: tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer") self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path) self.max_length = max_length + self.min_length = min_length empty = self.tokenizer('')["input_ids"] if has_start_token: @@ -471,6 +472,8 @@ class SDTokenizer: batch.append((self.end_token, 1.0, 0)) if self.pad_to_max_length: batch.extend([(pad_token, 1.0, 0)] * (self.max_length - len(batch))) + if self.min_length is not None and len(batch) < self.min_length: + batch.extend([(pad_token, 1.0, 0)] * (self.min_length - len(batch))) if not return_word_ids: batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens] From b416be7d78518b167b6e757ee563e9f8bb5a34cc Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 27 Feb 2024 01:52:23 -0500 Subject: [PATCH 056/378] Make the text projection saved in the checkpoint the right format. --- comfy/diffusers_convert.py | 4 ++++ comfy/utils.py | 2 +- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/comfy/diffusers_convert.py b/comfy/diffusers_convert.py index a9eb9302..8e3ca94e 100644 --- a/comfy/diffusers_convert.py +++ b/comfy/diffusers_convert.py @@ -237,6 +237,10 @@ def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""): capture_qkv_bias[k_pre][code2idx[k_code]] = v continue + text_proj = "transformer.text_projection.weight" + if k.endswith(text_proj): + new_state_dict[k.replace(text_proj, "text_projection")] = v.transpose(0, 1).contiguous() + relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k) new_state_dict[relabelled_key] = v diff --git a/comfy/utils.py b/comfy/utils.py index c471024d..41f730c8 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -110,7 +110,7 @@ def clip_text_transformers_convert(sd, prefix_from, prefix_to): tp = "{}text_projection".format(prefix_from) if tp in sd: - sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp).transpose(0, 1) + sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp).transpose(0, 1).contiguous() return sd From 1e0fcc9a658dac305660c982a6bc0ea9b5657cf7 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 27 Feb 2024 02:07:40 -0500 Subject: [PATCH 057/378] Make XL checkpoints save in a more standard format. --- comfy/supported_models.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/comfy/supported_models.py b/comfy/supported_models.py index dbc3cf26..5d57a31a 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -190,12 +190,16 @@ class SDXL(supported_models_base.BASE): replace_prefix = {} keys_to_replace = {} state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g") - if "clip_g.transformer.text_model.embeddings.position_ids" in state_dict_g: - state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids") for k in state_dict: if k.startswith("clip_l"): state_dict_g[k] = state_dict[k] + state_dict_g["clip_l.transformer.text_model.embeddings.position_ids"] = torch.arange(77).expand((1, -1)) + pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"] + for p in pop_keys: + if p in state_dict_g: + state_dict_g.pop(p) + replace_prefix["clip_g"] = "conditioner.embedders.1.model" replace_prefix["clip_l"] = "conditioner.embedders.0" state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix) From d46583ecece5014f23f9f47f7952c8aecd8cc491 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 27 Feb 2024 15:12:33 -0500 Subject: [PATCH 058/378] Playground V2.5 support with ModelSamplingContinuousEDM node. Use ModelSamplingContinuousEDM with edm_playground_v2.5 selected. --- comfy/latent_formats.py | 27 +++++++++++++++++++++++++++ comfy/model_sampling.py | 13 +++++++++---- comfy/samplers.py | 2 +- comfy_extras/nodes_model_advanced.py | 13 +++++++++++-- 4 files changed, 48 insertions(+), 7 deletions(-) diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py index 03fd59e3..674364e7 100644 --- a/comfy/latent_formats.py +++ b/comfy/latent_formats.py @@ -1,3 +1,4 @@ +import torch class LatentFormat: scale_factor = 1.0 @@ -34,6 +35,32 @@ class SDXL(LatentFormat): ] self.taesd_decoder_name = "taesdxl_decoder" +class SDXL_Playground_2_5(LatentFormat): + def __init__(self): + self.scale_factor = 0.5 + self.latents_mean = torch.tensor([-1.6574, 1.886, -1.383, 2.5155]).view(1, 4, 1, 1) + self.latents_std = torch.tensor([8.4927, 5.9022, 6.5498, 5.2299]).view(1, 4, 1, 1) + + self.latent_rgb_factors = [ + # R G B + [ 0.3920, 0.4054, 0.4549], + [-0.2634, -0.0196, 0.0653], + [ 0.0568, 0.1687, -0.0755], + [-0.3112, -0.2359, -0.2076] + ] + self.taesd_decoder_name = "taesdxl_decoder" + + def process_in(self, latent): + latents_mean = self.latents_mean.to(latent.device, latent.dtype) + latents_std = self.latents_std.to(latent.device, latent.dtype) + return (latent - latents_mean) * self.scale_factor / latents_std + + def process_out(self, latent): + latents_mean = self.latents_mean.to(latent.device, latent.dtype) + latents_std = self.latents_std.to(latent.device, latent.dtype) + return latent * latents_std / self.scale_factor + latents_mean + + class SD_X4(LatentFormat): def __init__(self): self.scale_factor = 0.08333 diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py index 97e91a01..e7f8bc6a 100644 --- a/comfy/model_sampling.py +++ b/comfy/model_sampling.py @@ -17,6 +17,11 @@ class V_PREDICTION(EPS): sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 +class EDM(V_PREDICTION): + def calculate_denoised(self, sigma, model_output, model_input): + sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) + return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 + class ModelSamplingDiscrete(torch.nn.Module): def __init__(self, model_config=None): @@ -92,8 +97,6 @@ class ModelSamplingDiscrete(torch.nn.Module): class ModelSamplingContinuousEDM(torch.nn.Module): def __init__(self, model_config=None): super().__init__() - self.sigma_data = 1.0 - if model_config is not None: sampling_settings = model_config.sampling_settings else: @@ -101,9 +104,11 @@ class ModelSamplingContinuousEDM(torch.nn.Module): sigma_min = sampling_settings.get("sigma_min", 0.002) sigma_max = sampling_settings.get("sigma_max", 120.0) - self.set_sigma_range(sigma_min, sigma_max) + sigma_data = sampling_settings.get("sigma_data", 1.0) + self.set_parameters(sigma_min, sigma_max, sigma_data) - def set_sigma_range(self, sigma_min, sigma_max): + def set_parameters(self, sigma_min, sigma_max, sigma_data): + self.sigma_data = sigma_data sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), 1000).exp() self.register_buffer('sigmas', sigmas) #for compatibility with some schedulers diff --git a/comfy/samplers.py b/comfy/samplers.py index 491c95d3..e5569322 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -588,7 +588,7 @@ def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model calculate_start_end_timesteps(model, negative) calculate_start_end_timesteps(model, positive) - if latent_image is not None: + if latent_image is not None and torch.count_nonzero(latent_image) > 0: #Don't shift the empty latent image. latent_image = model.process_latent_in(latent_image) if hasattr(model, 'extra_conds'): diff --git a/comfy_extras/nodes_model_advanced.py b/comfy_extras/nodes_model_advanced.py index 1b3f3945..21af4b73 100644 --- a/comfy_extras/nodes_model_advanced.py +++ b/comfy_extras/nodes_model_advanced.py @@ -1,6 +1,7 @@ import folder_paths import comfy.sd import comfy.model_sampling +import comfy.latent_formats import torch class LCM(comfy.model_sampling.EPS): @@ -135,7 +136,7 @@ class ModelSamplingContinuousEDM: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), - "sampling": (["v_prediction", "eps"],), + "sampling": (["v_prediction", "edm_playground_v2.5", "eps"],), "sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), "sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), }} @@ -148,17 +149,25 @@ class ModelSamplingContinuousEDM: def patch(self, model, sampling, sigma_max, sigma_min): m = model.clone() + latent_format = None + sigma_data = 1.0 if sampling == "eps": sampling_type = comfy.model_sampling.EPS elif sampling == "v_prediction": sampling_type = comfy.model_sampling.V_PREDICTION + elif sampling == "edm_playground_v2.5": + sampling_type = comfy.model_sampling.EDM + sigma_data = 0.5 + latent_format = comfy.latent_formats.SDXL_Playground_2_5() class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingContinuousEDM, sampling_type): pass model_sampling = ModelSamplingAdvanced(model.model.model_config) - model_sampling.set_sigma_range(sigma_min, sigma_max) + model_sampling.set_parameters(sigma_min, sigma_max, sigma_data) m.add_object_patch("model_sampling", model_sampling) + if latent_format is not None: + m.add_object_patch("latent_format", latent_format) return (m, ) class RescaleCFG: From 8daedc5bf2ac106f1920c634866198c82e06997e Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 27 Feb 2024 18:03:03 -0500 Subject: [PATCH 059/378] Auto detect playground v2.5 model. --- comfy/model_base.py | 6 +++++- comfy/supported_models.py | 8 +++++++- 2 files changed, 12 insertions(+), 2 deletions(-) diff --git a/comfy/model_base.py b/comfy/model_base.py index 421f271b..170b1fd4 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -15,9 +15,10 @@ class ModelType(Enum): V_PREDICTION = 2 V_PREDICTION_EDM = 3 STABLE_CASCADE = 4 + EDM = 5 -from comfy.model_sampling import EPS, V_PREDICTION, ModelSamplingDiscrete, ModelSamplingContinuousEDM, StableCascadeSampling +from comfy.model_sampling import EPS, V_PREDICTION, EDM, ModelSamplingDiscrete, ModelSamplingContinuousEDM, StableCascadeSampling def model_sampling(model_config, model_type): @@ -33,6 +34,9 @@ def model_sampling(model_config, model_type): elif model_type == ModelType.STABLE_CASCADE: c = EPS s = StableCascadeSampling + elif model_type == ModelType.EDM: + c = EDM + s = ModelSamplingContinuousEDM class ModelSampling(s, c): pass diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 5d57a31a..74908216 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -163,7 +163,13 @@ class SDXL(supported_models_base.BASE): latent_format = latent_formats.SDXL def model_type(self, state_dict, prefix=""): - if "v_pred" in state_dict: + if 'edm_mean' in state_dict and 'edm_std' in state_dict: #Playground V2.5 + self.latent_format = latent_formats.SDXL_Playground_2_5() + self.sampling_settings["sigma_data"] = 0.5 + self.sampling_settings["sigma_max"] = 80.0 + self.sampling_settings["sigma_min"] = 0.002 + return model_base.ModelType.EDM + elif "v_pred" in state_dict: return model_base.ModelType.V_PREDICTION else: return model_base.ModelType.EPS From 37a86e4618098ef1e0d692d0953f072388cbc673 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 28 Feb 2024 03:57:41 -0500 Subject: [PATCH 060/378] Remove duplicate text_projection key from some saved models. --- comfy/diffusers_convert.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/comfy/diffusers_convert.py b/comfy/diffusers_convert.py index 8e3ca94e..eb561933 100644 --- a/comfy/diffusers_convert.py +++ b/comfy/diffusers_convert.py @@ -240,9 +240,9 @@ def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""): text_proj = "transformer.text_projection.weight" if k.endswith(text_proj): new_state_dict[k.replace(text_proj, "text_projection")] = v.transpose(0, 1).contiguous() - - relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k) - new_state_dict[relabelled_key] = v + else: + relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k) + new_state_dict[relabelled_key] = v for k_pre, tensors in capture_qkv_weight.items(): if None in tensors: From b3e97fc7141681b1fa6da3ee6701c0f9a31d38f8 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 28 Feb 2024 11:55:06 -0500 Subject: [PATCH 061/378] Koala 700M and 1B support. Use the UNET Loader node to load the unet file to use them. --- .../modules/diffusionmodules/openaimodel.py | 48 ++++++++++--------- comfy/model_detection.py | 23 +++++++-- comfy/supported_models.py | 22 ++++++++- 3 files changed, 66 insertions(+), 27 deletions(-) diff --git a/comfy/ldm/modules/diffusionmodules/openaimodel.py b/comfy/ldm/modules/diffusionmodules/openaimodel.py index 998afd97..c5477025 100644 --- a/comfy/ldm/modules/diffusionmodules/openaimodel.py +++ b/comfy/ldm/modules/diffusionmodules/openaimodel.py @@ -708,27 +708,30 @@ class UNetModel(nn.Module): device=device, operations=operations )] - if transformer_depth_middle >= 0: - mid_block += [get_attention_layer( # always uses a self-attn - ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim, - disable_self_attn=disable_middle_self_attn, use_checkpoint=use_checkpoint - ), - get_resblock( - merge_factor=merge_factor, - merge_strategy=merge_strategy, - video_kernel_size=video_kernel_size, - ch=ch, - time_embed_dim=time_embed_dim, - dropout=dropout, - out_channels=None, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - dtype=self.dtype, - device=device, - operations=operations - )] - self.middle_block = TimestepEmbedSequential(*mid_block) + + self.middle_block = None + if transformer_depth_middle >= -1: + if transformer_depth_middle >= 0: + mid_block += [get_attention_layer( # always uses a self-attn + ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim, + disable_self_attn=disable_middle_self_attn, use_checkpoint=use_checkpoint + ), + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=None, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations + )] + self.middle_block = TimestepEmbedSequential(*mid_block) self._feature_size += ch self.output_blocks = nn.ModuleList([]) @@ -858,7 +861,8 @@ class UNetModel(nn.Module): h = p(h, transformer_options) transformer_options["block"] = ("middle", 0) - h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) + if self.middle_block is not None: + h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) h = apply_control(h, control, 'middle') diff --git a/comfy/model_detection.py b/comfy/model_detection.py index 8fca6d8c..07ee8570 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -151,8 +151,10 @@ def detect_unet_config(state_dict, key_prefix): channel_mult.append(last_channel_mult) if "{}middle_block.1.proj_in.weight".format(key_prefix) in state_dict_keys: transformer_depth_middle = count_blocks(state_dict_keys, '{}middle_block.1.transformer_blocks.'.format(key_prefix) + '{}') - else: + elif "{}middle_block.0.in_layers.0.weight".format(key_prefix) in state_dict_keys: transformer_depth_middle = -1 + else: + transformer_depth_middle = -2 unet_config["in_channels"] = in_channels unet_config["out_channels"] = out_channels @@ -242,6 +244,7 @@ def unet_config_from_diffusers_unet(state_dict, dtype=None): down_blocks = count_blocks(state_dict, "down_blocks.{}") for i in range(down_blocks): attn_blocks = count_blocks(state_dict, "down_blocks.{}.attentions.".format(i) + '{}') + res_blocks = count_blocks(state_dict, "down_blocks.{}.resnets.".format(i) + '{}') for ab in range(attn_blocks): transformer_count = count_blocks(state_dict, "down_blocks.{}.attentions.{}.transformer_blocks.".format(i, ab) + '{}') transformer_depth.append(transformer_count) @@ -250,8 +253,8 @@ def unet_config_from_diffusers_unet(state_dict, dtype=None): attn_res *= 2 if attn_blocks == 0: - transformer_depth.append(0) - transformer_depth.append(0) + for i in range(res_blocks): + transformer_depth.append(0) match["transformer_depth"] = transformer_depth @@ -329,7 +332,19 @@ def unet_config_from_diffusers_unet(state_dict, dtype=None): 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'use_temporal_attention': False, 'use_temporal_resblock': False} - supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega] + KOALA_700M = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [1, 1, 1], 'transformer_depth': [0, 2, 5], 'transformer_depth_output': [0, 0, 2, 2, 5, 5], + 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + KOALA_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [1, 1, 1], 'transformer_depth': [0, 2, 6], 'transformer_depth_output': [0, 0, 2, 2, 6, 6], + 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 6, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega, KOALA_700M, KOALA_1B] for unet_config in supported_models: matches = True diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 74908216..37582103 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -234,6 +234,26 @@ class Segmind_Vega(SDXL): "use_temporal_attention": False, } +class KOALA_700M(SDXL): + unet_config = { + "model_channels": 320, + "use_linear_in_transformer": True, + "transformer_depth": [0, 2, 5], + "context_dim": 2048, + "adm_in_channels": 2816, + "use_temporal_attention": False, + } + +class KOALA_1B(SDXL): + unet_config = { + "model_channels": 320, + "use_linear_in_transformer": True, + "transformer_depth": [0, 2, 6], + "context_dim": 2048, + "adm_in_channels": 2816, + "use_temporal_attention": False, + } + class SVD_img2vid(supported_models_base.BASE): unet_config = { "model_channels": 320, @@ -380,5 +400,5 @@ class Stable_Cascade_B(Stable_Cascade_C): return out -models = [Stable_Zero123, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B] +models = [Stable_Zero123, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B] models += [SVD_img2vid] From cb7c3a2921cfc0805be0229b4634e1143d60e6fe Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 29 Feb 2024 13:09:43 -0500 Subject: [PATCH 062/378] Allow image_only_indicator to be None. --- .../modules/diffusionmodules/openaimodel.py | 3 +-- comfy/ldm/modules/diffusionmodules/util.py | 22 ++++++++++--------- comfy/model_base.py | 1 - 3 files changed, 13 insertions(+), 13 deletions(-) diff --git a/comfy/ldm/modules/diffusionmodules/openaimodel.py b/comfy/ldm/modules/diffusionmodules/openaimodel.py index c5477025..cf89ae01 100644 --- a/comfy/ldm/modules/diffusionmodules/openaimodel.py +++ b/comfy/ldm/modules/diffusionmodules/openaimodel.py @@ -484,7 +484,6 @@ class UNetModel(nn.Module): self.predict_codebook_ids = n_embed is not None self.default_num_video_frames = None - self.default_image_only_indicator = None time_embed_dim = model_channels * 4 self.time_embed = nn.Sequential( @@ -830,7 +829,7 @@ class UNetModel(nn.Module): transformer_patches = transformer_options.get("patches", {}) num_video_frames = kwargs.get("num_video_frames", self.default_num_video_frames) - image_only_indicator = kwargs.get("image_only_indicator", self.default_image_only_indicator) + image_only_indicator = kwargs.get("image_only_indicator", None) time_context = kwargs.get("time_context", None) assert (y is not None) == ( diff --git a/comfy/ldm/modules/diffusionmodules/util.py b/comfy/ldm/modules/diffusionmodules/util.py index 5a6aa7d7..ce14ad5e 100644 --- a/comfy/ldm/modules/diffusionmodules/util.py +++ b/comfy/ldm/modules/diffusionmodules/util.py @@ -46,23 +46,25 @@ class AlphaBlender(nn.Module): else: raise ValueError(f"unknown merge strategy {self.merge_strategy}") - def get_alpha(self, image_only_indicator: torch.Tensor) -> torch.Tensor: + def get_alpha(self, image_only_indicator: torch.Tensor, device) -> torch.Tensor: # skip_time_mix = rearrange(repeat(skip_time_mix, 'b -> (b t) () () ()', t=t), '(b t) 1 ... -> b 1 t ...', t=t) if self.merge_strategy == "fixed": # make shape compatible # alpha = repeat(self.mix_factor, '1 -> b () t () ()', t=t, b=bs) - alpha = self.mix_factor.to(image_only_indicator.device) + alpha = self.mix_factor.to(device) elif self.merge_strategy == "learned": - alpha = torch.sigmoid(self.mix_factor.to(image_only_indicator.device)) + alpha = torch.sigmoid(self.mix_factor.to(device)) # make shape compatible # alpha = repeat(alpha, '1 -> s () ()', s = t * bs) elif self.merge_strategy == "learned_with_images": - assert image_only_indicator is not None, "need image_only_indicator ..." - alpha = torch.where( - image_only_indicator.bool(), - torch.ones(1, 1, device=image_only_indicator.device), - rearrange(torch.sigmoid(self.mix_factor.to(image_only_indicator.device)), "... -> ... 1"), - ) + if image_only_indicator is None: + alpha = rearrange(torch.sigmoid(self.mix_factor.to(device)), "... -> ... 1") + else: + alpha = torch.where( + image_only_indicator.bool(), + torch.ones(1, 1, device=image_only_indicator.device), + rearrange(torch.sigmoid(self.mix_factor.to(image_only_indicator.device)), "... -> ... 1"), + ) alpha = rearrange(alpha, self.rearrange_pattern) # make shape compatible # alpha = repeat(alpha, '1 -> s () ()', s = t * bs) @@ -76,7 +78,7 @@ class AlphaBlender(nn.Module): x_temporal, image_only_indicator=None, ) -> torch.Tensor: - alpha = self.get_alpha(image_only_indicator) + alpha = self.get_alpha(image_only_indicator, x_spatial.device) x = ( alpha.to(x_spatial.dtype) * x_spatial + (1.0 - alpha).to(x_spatial.dtype) * x_temporal diff --git a/comfy/model_base.py b/comfy/model_base.py index 170b1fd4..69117dd0 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -372,7 +372,6 @@ class SVD_img2vid(BaseModel): if "time_conditioning" in kwargs: out["time_context"] = comfy.conds.CONDCrossAttn(kwargs["time_conditioning"]) - out['image_only_indicator'] = comfy.conds.CONDConstant(torch.zeros((1,), device=device)) out['num_video_frames'] = comfy.conds.CONDConstant(noise.shape[0]) return out From c62e8361676e53f86422e5cb5511673ef3b61e78 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 1 Mar 2024 12:54:38 -0500 Subject: [PATCH 063/378] Move noise scaling to object with sampling math. --- comfy/model_sampling.py | 8 ++++++++ comfy/samplers.py | 8 +------- 2 files changed, 9 insertions(+), 7 deletions(-) diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py index e7f8bc6a..b46202b7 100644 --- a/comfy/model_sampling.py +++ b/comfy/model_sampling.py @@ -11,6 +11,14 @@ class EPS: sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) return model_input - model_output * sigma + def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): + if max_denoise: + noise = noise * torch.sqrt(1.0 + sigma ** 2.0) + else: + noise = noise * sigma + if latent_image is not None: + noise += latent_image + return noise class V_PREDICTION(EPS): def calculate_denoised(self, sigma, model_output, model_input): diff --git a/comfy/samplers.py b/comfy/samplers.py index e5569322..46d444f5 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -534,19 +534,13 @@ class KSAMPLER(Sampler): else: model_k.noise = noise - if self.max_denoise(model_wrap, sigmas): - noise = noise * torch.sqrt(1.0 + sigmas[0] ** 2.0) - else: - noise = noise * sigmas[0] + noise = model_wrap.inner_model.model_sampling.noise_scaling(sigmas[0], noise, latent_image, self.max_denoise(model_wrap, sigmas)) k_callback = None total_steps = len(sigmas) - 1 if callback is not None: k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps) - if latent_image is not None: - noise += latent_image - samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options) return samples From 00425563c07171f478e7809d3b89cc3df770ebbd Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 1 Mar 2024 14:24:41 -0500 Subject: [PATCH 064/378] Cleanup: Use sampling noise scaling function for inpainting. --- comfy/samplers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/samplers.py b/comfy/samplers.py index 46d444f5..9c50d712 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -278,7 +278,7 @@ class KSamplerX0Inpaint(torch.nn.Module): def forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, model_options={}, seed=None): if denoise_mask is not None: latent_mask = 1. - denoise_mask - x = x * denoise_mask + (self.latent_image + self.noise * sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1))) * latent_mask + x = x * denoise_mask + self.inner_model.inner_model.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1)), self.noise, self.latent_image) * latent_mask out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, model_options=model_options, seed=seed) if denoise_mask is not None: out = out * denoise_mask + self.latent_image * latent_mask From 9f71e4b62d19708ad3a7b57db969673bd9159dbb Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 2 Mar 2024 11:43:27 -0500 Subject: [PATCH 065/378] Let model patches patch sub objects. --- comfy/model_patcher.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index a88b737c..f29781f3 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -176,10 +176,10 @@ class ModelPatcher: def patch_model(self, device_to=None, patch_weights=True): for k in self.object_patches: - old = getattr(self.model, k) + old = comfy.utils.get_attr(self.model, k) if k not in self.object_patches_backup: self.object_patches_backup[k] = old - setattr(self.model, k, self.object_patches[k]) + comfy.utils.set_attr(self.model, k, self.object_patches[k]) if patch_weights: model_sd = self.model_state_dict() @@ -352,6 +352,6 @@ class ModelPatcher: keys = list(self.object_patches_backup.keys()) for k in keys: - setattr(self.model, k, self.object_patches_backup[k]) + comfy.utils.set_attr(self.model, k, self.object_patches_backup[k]) self.object_patches_backup = {} From 51df846598544471bdc2126d39c13dca66884f90 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 2 Mar 2024 11:44:06 -0500 Subject: [PATCH 066/378] Let conditioning specify custom concat conds. --- comfy/model_base.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/comfy/model_base.py b/comfy/model_base.py index 69117dd0..a9de1366 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -167,6 +167,10 @@ class BaseModel(torch.nn.Module): if cross_attn_cnet is not None: out['crossattn_controlnet'] = comfy.conds.CONDCrossAttn(cross_attn_cnet) + c_concat = kwargs.get("noise_concat", None) + if c_concat is not None: + out['c_concat'] = comfy.conds.CONDNoiseShape(data) + return out def load_model_weights(self, sd, unet_prefix=""): From dce3555339a0f8883757bc44c358f4d0248e7b1e Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 2 Mar 2024 17:16:31 -0500 Subject: [PATCH 067/378] Add some tesla pascal GPUs to the fp16 working but slower list. --- comfy/model_management.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index adcc0e8a..9f924883 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -753,7 +753,7 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma #FP16 is confirmed working on a 1080 (GP104) but it's a bit slower than FP32 so it should only be enabled #when the model doesn't actually fit on the card #TODO: actually test if GP106 and others have the same type of behavior - nvidia_10_series = ["1080", "1070", "titan x", "p3000", "p3200", "p4000", "p4200", "p5000", "p5200", "p6000", "1060", "1050"] + nvidia_10_series = ["1080", "1070", "titan x", "p3000", "p3200", "p4000", "p4200", "p5000", "p5200", "p6000", "1060", "1050", "p40", "p100", "p6", "p4"] for x in nvidia_10_series: if x in props.name.lower(): fp16_works = True From 1abf8374ec690972ba512a50eeaa767935536441 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 2 Mar 2024 17:27:23 -0500 Subject: [PATCH 068/378] utils.set_attr can now be used to set any attribute. The old set_attr has been renamed to set_attr_param. --- comfy/controlnet.py | 4 ++-- comfy/model_patcher.py | 7 +++---- comfy/utils.py | 7 +++++-- 3 files changed, 10 insertions(+), 8 deletions(-) diff --git a/comfy/controlnet.py b/comfy/controlnet.py index 41619758..f859a50d 100644 --- a/comfy/controlnet.py +++ b/comfy/controlnet.py @@ -287,13 +287,13 @@ class ControlLora(ControlNet): for k in sd: weight = sd[k] try: - comfy.utils.set_attr(self.control_model, k, weight) + comfy.utils.set_attr_param(self.control_model, k, weight) except: pass for k in self.control_weights: if k not in {"lora_controlnet"}: - comfy.utils.set_attr(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device())) + comfy.utils.set_attr_param(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device())) def copy(self): c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling) diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index f29781f3..604e3477 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -176,10 +176,9 @@ class ModelPatcher: def patch_model(self, device_to=None, patch_weights=True): for k in self.object_patches: - old = comfy.utils.get_attr(self.model, k) + old = comfy.utils.set_attr(self.model, k, self.object_patches[k]) if k not in self.object_patches_backup: self.object_patches_backup[k] = old - comfy.utils.set_attr(self.model, k, self.object_patches[k]) if patch_weights: model_sd = self.model_state_dict() @@ -203,7 +202,7 @@ class ModelPatcher: if inplace_update: comfy.utils.copy_to_param(self.model, key, out_weight) else: - comfy.utils.set_attr(self.model, key, out_weight) + comfy.utils.set_attr_param(self.model, key, out_weight) del temp_weight if device_to is not None: @@ -342,7 +341,7 @@ class ModelPatcher: comfy.utils.copy_to_param(self.model, k, self.backup[k]) else: for k in keys: - comfy.utils.set_attr(self.model, k, self.backup[k]) + comfy.utils.set_attr_param(self.model, k, self.backup[k]) self.backup = {} diff --git a/comfy/utils.py b/comfy/utils.py index 41f730c8..5deb14cd 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -294,8 +294,11 @@ def set_attr(obj, attr, value): for name in attrs[:-1]: obj = getattr(obj, name) prev = getattr(obj, attrs[-1]) - setattr(obj, attrs[-1], torch.nn.Parameter(value, requires_grad=False)) - del prev + setattr(obj, attrs[-1], value) + return prev + +def set_attr_param(obj, attr, value): + return set_attr(obj, attr, torch.nn.Parameter(value, requires_grad=False)) def copy_to_param(obj, attr, value): # inplace update tensor instead of replacing it From 727021bdea69b2fd2594e08410fd1be3bc748721 Mon Sep 17 00:00:00 2001 From: Shiimizu Date: Sun, 3 Mar 2024 12:34:13 -0800 Subject: [PATCH 069/378] Implement Differential Diffusion (#2876) * Implement Differential Diffusion * Cleanup. * Fix. * Masks should be applied at full strength. * Fix colors. * Register the node. * Cleaner code. * Fix issue with getting unipc sampler. * Adjust thresholds. * Switch to linear thresholds. * Only calculate nearest_idx on valid thresholds. --- comfy/samplers.py | 2 + comfy_extras/nodes_differential_diffusion.py | 97 ++++++++++++++++++++ nodes.py | 1 + 3 files changed, 100 insertions(+) create mode 100644 comfy_extras/nodes_differential_diffusion.py diff --git a/comfy/samplers.py b/comfy/samplers.py index 9c50d712..b7ef6b96 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -277,6 +277,8 @@ class KSamplerX0Inpaint(torch.nn.Module): self.inner_model = model def forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, model_options={}, seed=None): if denoise_mask is not None: + if "denoise_mask_function" in model_options: + denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask) latent_mask = 1. - denoise_mask x = x * denoise_mask + self.inner_model.inner_model.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1)), self.noise, self.latent_image) * latent_mask out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, model_options=model_options, seed=seed) diff --git a/comfy_extras/nodes_differential_diffusion.py b/comfy_extras/nodes_differential_diffusion.py new file mode 100644 index 00000000..48c95602 --- /dev/null +++ b/comfy_extras/nodes_differential_diffusion.py @@ -0,0 +1,97 @@ +# code adapted from https://github.com/exx8/differential-diffusion + +import torch +import inspect + +class DifferentialDiffusion(): + @classmethod + def INPUT_TYPES(s): + return {"required": {"model": ("MODEL", ), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "apply" + CATEGORY = "_for_testing" + INIT = False + + @classmethod + def IS_CHANGED(s, *args, **kwargs): + DifferentialDiffusion.INIT = s.INIT = True + return "" + + def __init__(self) -> None: + DifferentialDiffusion.INIT = False + self.sigmas: torch.Tensor = None + self.thresholds: torch.Tensor = None + self.mask_i = None + self.valid_sigmas = False + self.varying_sigmas_samplers = ["dpmpp_2s", "dpmpp_sde", "dpm_2", "heun", "restart"] + + def apply(self, model): + model = model.clone() + model.model_options["denoise_mask_function"] = self.forward + return (model,) + + def init_sigmas(self, sigma: torch.Tensor, denoise_mask: torch.Tensor): + self.__init__() + self.sigmas, sampler = find_outer_instance("sigmas", callback=get_sigmas_and_sampler) or (None, "") + self.valid_sigmas = not ("sample_" not in sampler or any(s in sampler for s in self.varying_sigmas_samplers)) or "generic" in sampler + if self.sigmas is None: + self.sigmas = sigma[:1].repeat(2) + self.sigmas[-1].zero_() + self.sigmas_min = self.sigmas.min() + self.sigmas_max = self.sigmas.max() + self.thresholds = torch.linspace(1, 0, self.sigmas.shape[0], dtype=sigma.dtype, device=sigma.device) + self.thresholds_min_len = self.thresholds.shape[0] - 1 + if self.valid_sigmas: + thresholds = self.thresholds[:-1].reshape(-1, 1, 1, 1, 1) + mask = denoise_mask.unsqueeze(0) + mask = (mask >= thresholds).to(denoise_mask.dtype) + self.mask_i = iter(mask) + + def forward(self, sigma: torch.Tensor, denoise_mask: torch.Tensor): + if self.sigmas is None or DifferentialDiffusion.INIT: + self.init_sigmas(sigma, denoise_mask) + if self.valid_sigmas: + try: + return next(self.mask_i) + except StopIteration: + self.valid_sigmas = False + if self.thresholds_min_len > 1: + nearest_idx = (self.sigmas - sigma[0]).abs().argmin() + if not self.thresholds_min_len > nearest_idx: + nearest_idx = -2 + threshold = self.thresholds[nearest_idx] + else: + threshold = (sigma[0] - self.sigmas_min) / (self.sigmas_max - self.sigmas_min) + return (denoise_mask >= threshold).to(denoise_mask.dtype) + +def get_sigmas_and_sampler(frame, target): + found = frame.f_locals[target] + if isinstance(found, torch.Tensor) and found[-1] < 0.1: + return found, frame.f_code.co_name + return False + +def find_outer_instance(target: str, target_type=None, callback=None): + frame = inspect.currentframe() + i = 0 + while frame and i < 100: + if target in frame.f_locals: + if callback is not None: + res = callback(frame, target) + if res: + return res + else: + found = frame.f_locals[target] + if isinstance(found, target_type): + return found + frame = frame.f_back + i += 1 + return None + + +NODE_CLASS_MAPPINGS = { + "DifferentialDiffusion": DifferentialDiffusion, +} +NODE_DISPLAY_NAME_MAPPINGS = { + "DifferentialDiffusion": "Differential Diffusion", +} diff --git a/nodes.py b/nodes.py index 1846797b..2cfea1a7 100644 --- a/nodes.py +++ b/nodes.py @@ -1961,6 +1961,7 @@ def init_custom_nodes(): "nodes_photomaker.py", "nodes_cond.py", "nodes_stable_cascade.py", + "nodes_differential_diffusion.py", ] for node_file in extras_files: From 12c1080ebc9095b3878d7b3aa994a6b75b308e0a Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 3 Mar 2024 15:11:13 -0500 Subject: [PATCH 070/378] Simplify differential diffusion code. --- comfy/model_patcher.py | 3 + comfy/samplers.py | 7 +- comfy_extras/nodes_differential_diffusion.py | 87 ++++---------------- 3 files changed, 23 insertions(+), 74 deletions(-) diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index 604e3477..4a5d42b0 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -67,6 +67,9 @@ class ModelPatcher: def set_model_unet_function_wrapper(self, unet_wrapper_function): self.model_options["model_function_wrapper"] = unet_wrapper_function + def set_model_denoise_mask_function(self, denoise_mask_function): + self.model_options["denoise_mask_function"] = denoise_mask_function + def set_model_patch(self, patch, name): to = self.model_options["transformer_options"] if "patches" not in to: diff --git a/comfy/samplers.py b/comfy/samplers.py index b7ef6b96..6863be4e 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -272,13 +272,14 @@ class CFGNoisePredictor(torch.nn.Module): return self.apply_model(*args, **kwargs) class KSamplerX0Inpaint(torch.nn.Module): - def __init__(self, model): + def __init__(self, model, sigmas): super().__init__() self.inner_model = model + self.sigmas = sigmas def forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, model_options={}, seed=None): if denoise_mask is not None: if "denoise_mask_function" in model_options: - denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask) + denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask, extra_options={"model": self.inner_model, "sigmas": self.sigmas}) latent_mask = 1. - denoise_mask x = x * denoise_mask + self.inner_model.inner_model.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1)), self.noise, self.latent_image) * latent_mask out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, model_options=model_options, seed=seed) @@ -528,7 +529,7 @@ class KSAMPLER(Sampler): def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False): extra_args["denoise_mask"] = denoise_mask - model_k = KSamplerX0Inpaint(model_wrap) + model_k = KSamplerX0Inpaint(model_wrap, sigmas) model_k.latent_image = latent_image if self.inpaint_options.get("random", False): #TODO: Should this be the default? generator = torch.manual_seed(extra_args.get("seed", 41) + 1) diff --git a/comfy_extras/nodes_differential_diffusion.py b/comfy_extras/nodes_differential_diffusion.py index 48c95602..7e858a71 100644 --- a/comfy_extras/nodes_differential_diffusion.py +++ b/comfy_extras/nodes_differential_diffusion.py @@ -1,7 +1,6 @@ # code adapted from https://github.com/exx8/differential-diffusion import torch -import inspect class DifferentialDiffusion(): @classmethod @@ -13,82 +12,28 @@ class DifferentialDiffusion(): CATEGORY = "_for_testing" INIT = False - @classmethod - def IS_CHANGED(s, *args, **kwargs): - DifferentialDiffusion.INIT = s.INIT = True - return "" - - def __init__(self) -> None: - DifferentialDiffusion.INIT = False - self.sigmas: torch.Tensor = None - self.thresholds: torch.Tensor = None - self.mask_i = None - self.valid_sigmas = False - self.varying_sigmas_samplers = ["dpmpp_2s", "dpmpp_sde", "dpm_2", "heun", "restart"] - def apply(self, model): model = model.clone() - model.model_options["denoise_mask_function"] = self.forward + model.set_model_denoise_mask_function(self.forward) return (model,) - - def init_sigmas(self, sigma: torch.Tensor, denoise_mask: torch.Tensor): - self.__init__() - self.sigmas, sampler = find_outer_instance("sigmas", callback=get_sigmas_and_sampler) or (None, "") - self.valid_sigmas = not ("sample_" not in sampler or any(s in sampler for s in self.varying_sigmas_samplers)) or "generic" in sampler - if self.sigmas is None: - self.sigmas = sigma[:1].repeat(2) - self.sigmas[-1].zero_() - self.sigmas_min = self.sigmas.min() - self.sigmas_max = self.sigmas.max() - self.thresholds = torch.linspace(1, 0, self.sigmas.shape[0], dtype=sigma.dtype, device=sigma.device) - self.thresholds_min_len = self.thresholds.shape[0] - 1 - if self.valid_sigmas: - thresholds = self.thresholds[:-1].reshape(-1, 1, 1, 1, 1) - mask = denoise_mask.unsqueeze(0) - mask = (mask >= thresholds).to(denoise_mask.dtype) - self.mask_i = iter(mask) - - def forward(self, sigma: torch.Tensor, denoise_mask: torch.Tensor): - if self.sigmas is None or DifferentialDiffusion.INIT: - self.init_sigmas(sigma, denoise_mask) - if self.valid_sigmas: - try: - return next(self.mask_i) - except StopIteration: - self.valid_sigmas = False - if self.thresholds_min_len > 1: - nearest_idx = (self.sigmas - sigma[0]).abs().argmin() - if not self.thresholds_min_len > nearest_idx: - nearest_idx = -2 - threshold = self.thresholds[nearest_idx] - else: - threshold = (sigma[0] - self.sigmas_min) / (self.sigmas_max - self.sigmas_min) + + def forward(self, sigma: torch.Tensor, denoise_mask: torch.Tensor, extra_options: dict): + model = extra_options["model"] + step_sigmas = extra_options["sigmas"] + sigma_to = model.inner_model.model_sampling.sigma_min + if step_sigmas[-1] > sigma_to: + sigma_to = step_sigmas[-1] + sigma_from = step_sigmas[0] + + ts_from = model.inner_model.model_sampling.timestep(sigma_from) + ts_to = model.inner_model.model_sampling.timestep(sigma_to) + current_ts = model.inner_model.model_sampling.timestep(sigma) + + threshold = (current_ts - ts_to) / (ts_from - ts_to) + return (denoise_mask >= threshold).to(denoise_mask.dtype) -def get_sigmas_and_sampler(frame, target): - found = frame.f_locals[target] - if isinstance(found, torch.Tensor) and found[-1] < 0.1: - return found, frame.f_code.co_name - return False -def find_outer_instance(target: str, target_type=None, callback=None): - frame = inspect.currentframe() - i = 0 - while frame and i < 100: - if target in frame.f_locals: - if callback is not None: - res = callback(frame, target) - if res: - return res - else: - found = frame.f_locals[target] - if isinstance(found, target_type): - return found - frame = frame.f_back - i += 1 - return None - - NODE_CLASS_MAPPINGS = { "DifferentialDiffusion": DifferentialDiffusion, } From 0db3111b5f488a7a456472bcee1274b5b4424857 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 3 Mar 2024 16:25:16 -0500 Subject: [PATCH 071/378] Disable site dir in updater when doing pip install. --- .ci/update_windows/update.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.ci/update_windows/update.py b/.ci/update_windows/update.py index 6067d1a1..127247b2 100755 --- a/.ci/update_windows/update.py +++ b/.ci/update_windows/update.py @@ -104,7 +104,7 @@ if self_update and not files_equal(update_py_path, repo_update_py_path) and file if not os.path.exists(req_path) or not files_equal(repo_req_path, req_path): import subprocess try: - subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-r', repo_req_path]) + subprocess.check_call([sys.executable, '-s', '-m', 'pip', 'install', '-r', repo_req_path]) shutil.copy(repo_req_path, req_path) except: pass From b2e1744a164edd617efe90831b30d1eba8ec6765 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 4 Mar 2024 00:31:59 -0500 Subject: [PATCH 072/378] Add a ThresholdMask node. --- comfy_extras/nodes_mask.py | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/comfy_extras/nodes_mask.py b/comfy_extras/nodes_mask.py index a7d164bf..29589b4a 100644 --- a/comfy_extras/nodes_mask.py +++ b/comfy_extras/nodes_mask.py @@ -341,6 +341,24 @@ class GrowMask: out.append(output) return (torch.stack(out, dim=0),) +class ThresholdMask: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "mask": ("MASK",), + "value": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + FUNCTION = "image_to_mask" + + def image_to_mask(self, mask, value): + mask = (mask > value).float() + return (mask,) NODE_CLASS_MAPPINGS = { @@ -355,6 +373,7 @@ NODE_CLASS_MAPPINGS = { "MaskComposite": MaskComposite, "FeatherMask": FeatherMask, "GrowMask": GrowMask, + "ThresholdMask": ThresholdMask, } NODE_DISPLAY_NAME_MAPPINGS = { From 0490ce8244b19cf95a9d9d77d227d540eca43ac2 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 4 Mar 2024 00:43:09 -0500 Subject: [PATCH 073/378] Fix differential diffusion node for batches. --- comfy_extras/nodes_differential_diffusion.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy_extras/nodes_differential_diffusion.py b/comfy_extras/nodes_differential_diffusion.py index 7e858a71..98dbbf10 100644 --- a/comfy_extras/nodes_differential_diffusion.py +++ b/comfy_extras/nodes_differential_diffusion.py @@ -27,7 +27,7 @@ class DifferentialDiffusion(): ts_from = model.inner_model.model_sampling.timestep(sigma_from) ts_to = model.inner_model.model_sampling.timestep(sigma_to) - current_ts = model.inner_model.model_sampling.timestep(sigma) + current_ts = model.inner_model.model_sampling.timestep(sigma[0]) threshold = (current_ts - ts_to) / (ts_from - ts_to) From 478f71a2490a5e213db2eb26552550b3f51e005c Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 4 Mar 2024 08:51:25 -0500 Subject: [PATCH 074/378] Remove useless check. --- comfy/model_sampling.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py index b46202b7..d325f76d 100644 --- a/comfy/model_sampling.py +++ b/comfy/model_sampling.py @@ -16,8 +16,8 @@ class EPS: noise = noise * torch.sqrt(1.0 + sigma ** 2.0) else: noise = noise * sigma - if latent_image is not None: - noise += latent_image + + noise += latent_image return noise class V_PREDICTION(EPS): From caddef8d8863adaa3d85faa03e3e2f1c6a04fb58 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 4 Mar 2024 09:03:59 -0500 Subject: [PATCH 075/378] Auto disable cuda malloc on unsupported GPUs on Linux. --- cuda_malloc.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/cuda_malloc.py b/cuda_malloc.py index 144cdacd..70e7ecf9 100644 --- a/cuda_malloc.py +++ b/cuda_malloc.py @@ -1,6 +1,7 @@ import os import importlib.util from comfy.cli_args import args +import subprocess #Can't use pytorch to get the GPU names because the cuda malloc has to be set before the first import. def get_gpu_names(): @@ -34,7 +35,12 @@ def get_gpu_names(): return gpu_names return enum_display_devices() else: - return set() + gpu_names = set() + out = subprocess.check_output(['nvidia-smi', '-L']) + for l in out.split(b'\n'): + if len(l) > 0: + gpu_names.add(l.decode('utf-8').split(' (UUID')[0]) + return gpu_names blacklist = {"GeForce GTX TITAN X", "GeForce GTX 980", "GeForce GTX 970", "GeForce GTX 960", "GeForce GTX 950", "GeForce 945M", "GeForce 940M", "GeForce 930M", "GeForce 920M", "GeForce 910M", "GeForce GTX 750", "GeForce GTX 745", "Quadro K620", From 6d8834f08f7b5243cba925665f4a5a7296142383 Mon Sep 17 00:00:00 2001 From: Dmytro Mishkin Date: Mon, 4 Mar 2024 18:50:28 +0100 Subject: [PATCH 076/378] Add Morphology nodes from kornia (#2781) * import kornia * Added morphology nodexs * Add kornia to requirements * fix choices * options, also move to postprocessors * fix placing and step --- comfy_extras/nodes_canny.py | 270 +------------------------------ comfy_extras/nodes_morphology.py | 49 ++++++ nodes.py | 1 + requirements.txt | 1 + 4 files changed, 52 insertions(+), 269 deletions(-) create mode 100644 comfy_extras/nodes_morphology.py diff --git a/comfy_extras/nodes_canny.py b/comfy_extras/nodes_canny.py index 730dded5..8138b5f7 100644 --- a/comfy_extras/nodes_canny.py +++ b/comfy_extras/nodes_canny.py @@ -5,275 +5,7 @@ import torch import torch.nn.functional as F import comfy.model_management -def get_canny_nms_kernel(device=None, dtype=None): - """Utility function that returns 3x3 kernels for the Canny Non-maximal suppression.""" - return torch.tensor( - [ - [[[0.0, 0.0, 0.0], [0.0, 1.0, -1.0], [0.0, 0.0, 0.0]]], - [[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, -1.0]]], - [[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, -1.0, 0.0]]], - [[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [-1.0, 0.0, 0.0]]], - [[[0.0, 0.0, 0.0], [-1.0, 1.0, 0.0], [0.0, 0.0, 0.0]]], - [[[-1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]], - [[[0.0, -1.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]], - [[[0.0, 0.0, -1.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]], - ], - device=device, - dtype=dtype, - ) - - -def get_hysteresis_kernel(device=None, dtype=None): - """Utility function that returns the 3x3 kernels for the Canny hysteresis.""" - return torch.tensor( - [ - [[[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 0.0]]], - [[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 1.0]]], - [[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]]], - [[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [1.0, 0.0, 0.0]]], - [[[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 0.0]]], - [[[1.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]], - [[[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]], - [[[0.0, 0.0, 1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]], - ], - device=device, - dtype=dtype, - ) - -def gaussian_blur_2d(img, kernel_size, sigma): - ksize_half = (kernel_size - 1) * 0.5 - - x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size) - - pdf = torch.exp(-0.5 * (x / sigma).pow(2)) - - x_kernel = pdf / pdf.sum() - x_kernel = x_kernel.to(device=img.device, dtype=img.dtype) - - kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :]) - kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1]) - - padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2] - - img = torch.nn.functional.pad(img, padding, mode="reflect") - img = torch.nn.functional.conv2d(img, kernel2d, groups=img.shape[-3]) - - return img - -def get_sobel_kernel2d(device=None, dtype=None): - kernel_x = torch.tensor([[-1.0, 0.0, 1.0], [-2.0, 0.0, 2.0], [-1.0, 0.0, 1.0]], device=device, dtype=dtype) - kernel_y = kernel_x.transpose(0, 1) - return torch.stack([kernel_x, kernel_y]) - -def spatial_gradient(input, normalized: bool = True): - r"""Compute the first order image derivative in both x and y using a Sobel operator. - .. image:: _static/img/spatial_gradient.png - Args: - input: input image tensor with shape :math:`(B, C, H, W)`. - mode: derivatives modality, can be: `sobel` or `diff`. - order: the order of the derivatives. - normalized: whether the output is normalized. - Return: - the derivatives of the input feature map. with shape :math:`(B, C, 2, H, W)`. - .. note:: - See a working example `here `__. - Examples: - >>> input = torch.rand(1, 3, 4, 4) - >>> output = spatial_gradient(input) # 1x3x2x4x4 - >>> output.shape - torch.Size([1, 3, 2, 4, 4]) - """ - # KORNIA_CHECK_IS_TENSOR(input) - # KORNIA_CHECK_SHAPE(input, ['B', 'C', 'H', 'W']) - - # allocate kernel - kernel = get_sobel_kernel2d(device=input.device, dtype=input.dtype) - if normalized: - kernel = normalize_kernel2d(kernel) - - # prepare kernel - b, c, h, w = input.shape - tmp_kernel = kernel[:, None, ...] - - # Pad with "replicate for spatial dims, but with zeros for channel - spatial_pad = [kernel.size(1) // 2, kernel.size(1) // 2, kernel.size(2) // 2, kernel.size(2) // 2] - out_channels: int = 2 - padded_inp = torch.nn.functional.pad(input.reshape(b * c, 1, h, w), spatial_pad, 'replicate') - out = F.conv2d(padded_inp, tmp_kernel, groups=1, padding=0, stride=1) - return out.reshape(b, c, out_channels, h, w) - -def rgb_to_grayscale(image, rgb_weights = None): - r"""Convert a RGB image to grayscale version of image. - - .. image:: _static/img/rgb_to_grayscale.png - - The image data is assumed to be in the range of (0, 1). - - Args: - image: RGB image to be converted to grayscale with shape :math:`(*,3,H,W)`. - rgb_weights: Weights that will be applied on each channel (RGB). - The sum of the weights should add up to one. - Returns: - grayscale version of the image with shape :math:`(*,1,H,W)`. - - .. note:: - See a working example `here `__. - - Example: - >>> input = torch.rand(2, 3, 4, 5) - >>> gray = rgb_to_grayscale(input) # 2x1x4x5 - """ - - if len(image.shape) < 3 or image.shape[-3] != 3: - raise ValueError(f"Input size must have a shape of (*, 3, H, W). Got {image.shape}") - - if rgb_weights is None: - # 8 bit images - if image.dtype == torch.uint8: - rgb_weights = torch.tensor([76, 150, 29], device=image.device, dtype=torch.uint8) - # floating point images - elif image.dtype in (torch.float16, torch.float32, torch.float64): - rgb_weights = torch.tensor([0.299, 0.587, 0.114], device=image.device, dtype=image.dtype) - else: - raise TypeError(f"Unknown data type: {image.dtype}") - else: - # is tensor that we make sure is in the same device/dtype - rgb_weights = rgb_weights.to(image) - - # unpack the color image channels with RGB order - r: Tensor = image[..., 0:1, :, :] - g: Tensor = image[..., 1:2, :, :] - b: Tensor = image[..., 2:3, :, :] - - w_r, w_g, w_b = rgb_weights.unbind() - return w_r * r + w_g * g + w_b * b - -def canny( - input, - low_threshold = 0.1, - high_threshold = 0.2, - kernel_size = 5, - sigma = 1, - hysteresis = True, - eps = 1e-6, -): - r"""Find edges of the input image and filters them using the Canny algorithm. - .. image:: _static/img/canny.png - Args: - input: input image tensor with shape :math:`(B,C,H,W)`. - low_threshold: lower threshold for the hysteresis procedure. - high_threshold: upper threshold for the hysteresis procedure. - kernel_size: the size of the kernel for the gaussian blur. - sigma: the standard deviation of the kernel for the gaussian blur. - hysteresis: if True, applies the hysteresis edge tracking. - Otherwise, the edges are divided between weak (0.5) and strong (1) edges. - eps: regularization number to avoid NaN during backprop. - Returns: - - the canny edge magnitudes map, shape of :math:`(B,1,H,W)`. - - the canny edge detection filtered by thresholds and hysteresis, shape of :math:`(B,1,H,W)`. - .. note:: - See a working example `here `__. - Example: - >>> input = torch.rand(5, 3, 4, 4) - >>> magnitude, edges = canny(input) # 5x3x4x4 - >>> magnitude.shape - torch.Size([5, 1, 4, 4]) - >>> edges.shape - torch.Size([5, 1, 4, 4]) - """ - # KORNIA_CHECK_IS_TENSOR(input) - # KORNIA_CHECK_SHAPE(input, ['B', 'C', 'H', 'W']) - # KORNIA_CHECK( - # low_threshold <= high_threshold, - # "Invalid input thresholds. low_threshold should be smaller than the high_threshold. Got: " - # f"{low_threshold}>{high_threshold}", - # ) - # KORNIA_CHECK(0 < low_threshold < 1, f'Invalid low threshold. Should be in range (0, 1). Got: {low_threshold}') - # KORNIA_CHECK(0 < high_threshold < 1, f'Invalid high threshold. Should be in range (0, 1). Got: {high_threshold}') - - device = input.device - dtype = input.dtype - - # To Grayscale - if input.shape[1] == 3: - input = rgb_to_grayscale(input) - - # Gaussian filter - blurred: Tensor = gaussian_blur_2d(input, kernel_size, sigma) - - # Compute the gradients - gradients: Tensor = spatial_gradient(blurred, normalized=False) - - # Unpack the edges - gx: Tensor = gradients[:, :, 0] - gy: Tensor = gradients[:, :, 1] - - # Compute gradient magnitude and angle - magnitude: Tensor = torch.sqrt(gx * gx + gy * gy + eps) - angle: Tensor = torch.atan2(gy, gx) - - # Radians to Degrees - angle = 180.0 * angle / math.pi - - # Round angle to the nearest 45 degree - angle = torch.round(angle / 45) * 45 - - # Non-maximal suppression - nms_kernels: Tensor = get_canny_nms_kernel(device, dtype) - nms_magnitude: Tensor = F.conv2d(magnitude, nms_kernels, padding=nms_kernels.shape[-1] // 2) - - # Get the indices for both directions - positive_idx: Tensor = (angle / 45) % 8 - positive_idx = positive_idx.long() - - negative_idx: Tensor = ((angle / 45) + 4) % 8 - negative_idx = negative_idx.long() - - # Apply the non-maximum suppression to the different directions - channel_select_filtered_positive: Tensor = torch.gather(nms_magnitude, 1, positive_idx) - channel_select_filtered_negative: Tensor = torch.gather(nms_magnitude, 1, negative_idx) - - channel_select_filtered: Tensor = torch.stack( - [channel_select_filtered_positive, channel_select_filtered_negative], 1 - ) - - is_max: Tensor = channel_select_filtered.min(dim=1)[0] > 0.0 - - magnitude = magnitude * is_max - - # Threshold - edges: Tensor = F.threshold(magnitude, low_threshold, 0.0) - - low: Tensor = magnitude > low_threshold - high: Tensor = magnitude > high_threshold - - edges = low * 0.5 + high * 0.5 - edges = edges.to(dtype) - - # Hysteresis - if hysteresis: - edges_old: Tensor = -torch.ones(edges.shape, device=edges.device, dtype=dtype) - hysteresis_kernels: Tensor = get_hysteresis_kernel(device, dtype) - - while ((edges_old - edges).abs() != 0).any(): - weak: Tensor = (edges == 0.5).float() - strong: Tensor = (edges == 1).float() - - hysteresis_magnitude: Tensor = F.conv2d( - edges, hysteresis_kernels, padding=hysteresis_kernels.shape[-1] // 2 - ) - hysteresis_magnitude = (hysteresis_magnitude == 1).any(1, keepdim=True).to(dtype) - hysteresis_magnitude = hysteresis_magnitude * weak + strong - - edges_old = edges.clone() - edges = hysteresis_magnitude + (hysteresis_magnitude == 0) * weak * 0.5 - - edges = hysteresis_magnitude - - return magnitude, edges +from kornia.filters import canny class Canny: diff --git a/comfy_extras/nodes_morphology.py b/comfy_extras/nodes_morphology.py new file mode 100644 index 00000000..071521d8 --- /dev/null +++ b/comfy_extras/nodes_morphology.py @@ -0,0 +1,49 @@ +import torch +import comfy.model_management + +from kornia.morphology import dilation, erosion, opening, closing, gradient, top_hat, bottom_hat + + +class Morphology: + @classmethod + def INPUT_TYPES(s): + return {"required": {"image": ("IMAGE",), + "operation": (["erode", "dilate", "open", "close", "gradient", "bottom_hat", "top_hat"],), + "kernel_size": ("INT", {"default": 3, "min": 3, "max": 999, "step": 1}), + }} + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "process" + + CATEGORY = "image/postprocessing" + + def process(self, image, operation, kernel_size): + device = comfy.model_management.get_torch_device() + kernel = torch.ones(kernel_size, kernel_size, device=device) + image_k = image.to(device).movedim(-1, 1) + if operation == "erode": + output = erosion(image_k, kernel) + elif operation == "dilate": + output = dilation(image_k, kernel) + elif operation == "open": + output = opening(image_k, kernel) + elif operation == "close": + output = closing(image_k, kernel) + elif operation == "gradient": + output = gradient(image_k, kernel) + elif operation == "top_hat": + output = top_hat(image_k, kernel) + elif operation == "bottom_hat": + output = bottom_hat(image_k, kernel) + else: + raise ValueError(f"Invalid operation {operation} for morphology. Must be one of 'erode', 'dilate', 'open', 'close', 'gradient', 'tophat', 'bottomhat'") + img_out = output.to(comfy.model_management.intermediate_device()).movedim(1, -1) + return (img_out,) + +NODE_CLASS_MAPPINGS = { + "Morphology": Morphology, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "Morphology": "ImageMorphology", +} \ No newline at end of file diff --git a/nodes.py b/nodes.py index 2cfea1a7..d8a4e822 100644 --- a/nodes.py +++ b/nodes.py @@ -1960,6 +1960,7 @@ def init_custom_nodes(): "nodes_sdupscale.py", "nodes_photomaker.py", "nodes_cond.py", + "nodes_morphology.py", "nodes_stable_cascade.py", "nodes_differential_diffusion.py", ] diff --git a/requirements.txt b/requirements.txt index e804618e..e7d8c0e9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -10,3 +10,4 @@ Pillow scipy tqdm psutil +kornia>=0.7.1 From b7b55931660c26524cbee96b65f7d745a38de86d Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 4 Mar 2024 13:06:13 -0500 Subject: [PATCH 077/378] Fix nightly workflow and update other workflows. --- .github/workflows/windows_release_dependencies.yml | 2 +- .github/workflows/windows_release_nightly_pytorch.yml | 4 ++-- .github/workflows/windows_release_package.yml | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/windows_release_dependencies.yml b/.github/workflows/windows_release_dependencies.yml index e0841fdf..ffd3e221 100644 --- a/.github/workflows/windows_release_dependencies.yml +++ b/.github/workflows/windows_release_dependencies.yml @@ -24,7 +24,7 @@ on: description: 'python patch version' required: true type: string - default: "6" + default: "8" # push: # branches: # - master diff --git a/.github/workflows/windows_release_nightly_pytorch.yml b/.github/workflows/windows_release_nightly_pytorch.yml index 56830935..672a7f22 100644 --- a/.github/workflows/windows_release_nightly_pytorch.yml +++ b/.github/workflows/windows_release_nightly_pytorch.yml @@ -19,7 +19,7 @@ on: description: 'python patch version' required: true type: string - default: "1" + default: "2" # push: # branches: # - master @@ -49,7 +49,7 @@ jobs: echo 'import site' >> ./python3${{ inputs.python_minor }}._pth curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py ./python.exe get-pip.py - python -m pip wheel torch torchvision torchaudio --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2 -w ../temp_wheel_dir + python -m pip wheel torch torchvision torchaudio mpmath==1.3.0 --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2 -w ../temp_wheel_dir ls ../temp_wheel_dir ./python.exe -s -m pip install --pre ../temp_wheel_dir/* sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth diff --git a/.github/workflows/windows_release_package.yml b/.github/workflows/windows_release_package.yml index 87d37c24..4e3cdabd 100644 --- a/.github/workflows/windows_release_package.yml +++ b/.github/workflows/windows_release_package.yml @@ -19,7 +19,7 @@ on: description: 'python patch version' required: true type: string - default: "6" + default: "8" # push: # branches: # - master From a38b9b3ac152fb5679dad03813a93c09e0a4d15e Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 4 Mar 2024 13:24:08 -0500 Subject: [PATCH 078/378] Add debugging info for when comfy_extra nodes fail to import. --- nodes.py | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/nodes.py b/nodes.py index d8a4e822..ea49d549 100644 --- a/nodes.py +++ b/nodes.py @@ -1965,7 +1965,20 @@ def init_custom_nodes(): "nodes_differential_diffusion.py", ] + import_failed = [] for node_file in extras_files: - load_custom_node(os.path.join(extras_dir, node_file)) + if not load_custom_node(os.path.join(extras_dir, node_file)): + import_failed.append(node_file) load_custom_nodes() + + if len(import_failed) > 0: + print("WARNING: some comfy_extras/ nodes did not import correctly. This may be because they are missing some dependencies.\n") + for node in import_failed: + print("IMPORT FAILED: {}".format(node)) + print("\nThis issue might be caused by missing dependencies.") + if args.windows_standalone_build: + print("Please run the update script: update/update_comfyui.bat") + else: + print("Please do a: pip install -r requirements.txt") + print() From 10860bcd28ca31f0206424127b562aa2c80fbd56 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 5 Mar 2024 15:15:20 -0500 Subject: [PATCH 079/378] Add compression_ratio to controlnet code. --- comfy/controlnet.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/comfy/controlnet.py b/comfy/controlnet.py index f859a50d..602b2d3b 100644 --- a/comfy/controlnet.py +++ b/comfy/controlnet.py @@ -37,6 +37,7 @@ class ControlBase: self.timestep_percent_range = (0.0, 1.0) self.global_average_pooling = False self.timestep_range = None + self.compression_ratio = 8 if device is None: device = comfy.model_management.get_torch_device() @@ -158,11 +159,11 @@ class ControlNet(ControlBase): dtype = self.manual_cast_dtype output_dtype = x_noisy.dtype - if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]: + if self.cond_hint is None or x_noisy.shape[2] * self.compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.compression_ratio != self.cond_hint.shape[3]: if self.cond_hint is not None: del self.cond_hint self.cond_hint = None - self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(dtype).to(self.device) + self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * self.compression_ratio, x_noisy.shape[2] * self.compression_ratio, 'nearest-exact', "center").to(dtype).to(self.device) if x_noisy.shape[0] != self.cond_hint.shape[0]: self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) @@ -456,12 +457,12 @@ class T2IAdapter(ControlBase): else: return None - if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]: + if self.cond_hint is None or x_noisy.shape[2] * self.compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.compression_ratio != self.cond_hint.shape[3]: if self.cond_hint is not None: del self.cond_hint self.control_input = None self.cond_hint = None - width, height = self.scale_image_to(x_noisy.shape[3] * 8, x_noisy.shape[2] * 8) + width, height = self.scale_image_to(x_noisy.shape[3] * self.compression_ratio, x_noisy.shape[2] * self.compression_ratio) self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, width, height, 'nearest-exact', "center").float().to(self.device) if self.channels_in == 1 and self.cond_hint.shape[1] > 1: self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True) From 03e83bb5d052be16aaad7599ffeb1eade9481508 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 6 Mar 2024 02:25:42 -0500 Subject: [PATCH 080/378] Support stable cascade canny controlnet. --- comfy/controlnet.py | 15 ++++-- comfy/ldm/cascade/controlnet.py | 94 +++++++++++++++++++++++++++++++++ comfy/ldm/cascade/stage_c.py | 17 +++--- 3 files changed, 116 insertions(+), 10 deletions(-) create mode 100644 comfy/ldm/cascade/controlnet.py diff --git a/comfy/controlnet.py b/comfy/controlnet.py index 602b2d3b..39344a4d 100644 --- a/comfy/controlnet.py +++ b/comfy/controlnet.py @@ -9,6 +9,7 @@ import comfy.ops import comfy.cldm.cldm import comfy.t2i_adapter.adapter +import comfy.ldm.cascade.controlnet def broadcast_image_to(tensor, target_batch_size, batched_number): @@ -78,6 +79,7 @@ class ControlBase: c.strength = self.strength c.timestep_percent_range = self.timestep_percent_range c.global_average_pooling = self.global_average_pooling + c.compression_ratio = self.compression_ratio def inference_memory_requirements(self, dtype): if self.previous_controlnet is not None: @@ -433,11 +435,12 @@ def load_controlnet(ckpt_path, model=None): return control class T2IAdapter(ControlBase): - def __init__(self, t2i_model, channels_in, device=None): + def __init__(self, t2i_model, channels_in, compression_ratio, device=None): super().__init__(device) self.t2i_model = t2i_model self.channels_in = channels_in self.control_input = None + self.compression_ratio = compression_ratio def scale_image_to(self, width, height): unshuffle_amount = self.t2i_model.unshuffle_amount @@ -482,11 +485,13 @@ class T2IAdapter(ControlBase): return self.control_merge(control_input, mid, control_prev, x_noisy.dtype) def copy(self): - c = T2IAdapter(self.t2i_model, self.channels_in) + c = T2IAdapter(self.t2i_model, self.channels_in, self.compression_ratio) self.copy_to(c) return c def load_t2i_adapter(t2i_data): + compression_ratio = 8 + if 'adapter' in t2i_data: t2i_data = t2i_data['adapter'] if 'adapter.body.0.resnets.0.block1.weight' in t2i_data: #diffusers format @@ -514,8 +519,12 @@ def load_t2i_adapter(t2i_data): if cin == 256 or cin == 768: xl = True model_ad = comfy.t2i_adapter.adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv, xl=xl) + elif "backbone.0.0.weight" in keys: + model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.0.weight'].shape[1], proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63]) + compression_ratio = 32 else: return None + missing, unexpected = model_ad.load_state_dict(t2i_data) if len(missing) > 0: print("t2i missing", missing) @@ -523,4 +532,4 @@ def load_t2i_adapter(t2i_data): if len(unexpected) > 0: print("t2i unexpected", unexpected) - return T2IAdapter(model_ad, model_ad.input_channels) + return T2IAdapter(model_ad, model_ad.input_channels, compression_ratio) diff --git a/comfy/ldm/cascade/controlnet.py b/comfy/ldm/cascade/controlnet.py new file mode 100644 index 00000000..fdb1ba4d --- /dev/null +++ b/comfy/ldm/cascade/controlnet.py @@ -0,0 +1,94 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Stability AI + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import torch +import torchvision +from torch import nn +from .common import LayerNorm2d_op + + +class CNetResBlock(nn.Module): + def __init__(self, c, dtype=None, device=None, operations=None): + super().__init__() + self.blocks = nn.Sequential( + LayerNorm2d_op(operations)(c, dtype=dtype, device=device), + nn.GELU(), + operations.Conv2d(c, c, kernel_size=3, padding=1), + LayerNorm2d_op(operations)(c, dtype=dtype, device=device), + nn.GELU(), + operations.Conv2d(c, c, kernel_size=3, padding=1), + ) + + def forward(self, x): + return x + self.blocks(x) + + +class ControlNet(nn.Module): + def __init__(self, c_in=3, c_proj=2048, proj_blocks=None, bottleneck_mode=None, dtype=None, device=None, operations=nn): + super().__init__() + if bottleneck_mode is None: + bottleneck_mode = 'effnet' + self.proj_blocks = proj_blocks + if bottleneck_mode == 'effnet': + embd_channels = 1280 + self.backbone = torchvision.models.efficientnet_v2_s().features.eval() + if c_in != 3: + in_weights = self.backbone[0][0].weight.data + self.backbone[0][0] = operations.Conv2d(c_in, 24, kernel_size=3, stride=2, bias=False, dtype=dtype, device=device) + if c_in > 3: + # nn.init.constant_(self.backbone[0][0].weight, 0) + self.backbone[0][0].weight.data[:, :3] = in_weights[:, :3].clone() + else: + self.backbone[0][0].weight.data = in_weights[:, :c_in].clone() + elif bottleneck_mode == 'simple': + embd_channels = c_in + self.backbone = nn.Sequential( + operations.Conv2d(embd_channels, embd_channels * 4, kernel_size=3, padding=1, dtype=dtype, device=device), + nn.LeakyReLU(0.2, inplace=True), + operations.Conv2d(embd_channels * 4, embd_channels, kernel_size=3, padding=1, dtype=dtype, device=device), + ) + elif bottleneck_mode == 'large': + self.backbone = nn.Sequential( + operations.Conv2d(c_in, 4096 * 4, kernel_size=1, dtype=dtype, device=device), + nn.LeakyReLU(0.2, inplace=True), + operations.Conv2d(4096 * 4, 1024, kernel_size=1, dtype=dtype, device=device), + *[CNetResBlock(1024) for _ in range(8)], + operations.Conv2d(1024, 1280, kernel_size=1, dtype=dtype, device=device), + ) + embd_channels = 1280 + else: + raise ValueError(f'Unknown bottleneck mode: {bottleneck_mode}') + self.projections = nn.ModuleList() + for _ in range(len(proj_blocks)): + self.projections.append(nn.Sequential( + operations.Conv2d(embd_channels, embd_channels, kernel_size=1, bias=False, dtype=dtype, device=device), + nn.LeakyReLU(0.2, inplace=True), + operations.Conv2d(embd_channels, c_proj, kernel_size=1, bias=False, dtype=dtype, device=device), + )) + # nn.init.constant_(self.projections[-1][-1].weight, 0) # zero output projection + self.xl = False + self.input_channels = c_in + self.unshuffle_amount = 8 + + def forward(self, x): + print(x) + x = self.backbone(x) + proj_outputs = [None for _ in range(max(self.proj_blocks) + 1)] + for i, idx in enumerate(self.proj_blocks): + proj_outputs[idx] = self.projections[i](x) + return proj_outputs diff --git a/comfy/ldm/cascade/stage_c.py b/comfy/ldm/cascade/stage_c.py index 08e33ade..67c1e52b 100644 --- a/comfy/ldm/cascade/stage_c.py +++ b/comfy/ldm/cascade/stage_c.py @@ -194,10 +194,10 @@ class StageC(nn.Module): hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, ResBlock)): if cnet is not None: - next_cnet = cnet() + next_cnet = cnet.pop() if next_cnet is not None: x = x + nn.functional.interpolate(next_cnet, size=x.shape[-2:], mode='bilinear', - align_corners=True) + align_corners=True).to(x.dtype) x = block(x) elif isinstance(block, AttnBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, @@ -228,10 +228,10 @@ class StageC(nn.Module): x = torch.nn.functional.interpolate(x, skip.shape[-2:], mode='bilinear', align_corners=True) if cnet is not None: - next_cnet = cnet() + next_cnet = cnet.pop() if next_cnet is not None: x = x + nn.functional.interpolate(next_cnet, size=x.shape[-2:], mode='bilinear', - align_corners=True) + align_corners=True).to(x.dtype) x = block(x, skip) elif isinstance(block, AttnBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, @@ -248,7 +248,7 @@ class StageC(nn.Module): x = upscaler(x) return x - def forward(self, x, r, clip_text, clip_text_pooled, clip_img, cnet=None, **kwargs): + def forward(self, x, r, clip_text, clip_text_pooled, clip_img, control=None, **kwargs): # Process the conditioning embeddings r_embed = self.gen_r_embedding(r).to(dtype=x.dtype) for c in self.t_conds: @@ -256,10 +256,13 @@ class StageC(nn.Module): r_embed = torch.cat([r_embed, self.gen_r_embedding(t_cond).to(dtype=x.dtype)], dim=1) clip = self.gen_c_embeddings(clip_text, clip_text_pooled, clip_img) + if control is not None: + cnet = control.get("input") + else: + cnet = None + # Model Blocks x = self.embedding(x) - if cnet is not None: - cnet = ControlNetDeliverer(cnet) level_outputs = self._down_encode(x, r_embed, clip, cnet) x = self._up_decode(level_outputs, r_embed, clip, cnet) return self.clf(x) From 03e6e81629b0eb5351f82fcbaafad8698253bfbc Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 6 Mar 2024 02:56:13 -0500 Subject: [PATCH 081/378] Set upscale algorithm to bilinear for stable cascade controlnet. --- comfy/controlnet.py | 15 ++++++++++----- comfy/ldm/cascade/controlnet.py | 1 - 2 files changed, 10 insertions(+), 6 deletions(-) diff --git a/comfy/controlnet.py b/comfy/controlnet.py index 39344a4d..356d5c0e 100644 --- a/comfy/controlnet.py +++ b/comfy/controlnet.py @@ -39,6 +39,7 @@ class ControlBase: self.global_average_pooling = False self.timestep_range = None self.compression_ratio = 8 + self.upscale_algorithm = 'nearest-exact' if device is None: device = comfy.model_management.get_torch_device() @@ -80,6 +81,7 @@ class ControlBase: c.timestep_percent_range = self.timestep_percent_range c.global_average_pooling = self.global_average_pooling c.compression_ratio = self.compression_ratio + c.upscale_algorithm = self.upscale_algorithm def inference_memory_requirements(self, dtype): if self.previous_controlnet is not None: @@ -165,7 +167,7 @@ class ControlNet(ControlBase): if self.cond_hint is not None: del self.cond_hint self.cond_hint = None - self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * self.compression_ratio, x_noisy.shape[2] * self.compression_ratio, 'nearest-exact', "center").to(dtype).to(self.device) + self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * self.compression_ratio, x_noisy.shape[2] * self.compression_ratio, self.upscale_algorithm, "center").to(dtype).to(self.device) if x_noisy.shape[0] != self.cond_hint.shape[0]: self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) @@ -435,12 +437,13 @@ def load_controlnet(ckpt_path, model=None): return control class T2IAdapter(ControlBase): - def __init__(self, t2i_model, channels_in, compression_ratio, device=None): + def __init__(self, t2i_model, channels_in, compression_ratio, upscale_algorithm, device=None): super().__init__(device) self.t2i_model = t2i_model self.channels_in = channels_in self.control_input = None self.compression_ratio = compression_ratio + self.upscale_algorithm = upscale_algorithm def scale_image_to(self, width, height): unshuffle_amount = self.t2i_model.unshuffle_amount @@ -466,7 +469,7 @@ class T2IAdapter(ControlBase): self.control_input = None self.cond_hint = None width, height = self.scale_image_to(x_noisy.shape[3] * self.compression_ratio, x_noisy.shape[2] * self.compression_ratio) - self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, width, height, 'nearest-exact', "center").float().to(self.device) + self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, width, height, self.upscale_algorithm, "center").float().to(self.device) if self.channels_in == 1 and self.cond_hint.shape[1] > 1: self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True) if x_noisy.shape[0] != self.cond_hint.shape[0]: @@ -485,12 +488,13 @@ class T2IAdapter(ControlBase): return self.control_merge(control_input, mid, control_prev, x_noisy.dtype) def copy(self): - c = T2IAdapter(self.t2i_model, self.channels_in, self.compression_ratio) + c = T2IAdapter(self.t2i_model, self.channels_in, self.compression_ratio, self.upscale_algorithm) self.copy_to(c) return c def load_t2i_adapter(t2i_data): compression_ratio = 8 + upscale_algorithm = 'nearest-exact' if 'adapter' in t2i_data: t2i_data = t2i_data['adapter'] @@ -522,6 +526,7 @@ def load_t2i_adapter(t2i_data): elif "backbone.0.0.weight" in keys: model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.0.weight'].shape[1], proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63]) compression_ratio = 32 + upscale_algorithm = 'bilinear' else: return None @@ -532,4 +537,4 @@ def load_t2i_adapter(t2i_data): if len(unexpected) > 0: print("t2i unexpected", unexpected) - return T2IAdapter(model_ad, model_ad.input_channels, compression_ratio) + return T2IAdapter(model_ad, model_ad.input_channels, compression_ratio, upscale_algorithm) diff --git a/comfy/ldm/cascade/controlnet.py b/comfy/ldm/cascade/controlnet.py index fdb1ba4d..c5757308 100644 --- a/comfy/ldm/cascade/controlnet.py +++ b/comfy/ldm/cascade/controlnet.py @@ -86,7 +86,6 @@ class ControlNet(nn.Module): self.unshuffle_amount = 8 def forward(self, x): - print(x) x = self.backbone(x) proj_outputs = [None for _ in range(max(self.proj_blocks) + 1)] for i, idx in enumerate(self.proj_blocks): From 5f60ee246e4a14a8d835a9653a193c8e651535e5 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 7 Mar 2024 00:58:52 -0500 Subject: [PATCH 082/378] Support loading the sr cascade controlnet. --- comfy/controlnet.py | 4 ++++ comfy/ldm/cascade/controlnet.py | 2 +- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/comfy/controlnet.py b/comfy/controlnet.py index 356d5c0e..1352d141 100644 --- a/comfy/controlnet.py +++ b/comfy/controlnet.py @@ -527,6 +527,10 @@ def load_t2i_adapter(t2i_data): model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.0.weight'].shape[1], proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63]) compression_ratio = 32 upscale_algorithm = 'bilinear' + elif "backbone.10.blocks.0.weight" in keys: + model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.weight'].shape[1], bottleneck_mode="large", proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63]) + compression_ratio = 1 + upscale_algorithm = 'nearest-exact' else: return None diff --git a/comfy/ldm/cascade/controlnet.py b/comfy/ldm/cascade/controlnet.py index c5757308..5dac5939 100644 --- a/comfy/ldm/cascade/controlnet.py +++ b/comfy/ldm/cascade/controlnet.py @@ -67,7 +67,7 @@ class ControlNet(nn.Module): operations.Conv2d(c_in, 4096 * 4, kernel_size=1, dtype=dtype, device=device), nn.LeakyReLU(0.2, inplace=True), operations.Conv2d(4096 * 4, 1024, kernel_size=1, dtype=dtype, device=device), - *[CNetResBlock(1024) for _ in range(8)], + *[CNetResBlock(1024, dtype=dtype, device=device, operations=operations) for _ in range(8)], operations.Conv2d(1024, 1280, kernel_size=1, dtype=dtype, device=device), ) embd_channels = 1280 From 3f75419e2e656575309f391769c66813a9ec078d Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 7 Mar 2024 01:48:31 -0500 Subject: [PATCH 083/378] Add a node to use the super resolution controlnet. --- comfy_extras/nodes_stable_cascade.py | 31 ++++++++++++++++++++++++++++ 1 file changed, 31 insertions(+) diff --git a/comfy_extras/nodes_stable_cascade.py b/comfy_extras/nodes_stable_cascade.py index b795d008..27596fc6 100644 --- a/comfy_extras/nodes_stable_cascade.py +++ b/comfy_extras/nodes_stable_cascade.py @@ -102,8 +102,39 @@ class StableCascade_StageB_Conditioning: c.append(n) return (c, ) +class StableCascade_SuperResolutionControlnet: + def __init__(self, device="cpu"): + self.device = device + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image": ("IMAGE",), + "vae": ("VAE", ), + }} + RETURN_TYPES = ("IMAGE", "LATENT", "LATENT") + RETURN_NAMES = ("controlnet_input", "stage_c", "stage_b") + FUNCTION = "generate" + + CATEGORY = "_for_testing/stable_cascade" + + def generate(self, image, vae): + width = image.shape[-2] + height = image.shape[-3] + batch_size = image.shape[0] + controlnet_input = vae.encode(image[:,:,:,:3]).movedim(1, -1) + + c_latent = torch.zeros([batch_size, 16, height // 16, width // 16]) + b_latent = torch.zeros([batch_size, 4, height // 2, width // 2]) + return (controlnet_input, { + "samples": c_latent, + }, { + "samples": b_latent, + }) + NODE_CLASS_MAPPINGS = { "StableCascade_EmptyLatentImage": StableCascade_EmptyLatentImage, "StableCascade_StageB_Conditioning": StableCascade_StageB_Conditioning, "StableCascade_StageC_VAEEncode": StableCascade_StageC_VAEEncode, + "StableCascade_SuperResolutionControlnet": StableCascade_SuperResolutionControlnet, } From 55f37baae85a5f3ef6d87743445fcce1f0477ba9 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 7 Mar 2024 01:49:20 -0500 Subject: [PATCH 084/378] Move some stable cascade nodes outside of _for_testing. --- comfy_extras/nodes_stable_cascade.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/comfy_extras/nodes_stable_cascade.py b/comfy_extras/nodes_stable_cascade.py index 27596fc6..7e2d37d2 100644 --- a/comfy_extras/nodes_stable_cascade.py +++ b/comfy_extras/nodes_stable_cascade.py @@ -37,7 +37,7 @@ class StableCascade_EmptyLatentImage: RETURN_NAMES = ("stage_c", "stage_b") FUNCTION = "generate" - CATEGORY = "_for_testing/stable_cascade" + CATEGORY = "latent/stable_cascade" def generate(self, width, height, compression, batch_size=1): c_latent = torch.zeros([batch_size, 16, height // compression, width // compression]) @@ -63,7 +63,7 @@ class StableCascade_StageC_VAEEncode: RETURN_NAMES = ("stage_c", "stage_b") FUNCTION = "generate" - CATEGORY = "_for_testing/stable_cascade" + CATEGORY = "latent/stable_cascade" def generate(self, image, vae, compression): width = image.shape[-2] @@ -91,7 +91,7 @@ class StableCascade_StageB_Conditioning: FUNCTION = "set_prior" - CATEGORY = "_for_testing/stable_cascade" + CATEGORY = "conditioning/stable_cascade" def set_prior(self, conditioning, stage_c): c = [] From 314d28c251a69ca1cb42f888635e66ceeba5a03a Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 7 Mar 2024 15:07:47 -0500 Subject: [PATCH 085/378] Pass extra_pnginfo as None when not in input data. --- execution.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/execution.py b/execution.py index 3e9d53b0..5f8ea835 100644 --- a/execution.py +++ b/execution.py @@ -35,8 +35,7 @@ def get_input_data(inputs, class_def, unique_id, outputs={}, prompt={}, extra_da if h[x] == "PROMPT": input_data_all[x] = [prompt] if h[x] == "EXTRA_PNGINFO": - if "extra_pnginfo" in extra_data: - input_data_all[x] = [extra_data['extra_pnginfo']] + input_data_all[x] = [extra_data.get('extra_pnginfo', None)] if h[x] == "UNIQUE_ID": input_data_all[x] = [unique_id] return input_data_all From 0a4675266e1cb68da5a04fcb533d85ade6b84ec3 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 8 Mar 2024 18:43:13 -0500 Subject: [PATCH 086/378] Make message about missing dependencies more clear. --- nodes.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nodes.py b/nodes.py index ea49d549..c334cdb9 100644 --- a/nodes.py +++ b/nodes.py @@ -1976,7 +1976,7 @@ def init_custom_nodes(): print("WARNING: some comfy_extras/ nodes did not import correctly. This may be because they are missing some dependencies.\n") for node in import_failed: print("IMPORT FAILED: {}".format(node)) - print("\nThis issue might be caused by missing dependencies.") + print("\nThis issue might be caused by missing newly dependencies added the last time you updated ComfyUI.") if args.windows_standalone_build: print("Please run the update script: update/update_comfyui.bat") else: From a9ee9589b72aa0e2931f1c0705524c56adaee26d Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 9 Mar 2024 08:21:43 -0500 Subject: [PATCH 087/378] Add SamplerEulerAncestral node. --- comfy_extras/nodes_custom_sampler.py | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index 99f9ea7d..0ad1246a 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -228,6 +228,23 @@ class SamplerDPMPP_SDE: sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r}) return (sampler, ) +class SamplerEulerAncestral: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, eta, s_noise): + sampler = comfy.samplers.ksampler("euler_ancestral", {"eta": eta, "s_noise": s_noise}) + return (sampler, ) + class SamplerCustom: @classmethod def INPUT_TYPES(s): @@ -288,6 +305,7 @@ NODE_CLASS_MAPPINGS = { "VPScheduler": VPScheduler, "SDTurboScheduler": SDTurboScheduler, "KSamplerSelect": KSamplerSelect, + "SamplerEulerAncestral": SamplerEulerAncestral, "SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE, "SamplerDPMPP_SDE": SamplerDPMPP_SDE, "SplitSigmas": SplitSigmas, From 4656273e727d67338053aaa6a5502aa0ef932893 Mon Sep 17 00:00:00 2001 From: MoonRide303 Date: Sat, 9 Mar 2024 19:32:33 +0100 Subject: [PATCH 088/378] Added additional nodes for CLIP merging --- comfy_extras/nodes_model_merging.py | 46 +++++++++++++++++++++++++++++ 1 file changed, 46 insertions(+) diff --git a/comfy_extras/nodes_model_merging.py b/comfy_extras/nodes_model_merging.py index d594cf49..a25b73ca 100644 --- a/comfy_extras/nodes_model_merging.py +++ b/comfy_extras/nodes_model_merging.py @@ -87,6 +87,50 @@ class CLIPMergeSimple: m.add_patches({k: kp[k]}, 1.0 - ratio, ratio) return (m, ) + +class CLIPSubtract: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip1": ("CLIP",), + "clip2": ("CLIP",), + "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "merge" + + CATEGORY = "advanced/model_merging" + + def merge(self, clip1, clip2, multiplier): + m = clip1.clone() + kp = clip2.get_key_patches() + for k in kp: + if k.endswith(".position_ids") or k.endswith(".logit_scale"): + continue + m.add_patches({k: kp[k]}, - multiplier, multiplier) + return (m, ) + + +class CLIPAdd: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip1": ("CLIP",), + "clip2": ("CLIP",), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "merge" + + CATEGORY = "advanced/model_merging" + + def merge(self, clip1, clip2): + m = clip1.clone() + kp = clip2.get_key_patches() + for k in kp: + if k.endswith(".position_ids") or k.endswith(".logit_scale"): + continue + m.add_patches({k: kp[k]}, 1.0, 1.0) + return (m, ) + + class ModelMergeBlocks: @classmethod def INPUT_TYPES(s): @@ -279,6 +323,8 @@ NODE_CLASS_MAPPINGS = { "ModelMergeAdd": ModelAdd, "CheckpointSave": CheckpointSave, "CLIPMergeSimple": CLIPMergeSimple, + "CLIPMergeSubtract": CLIPSubtract, + "CLIPMergeAdd": CLIPAdd, "CLIPSave": CLIPSave, "VAESave": VAESave, } From 65397ce601030c9f5934b0b277a019bdaa911a5f Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 10 Mar 2024 11:37:08 -0400 Subject: [PATCH 089/378] Replace prints with logging and add --verbose argument. --- comfy/cli_args.py | 10 ++++++++++ comfy/clip_vision.py | 3 ++- comfy/controlnet.py | 18 +++++++++++------ comfy/diffusers_convert.py | 3 ++- comfy/lora.py | 3 ++- comfy/model_base.py | 9 +++++---- comfy/model_detection.py | 3 ++- comfy/model_management.py | 41 +++++++++++++++++++------------------- comfy/model_patcher.py | 18 ++++++++++------- comfy/sd.py | 33 +++++++++++++++--------------- comfy/sd1_clip.py | 9 ++++----- comfy/utils.py | 5 +++-- 12 files changed, 90 insertions(+), 65 deletions(-) diff --git a/comfy/cli_args.py b/comfy/cli_args.py index b4bbfbfa..757fc245 100644 --- a/comfy/cli_args.py +++ b/comfy/cli_args.py @@ -114,6 +114,9 @@ parser.add_argument("--disable-metadata", action="store_true", help="Disable sav parser.add_argument("--multi-user", action="store_true", help="Enables per-user storage.") +parser.add_argument("--verbose", action="store_true", help="Enables more debug prints.") + + if comfy.options.args_parsing: args = parser.parse_args() else: @@ -124,3 +127,10 @@ if args.windows_standalone_build: if args.disable_auto_launch: args.auto_launch = False + +import logging +logging_level = logging.WARNING +if args.verbose: + logging_level = logging.DEBUG + +logging.basicConfig(format="%(message)s", level=logging_level) diff --git a/comfy/clip_vision.py b/comfy/clip_vision.py index 8c77ee7a..acc86be8 100644 --- a/comfy/clip_vision.py +++ b/comfy/clip_vision.py @@ -2,6 +2,7 @@ from .utils import load_torch_file, transformers_convert, state_dict_prefix_repl import os import torch import json +import logging import comfy.ops import comfy.model_patcher @@ -99,7 +100,7 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False): clip = ClipVisionModel(json_config) m, u = clip.load_sd(sd) if len(m) > 0: - print("missing clip vision:", m) + logging.warning("missing clip vision: {}".format(m)) u = set(u) keys = list(sd.keys()) for k in keys: diff --git a/comfy/controlnet.py b/comfy/controlnet.py index 1352d141..a5e7b23f 100644 --- a/comfy/controlnet.py +++ b/comfy/controlnet.py @@ -1,6 +1,7 @@ import torch import math import os +import logging import comfy.utils import comfy.model_management import comfy.model_detection @@ -367,7 +368,7 @@ def load_controlnet(ckpt_path, model=None): leftover_keys = controlnet_data.keys() if len(leftover_keys) > 0: - print("leftover keys:", leftover_keys) + logging.warning("leftover keys: {}".format(leftover_keys)) controlnet_data = new_sd pth_key = 'control_model.zero_convs.0.0.weight' @@ -382,7 +383,7 @@ def load_controlnet(ckpt_path, model=None): else: net = load_t2i_adapter(controlnet_data) if net is None: - print("error checkpoint does not contain controlnet or t2i adapter data", ckpt_path) + logging.error("error checkpoint does not contain controlnet or t2i adapter data {}".format(ckpt_path)) return net if controlnet_config is None: @@ -417,7 +418,7 @@ def load_controlnet(ckpt_path, model=None): cd = controlnet_data[x] cd += model_sd[sd_key].type(cd.dtype).to(cd.device) else: - print("WARNING: Loaded a diff controlnet without a model. It will very likely not work.") + logging.warning("WARNING: Loaded a diff controlnet without a model. It will very likely not work.") class WeightsLoader(torch.nn.Module): pass @@ -426,7 +427,12 @@ def load_controlnet(ckpt_path, model=None): missing, unexpected = w.load_state_dict(controlnet_data, strict=False) else: missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False) - print(missing, unexpected) + + if len(missing) > 0: + logging.warning("missing controlnet keys: {}".format(missing)) + + if len(unexpected) > 0: + logging.info("unexpected controlnet keys: {}".format(unexpected)) global_average_pooling = False filename = os.path.splitext(ckpt_path)[0] @@ -536,9 +542,9 @@ def load_t2i_adapter(t2i_data): missing, unexpected = model_ad.load_state_dict(t2i_data) if len(missing) > 0: - print("t2i missing", missing) + logging.warning("t2i missing {}".format(missing)) if len(unexpected) > 0: - print("t2i unexpected", unexpected) + logging.info("t2i unexpected {}".format(unexpected)) return T2IAdapter(model_ad, model_ad.input_channels, compression_ratio, upscale_algorithm) diff --git a/comfy/diffusers_convert.py b/comfy/diffusers_convert.py index eb561933..18398cb3 100644 --- a/comfy/diffusers_convert.py +++ b/comfy/diffusers_convert.py @@ -1,5 +1,6 @@ import re import torch +import logging # conversion code from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py @@ -177,7 +178,7 @@ def convert_vae_state_dict(vae_state_dict): for k, v in new_state_dict.items(): for weight_name in weights_to_convert: if f"mid.attn_1.{weight_name}.weight" in k: - print(f"Reshaping {k} for SD format") + logging.info(f"Reshaping {k} for SD format") new_state_dict[k] = reshape_weight_for_sd(v) return new_state_dict diff --git a/comfy/lora.py b/comfy/lora.py index 21b9897a..637380d5 100644 --- a/comfy/lora.py +++ b/comfy/lora.py @@ -1,4 +1,5 @@ import comfy.utils +import logging LORA_CLIP_MAP = { "mlp.fc1": "mlp_fc1", @@ -156,7 +157,7 @@ def load_lora(lora, to_load): for x in lora.keys(): if x not in loaded_keys: - print("lora key not loaded", x) + logging.warning("lora key not loaded: {}".format(x)) return patch_dict def model_lora_keys_clip(model, key_map={}): diff --git a/comfy/model_base.py b/comfy/model_base.py index a9de1366..a2514ca5 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -1,4 +1,5 @@ import torch +import logging from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep from comfy.ldm.cascade.stage_c import StageC from comfy.ldm.cascade.stage_b import StageB @@ -66,8 +67,8 @@ class BaseModel(torch.nn.Module): if self.adm_channels is None: self.adm_channels = 0 self.inpaint_model = False - print("model_type", model_type.name) - print("adm", self.adm_channels) + logging.warning("model_type {}".format(model_type.name)) + logging.info("adm {}".format(self.adm_channels)) def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs): sigma = t @@ -183,10 +184,10 @@ class BaseModel(torch.nn.Module): to_load = self.model_config.process_unet_state_dict(to_load) m, u = self.diffusion_model.load_state_dict(to_load, strict=False) if len(m) > 0: - print("unet missing:", m) + logging.warning("unet missing: {}".format(m)) if len(u) > 0: - print("unet unexpected:", u) + logging.warning("unet unexpected: {}".format(u)) del to_load return self diff --git a/comfy/model_detection.py b/comfy/model_detection.py index 07ee8570..b7c3be30 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -1,5 +1,6 @@ import comfy.supported_models import comfy.supported_models_base +import logging def count_blocks(state_dict_keys, prefix_string): count = 0 @@ -186,7 +187,7 @@ def model_config_from_unet_config(unet_config): if model_config.matches(unet_config): return model_config(unet_config) - print("no match", unet_config) + logging.error("no match {}".format(unet_config)) return None def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=False): diff --git a/comfy/model_management.py b/comfy/model_management.py index 9f924883..dd262e26 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -1,4 +1,5 @@ import psutil +import logging from enum import Enum from comfy.cli_args import args import comfy.utils @@ -29,7 +30,7 @@ lowvram_available = True xpu_available = False if args.deterministic: - print("Using deterministic algorithms for pytorch") + logging.warning("Using deterministic algorithms for pytorch") torch.use_deterministic_algorithms(True, warn_only=True) directml_enabled = False @@ -41,7 +42,7 @@ if args.directml is not None: directml_device = torch_directml.device() else: directml_device = torch_directml.device(device_index) - print("Using directml with device:", torch_directml.device_name(device_index)) + logging.warning("Using directml with device: {}".format(torch_directml.device_name(device_index))) # torch_directml.disable_tiled_resources(True) lowvram_available = False #TODO: need to find a way to get free memory in directml before this can be enabled by default. @@ -117,10 +118,10 @@ def get_total_memory(dev=None, torch_total_too=False): total_vram = get_total_memory(get_torch_device()) / (1024 * 1024) total_ram = psutil.virtual_memory().total / (1024 * 1024) -print("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram)) +logging.warning("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram)) if not args.normalvram and not args.cpu: if lowvram_available and total_vram <= 4096: - print("Trying to enable lowvram mode because your GPU seems to have 4GB or less. If you don't want this use: --normalvram") + logging.warning("Trying to enable lowvram mode because your GPU seems to have 4GB or less. If you don't want this use: --normalvram") set_vram_to = VRAMState.LOW_VRAM try: @@ -143,12 +144,10 @@ else: pass try: XFORMERS_VERSION = xformers.version.__version__ - print("xformers version:", XFORMERS_VERSION) + logging.warning("xformers version: {}".format(XFORMERS_VERSION)) if XFORMERS_VERSION.startswith("0.0.18"): - print() - print("WARNING: This version of xformers has a major bug where you will get black images when generating high resolution images.") - print("Please downgrade or upgrade xformers to a different version.") - print() + logging.warning("\nWARNING: This version of xformers has a major bug where you will get black images when generating high resolution images.") + logging.warning("Please downgrade or upgrade xformers to a different version.\n") XFORMERS_ENABLED_VAE = False except: pass @@ -213,11 +212,11 @@ elif args.highvram or args.gpu_only: FORCE_FP32 = False FORCE_FP16 = False if args.force_fp32: - print("Forcing FP32, if this improves things please report it.") + logging.warning("Forcing FP32, if this improves things please report it.") FORCE_FP32 = True if args.force_fp16: - print("Forcing FP16.") + logging.warning("Forcing FP16.") FORCE_FP16 = True if lowvram_available: @@ -231,12 +230,12 @@ if cpu_state != CPUState.GPU: if cpu_state == CPUState.MPS: vram_state = VRAMState.SHARED -print(f"Set vram state to: {vram_state.name}") +logging.warning(f"Set vram state to: {vram_state.name}") DISABLE_SMART_MEMORY = args.disable_smart_memory if DISABLE_SMART_MEMORY: - print("Disabling smart memory management") + logging.warning("Disabling smart memory management") def get_torch_device_name(device): if hasattr(device, 'type'): @@ -254,11 +253,11 @@ def get_torch_device_name(device): return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device)) try: - print("Device:", get_torch_device_name(get_torch_device())) + logging.warning("Device: {}".format(get_torch_device_name(get_torch_device()))) except: - print("Could not pick default device.") + logging.warning("Could not pick default device.") -print("VAE dtype:", VAE_DTYPE) +logging.warning("VAE dtype: {}".format(VAE_DTYPE)) current_loaded_models = [] @@ -301,7 +300,7 @@ class LoadedModel: raise e if lowvram_model_memory > 0: - print("loading in lowvram mode", lowvram_model_memory/(1024 * 1024)) + logging.warning("loading in lowvram mode {}".format(lowvram_model_memory/(1024 * 1024))) mem_counter = 0 for m in self.real_model.modules(): if hasattr(m, "comfy_cast_weights"): @@ -314,7 +313,7 @@ class LoadedModel: elif hasattr(m, "weight"): #only modules with comfy_cast_weights can be set to lowvram mode m.to(self.device) mem_counter += module_size(m) - print("lowvram: loaded module regularly", m) + logging.warning("lowvram: loaded module regularly {}".format(m)) self.model_accelerated = True @@ -348,7 +347,7 @@ def unload_model_clones(model): to_unload = [i] + to_unload for i in to_unload: - print("unload clone", i) + logging.warning("unload clone {}".format(i)) current_loaded_models.pop(i).model_unload() def free_memory(memory_required, device, keep_loaded=[]): @@ -390,7 +389,7 @@ def load_models_gpu(models, memory_required=0): models_already_loaded.append(loaded_model) else: if hasattr(x, "model"): - print(f"Requested to load {x.model.__class__.__name__}") + logging.warning(f"Requested to load {x.model.__class__.__name__}") models_to_load.append(loaded_model) if len(models_to_load) == 0: @@ -400,7 +399,7 @@ def load_models_gpu(models, memory_required=0): free_memory(extra_mem, d, models_already_loaded) return - print(f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}") + logging.warning(f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}") total_memory_required = {} for loaded_model in models_to_load: diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index 4a5d42b0..5e578dff 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -1,6 +1,7 @@ import torch import copy import inspect +import logging import comfy.utils import comfy.model_management @@ -187,7 +188,7 @@ class ModelPatcher: model_sd = self.model_state_dict() for key in self.patches: if key not in model_sd: - print("could not patch. key doesn't exist in model:", key) + logging.warning("could not patch. key doesn't exist in model: {}".format(key)) continue weight = model_sd[key] @@ -236,7 +237,7 @@ class ModelPatcher: w1 = v[0] if alpha != 0.0: if w1.shape != weight.shape: - print("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape)) + logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape)) else: weight += alpha * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype) elif patch_type == "lora": #lora/locon @@ -252,7 +253,7 @@ class ModelPatcher: try: weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(weight.shape).type(weight.dtype) except Exception as e: - print("ERROR", key, e) + logging.error("ERROR {} {} {}".format(patch_type, key, e)) elif patch_type == "lokr": w1 = v[0] w2 = v[1] @@ -291,7 +292,7 @@ class ModelPatcher: try: weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype) except Exception as e: - print("ERROR", key, e) + logging.error("ERROR {} {} {}".format(patch_type, key, e)) elif patch_type == "loha": w1a = v[0] w1b = v[1] @@ -320,7 +321,7 @@ class ModelPatcher: try: weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype) except Exception as e: - print("ERROR", key, e) + logging.error("ERROR {} {} {}".format(patch_type, key, e)) elif patch_type == "glora": if v[4] is not None: alpha *= v[4] / v[0].shape[0] @@ -330,9 +331,12 @@ class ModelPatcher: b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, torch.float32) b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, torch.float32) - weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype) + try: + weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype) + except Exception as e: + logging.error("ERROR {} {} {}".format(patch_type, key, e)) else: - print("patch type not recognized", patch_type, key) + logging.warning("patch type not recognized {} {}".format(patch_type, key)) return weight diff --git a/comfy/sd.py b/comfy/sd.py index fd5d604e..3e4b9e47 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -1,5 +1,6 @@ import torch from enum import Enum +import logging from comfy import model_management from .ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine @@ -37,7 +38,7 @@ def load_model_weights(model, sd): w = sd.pop(x) del w if len(m) > 0: - print("missing", m) + logging.warning("missing {}".format(m)) return model def load_clip_weights(model, sd): @@ -81,7 +82,7 @@ def load_lora_for_models(model, clip, lora, strength_model, strength_clip): k1 = set(k1) for x in loaded: if (x not in k) and (x not in k1): - print("NOT LOADED", x) + logging.warning("NOT LOADED {}".format(x)) return (new_modelpatcher, new_clip) @@ -225,10 +226,10 @@ class VAE: m, u = self.first_stage_model.load_state_dict(sd, strict=False) if len(m) > 0: - print("Missing VAE keys", m) + logging.warning("Missing VAE keys {}".format(m)) if len(u) > 0: - print("Leftover VAE keys", u) + logging.info("Leftover VAE keys {}".format(u)) if device is None: device = model_management.vae_device() @@ -291,7 +292,7 @@ class VAE: samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device) pixel_samples[x:x+batch_number] = self.process_output(self.first_stage_model.decode(samples).to(self.output_device).float()) except model_management.OOM_EXCEPTION as e: - print("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.") + logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.") pixel_samples = self.decode_tiled_(samples_in) pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1) @@ -317,7 +318,7 @@ class VAE: samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).to(self.output_device).float() except model_management.OOM_EXCEPTION as e: - print("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.") + logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.") samples = self.encode_tiled_(pixel_samples) return samples @@ -393,10 +394,10 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI for c in clip_data: m, u = clip.load_sd(c) if len(m) > 0: - print("clip missing:", m) + logging.warning("clip missing: {}".format(m)) if len(u) > 0: - print("clip unexpected:", u) + logging.info("clip unexpected: {}".format(u)) return clip def load_gligen(ckpt_path): @@ -534,21 +535,21 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o clip = CLIP(clip_target, embedding_directory=embedding_directory) m, u = clip.load_sd(clip_sd, full_model=True) if len(m) > 0: - print("clip missing:", m) + logging.warning("clip missing: {}".format(m)) if len(u) > 0: - print("clip unexpected:", u) + logging.info("clip unexpected {}:".format(u)) else: - print("no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.") + logging.warning("no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.") left_over = sd.keys() if len(left_over) > 0: - print("left over keys:", left_over) + logging.info("left over keys: {}".format(left_over)) if output_model: model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device(), current_device=inital_load_device) if inital_load_device != torch.device("cpu"): - print("loaded straight to GPU") + logging.warning("loaded straight to GPU") model_management.load_model_gpu(model_patcher) return (model_patcher, clip, vae, clipvision) @@ -577,7 +578,7 @@ def load_unet_state_dict(sd): #load unet in diffusers format if k in sd: new_sd[diffusers_keys[k]] = sd.pop(k) else: - print(diffusers_keys[k], k) + logging.warning("{} {}".format(diffusers_keys[k], k)) offload_device = model_management.unet_offload_device() unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes) @@ -588,14 +589,14 @@ def load_unet_state_dict(sd): #load unet in diffusers format model.load_model_weights(new_sd, "") left_over = sd.keys() if len(left_over) > 0: - print("left over keys in unet:", left_over) + logging.warning("left over keys in unet: {}".format(left_over)) return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device) def load_unet(unet_path): sd = comfy.utils.load_torch_file(unet_path) model = load_unet_state_dict(sd) if model is None: - print("ERROR UNSUPPORTED UNET", unet_path) + logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path)) raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path)) return model diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py index 87e3eaa4..ff6db0d2 100644 --- a/comfy/sd1_clip.py +++ b/comfy/sd1_clip.py @@ -8,6 +8,7 @@ import zipfile from . import model_management import comfy.clip_model import json +import logging def gen_empty_tokens(special_tokens, length): start_token = special_tokens.get("start", None) @@ -137,7 +138,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): tokens_temp += [next_new_token] next_new_token += 1 else: - print("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored", y.shape[0], current_embeds.weight.shape[1]) + logging.warning("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored {} != {}".format(y.shape[0], current_embeds.weight.shape[1])) while len(tokens_temp) < len(x): tokens_temp += [self.special_tokens["pad"]] out_tokens += [tokens_temp] @@ -329,9 +330,7 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No else: embed = torch.load(embed_path, map_location="cpu") except Exception as e: - print(traceback.format_exc()) - print() - print("error loading embedding, skipping loading:", embedding_name) + logging.warning("{}\n\nerror loading embedding, skipping loading: {}".format(traceback.format_exc(), embedding_name)) return None if embed_out is None: @@ -422,7 +421,7 @@ class SDTokenizer: embedding_name = word[len(self.embedding_identifier):].strip('\n') embed, leftover = self._try_get_embedding(embedding_name) if embed is None: - print(f"warning, embedding:{embedding_name} does not exist, ignoring") + logging.warning(f"warning, embedding:{embedding_name} does not exist, ignoring") else: if len(embed.shape) == 1: tokens.append([(embed, weight)]) diff --git a/comfy/utils.py b/comfy/utils.py index 5deb14cd..8caecd86 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -5,6 +5,7 @@ import comfy.checkpoint_pickle import safetensors.torch import numpy as np from PIL import Image +import logging def load_torch_file(ckpt, safe_load=False, device=None): if device is None: @@ -14,14 +15,14 @@ def load_torch_file(ckpt, safe_load=False, device=None): else: if safe_load: if not 'weights_only' in torch.load.__code__.co_varnames: - print("Warning torch.load doesn't support weights_only on this pytorch version, loading unsafely.") + logging.warning("Warning torch.load doesn't support weights_only on this pytorch version, loading unsafely.") safe_load = False if safe_load: pl_sd = torch.load(ckpt, map_location=device, weights_only=True) else: pl_sd = torch.load(ckpt, map_location=device, pickle_module=comfy.checkpoint_pickle) if "global_step" in pl_sd: - print(f"Global Step: {pl_sd['global_step']}") + logging.info(f"Global Step: {pl_sd['global_step']}") if "state_dict" in pl_sd: sd = pl_sd["state_dict"] else: From 03f4cfb7cda62bf40e4a10f6f00ca161bb68a48e Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 11 Mar 2024 00:56:41 -0400 Subject: [PATCH 090/378] Replace more prints with logging. --- latent_preview.py | 3 ++- nodes.py | 29 +++++++++++++++-------------- 2 files changed, 17 insertions(+), 15 deletions(-) diff --git a/latent_preview.py b/latent_preview.py index 61754751..4dbcbf45 100644 --- a/latent_preview.py +++ b/latent_preview.py @@ -6,6 +6,7 @@ from comfy.cli_args import args, LatentPreviewMethod from comfy.taesd.taesd import TAESD import folder_paths import comfy.utils +import logging MAX_PREVIEW_RESOLUTION = 512 @@ -70,7 +71,7 @@ def get_previewer(device, latent_format): taesd = TAESD(None, taesd_decoder_path).to(device) previewer = TAESDPreviewerImpl(taesd) else: - print("Warning: TAESD previews enabled, but could not find models/vae_approx/{}".format(latent_format.taesd_decoder_name)) + logging.warning("Warning: TAESD previews enabled, but could not find models/vae_approx/{}".format(latent_format.taesd_decoder_name)) if previewer is None: if latent_format.latent_rgb_factors is not None: diff --git a/nodes.py b/nodes.py index c334cdb9..ca14677d 100644 --- a/nodes.py +++ b/nodes.py @@ -8,6 +8,7 @@ import traceback import math import time import random +import logging from PIL import Image, ImageOps, ImageSequence from PIL.PngImagePlugin import PngInfo @@ -83,7 +84,7 @@ class ConditioningAverage : out = [] if len(conditioning_from) > 1: - print("Warning: ConditioningAverage conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.") + logging.warning("Warning: ConditioningAverage conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.") cond_from = conditioning_from[0][0] pooled_output_from = conditioning_from[0][1].get("pooled_output", None) @@ -122,7 +123,7 @@ class ConditioningConcat: out = [] if len(conditioning_from) > 1: - print("Warning: ConditioningConcat conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.") + logging.warning("Warning: ConditioningConcat conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.") cond_from = conditioning_from[0][0] @@ -1899,11 +1900,11 @@ def load_custom_node(module_path, ignore=set()): NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS) return True else: - print(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS.") + logging.warning(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS.") return False except Exception as e: - print(traceback.format_exc()) - print(f"Cannot import {module_path} module for custom nodes:", e) + logging.warning(traceback.format_exc()) + logging.warning(f"Cannot import {module_path} module for custom nodes:", e) return False def load_custom_nodes(): @@ -1924,14 +1925,14 @@ def load_custom_nodes(): node_import_times.append((time.perf_counter() - time_before, module_path, success)) if len(node_import_times) > 0: - print("\nImport times for custom nodes:") + logging.warning("\nImport times for custom nodes:") for n in sorted(node_import_times): if n[2]: import_message = "" else: import_message = " (IMPORT FAILED)" - print("{:6.1f} seconds{}:".format(n[0], import_message), n[1]) - print() + logging.warning("{:6.1f} seconds{}: {}".format(n[0], import_message, n[1])) + logging.warning("") def init_custom_nodes(): extras_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras") @@ -1973,12 +1974,12 @@ def init_custom_nodes(): load_custom_nodes() if len(import_failed) > 0: - print("WARNING: some comfy_extras/ nodes did not import correctly. This may be because they are missing some dependencies.\n") + logging.warning("WARNING: some comfy_extras/ nodes did not import correctly. This may be because they are missing some dependencies.\n") for node in import_failed: - print("IMPORT FAILED: {}".format(node)) - print("\nThis issue might be caused by missing newly dependencies added the last time you updated ComfyUI.") + logging.warning("IMPORT FAILED: {}".format(node)) + logging.warning("\nThis issue might be caused by new missing dependencies added the last time you updated ComfyUI.") if args.windows_standalone_build: - print("Please run the update script: update/update_comfyui.bat") + logging.warning("Please run the update script: update/update_comfyui.bat") else: - print("Please do a: pip install -r requirements.txt") - print() + logging.warning("Please do a: pip install -r requirements.txt") + logging.warning("") From dc6d4151a2dc411e69361e219af3a81863fc6dc4 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 11 Mar 2024 12:30:11 -0400 Subject: [PATCH 091/378] Not needed anymore. --- server.py | 11 ++--------- 1 file changed, 2 insertions(+), 9 deletions(-) diff --git a/server.py b/server.py index c6132cdf..7b4f910e 100644 --- a/server.py +++ b/server.py @@ -15,15 +15,8 @@ from PIL import Image, ImageOps from PIL.PngImagePlugin import PngInfo from io import BytesIO -try: - import aiohttp - from aiohttp import web -except ImportError: - print("Module 'aiohttp' not installed. Please install it via:") - print("pip install aiohttp") - print("or") - print("pip install -r requirements.txt") - sys.exit() +import aiohttp +from aiohttp import web import mimetypes from comfy.cli_args import args From 0ed72befe18e086ac160f1a55aa69b37c928ebb9 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 11 Mar 2024 13:54:56 -0400 Subject: [PATCH 092/378] Change log levels. Logging level now defaults to info. --verbose sets it to debug. --- comfy/cli_args.py | 2 +- comfy/controlnet.py | 4 ++-- comfy/diffusers_convert.py | 2 +- comfy/model_base.py | 4 ++-- comfy/model_management.py | 28 ++++++++++++++-------------- comfy/sd.py | 12 ++++++------ comfy/utils.py | 2 +- nodes.py | 6 +++--- server.py | 15 ++++++++------- 9 files changed, 38 insertions(+), 37 deletions(-) diff --git a/comfy/cli_args.py b/comfy/cli_args.py index 757fc245..353bb51e 100644 --- a/comfy/cli_args.py +++ b/comfy/cli_args.py @@ -129,7 +129,7 @@ if args.disable_auto_launch: args.auto_launch = False import logging -logging_level = logging.WARNING +logging_level = logging.INFO if args.verbose: logging_level = logging.DEBUG diff --git a/comfy/controlnet.py b/comfy/controlnet.py index a5e7b23f..1a72412b 100644 --- a/comfy/controlnet.py +++ b/comfy/controlnet.py @@ -432,7 +432,7 @@ def load_controlnet(ckpt_path, model=None): logging.warning("missing controlnet keys: {}".format(missing)) if len(unexpected) > 0: - logging.info("unexpected controlnet keys: {}".format(unexpected)) + logging.debug("unexpected controlnet keys: {}".format(unexpected)) global_average_pooling = False filename = os.path.splitext(ckpt_path)[0] @@ -545,6 +545,6 @@ def load_t2i_adapter(t2i_data): logging.warning("t2i missing {}".format(missing)) if len(unexpected) > 0: - logging.info("t2i unexpected {}".format(unexpected)) + logging.debug("t2i unexpected {}".format(unexpected)) return T2IAdapter(model_ad, model_ad.input_channels, compression_ratio, upscale_algorithm) diff --git a/comfy/diffusers_convert.py b/comfy/diffusers_convert.py index 18398cb3..08018c54 100644 --- a/comfy/diffusers_convert.py +++ b/comfy/diffusers_convert.py @@ -178,7 +178,7 @@ def convert_vae_state_dict(vae_state_dict): for k, v in new_state_dict.items(): for weight_name in weights_to_convert: if f"mid.attn_1.{weight_name}.weight" in k: - logging.info(f"Reshaping {k} for SD format") + logging.debug(f"Reshaping {k} for SD format") new_state_dict[k] = reshape_weight_for_sd(v) return new_state_dict diff --git a/comfy/model_base.py b/comfy/model_base.py index a2514ca5..5da71e63 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -67,8 +67,8 @@ class BaseModel(torch.nn.Module): if self.adm_channels is None: self.adm_channels = 0 self.inpaint_model = False - logging.warning("model_type {}".format(model_type.name)) - logging.info("adm {}".format(self.adm_channels)) + logging.info("model_type {}".format(model_type.name)) + logging.debug("adm {}".format(self.adm_channels)) def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs): sigma = t diff --git a/comfy/model_management.py b/comfy/model_management.py index dd262e26..2f0a0a62 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -30,7 +30,7 @@ lowvram_available = True xpu_available = False if args.deterministic: - logging.warning("Using deterministic algorithms for pytorch") + logging.info("Using deterministic algorithms for pytorch") torch.use_deterministic_algorithms(True, warn_only=True) directml_enabled = False @@ -42,7 +42,7 @@ if args.directml is not None: directml_device = torch_directml.device() else: directml_device = torch_directml.device(device_index) - logging.warning("Using directml with device: {}".format(torch_directml.device_name(device_index))) + logging.info("Using directml with device: {}".format(torch_directml.device_name(device_index))) # torch_directml.disable_tiled_resources(True) lowvram_available = False #TODO: need to find a way to get free memory in directml before this can be enabled by default. @@ -118,7 +118,7 @@ def get_total_memory(dev=None, torch_total_too=False): total_vram = get_total_memory(get_torch_device()) / (1024 * 1024) total_ram = psutil.virtual_memory().total / (1024 * 1024) -logging.warning("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram)) +logging.info("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram)) if not args.normalvram and not args.cpu: if lowvram_available and total_vram <= 4096: logging.warning("Trying to enable lowvram mode because your GPU seems to have 4GB or less. If you don't want this use: --normalvram") @@ -144,7 +144,7 @@ else: pass try: XFORMERS_VERSION = xformers.version.__version__ - logging.warning("xformers version: {}".format(XFORMERS_VERSION)) + logging.info("xformers version: {}".format(XFORMERS_VERSION)) if XFORMERS_VERSION.startswith("0.0.18"): logging.warning("\nWARNING: This version of xformers has a major bug where you will get black images when generating high resolution images.") logging.warning("Please downgrade or upgrade xformers to a different version.\n") @@ -212,11 +212,11 @@ elif args.highvram or args.gpu_only: FORCE_FP32 = False FORCE_FP16 = False if args.force_fp32: - logging.warning("Forcing FP32, if this improves things please report it.") + logging.info("Forcing FP32, if this improves things please report it.") FORCE_FP32 = True if args.force_fp16: - logging.warning("Forcing FP16.") + logging.info("Forcing FP16.") FORCE_FP16 = True if lowvram_available: @@ -230,12 +230,12 @@ if cpu_state != CPUState.GPU: if cpu_state == CPUState.MPS: vram_state = VRAMState.SHARED -logging.warning(f"Set vram state to: {vram_state.name}") +logging.info(f"Set vram state to: {vram_state.name}") DISABLE_SMART_MEMORY = args.disable_smart_memory if DISABLE_SMART_MEMORY: - logging.warning("Disabling smart memory management") + logging.info("Disabling smart memory management") def get_torch_device_name(device): if hasattr(device, 'type'): @@ -253,11 +253,11 @@ def get_torch_device_name(device): return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device)) try: - logging.warning("Device: {}".format(get_torch_device_name(get_torch_device()))) + logging.info("Device: {}".format(get_torch_device_name(get_torch_device()))) except: logging.warning("Could not pick default device.") -logging.warning("VAE dtype: {}".format(VAE_DTYPE)) +logging.info("VAE dtype: {}".format(VAE_DTYPE)) current_loaded_models = [] @@ -300,7 +300,7 @@ class LoadedModel: raise e if lowvram_model_memory > 0: - logging.warning("loading in lowvram mode {}".format(lowvram_model_memory/(1024 * 1024))) + logging.info("loading in lowvram mode {}".format(lowvram_model_memory/(1024 * 1024))) mem_counter = 0 for m in self.real_model.modules(): if hasattr(m, "comfy_cast_weights"): @@ -347,7 +347,7 @@ def unload_model_clones(model): to_unload = [i] + to_unload for i in to_unload: - logging.warning("unload clone {}".format(i)) + logging.debug("unload clone {}".format(i)) current_loaded_models.pop(i).model_unload() def free_memory(memory_required, device, keep_loaded=[]): @@ -389,7 +389,7 @@ def load_models_gpu(models, memory_required=0): models_already_loaded.append(loaded_model) else: if hasattr(x, "model"): - logging.warning(f"Requested to load {x.model.__class__.__name__}") + logging.info(f"Requested to load {x.model.__class__.__name__}") models_to_load.append(loaded_model) if len(models_to_load) == 0: @@ -399,7 +399,7 @@ def load_models_gpu(models, memory_required=0): free_memory(extra_mem, d, models_already_loaded) return - logging.warning(f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}") + logging.info(f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}") total_memory_required = {} for loaded_model in models_to_load: diff --git a/comfy/sd.py b/comfy/sd.py index 3e4b9e47..85821120 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -229,7 +229,7 @@ class VAE: logging.warning("Missing VAE keys {}".format(m)) if len(u) > 0: - logging.info("Leftover VAE keys {}".format(u)) + logging.debug("Leftover VAE keys {}".format(u)) if device is None: device = model_management.vae_device() @@ -397,7 +397,7 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI logging.warning("clip missing: {}".format(m)) if len(u) > 0: - logging.info("clip unexpected: {}".format(u)) + logging.debug("clip unexpected: {}".format(u)) return clip def load_gligen(ckpt_path): @@ -538,18 +538,18 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o logging.warning("clip missing: {}".format(m)) if len(u) > 0: - logging.info("clip unexpected {}:".format(u)) + logging.debug("clip unexpected {}:".format(u)) else: logging.warning("no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.") left_over = sd.keys() if len(left_over) > 0: - logging.info("left over keys: {}".format(left_over)) + logging.debug("left over keys: {}".format(left_over)) if output_model: model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device(), current_device=inital_load_device) if inital_load_device != torch.device("cpu"): - logging.warning("loaded straight to GPU") + logging.info("loaded straight to GPU") model_management.load_model_gpu(model_patcher) return (model_patcher, clip, vae, clipvision) @@ -589,7 +589,7 @@ def load_unet_state_dict(sd): #load unet in diffusers format model.load_model_weights(new_sd, "") left_over = sd.keys() if len(left_over) > 0: - logging.warning("left over keys in unet: {}".format(left_over)) + logging.info("left over keys in unet: {}".format(left_over)) return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device) def load_unet(unet_path): diff --git a/comfy/utils.py b/comfy/utils.py index 8caecd86..ab47b8f2 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -22,7 +22,7 @@ def load_torch_file(ckpt, safe_load=False, device=None): else: pl_sd = torch.load(ckpt, map_location=device, pickle_module=comfy.checkpoint_pickle) if "global_step" in pl_sd: - logging.info(f"Global Step: {pl_sd['global_step']}") + logging.debug(f"Global Step: {pl_sd['global_step']}") if "state_dict" in pl_sd: sd = pl_sd["state_dict"] else: diff --git a/nodes.py b/nodes.py index ca14677d..e2f35dc2 100644 --- a/nodes.py +++ b/nodes.py @@ -1925,14 +1925,14 @@ def load_custom_nodes(): node_import_times.append((time.perf_counter() - time_before, module_path, success)) if len(node_import_times) > 0: - logging.warning("\nImport times for custom nodes:") + logging.info("\nImport times for custom nodes:") for n in sorted(node_import_times): if n[2]: import_message = "" else: import_message = " (IMPORT FAILED)" - logging.warning("{:6.1f} seconds{}: {}".format(n[0], import_message, n[1])) - logging.warning("") + logging.info("{:6.1f} seconds{}: {}".format(n[0], import_message, n[1])) + logging.info("") def init_custom_nodes(): extras_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras") diff --git a/server.py b/server.py index 7b4f910e..d748a47d 100644 --- a/server.py +++ b/server.py @@ -17,6 +17,7 @@ from io import BytesIO import aiohttp from aiohttp import web +import logging import mimetypes from comfy.cli_args import args @@ -33,7 +34,7 @@ async def send_socket_catch_exception(function, message): try: await function(message) except (aiohttp.ClientError, aiohttp.ClientPayloadError, ConnectionResetError) as err: - print("send error:", err) + logging.warning("send error: {}".format(err)) @web.middleware async def cache_control(request: web.Request, handler): @@ -111,7 +112,7 @@ class PromptServer(): async for msg in ws: if msg.type == aiohttp.WSMsgType.ERROR: - print('ws connection closed with exception %s' % ws.exception()) + logging.warning('ws connection closed with exception %s' % ws.exception()) finally: self.sockets.pop(sid, None) return ws @@ -446,7 +447,7 @@ class PromptServer(): @routes.post("/prompt") async def post_prompt(request): - print("got prompt") + logging.info("got prompt") resp_code = 200 out_string = "" json_data = await request.json() @@ -478,7 +479,7 @@ class PromptServer(): response = {"prompt_id": prompt_id, "number": number, "node_errors": valid[3]} return web.json_response(response) else: - print("invalid prompt:", valid[1]) + logging.warning("invalid prompt: {}".format(valid[1])) return web.json_response({"error": valid[1], "node_errors": valid[3]}, status=400) else: return web.json_response({"error": "no prompt", "node_errors": []}, status=400) @@ -626,8 +627,8 @@ class PromptServer(): await site.start() if verbose: - print("Starting server\n") - print("To see the GUI go to: http://{}:{}".format(address, port)) + logging.info("Starting server\n") + logging.info("To see the GUI go to: http://{}:{}".format(address, port)) if call_on_start is not None: call_on_start(address, port) @@ -639,7 +640,7 @@ class PromptServer(): try: json_data = handler(json_data) except Exception as e: - print(f"[ERROR] An error occurred during the on_prompt_handler processing") + logging.warning(f"[ERROR] An error occurred during the on_prompt_handler processing") traceback.print_exc() return json_data From 2a813c3b09292c9aeab622ddf65d77e5d8171d0d Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 11 Mar 2024 16:24:47 -0400 Subject: [PATCH 093/378] Switch some more prints to logging. --- comfy/ldm/modules/attention.py | 15 ++++++++------- comfy/ldm/modules/diffusionmodules/model.py | 13 +++++++------ .../modules/diffusionmodules/openaimodel.py | 5 +++-- comfy/ldm/modules/sub_quadratic_attention.py | 3 ++- comfy/samplers.py | 3 ++- comfy_extras/nodes_freelunch.py | 6 +++--- comfy_extras/nodes_hypernetwork.py | 3 ++- main.py | 18 +++++++++--------- nodes.py | 2 +- server.py | 6 +++--- 10 files changed, 40 insertions(+), 34 deletions(-) diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py index 48399bc0..f116efee 100644 --- a/comfy/ldm/modules/attention.py +++ b/comfy/ldm/modules/attention.py @@ -4,6 +4,7 @@ import torch.nn.functional as F from torch import nn, einsum from einops import rearrange, repeat from typing import Optional, Any +import logging from .diffusionmodules.util import checkpoint, AlphaBlender, timestep_embedding from .sub_quadratic_attention import efficient_dot_product_attention @@ -20,7 +21,7 @@ ops = comfy.ops.disable_weight_init # CrossAttn precision handling if args.dont_upcast_attention: - print("disabling upcasting of attention") + logging.info("disabling upcasting of attention") _ATTN_PRECISION = "fp16" else: _ATTN_PRECISION = "fp32" @@ -274,12 +275,12 @@ def attention_split(q, k, v, heads, mask=None): model_management.soft_empty_cache(True) if cleared_cache == False: cleared_cache = True - print("out of memory error, emptying cache and trying again") + logging.warning("out of memory error, emptying cache and trying again") continue steps *= 2 if steps > 64: raise e - print("out of memory error, increasing steps and trying again", steps) + logging.warning("out of memory error, increasing steps and trying again {}".format(steps)) else: raise e @@ -351,17 +352,17 @@ def attention_pytorch(q, k, v, heads, mask=None): optimized_attention = attention_basic if model_management.xformers_enabled(): - print("Using xformers cross attention") + logging.info("Using xformers cross attention") optimized_attention = attention_xformers elif model_management.pytorch_attention_enabled(): - print("Using pytorch cross attention") + logging.info("Using pytorch cross attention") optimized_attention = attention_pytorch else: if args.use_split_cross_attention: - print("Using split optimization for cross attention") + logging.info("Using split optimization for cross attention") optimized_attention = attention_split else: - print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention") + logging.info("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention") optimized_attention = attention_sub_quad optimized_attention_masked = optimized_attention diff --git a/comfy/ldm/modules/diffusionmodules/model.py b/comfy/ldm/modules/diffusionmodules/model.py index cc81c1f2..fabc5c5e 100644 --- a/comfy/ldm/modules/diffusionmodules/model.py +++ b/comfy/ldm/modules/diffusionmodules/model.py @@ -5,6 +5,7 @@ import torch.nn as nn import numpy as np from einops import rearrange from typing import Optional, Any +import logging from comfy import model_management import comfy.ops @@ -190,7 +191,7 @@ def slice_attention(q, k, v): steps *= 2 if steps > 128: raise e - print("out of memory error, increasing steps and trying again", steps) + logging.warning("out of memory error, increasing steps and trying again {}".format(steps)) return r1 @@ -235,7 +236,7 @@ def pytorch_attention(q, k, v): out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False) out = out.transpose(2, 3).reshape(B, C, H, W) except model_management.OOM_EXCEPTION as e: - print("scaled_dot_product_attention OOMed: switched to slice attention") + logging.warning("scaled_dot_product_attention OOMed: switched to slice attention") out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W) return out @@ -268,13 +269,13 @@ class AttnBlock(nn.Module): padding=0) if model_management.xformers_enabled_vae(): - print("Using xformers attention in VAE") + logging.info("Using xformers attention in VAE") self.optimized_attention = xformers_attention elif model_management.pytorch_attention_enabled(): - print("Using pytorch attention in VAE") + logging.info("Using pytorch attention in VAE") self.optimized_attention = pytorch_attention else: - print("Using split attention in VAE") + logging.info("Using split attention in VAE") self.optimized_attention = normal_attention def forward(self, x): @@ -562,7 +563,7 @@ class Decoder(nn.Module): block_in = ch*ch_mult[self.num_resolutions-1] curr_res = resolution // 2**(self.num_resolutions-1) self.z_shape = (1,z_channels,curr_res,curr_res) - print("Working with z of shape {} = {} dimensions.".format( + logging.debug("Working with z of shape {} = {} dimensions.".format( self.z_shape, np.prod(self.z_shape))) # z to block_in diff --git a/comfy/ldm/modules/diffusionmodules/openaimodel.py b/comfy/ldm/modules/diffusionmodules/openaimodel.py index cf89ae01..d782eff3 100644 --- a/comfy/ldm/modules/diffusionmodules/openaimodel.py +++ b/comfy/ldm/modules/diffusionmodules/openaimodel.py @@ -4,6 +4,7 @@ import torch as th import torch.nn as nn import torch.nn.functional as F from einops import rearrange +import logging from .util import ( checkpoint, @@ -359,7 +360,7 @@ def apply_control(h, control, name): try: h += ctrl except: - print("warning control could not be applied", h.shape, ctrl.shape) + logging.warning("warning control could not be applied {} {}".format(h.shape, ctrl.shape)) return h class UNetModel(nn.Module): @@ -496,7 +497,7 @@ class UNetModel(nn.Module): if isinstance(self.num_classes, int): self.label_emb = nn.Embedding(num_classes, time_embed_dim, dtype=self.dtype, device=device) elif self.num_classes == "continuous": - print("setting up linear c_adm embedding layer") + logging.debug("setting up linear c_adm embedding layer") self.label_emb = nn.Linear(1, time_embed_dim) elif self.num_classes == "sequential": assert adm_in_channels is not None diff --git a/comfy/ldm/modules/sub_quadratic_attention.py b/comfy/ldm/modules/sub_quadratic_attention.py index cb0896b0..1bc4138c 100644 --- a/comfy/ldm/modules/sub_quadratic_attention.py +++ b/comfy/ldm/modules/sub_quadratic_attention.py @@ -14,6 +14,7 @@ import torch from torch import Tensor from torch.utils.checkpoint import checkpoint import math +import logging try: from typing import Optional, NamedTuple, List, Protocol @@ -170,7 +171,7 @@ def _get_attention_scores_no_kv_chunking( attn_probs = attn_scores.softmax(dim=-1) del attn_scores except model_management.OOM_EXCEPTION: - print("ran out of memory while running softmax in _get_attention_scores_no_kv_chunking, trying slower in place softmax instead") + logging.warning("ran out of memory while running softmax in _get_attention_scores_no_kv_chunking, trying slower in place softmax instead") attn_scores -= attn_scores.max(dim=-1, keepdim=True).values torch.exp(attn_scores, out=attn_scores) summed = torch.sum(attn_scores, dim=-1, keepdim=True) diff --git a/comfy/samplers.py b/comfy/samplers.py index 6863be4e..16b4514e 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -4,6 +4,7 @@ import torch import collections from comfy import model_management import math +import logging def get_area_and_mult(conds, x_in, timestep_in): area = (x_in.shape[2], x_in.shape[3], 0, 0) @@ -625,7 +626,7 @@ def calculate_sigmas_scheduler(model, scheduler_name, steps): elif scheduler_name == "sgm_uniform": sigmas = normal_scheduler(model, steps, sgm=True) else: - print("error invalid scheduler", scheduler_name) + logging.error("error invalid scheduler {}".format(scheduler_name)) return sigmas def sampler_object(name): diff --git a/comfy_extras/nodes_freelunch.py b/comfy_extras/nodes_freelunch.py index 7764aa0b..6f1d87bf 100644 --- a/comfy_extras/nodes_freelunch.py +++ b/comfy_extras/nodes_freelunch.py @@ -1,7 +1,7 @@ #code originally taken from: https://github.com/ChenyangSi/FreeU (under MIT License) import torch - +import logging def Fourier_filter(x, threshold, scale): # FFT @@ -49,7 +49,7 @@ class FreeU: try: hsp = Fourier_filter(hsp, threshold=1, scale=scale[1]) except: - print("Device", hsp.device, "does not support the torch.fft functions used in the FreeU node, switching to CPU.") + logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device)) on_cpu_devices[hsp.device] = True hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) else: @@ -95,7 +95,7 @@ class FreeU_V2: try: hsp = Fourier_filter(hsp, threshold=1, scale=scale[1]) except: - print("Device", hsp.device, "does not support the torch.fft functions used in the FreeU node, switching to CPU.") + logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device)) on_cpu_devices[hsp.device] = True hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) else: diff --git a/comfy_extras/nodes_hypernetwork.py b/comfy_extras/nodes_hypernetwork.py index f692945a..cafafa6a 100644 --- a/comfy_extras/nodes_hypernetwork.py +++ b/comfy_extras/nodes_hypernetwork.py @@ -1,6 +1,7 @@ import comfy.utils import folder_paths import torch +import logging def load_hypernetwork_patch(path, strength): sd = comfy.utils.load_torch_file(path, safe_load=True) @@ -23,7 +24,7 @@ def load_hypernetwork_patch(path, strength): } if activation_func not in valid_activation: - print("Unsupported Hypernetwork format, if you report it I might implement it.", path, " ", activation_func, is_layer_norm, use_dropout, activate_output, last_layer_dropout) + logging.error("Unsupported Hypernetwork format, if you report it I might implement it. {} {} {} {} {} {}".format(path, activation_func, is_layer_norm, use_dropout, activate_output, last_layer_dropout)) return None out = {} diff --git a/main.py b/main.py index 5d07ce2d..3dee72e3 100644 --- a/main.py +++ b/main.py @@ -54,15 +54,15 @@ import threading import gc from comfy.cli_args import args +import logging if os.name == "nt": - import logging logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage()) if __name__ == "__main__": if args.cuda_device is not None: os.environ['CUDA_VISIBLE_DEVICES'] = str(args.cuda_device) - print("Set cuda device to:", args.cuda_device) + logging.info("Set cuda device to: {}".format(args.cuda_device)) if args.deterministic: if 'CUBLAS_WORKSPACE_CONFIG' not in os.environ: @@ -88,7 +88,7 @@ def cuda_malloc_warning(): if b in device_name: cuda_malloc_warning = True if cuda_malloc_warning: - print("\nWARNING: this card most likely does not support cuda-malloc, if you get \"CUDA error\" please run ComfyUI with: --disable-cuda-malloc\n") + logging.warning("\nWARNING: this card most likely does not support cuda-malloc, if you get \"CUDA error\" please run ComfyUI with: --disable-cuda-malloc\n") def prompt_worker(q, server): e = execution.PromptExecutor(server) @@ -121,7 +121,7 @@ def prompt_worker(q, server): current_time = time.perf_counter() execution_time = current_time - execution_start_time - print("Prompt executed in {:.2f} seconds".format(execution_time)) + logging.info("Prompt executed in {:.2f} seconds".format(execution_time)) flags = q.get_flags() free_memory = flags.get("free_memory", False) @@ -182,14 +182,14 @@ def load_extra_path_config(yaml_path): full_path = y if base_path is not None: full_path = os.path.join(base_path, full_path) - print("Adding extra search path", x, full_path) + logging.info("Adding extra search path {} {}".format(x, full_path)) folder_paths.add_model_folder_path(x, full_path) if __name__ == "__main__": if args.temp_directory: temp_dir = os.path.join(os.path.abspath(args.temp_directory), "temp") - print(f"Setting temp directory to: {temp_dir}") + logging.info(f"Setting temp directory to: {temp_dir}") folder_paths.set_temp_directory(temp_dir) cleanup_temp() @@ -224,7 +224,7 @@ if __name__ == "__main__": if args.output_directory: output_dir = os.path.abspath(args.output_directory) - print(f"Setting output directory to: {output_dir}") + logging.info(f"Setting output directory to: {output_dir}") folder_paths.set_output_directory(output_dir) #These are the default folders that checkpoints, clip and vae models will be saved to when using CheckpointSave, etc.. nodes @@ -234,7 +234,7 @@ if __name__ == "__main__": if args.input_directory: input_dir = os.path.abspath(args.input_directory) - print(f"Setting input directory to: {input_dir}") + logging.info(f"Setting input directory to: {input_dir}") folder_paths.set_input_directory(input_dir) if args.quick_test_for_ci: @@ -252,6 +252,6 @@ if __name__ == "__main__": try: loop.run_until_complete(run(server, address=args.listen, port=args.port, verbose=not args.dont_print_server, call_on_start=call_on_start)) except KeyboardInterrupt: - print("\nStopped server") + logging.info("\nStopped server") cleanup_temp() diff --git a/nodes.py b/nodes.py index e2f35dc2..453f6e60 100644 --- a/nodes.py +++ b/nodes.py @@ -1904,7 +1904,7 @@ def load_custom_node(module_path, ignore=set()): return False except Exception as e: logging.warning(traceback.format_exc()) - logging.warning(f"Cannot import {module_path} module for custom nodes:", e) + logging.warning(f"Cannot import {module_path} module for custom nodes: {e}") return False def load_custom_nodes(): diff --git a/server.py b/server.py index d748a47d..5642bd5e 100644 --- a/server.py +++ b/server.py @@ -413,8 +413,8 @@ class PromptServer(): try: out[x] = node_info(x) except Exception as e: - print(f"[ERROR] An error occurred while retrieving information for the '{x}' node.", file=sys.stderr) - traceback.print_exc() + logging.error(f"[ERROR] An error occurred while retrieving information for the '{x}' node.") + logging.error(traceback.format_exc()) return web.json_response(out) @routes.get("/object_info/{node_class}") @@ -641,6 +641,6 @@ class PromptServer(): json_data = handler(json_data) except Exception as e: logging.warning(f"[ERROR] An error occurred during the on_prompt_handler processing") - traceback.print_exc() + logging.warning(traceback.format_exc()) return json_data From e7b8e240f76d2afe0eed6db7a35833923792ae94 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 12 Mar 2024 04:34:34 -0400 Subject: [PATCH 094/378] Add SamplerLMS node. --- comfy_extras/nodes_custom_sampler.py | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index 0ad1246a..f8e5f975 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -245,6 +245,22 @@ class SamplerEulerAncestral: sampler = comfy.samplers.ksampler("euler_ancestral", {"eta": eta, "s_noise": s_noise}) return (sampler, ) +class SamplerLMS: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"order": ("INT", {"default": 4, "min": 1, "max": 100}), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, order): + sampler = comfy.samplers.ksampler("lms", {"order": order}) + return (sampler, ) + class SamplerCustom: @classmethod def INPUT_TYPES(s): @@ -306,6 +322,7 @@ NODE_CLASS_MAPPINGS = { "SDTurboScheduler": SDTurboScheduler, "KSamplerSelect": KSamplerSelect, "SamplerEulerAncestral": SamplerEulerAncestral, + "SamplerLMS": SamplerLMS, "SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE, "SamplerDPMPP_SDE": SamplerDPMPP_SDE, "SplitSigmas": SplitSigmas, From eda87043862f743b0a0467735f8531f7c4709b3a Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 12 Mar 2024 12:16:37 -0400 Subject: [PATCH 095/378] Add SamplerDPMPP_3M_SDE node. --- comfy_extras/nodes_custom_sampler.py | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index f8e5f975..8b808ce0 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -181,6 +181,28 @@ class KSamplerSelect: sampler = comfy.samplers.sampler_object(sampler_name) return (sampler, ) +class SamplerDPMPP_3M_SDE: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "noise_device": (['gpu', 'cpu'], ), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, eta, s_noise, noise_device): + if noise_device == 'cpu': + sampler_name = "dpmpp_3m_sde" + else: + sampler_name = "dpmpp_3m_sde_gpu" + sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise}) + return (sampler, ) + class SamplerDPMPP_2M_SDE: @classmethod def INPUT_TYPES(s): @@ -323,6 +345,7 @@ NODE_CLASS_MAPPINGS = { "KSamplerSelect": KSamplerSelect, "SamplerEulerAncestral": SamplerEulerAncestral, "SamplerLMS": SamplerLMS, + "SamplerDPMPP_3M_SDE": SamplerDPMPP_3M_SDE, "SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE, "SamplerDPMPP_SDE": SamplerDPMPP_SDE, "SplitSigmas": SplitSigmas, From db8b59ecff7be40377d17ea69487f442b469c536 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 13 Mar 2024 19:04:41 -0400 Subject: [PATCH 096/378] Lower memory usage for loras in lowvram mode at the cost of perf. --- comfy/model_management.py | 36 +++------------- comfy/model_patcher.py | 91 +++++++++++++++++++++++++++++++-------- comfy/ops.py | 22 ++++++++++ 3 files changed, 101 insertions(+), 48 deletions(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index 2f0a0a62..66fa918b 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -272,7 +272,6 @@ def module_size(module): class LoadedModel: def __init__(self, model): self.model = model - self.model_accelerated = False self.device = model.load_device def model_memory(self): @@ -285,52 +284,27 @@ class LoadedModel: return self.model_memory() def model_load(self, lowvram_model_memory=0): - patch_model_to = None - if lowvram_model_memory == 0: - patch_model_to = self.device + patch_model_to = self.device self.model.model_patches_to(self.device) self.model.model_patches_to(self.model.model_dtype()) try: - self.real_model = self.model.patch_model(device_to=patch_model_to) #TODO: do something with loras and offloading to CPU + if lowvram_model_memory > 0: + self.real_model = self.model.patch_model_lowvram(device_to=patch_model_to, lowvram_model_memory=lowvram_model_memory) + else: + self.real_model = self.model.patch_model(device_to=patch_model_to) except Exception as e: self.model.unpatch_model(self.model.offload_device) self.model_unload() raise e - if lowvram_model_memory > 0: - logging.info("loading in lowvram mode {}".format(lowvram_model_memory/(1024 * 1024))) - mem_counter = 0 - for m in self.real_model.modules(): - if hasattr(m, "comfy_cast_weights"): - m.prev_comfy_cast_weights = m.comfy_cast_weights - m.comfy_cast_weights = True - module_mem = module_size(m) - if mem_counter + module_mem < lowvram_model_memory: - m.to(self.device) - mem_counter += module_mem - elif hasattr(m, "weight"): #only modules with comfy_cast_weights can be set to lowvram mode - m.to(self.device) - mem_counter += module_size(m) - logging.warning("lowvram: loaded module regularly {}".format(m)) - - self.model_accelerated = True - if is_intel_xpu() and not args.disable_ipex_optimize: self.real_model = torch.xpu.optimize(self.real_model.eval(), inplace=True, auto_kernel_selection=True, graph_mode=True) return self.real_model def model_unload(self): - if self.model_accelerated: - for m in self.real_model.modules(): - if hasattr(m, "prev_comfy_cast_weights"): - m.comfy_cast_weights = m.prev_comfy_cast_weights - del m.prev_comfy_cast_weights - - self.model_accelerated = False - self.model.unpatch_model(self.model.offload_device) self.model.model_patches_to(self.model.offload_device) diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index 5e578dff..475fa812 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -24,6 +24,7 @@ class ModelPatcher: self.current_device = current_device self.weight_inplace_update = weight_inplace_update + self.model_lowvram = False def model_size(self): if self.size > 0: @@ -178,6 +179,27 @@ class ModelPatcher: sd.pop(k) return sd + def patch_weight_to_device(self, key, device_to=None): + if key not in self.patches: + return + + weight = comfy.utils.get_attr(self.model, key) + + inplace_update = self.weight_inplace_update + + if key not in self.backup: + self.backup[key] = weight.to(device=self.offload_device, copy=inplace_update) + + if device_to is not None: + temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True) + else: + temp_weight = weight.to(torch.float32, copy=True) + out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype) + if inplace_update: + comfy.utils.copy_to_param(self.model, key, out_weight) + else: + comfy.utils.set_attr_param(self.model, key, out_weight) + def patch_model(self, device_to=None, patch_weights=True): for k in self.object_patches: old = comfy.utils.set_attr(self.model, k, self.object_patches[k]) @@ -191,23 +213,7 @@ class ModelPatcher: logging.warning("could not patch. key doesn't exist in model: {}".format(key)) continue - weight = model_sd[key] - - inplace_update = self.weight_inplace_update - - if key not in self.backup: - self.backup[key] = weight.to(device=self.offload_device, copy=inplace_update) - - if device_to is not None: - temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True) - else: - temp_weight = weight.to(torch.float32, copy=True) - out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype) - if inplace_update: - comfy.utils.copy_to_param(self.model, key, out_weight) - else: - comfy.utils.set_attr_param(self.model, key, out_weight) - del temp_weight + self.patch_weight_to_device(key, device_to) if device_to is not None: self.model.to(device_to) @@ -215,6 +221,47 @@ class ModelPatcher: return self.model + def patch_model_lowvram(self, device_to=None, lowvram_model_memory=0): + self.patch_model(device_to, patch_weights=False) + + logging.info("loading in lowvram mode {}".format(lowvram_model_memory/(1024 * 1024))) + class LowVramPatch: + def __init__(self, key, model_patcher): + self.key = key + self.model_patcher = model_patcher + def __call__(self, weight): + return self.model_patcher.calculate_weight(self.model_patcher.patches[self.key], weight, self.key) + + mem_counter = 0 + for n, m in self.model.named_modules(): + lowvram_weight = False + if hasattr(m, "comfy_cast_weights"): + module_mem = comfy.model_management.module_size(m) + if mem_counter + module_mem >= lowvram_model_memory: + lowvram_weight = True + + weight_key = "{}.weight".format(n) + bias_key = "{}.bias".format(n) + + if lowvram_weight: + if weight_key in self.patches: + m.weight_function = LowVramPatch(weight_key, self) + if bias_key in self.patches: + m.bias_function = LowVramPatch(weight_key, self) + + m.prev_comfy_cast_weights = m.comfy_cast_weights + m.comfy_cast_weights = True + else: + if hasattr(m, "weight"): + self.patch_weight_to_device(weight_key, device_to) + self.patch_weight_to_device(bias_key, device_to) + m.to(device_to) + mem_counter += comfy.model_management.module_size(m) + logging.debug("lowvram: loaded module regularly {}".format(m)) + + self.model_lowvram = True + return self.model + def calculate_weight(self, patches, weight, key): for p in patches: alpha = p[0] @@ -341,6 +388,16 @@ class ModelPatcher: return weight def unpatch_model(self, device_to=None): + if self.model_lowvram: + for m in self.model.modules(): + if hasattr(m, "prev_comfy_cast_weights"): + m.comfy_cast_weights = m.prev_comfy_cast_weights + del m.prev_comfy_cast_weights + m.weight_function = None + m.bias_function = None + + self.model_lowvram = False + keys = list(self.backup.keys()) if self.weight_inplace_update: diff --git a/comfy/ops.py b/comfy/ops.py index 517688e8..cfdec355 100644 --- a/comfy/ops.py +++ b/comfy/ops.py @@ -24,13 +24,20 @@ def cast_bias_weight(s, input): non_blocking = comfy.model_management.device_supports_non_blocking(input.device) if s.bias is not None: bias = s.bias.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking) + if s.bias_function is not None: + bias = s.bias_function(bias) weight = s.weight.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking) + if s.weight_function is not None: + weight = s.weight_function(weight) return weight, bias class disable_weight_init: class Linear(torch.nn.Linear): comfy_cast_weights = False + weight_function = None + bias_function = None + def reset_parameters(self): return None @@ -46,6 +53,9 @@ class disable_weight_init: class Conv2d(torch.nn.Conv2d): comfy_cast_weights = False + weight_function = None + bias_function = None + def reset_parameters(self): return None @@ -61,6 +71,9 @@ class disable_weight_init: class Conv3d(torch.nn.Conv3d): comfy_cast_weights = False + weight_function = None + bias_function = None + def reset_parameters(self): return None @@ -76,6 +89,9 @@ class disable_weight_init: class GroupNorm(torch.nn.GroupNorm): comfy_cast_weights = False + weight_function = None + bias_function = None + def reset_parameters(self): return None @@ -92,6 +108,9 @@ class disable_weight_init: class LayerNorm(torch.nn.LayerNorm): comfy_cast_weights = False + weight_function = None + bias_function = None + def reset_parameters(self): return None @@ -111,6 +130,9 @@ class disable_weight_init: class ConvTranspose2d(torch.nn.ConvTranspose2d): comfy_cast_weights = False + weight_function = None + bias_function = None + def reset_parameters(self): return None From 448d9263a258062344e25135fc49d26a7e60887a Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 14 Mar 2024 09:30:21 -0400 Subject: [PATCH 097/378] Fix control loras breaking. --- comfy/controlnet.py | 4 ++-- comfy/ops.py | 40 ++++++++++------------------------------ 2 files changed, 12 insertions(+), 32 deletions(-) diff --git a/comfy/controlnet.py b/comfy/controlnet.py index 1a72412b..b6941d8c 100644 --- a/comfy/controlnet.py +++ b/comfy/controlnet.py @@ -201,7 +201,7 @@ class ControlNet(ControlBase): super().cleanup() class ControlLoraOps: - class Linear(torch.nn.Module): + class Linear(torch.nn.Module, comfy.ops.CastWeightBiasOp): def __init__(self, in_features: int, out_features: int, bias: bool = True, device=None, dtype=None) -> None: factory_kwargs = {'device': device, 'dtype': dtype} @@ -220,7 +220,7 @@ class ControlLoraOps: else: return torch.nn.functional.linear(input, weight, bias) - class Conv2d(torch.nn.Module): + class Conv2d(torch.nn.Module, comfy.ops.CastWeightBiasOp): def __init__( self, in_channels, diff --git a/comfy/ops.py b/comfy/ops.py index cfdec355..eb650768 100644 --- a/comfy/ops.py +++ b/comfy/ops.py @@ -31,13 +31,13 @@ def cast_bias_weight(s, input): weight = s.weight_function(weight) return weight, bias +class CastWeightBiasOp: + comfy_cast_weights = False + weight_function = None + bias_function = None class disable_weight_init: - class Linear(torch.nn.Linear): - comfy_cast_weights = False - weight_function = None - bias_function = None - + class Linear(torch.nn.Linear, CastWeightBiasOp): def reset_parameters(self): return None @@ -51,11 +51,7 @@ class disable_weight_init: else: return super().forward(*args, **kwargs) - class Conv2d(torch.nn.Conv2d): - comfy_cast_weights = False - weight_function = None - bias_function = None - + class Conv2d(torch.nn.Conv2d, CastWeightBiasOp): def reset_parameters(self): return None @@ -69,11 +65,7 @@ class disable_weight_init: else: return super().forward(*args, **kwargs) - class Conv3d(torch.nn.Conv3d): - comfy_cast_weights = False - weight_function = None - bias_function = None - + class Conv3d(torch.nn.Conv3d, CastWeightBiasOp): def reset_parameters(self): return None @@ -87,11 +79,7 @@ class disable_weight_init: else: return super().forward(*args, **kwargs) - class GroupNorm(torch.nn.GroupNorm): - comfy_cast_weights = False - weight_function = None - bias_function = None - + class GroupNorm(torch.nn.GroupNorm, CastWeightBiasOp): def reset_parameters(self): return None @@ -106,11 +94,7 @@ class disable_weight_init: return super().forward(*args, **kwargs) - class LayerNorm(torch.nn.LayerNorm): - comfy_cast_weights = False - weight_function = None - bias_function = None - + class LayerNorm(torch.nn.LayerNorm, CastWeightBiasOp): def reset_parameters(self): return None @@ -128,11 +112,7 @@ class disable_weight_init: else: return super().forward(*args, **kwargs) - class ConvTranspose2d(torch.nn.ConvTranspose2d): - comfy_cast_weights = False - weight_function = None - bias_function = None - + class ConvTranspose2d(torch.nn.ConvTranspose2d, CastWeightBiasOp): def reset_parameters(self): return None From f2fe635c9f56a8e78866f59b3f110585e75b42f4 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 15 Mar 2024 19:34:22 -0400 Subject: [PATCH 098/378] SamplerDPMAdaptative node to test the different options. --- comfy/samplers.py | 4 ++-- comfy_extras/nodes_custom_sampler.py | 28 ++++++++++++++++++++++++++++ 2 files changed, 30 insertions(+), 2 deletions(-) diff --git a/comfy/samplers.py b/comfy/samplers.py index 16b4514e..d721cb2e 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -559,11 +559,11 @@ def ksampler(sampler_name, extra_options={}, inpaint_options={}): return k_diffusion_sampling.sample_dpm_fast(model, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=callback, disable=disable) sampler_function = dpm_fast_function elif sampler_name == "dpm_adaptive": - def dpm_adaptive_function(model, noise, sigmas, extra_args, callback, disable): + def dpm_adaptive_function(model, noise, sigmas, extra_args, callback, disable, **extra_options): sigma_min = sigmas[-1] if sigma_min == 0: sigma_min = sigmas[-2] - return k_diffusion_sampling.sample_dpm_adaptive(model, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=callback, disable=disable) + return k_diffusion_sampling.sample_dpm_adaptive(model, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=callback, disable=disable, **extra_options) sampler_function = dpm_adaptive_function else: sampler_function = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name)) diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index 8b808ce0..72ff7957 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -283,6 +283,33 @@ class SamplerLMS: sampler = comfy.samplers.ksampler("lms", {"order": order}) return (sampler, ) +class SamplerDPMAdaptative: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"order": ("INT", {"default": 3, "min": 2, "max": 3}), + "rtol": ("FLOAT", {"default": 0.05, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "atol": ("FLOAT", {"default": 0.0078, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "h_init": ("FLOAT", {"default": 0.05, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "pcoeff": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "icoeff": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "dcoeff": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "accept_safety": ("FLOAT", {"default": 0.81, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "eta": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise): + sampler = comfy.samplers.ksampler("dpm_adaptive", {"order": order, "rtol": rtol, "atol": atol, "h_init": h_init, "pcoeff": pcoeff, + "icoeff": icoeff, "dcoeff": dcoeff, "accept_safety": accept_safety, "eta": eta, + "s_noise":s_noise }) + return (sampler, ) + class SamplerCustom: @classmethod def INPUT_TYPES(s): @@ -348,6 +375,7 @@ NODE_CLASS_MAPPINGS = { "SamplerDPMPP_3M_SDE": SamplerDPMPP_3M_SDE, "SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE, "SamplerDPMPP_SDE": SamplerDPMPP_SDE, + "SamplerDPMAdaptative": SamplerDPMAdaptative, "SplitSigmas": SplitSigmas, "FlipSigmas": FlipSigmas, } From d7897fff2cfd38eac051fbc958a6f944bdf68cc9 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 16 Mar 2024 14:49:35 -0400 Subject: [PATCH 099/378] Move cascade scale factor from stage_a to latent_formats.py --- comfy/latent_formats.py | 2 +- comfy/ldm/cascade/stage_a.py | 9 +++------ 2 files changed, 4 insertions(+), 7 deletions(-) diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py index 674364e7..4ca466d9 100644 --- a/comfy/latent_formats.py +++ b/comfy/latent_formats.py @@ -95,7 +95,7 @@ class SC_Prior(LatentFormat): class SC_B(LatentFormat): def __init__(self): - self.scale_factor = 1.0 + self.scale_factor = 1.0 / 0.43 self.latent_rgb_factors = [ [ 0.1121, 0.2006, 0.1023], [-0.2093, -0.0222, -0.0195], diff --git a/comfy/ldm/cascade/stage_a.py b/comfy/ldm/cascade/stage_a.py index 260ccfc0..ca8867ea 100644 --- a/comfy/ldm/cascade/stage_a.py +++ b/comfy/ldm/cascade/stage_a.py @@ -163,11 +163,9 @@ class ResBlock(nn.Module): class StageA(nn.Module): - def __init__(self, levels=2, bottleneck_blocks=12, c_hidden=384, c_latent=4, codebook_size=8192, - scale_factor=0.43): # 0.3764 + def __init__(self, levels=2, bottleneck_blocks=12, c_hidden=384, c_latent=4, codebook_size=8192): super().__init__() self.c_latent = c_latent - self.scale_factor = scale_factor c_levels = [c_hidden // (2 ** i) for i in reversed(range(levels))] # Encoder blocks @@ -214,12 +212,11 @@ class StageA(nn.Module): x = self.down_blocks(x) if quantize: qe, (vq_loss, commit_loss), indices = self.vquantizer.forward(x, dim=1) - return qe / self.scale_factor, x / self.scale_factor, indices, vq_loss + commit_loss * 0.25 + return qe, x, indices, vq_loss + commit_loss * 0.25 else: - return x / self.scale_factor + return x def decode(self, x): - x = x * self.scale_factor x = self.up_blocks(x) x = self.out_block(x) return x From d3406d8d588d6b0c2da44c9bf378733a2077a14f Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 17 Mar 2024 08:57:49 -0400 Subject: [PATCH 100/378] Increase image batch nodes maximum values. --- comfy_extras/nodes_images.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/comfy_extras/nodes_images.py b/comfy_extras/nodes_images.py index 8f638bf8..af37666b 100644 --- a/comfy_extras/nodes_images.py +++ b/comfy_extras/nodes_images.py @@ -37,7 +37,7 @@ class RepeatImageBatch: @classmethod def INPUT_TYPES(s): return {"required": { "image": ("IMAGE",), - "amount": ("INT", {"default": 1, "min": 1, "max": 64}), + "amount": ("INT", {"default": 1, "min": 1, "max": 4096}), }} RETURN_TYPES = ("IMAGE",) FUNCTION = "repeat" @@ -52,8 +52,8 @@ class ImageFromBatch: @classmethod def INPUT_TYPES(s): return {"required": { "image": ("IMAGE",), - "batch_index": ("INT", {"default": 0, "min": 0, "max": 63}), - "length": ("INT", {"default": 1, "min": 1, "max": 64}), + "batch_index": ("INT", {"default": 0, "min": 0, "max": 4095}), + "length": ("INT", {"default": 1, "min": 1, "max": 4096}), }} RETURN_TYPES = ("IMAGE",) FUNCTION = "frombatch" From cacb022c4a5b9614f96086a866c8a4c4e9e85760 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 18 Mar 2024 00:26:23 -0400 Subject: [PATCH 101/378] Make saved SD1 checkpoints match more closely the official one. --- comfy/supported_models.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 37582103..e12935a2 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -45,6 +45,11 @@ class SD15(supported_models_base.BASE): return state_dict def process_clip_state_dict_for_saving(self, state_dict): + pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"] + for p in pop_keys: + if p in state_dict: + state_dict.pop(p) + replace_prefix = {"clip_l.": "cond_stage_model."} return utils.state_dict_prefix_replace(state_dict, replace_prefix) From b1a16d4500b89f4c7db2aadf41acb378efd20948 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 18 Mar 2024 13:50:11 -0400 Subject: [PATCH 102/378] Fix stable cascade img2img not working with all resolutions. --- comfy_extras/nodes_stable_cascade.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy_extras/nodes_stable_cascade.py b/comfy_extras/nodes_stable_cascade.py index 7e2d37d2..fcbbeb27 100644 --- a/comfy_extras/nodes_stable_cascade.py +++ b/comfy_extras/nodes_stable_cascade.py @@ -74,7 +74,7 @@ class StableCascade_StageC_VAEEncode: s = comfy.utils.common_upscale(image.movedim(-1,1), out_width, out_height, "bicubic", "center").movedim(1,-1) c_latent = vae.encode(s[:,:,:,:3]) - b_latent = torch.zeros([c_latent.shape[0], 4, height // 4, width // 4]) + b_latent = torch.zeros([c_latent.shape[0], 4, (height // 8) * 2, (width // 8) * 2]) return ({ "samples": c_latent, }, { From 0b78213bdaaa9021daa870c544be6ce86f54d30d Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 18 Mar 2024 15:51:23 -0400 Subject: [PATCH 103/378] Fix neg scale step. --- comfy_extras/nodes_perpneg.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy_extras/nodes_perpneg.py b/comfy_extras/nodes_perpneg.py index 64bbc1dc..dc73c552 100644 --- a/comfy_extras/nodes_perpneg.py +++ b/comfy_extras/nodes_perpneg.py @@ -10,7 +10,7 @@ class PerpNeg: def INPUT_TYPES(s): return {"required": {"model": ("MODEL", ), "empty_conditioning": ("CONDITIONING", ), - "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0}), + "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" From 40e124c6be01195eada95e8c319ca6ddf4fd1a17 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 18 Mar 2024 10:04:51 -0400 Subject: [PATCH 104/378] SV3D support. --- comfy/model_base.py | 30 +++++++++++++++++ comfy/supported_models.py | 37 ++++++++++++++++++++- comfy_extras/nodes_stable3d.py | 53 +++++++++++++++++++++++++++---- comfy_extras/nodes_video_model.py | 28 ++++++++++++++++ 4 files changed, 141 insertions(+), 7 deletions(-) diff --git a/comfy/model_base.py b/comfy/model_base.py index 5da71e63..bc019de5 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -380,6 +380,36 @@ class SVD_img2vid(BaseModel): out['num_video_frames'] = comfy.conds.CONDConstant(noise.shape[0]) return out +class SV3D_u(SVD_img2vid): + def encode_adm(self, **kwargs): + augmentation = kwargs.get("augmentation_level", 0) + + out = [] + out.append(self.embedder(torch.flatten(torch.Tensor([augmentation])))) + + flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0) + return flat + +class SV3D_p(SVD_img2vid): + def __init__(self, model_config, model_type=ModelType.V_PREDICTION_EDM, device=None): + super().__init__(model_config, model_type, device=device) + self.embedder_512 = Timestep(512) + + def encode_adm(self, **kwargs): + augmentation = kwargs.get("augmentation_level", 0) + elevation = kwargs.get("elevation", 0) #elevation and azimuth are in degrees here + azimuth = kwargs.get("azimuth", 0) + noise = kwargs.get("noise", None) + + out = [] + out.append(self.embedder(torch.flatten(torch.Tensor([augmentation])))) + out.append(self.embedder_512(torch.deg2rad(torch.fmod(torch.flatten(90 - torch.Tensor([elevation])), 360.0)))) + out.append(self.embedder_512(torch.deg2rad(torch.fmod(torch.flatten(torch.Tensor([azimuth])), 360.0)))) + + out = list(map(lambda a: utils.resize_to_batch_size(a, noise.shape[0]), out)) + return torch.cat(out, dim=1) + + class Stable_Zero123(BaseModel): def __init__(self, model_config, model_type=ModelType.EPS, device=None, cc_projection_weight=None, cc_projection_bias=None): super().__init__(model_config, model_type, device=device) diff --git a/comfy/supported_models.py b/comfy/supported_models.py index e12935a2..2ce9736b 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -284,6 +284,41 @@ class SVD_img2vid(supported_models_base.BASE): def clip_target(self): return None +class SV3D_u(SVD_img2vid): + unet_config = { + "model_channels": 320, + "in_channels": 8, + "use_linear_in_transformer": True, + "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0], + "context_dim": 1024, + "adm_in_channels": 256, + "use_temporal_attention": True, + "use_temporal_resblock": True + } + + vae_key_prefix = ["conditioner.embedders.1.encoder."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.SV3D_u(self, device=device) + return out + +class SV3D_p(SV3D_u): + unet_config = { + "model_channels": 320, + "in_channels": 8, + "use_linear_in_transformer": True, + "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0], + "context_dim": 1024, + "adm_in_channels": 1280, + "use_temporal_attention": True, + "use_temporal_resblock": True + } + + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.SV3D_p(self, device=device) + return out + class Stable_Zero123(supported_models_base.BASE): unet_config = { "context_dim": 768, @@ -405,5 +440,5 @@ class Stable_Cascade_B(Stable_Cascade_C): return out -models = [Stable_Zero123, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B] +models = [Stable_Zero123, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p] models += [SVD_img2vid] diff --git a/comfy_extras/nodes_stable3d.py b/comfy_extras/nodes_stable3d.py index 4375d8f9..be2e34c2 100644 --- a/comfy_extras/nodes_stable3d.py +++ b/comfy_extras/nodes_stable3d.py @@ -29,8 +29,8 @@ class StableZero123_Conditioning: "width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), - "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}), - "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}), + "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), + "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), }} RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") RETURN_NAMES = ("positive", "negative", "latent") @@ -62,10 +62,10 @@ class StableZero123_Conditioning_Batched: "width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), - "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}), - "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}), - "elevation_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}), - "azimuth_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}), + "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), + "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), + "elevation_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), + "azimuth_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), }} RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") RETURN_NAMES = ("positive", "negative", "latent") @@ -95,8 +95,49 @@ class StableZero123_Conditioning_Batched: latent = torch.zeros([batch_size, 4, height // 8, width // 8]) return (positive, negative, {"samples":latent, "batch_index": [0] * batch_size}) +class SV3D_Conditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_vision": ("CLIP_VISION",), + "init_image": ("IMAGE",), + "vae": ("VAE",), + "width": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), + "video_frames": ("INT", {"default": 21, "min": 1, "max": 4096}), + "elevation": ("FLOAT", {"default": 0.0, "min": -90.0, "max": 90.0, "step": 0.1, "round": False}), + }} + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + + FUNCTION = "encode" + + CATEGORY = "conditioning/3d_models" + + def encode(self, clip_vision, init_image, vae, width, height, video_frames, elevation): + output = clip_vision.encode_image(init_image) + pooled = output.image_embeds.unsqueeze(0) + pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) + encode_pixels = pixels[:,:,:,:3] + t = vae.encode(encode_pixels) + + azimuth = 0 + azimuth_increment = 360 / (max(video_frames, 2) - 1) + + elevations = [] + azimuths = [] + for i in range(video_frames): + elevations.append(elevation) + azimuths.append(azimuth) + azimuth += azimuth_increment + + positive = [[pooled, {"concat_latent_image": t, "elevation": elevations, "azimuth": azimuths}]] + negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t), "elevation": elevations, "azimuth": azimuths}]] + latent = torch.zeros([video_frames, 4, height // 8, width // 8]) + return (positive, negative, {"samples":latent}) + NODE_CLASS_MAPPINGS = { "StableZero123_Conditioning": StableZero123_Conditioning, "StableZero123_Conditioning_Batched": StableZero123_Conditioning_Batched, + "SV3D_Conditioning": SV3D_Conditioning, } diff --git a/comfy_extras/nodes_video_model.py b/comfy_extras/nodes_video_model.py index a5262565..1a0189ed 100644 --- a/comfy_extras/nodes_video_model.py +++ b/comfy_extras/nodes_video_model.py @@ -79,6 +79,33 @@ class VideoLinearCFGGuidance: m.set_model_sampler_cfg_function(linear_cfg) return (m, ) +class VideoTriangleCFGGuidance: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "sampling/video_models" + + def patch(self, model, min_cfg): + def linear_cfg(args): + cond = args["cond"] + uncond = args["uncond"] + cond_scale = args["cond_scale"] + period = 1.0 + values = torch.linspace(0, 1, cond.shape[0], device=cond.device) + values = 2 * (values / period - torch.floor(values / period + 0.5)).abs() + scale = (values * (cond_scale - min_cfg) + min_cfg).reshape((cond.shape[0], 1, 1, 1)) + + return uncond + scale * (cond - uncond) + + m = model.clone() + m.set_model_sampler_cfg_function(linear_cfg) + return (m, ) + class ImageOnlyCheckpointSave(comfy_extras.nodes_model_merging.CheckpointSave): CATEGORY = "_for_testing" @@ -98,6 +125,7 @@ NODE_CLASS_MAPPINGS = { "ImageOnlyCheckpointLoader": ImageOnlyCheckpointLoader, "SVD_img2vid_Conditioning": SVD_img2vid_Conditioning, "VideoLinearCFGGuidance": VideoLinearCFGGuidance, + "VideoTriangleCFGGuidance": VideoTriangleCFGGuidance, "ImageOnlyCheckpointSave": ImageOnlyCheckpointSave, } From 0c55f16c9e66eaa4915e288b34e4f848fb2d949f Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 19 Mar 2024 02:34:01 -0400 Subject: [PATCH 105/378] Remove code that should be useless now. --- nodes.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/nodes.py b/nodes.py index 453f6e60..d24df200 100644 --- a/nodes.py +++ b/nodes.py @@ -15,9 +15,6 @@ from PIL.PngImagePlugin import PngInfo import numpy as np import safetensors.torch -sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy")) - - import comfy.diffusers_load import comfy.samplers import comfy.sample From d14bdb18967f7413852a364747c49599de537eec Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 19 Mar 2024 11:17:37 -0400 Subject: [PATCH 106/378] Revert, NOTE: this will be removed again soon please fix your nodes. --- nodes.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/nodes.py b/nodes.py index d24df200..453f6e60 100644 --- a/nodes.py +++ b/nodes.py @@ -15,6 +15,9 @@ from PIL.PngImagePlugin import PngInfo import numpy as np import safetensors.torch +sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy")) + + import comfy.diffusers_load import comfy.samplers import comfy.sample From 150a3e946fe49990454b0b81e26f06cdd9fbab8f Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 20 Mar 2024 01:35:59 -0400 Subject: [PATCH 107/378] Make LCM sampler use the model noise scaling function. --- comfy/k_diffusion/sampling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py index 761c2e0e..57518c7b 100644 --- a/comfy/k_diffusion/sampling.py +++ b/comfy/k_diffusion/sampling.py @@ -748,7 +748,7 @@ def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, n x = denoised if sigmas[i + 1] > 0: - x += sigmas[i + 1] * noise_sampler(sigmas[i], sigmas[i + 1]) + x = model.inner_model.inner_model.model_sampling.noise_scaling(sigmas[i + 1] * s_in, noise_sampler(sigmas[i], sigmas[i + 1]), x) return x From c18a203a8abdd0fce24743a838fe0d0400d8ff09 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 20 Mar 2024 01:29:26 -0400 Subject: [PATCH 108/378] Don't unload model weights for non weight patches. --- comfy/model_management.py | 44 ++++++++++++++++++++++------ comfy/model_patcher.py | 60 ++++++++++++++++++++++++++------------- 2 files changed, 76 insertions(+), 28 deletions(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index 66fa918b..74958908 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -273,6 +273,7 @@ class LoadedModel: def __init__(self, model): self.model = model self.device = model.load_device + self.weights_loaded = False def model_memory(self): return self.model.model_size() @@ -289,11 +290,13 @@ class LoadedModel: self.model.model_patches_to(self.device) self.model.model_patches_to(self.model.model_dtype()) + load_weights = not self.weights_loaded + try: - if lowvram_model_memory > 0: + if lowvram_model_memory > 0 and load_weights: self.real_model = self.model.patch_model_lowvram(device_to=patch_model_to, lowvram_model_memory=lowvram_model_memory) else: - self.real_model = self.model.patch_model(device_to=patch_model_to) + self.real_model = self.model.patch_model(device_to=patch_model_to, patch_weights=load_weights) except Exception as e: self.model.unpatch_model(self.model.offload_device) self.model_unload() @@ -302,11 +305,13 @@ class LoadedModel: if is_intel_xpu() and not args.disable_ipex_optimize: self.real_model = torch.xpu.optimize(self.real_model.eval(), inplace=True, auto_kernel_selection=True, graph_mode=True) + self.weights_loaded = True return self.real_model - def model_unload(self): - self.model.unpatch_model(self.model.offload_device) + def model_unload(self, unpatch_weights=True): + self.model.unpatch_model(self.model.offload_device, unpatch_weights=unpatch_weights) self.model.model_patches_to(self.model.offload_device) + self.weights_loaded = self.weights_loaded and not unpatch_weights def __eq__(self, other): return self.model is other.model @@ -314,15 +319,35 @@ class LoadedModel: def minimum_inference_memory(): return (1024 * 1024 * 1024) -def unload_model_clones(model): +def unload_model_clones(loaded_model, unload_weights_only=True): + model = loaded_model.model + to_unload = [] for i in range(len(current_loaded_models)): if model.is_clone(current_loaded_models[i].model): to_unload = [i] + to_unload + if len(to_unload) == 0: + return + + same_weights = 0 for i in to_unload: - logging.debug("unload clone {}".format(i)) - current_loaded_models.pop(i).model_unload() + if model.clone_has_same_weights(current_loaded_models[i].model): + same_weights += 1 + + if same_weights == len(to_unload): + unload_weight = False + else: + unload_weight = True + + if unload_weights_only and unload_weight == False: + return + + for i in to_unload: + logging.debug("unload clone {} {}".format(i, unload_weight)) + current_loaded_models.pop(i).model_unload(unpatch_weights=unload_weight) + + loaded_model.weights_loaded = not unload_weight def free_memory(memory_required, device, keep_loaded=[]): unloaded_model = False @@ -377,13 +402,16 @@ def load_models_gpu(models, memory_required=0): total_memory_required = {} for loaded_model in models_to_load: - unload_model_clones(loaded_model.model) + unload_model_clones(loaded_model, unload_weights_only=True) #unload clones where the weights are different total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device) for device in total_memory_required: if device != torch.device("cpu"): free_memory(total_memory_required[device] * 1.3 + extra_mem, device, models_already_loaded) + for loaded_model in models_to_load: + unload_model_clones(loaded_model, unload_weights_only=False) #unload the rest of the clones where the weights can stay loaded + for loaded_model in models_to_load: model = loaded_model.model torch_dev = model.load_device diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index 475fa812..aa78302d 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -2,6 +2,7 @@ import torch import copy import inspect import logging +import uuid import comfy.utils import comfy.model_management @@ -25,6 +26,7 @@ class ModelPatcher: self.weight_inplace_update = weight_inplace_update self.model_lowvram = False + self.patches_uuid = uuid.uuid4() def model_size(self): if self.size > 0: @@ -39,10 +41,13 @@ class ModelPatcher: n.patches = {} for k in self.patches: n.patches[k] = self.patches[k][:] + n.patches_uuid = self.patches_uuid n.object_patches = self.object_patches.copy() n.model_options = copy.deepcopy(self.model_options) n.model_keys = self.model_keys + n.backup = self.backup + n.object_patches_backup = self.object_patches_backup return n def is_clone(self, other): @@ -50,6 +55,19 @@ class ModelPatcher: return True return False + def clone_has_same_weights(self, clone): + if not self.is_clone(clone): + return False + + if len(self.patches) == 0 and len(clone.patches) == 0: + return True + + if self.patches_uuid == clone.patches_uuid: + if len(self.patches) != len(clone.patches): + logging.warning("WARNING: something went wrong, same patch uuid but different length of patches.") + else: + return True + def memory_required(self, input_shape): return self.model.memory_required(input_shape=input_shape) @@ -154,6 +172,7 @@ class ModelPatcher: current_patches.append((strength_patch, patches[k], strength_model)) self.patches[k] = current_patches + self.patches_uuid = uuid.uuid4() return list(p) def get_key_patches(self, filter_prefix=None): @@ -387,31 +406,32 @@ class ModelPatcher: return weight - def unpatch_model(self, device_to=None): - if self.model_lowvram: - for m in self.model.modules(): - if hasattr(m, "prev_comfy_cast_weights"): - m.comfy_cast_weights = m.prev_comfy_cast_weights - del m.prev_comfy_cast_weights - m.weight_function = None - m.bias_function = None + def unpatch_model(self, device_to=None, unpatch_weights=True): + if unpatch_weights: + if self.model_lowvram: + for m in self.model.modules(): + if hasattr(m, "prev_comfy_cast_weights"): + m.comfy_cast_weights = m.prev_comfy_cast_weights + del m.prev_comfy_cast_weights + m.weight_function = None + m.bias_function = None - self.model_lowvram = False + self.model_lowvram = False - keys = list(self.backup.keys()) + keys = list(self.backup.keys()) - if self.weight_inplace_update: - for k in keys: - comfy.utils.copy_to_param(self.model, k, self.backup[k]) - else: - for k in keys: - comfy.utils.set_attr_param(self.model, k, self.backup[k]) + if self.weight_inplace_update: + for k in keys: + comfy.utils.copy_to_param(self.model, k, self.backup[k]) + else: + for k in keys: + comfy.utils.set_attr_param(self.model, k, self.backup[k]) - self.backup = {} + self.backup.clear() - if device_to is not None: - self.model.to(device_to) - self.current_device = device_to + if device_to is not None: + self.model.to(device_to) + self.current_device = device_to keys = list(self.object_patches_backup.keys()) for k in keys: From 4b9005e949224782236a8b914eae48bc503f1f18 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 20 Mar 2024 13:53:45 -0400 Subject: [PATCH 109/378] Fix regression with model merging. --- comfy/model_management.py | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index 74958908..11c97f29 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -319,16 +319,14 @@ class LoadedModel: def minimum_inference_memory(): return (1024 * 1024 * 1024) -def unload_model_clones(loaded_model, unload_weights_only=True): - model = loaded_model.model - +def unload_model_clones(model, unload_weights_only=True, force_unload=True): to_unload = [] for i in range(len(current_loaded_models)): if model.is_clone(current_loaded_models[i].model): to_unload = [i] + to_unload if len(to_unload) == 0: - return + return None same_weights = 0 for i in to_unload: @@ -340,14 +338,15 @@ def unload_model_clones(loaded_model, unload_weights_only=True): else: unload_weight = True - if unload_weights_only and unload_weight == False: - return + if not force_unload: + if unload_weights_only and unload_weight == False: + return None for i in to_unload: logging.debug("unload clone {} {}".format(i, unload_weight)) current_loaded_models.pop(i).model_unload(unpatch_weights=unload_weight) - loaded_model.weights_loaded = not unload_weight + return unload_weight def free_memory(memory_required, device, keep_loaded=[]): unloaded_model = False @@ -402,7 +401,7 @@ def load_models_gpu(models, memory_required=0): total_memory_required = {} for loaded_model in models_to_load: - unload_model_clones(loaded_model, unload_weights_only=True) #unload clones where the weights are different + unload_model_clones(loaded_model.model, unload_weights_only=True, force_unload=False) #unload clones where the weights are different total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device) for device in total_memory_required: @@ -410,7 +409,9 @@ def load_models_gpu(models, memory_required=0): free_memory(total_memory_required[device] * 1.3 + extra_mem, device, models_already_loaded) for loaded_model in models_to_load: - unload_model_clones(loaded_model, unload_weights_only=False) #unload the rest of the clones where the weights can stay loaded + weights_unloaded = unload_model_clones(loaded_model.model, unload_weights_only=False, force_unload=False) #unload the rest of the clones where the weights can stay loaded + if weights_unloaded is not None: + loaded_model.weights_loaded = not weights_unloaded for loaded_model in models_to_load: model = loaded_model.model From 5d875d77fe6e31a4b0bc6dc36f0441eba3b6afe1 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 20 Mar 2024 20:48:54 -0400 Subject: [PATCH 110/378] Fix regression with lcm not working with batches. --- comfy/k_diffusion/sampling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py index 57518c7b..7af01682 100644 --- a/comfy/k_diffusion/sampling.py +++ b/comfy/k_diffusion/sampling.py @@ -748,7 +748,7 @@ def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, n x = denoised if sigmas[i + 1] > 0: - x = model.inner_model.inner_model.model_sampling.noise_scaling(sigmas[i + 1] * s_in, noise_sampler(sigmas[i], sigmas[i + 1]), x) + x = model.inner_model.inner_model.model_sampling.noise_scaling(sigmas[i + 1], noise_sampler(sigmas[i], sigmas[i + 1]), x) return x From 062483823738ed610d8d074ba63910c90e9d45b7 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 21 Mar 2024 14:49:11 -0400 Subject: [PATCH 111/378] Add inverse noise scaling function. --- comfy/model_sampling.py | 3 +++ comfy/samplers.py | 1 + 2 files changed, 4 insertions(+) diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py index d325f76d..37976b32 100644 --- a/comfy/model_sampling.py +++ b/comfy/model_sampling.py @@ -20,6 +20,9 @@ class EPS: noise += latent_image return noise + def inverse_noise_scaling(self, sigma, latent): + return latent + class V_PREDICTION(EPS): def calculate_denoised(self, sigma, model_output, model_input): sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) diff --git a/comfy/samplers.py b/comfy/samplers.py index d721cb2e..3678dc81 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -546,6 +546,7 @@ class KSAMPLER(Sampler): k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps) samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options) + samples = model_wrap.inner_model.model_sampling.inverse_noise_scaling(sigmas[-1], samples) return samples From a28a9dc83684624ee2167c0b92d976bb68f2c606 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 22 Mar 2024 12:56:48 -0400 Subject: [PATCH 112/378] Add an example to use the SaveImageWebsocket node and enable it. --- ...ve.py.disabled => websocket_image_save.py} | 6 +- .../websockets_api_example_ws_images.py | 159 ++++++++++++++++++ 2 files changed, 160 insertions(+), 5 deletions(-) rename custom_nodes/{websocket_image_save.py.disabled => websocket_image_save.py} (84%) create mode 100644 script_examples/websockets_api_example_ws_images.py diff --git a/custom_nodes/websocket_image_save.py.disabled b/custom_nodes/websocket_image_save.py similarity index 84% rename from custom_nodes/websocket_image_save.py.disabled rename to custom_nodes/websocket_image_save.py index b85a5de8..5aa57364 100644 --- a/custom_nodes/websocket_image_save.py.disabled +++ b/custom_nodes/websocket_image_save.py @@ -10,10 +10,6 @@ import time #binary images on the websocket with a 8 byte header indicating the type #of binary message (first 4 bytes) and the image format (next 4 bytes). -#The reason this node is disabled by default is because there is a small -#issue when using it with the default ComfyUI web interface: When generating -#batches only the last image will be shown in the UI. - #Note that no metadata will be put in the images saved with this node. class SaveImageWebsocket: @@ -28,7 +24,7 @@ class SaveImageWebsocket: OUTPUT_NODE = True - CATEGORY = "image" + CATEGORY = "api/image" def save_images(self, images): pbar = comfy.utils.ProgressBar(images.shape[0]) diff --git a/script_examples/websockets_api_example_ws_images.py b/script_examples/websockets_api_example_ws_images.py new file mode 100644 index 00000000..73748862 --- /dev/null +++ b/script_examples/websockets_api_example_ws_images.py @@ -0,0 +1,159 @@ +#This is an example that uses the websockets api and the SaveImageWebsocket node to get images directly without +#them being saved to disk + +import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client) +import uuid +import json +import urllib.request +import urllib.parse + +server_address = "127.0.0.1:8188" +client_id = str(uuid.uuid4()) + +def queue_prompt(prompt): + p = {"prompt": prompt, "client_id": client_id} + data = json.dumps(p).encode('utf-8') + req = urllib.request.Request("http://{}/prompt".format(server_address), data=data) + return json.loads(urllib.request.urlopen(req).read()) + +def get_image(filename, subfolder, folder_type): + data = {"filename": filename, "subfolder": subfolder, "type": folder_type} + url_values = urllib.parse.urlencode(data) + with urllib.request.urlopen("http://{}/view?{}".format(server_address, url_values)) as response: + return response.read() + +def get_history(prompt_id): + with urllib.request.urlopen("http://{}/history/{}".format(server_address, prompt_id)) as response: + return json.loads(response.read()) + +def get_images(ws, prompt): + prompt_id = queue_prompt(prompt)['prompt_id'] + output_images = {} + current_node = "" + while True: + out = ws.recv() + if isinstance(out, str): + message = json.loads(out) + if message['type'] == 'executing': + data = message['data'] + if data['prompt_id'] == prompt_id: + if data['node'] is None: + break #Execution is done + else: + current_node = data['node'] + else: + if current_node == 'save_image_websocket_node': + images_output = output_images.get(current_node, []) + images_output.append(out[8:]) + output_images[current_node] = images_output + + return output_images + +prompt_text = """ +{ + "3": { + "class_type": "KSampler", + "inputs": { + "cfg": 8, + "denoise": 1, + "latent_image": [ + "5", + 0 + ], + "model": [ + "4", + 0 + ], + "negative": [ + "7", + 0 + ], + "positive": [ + "6", + 0 + ], + "sampler_name": "euler", + "scheduler": "normal", + "seed": 8566257, + "steps": 20 + } + }, + "4": { + "class_type": "CheckpointLoaderSimple", + "inputs": { + "ckpt_name": "v1-5-pruned-emaonly.ckpt" + } + }, + "5": { + "class_type": "EmptyLatentImage", + "inputs": { + "batch_size": 1, + "height": 512, + "width": 512 + } + }, + "6": { + "class_type": "CLIPTextEncode", + "inputs": { + "clip": [ + "4", + 1 + ], + "text": "masterpiece best quality girl" + } + }, + "7": { + "class_type": "CLIPTextEncode", + "inputs": { + "clip": [ + "4", + 1 + ], + "text": "bad hands" + } + }, + "8": { + "class_type": "VAEDecode", + "inputs": { + "samples": [ + "3", + 0 + ], + "vae": [ + "4", + 2 + ] + } + }, + "save_image_websocket_node": { + "class_type": "SaveImageWebsocket", + "inputs": { + "images": [ + "8", + 0 + ] + } + } +} +""" + +prompt = json.loads(prompt_text) +#set the text prompt for our positive CLIPTextEncode +prompt["6"]["inputs"]["text"] = "masterpiece best quality man" + +#set the seed for our KSampler node +prompt["3"]["inputs"]["seed"] = 5 + +ws = websocket.WebSocket() +ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id)) +images = get_images(ws, prompt) + +#Commented out code to display the output images: + +# for node_id in images: +# for image_data in images[node_id]: +# from PIL import Image +# import io +# image = Image.open(io.BytesIO(image_data)) +# image.show() + From 6a32c06f0646805456ac2744ece289962a2379c6 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 23 Mar 2024 17:27:10 -0400 Subject: [PATCH 113/378] Move cleanup_models to improve performance. --- execution.py | 1 - main.py | 1 + 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/execution.py b/execution.py index 5f8ea835..1b8f606a 100644 --- a/execution.py +++ b/execution.py @@ -368,7 +368,6 @@ class PromptExecutor: d = self.outputs_ui.pop(x) del d - comfy.model_management.cleanup_models() self.add_message("execution_cached", { "nodes": list(current_outputs) , "prompt_id": prompt_id}, broadcast=False) diff --git a/main.py b/main.py index 3dee72e3..b3a3ebea 100644 --- a/main.py +++ b/main.py @@ -139,6 +139,7 @@ def prompt_worker(q, server): if need_gc: current_time = time.perf_counter() if (current_time - last_gc_collect) > gc_collect_interval: + comfy.model_management.cleanup_models() gc.collect() comfy.model_management.soft_empty_cache() last_gc_collect = current_time From c6de09b02e20d748739fb6af58e196ebdd12825a Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 24 Mar 2024 02:36:30 -0400 Subject: [PATCH 114/378] Optimize memory unload strategy for more optimized performance. --- comfy/model_management.py | 25 ++++++++++++++++--------- 1 file changed, 16 insertions(+), 9 deletions(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index 11c97f29..715ca2ee 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -349,20 +349,27 @@ def unload_model_clones(model, unload_weights_only=True, force_unload=True): return unload_weight def free_memory(memory_required, device, keep_loaded=[]): - unloaded_model = False + unloaded_model = [] + can_unload = [] + for i in range(len(current_loaded_models) -1, -1, -1): - if not DISABLE_SMART_MEMORY: - if get_free_memory(device) > memory_required: - break shift_model = current_loaded_models[i] if shift_model.device == device: if shift_model not in keep_loaded: - m = current_loaded_models.pop(i) - m.model_unload() - del m - unloaded_model = True + can_unload.append((sys.getrefcount(shift_model.model), shift_model.model_memory(), i)) - if unloaded_model: + for x in sorted(can_unload): + i = x[-1] + if not DISABLE_SMART_MEMORY: + if get_free_memory(device) > memory_required: + break + current_loaded_models[i].model_unload() + unloaded_model.append(i) + + for i in sorted(unloaded_model, reverse=True): + current_loaded_models.pop(i) + + if len(unloaded_model) > 0: soft_empty_cache() else: if vram_state != VRAMState.HIGH_VRAM: From ae77590b4e0cbab55ac9edb67722092d375fe3ec Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 25 Mar 2024 18:09:23 -0400 Subject: [PATCH 115/378] dora_scale support for lora file. --- comfy/lora.py | 14 ++++++++++---- comfy/model_patcher.py | 25 +++++++++++++++++++++++++ 2 files changed, 35 insertions(+), 4 deletions(-) diff --git a/comfy/lora.py b/comfy/lora.py index 637380d5..096285bb 100644 --- a/comfy/lora.py +++ b/comfy/lora.py @@ -21,6 +21,12 @@ def load_lora(lora, to_load): alpha = lora[alpha_name].item() loaded_keys.add(alpha_name) + dora_scale_name = "{}.dora_scale".format(x) + dora_scale = None + if dora_scale_name in lora.keys(): + dora_scale = lora[dora_scale_name] + loaded_keys.add(dora_scale_name) + regular_lora = "{}.lora_up.weight".format(x) diffusers_lora = "{}_lora.up.weight".format(x) transformers_lora = "{}.lora_linear_layer.up.weight".format(x) @@ -44,7 +50,7 @@ def load_lora(lora, to_load): if mid_name is not None and mid_name in lora.keys(): mid = lora[mid_name] loaded_keys.add(mid_name) - patch_dict[to_load[x]] = ("lora", (lora[A_name], lora[B_name], alpha, mid)) + patch_dict[to_load[x]] = ("lora", (lora[A_name], lora[B_name], alpha, mid, dora_scale)) loaded_keys.add(A_name) loaded_keys.add(B_name) @@ -65,7 +71,7 @@ def load_lora(lora, to_load): loaded_keys.add(hada_t1_name) loaded_keys.add(hada_t2_name) - patch_dict[to_load[x]] = ("loha", (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2)) + patch_dict[to_load[x]] = ("loha", (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2, dora_scale)) loaded_keys.add(hada_w1_a_name) loaded_keys.add(hada_w1_b_name) loaded_keys.add(hada_w2_a_name) @@ -117,7 +123,7 @@ def load_lora(lora, to_load): loaded_keys.add(lokr_t2_name) if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None): - patch_dict[to_load[x]] = ("lokr", (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2)) + patch_dict[to_load[x]] = ("lokr", (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2, dora_scale)) #glora a1_name = "{}.a1.weight".format(x) @@ -125,7 +131,7 @@ def load_lora(lora, to_load): b1_name = "{}.b1.weight".format(x) b2_name = "{}.b2.weight".format(x) if a1_name in lora: - patch_dict[to_load[x]] = ("glora", (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha)) + patch_dict[to_load[x]] = ("glora", (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha, dora_scale)) loaded_keys.add(a1_name) loaded_keys.add(a2_name) loaded_keys.add(b1_name) diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index aa78302d..8dda84cf 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -7,6 +7,18 @@ import uuid import comfy.utils import comfy.model_management +def apply_weight_decompose(dora_scale, weight): + weight_norm = ( + weight.transpose(0, 1) + .reshape(weight.shape[1], -1) + .norm(dim=1, keepdim=True) + .reshape(weight.shape[1], *[1] * (weight.dim() - 1)) + .transpose(0, 1) + ) + + return weight * (dora_scale / weight_norm) + + class ModelPatcher: def __init__(self, model, load_device, offload_device, size=0, current_device=None, weight_inplace_update=False): self.size = size @@ -309,6 +321,7 @@ class ModelPatcher: elif patch_type == "lora": #lora/locon mat1 = comfy.model_management.cast_to_device(v[0], weight.device, torch.float32) mat2 = comfy.model_management.cast_to_device(v[1], weight.device, torch.float32) + dora_scale = v[4] if v[2] is not None: alpha *= v[2] / mat2.shape[0] if v[3] is not None: @@ -318,6 +331,8 @@ class ModelPatcher: mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1) try: weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(weight.shape).type(weight.dtype) + if dora_scale is not None: + weight = apply_weight_decompose(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight) except Exception as e: logging.error("ERROR {} {} {}".format(patch_type, key, e)) elif patch_type == "lokr": @@ -328,6 +343,7 @@ class ModelPatcher: w2_a = v[5] w2_b = v[6] t2 = v[7] + dora_scale = v[8] dim = None if w1 is None: @@ -357,6 +373,8 @@ class ModelPatcher: try: weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype) + if dora_scale is not None: + weight = apply_weight_decompose(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight) except Exception as e: logging.error("ERROR {} {} {}".format(patch_type, key, e)) elif patch_type == "loha": @@ -366,6 +384,7 @@ class ModelPatcher: alpha *= v[2] / w1b.shape[0] w2a = v[3] w2b = v[4] + dora_scale = v[7] if v[5] is not None: #cp decomposition t1 = v[5] t2 = v[6] @@ -386,12 +405,16 @@ class ModelPatcher: try: weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype) + if dora_scale is not None: + weight = apply_weight_decompose(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight) except Exception as e: logging.error("ERROR {} {} {}".format(patch_type, key, e)) elif patch_type == "glora": if v[4] is not None: alpha *= v[4] / v[0].shape[0] + dora_scale = v[5] + a1 = comfy.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, torch.float32) a2 = comfy.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, torch.float32) b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, torch.float32) @@ -399,6 +422,8 @@ class ModelPatcher: try: weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype) + if dora_scale is not None: + weight = apply_weight_decompose(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight) except Exception as e: logging.error("ERROR {} {} {}".format(patch_type, key, e)) else: From 11838e60f4aac495c6f0aed09415f3e8e0d2a402 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 26 Mar 2024 04:00:53 -0400 Subject: [PATCH 116/378] Increase the max resolution. --- nodes.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nodes.py b/nodes.py index 453f6e60..6c05e3a2 100644 --- a/nodes.py +++ b/nodes.py @@ -41,7 +41,7 @@ def before_node_execution(): def interrupt_processing(value=True): comfy.model_management.interrupt_current_processing(value) -MAX_RESOLUTION=8192 +MAX_RESOLUTION=16384 class CLIPTextEncode: @classmethod From c9673926fb2df5e82ccbfe2a746c428c148450f0 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 26 Mar 2024 04:07:30 -0400 Subject: [PATCH 117/378] Fix test. --- tests-ui/tests/groupNode.test.js | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests-ui/tests/groupNode.test.js b/tests-ui/tests/groupNode.test.js index e6ebedd9..53a5828d 100644 --- a/tests-ui/tests/groupNode.test.js +++ b/tests-ui/tests/groupNode.test.js @@ -947,7 +947,7 @@ describe("group node", () => { expect(p1.widgets.value.widget.options?.step).toBe(80); // width/height step * 10 expect(p2.widgets.value.widget.options?.min).toBe(16); // width/height min - expect(p2.widgets.value.widget.options?.max).toBe(8192); // width/height max + expect(p2.widgets.value.widget.options?.max).toBe(16384); // width/height max expect(p2.widgets.value.widget.options?.step).toBe(80); // width/height step * 10 expect(p1.widgets.value.value).toBe(128); From 2f93b9164636329b9e02fe8780cdd96c1300b90d Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 26 Mar 2024 19:33:40 -0400 Subject: [PATCH 118/378] Add Tesla GPUs to cuda malloc blacklist. --- cuda_malloc.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cuda_malloc.py b/cuda_malloc.py index 70e7ecf9..eb2857c5 100644 --- a/cuda_malloc.py +++ b/cuda_malloc.py @@ -47,7 +47,7 @@ blacklist = {"GeForce GTX TITAN X", "GeForce GTX 980", "GeForce GTX 970", "GeFor "Quadro K1200", "Quadro K2200", "Quadro M500", "Quadro M520", "Quadro M600", "Quadro M620", "Quadro M1000", "Quadro M1200", "Quadro M2000", "Quadro M2200", "Quadro M3000", "Quadro M4000", "Quadro M5000", "Quadro M5500", "Quadro M6000", "GeForce MX110", "GeForce MX130", "GeForce 830M", "GeForce 840M", "GeForce GTX 850M", "GeForce GTX 860M", - "GeForce GTX 1650", "GeForce GTX 1630" + "GeForce GTX 1650", "GeForce GTX 1630", "Tesla M4", "Tesla M6", "Tesla M10", "Tesla M40", "Tesla M60" } def cuda_malloc_supported(): From 8ae1e4d125100029d58f91e1938a1803abf6866c Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 27 Mar 2024 01:28:31 -0400 Subject: [PATCH 119/378] Make step on sharpen node smaller. --- comfy_extras/nodes_post_processing.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py index cb5c7d22..0110b472 100644 --- a/comfy_extras/nodes_post_processing.py +++ b/comfy_extras/nodes_post_processing.py @@ -204,13 +204,13 @@ class Sharpen: "default": 1.0, "min": 0.1, "max": 10.0, - "step": 0.1 + "step": 0.01 }), "alpha": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 5.0, - "step": 0.1 + "step": 0.01 }), }, } From 327ca1313d756c4b443790a53ab0afa1945d3f3e Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 27 Mar 2024 23:51:17 -0400 Subject: [PATCH 120/378] Support SDXS 0.9 --- comfy/model_detection.py | 8 +++++++- comfy/supported_models.py | 4 ++-- 2 files changed, 9 insertions(+), 3 deletions(-) diff --git a/comfy/model_detection.py b/comfy/model_detection.py index b7c3be30..bddbe2a4 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -345,7 +345,13 @@ def unet_config_from_diffusers_unet(state_dict, dtype=None): 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 6, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'use_temporal_attention': False, 'use_temporal_resblock': False} - supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega, KOALA_700M, KOALA_1B] + SD09_XS = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1], + 'transformer_depth': [1, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': True, + 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1], + 'use_temporal_attention': False, 'use_temporal_resblock': False, 'disable_self_attentions': [True, False, False]} + + supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega, KOALA_700M, KOALA_1B, SD09_XS] for unet_config in supported_models: matches = True diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 2ce9736b..5b2eb73f 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -70,8 +70,8 @@ class SD20(supported_models_base.BASE): def model_type(self, state_dict, prefix=""): if self.unet_config["in_channels"] == 4: #SD2.0 inpainting models are not v prediction k = "{}output_blocks.11.1.transformer_blocks.0.norm1.bias".format(prefix) - out = state_dict[k] - if torch.std(out, unbiased=False) > 0.09: # not sure how well this will actually work. I guess we will find out. + out = state_dict.get(k, None) + if out is not None and torch.std(out, unbiased=False) > 0.09: # not sure how well this will actually work. I guess we will find out. return model_base.ModelType.V_PREDICTION return model_base.ModelType.EPS From 5d8898c05668b5504f8ad5bc79779381d0af35b5 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 28 Mar 2024 18:01:04 -0400 Subject: [PATCH 121/378] Fix some performance issues with weight loading and unloading. Lower peak memory usage when changing model. Fix case where model weights would be unloaded and reloaded. --- comfy/model_management.py | 16 +++++++++++----- execution.py | 1 + 2 files changed, 12 insertions(+), 5 deletions(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index 715ca2ee..26216432 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -274,6 +274,7 @@ class LoadedModel: self.model = model self.device = model.load_device self.weights_loaded = False + self.real_model = None def model_memory(self): return self.model.model_size() @@ -312,6 +313,7 @@ class LoadedModel: self.model.unpatch_model(self.model.offload_device, unpatch_weights=unpatch_weights) self.model.model_patches_to(self.model.offload_device) self.weights_loaded = self.weights_loaded and not unpatch_weights + self.real_model = None def __eq__(self, other): return self.model is other.model @@ -326,7 +328,7 @@ def unload_model_clones(model, unload_weights_only=True, force_unload=True): to_unload = [i] + to_unload if len(to_unload) == 0: - return None + return True same_weights = 0 for i in to_unload: @@ -408,8 +410,8 @@ def load_models_gpu(models, memory_required=0): total_memory_required = {} for loaded_model in models_to_load: - unload_model_clones(loaded_model.model, unload_weights_only=True, force_unload=False) #unload clones where the weights are different - total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device) + if unload_model_clones(loaded_model.model, unload_weights_only=True, force_unload=False) == True:#unload clones where the weights are different + total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device) for device in total_memory_required: if device != torch.device("cpu"): @@ -448,11 +450,15 @@ def load_models_gpu(models, memory_required=0): def load_model_gpu(model): return load_models_gpu([model]) -def cleanup_models(): +def cleanup_models(keep_clone_weights_loaded=False): to_delete = [] for i in range(len(current_loaded_models)): if sys.getrefcount(current_loaded_models[i].model) <= 2: - to_delete = [i] + to_delete + if not keep_clone_weights_loaded: + to_delete = [i] + to_delete + #TODO: find a less fragile way to do this. + elif sys.getrefcount(current_loaded_models[i].real_model) <= 3: #references from .real_model + the .model + to_delete = [i] + to_delete for i in to_delete: x = current_loaded_models.pop(i) diff --git a/execution.py b/execution.py index 1b8f606a..35bdb77a 100644 --- a/execution.py +++ b/execution.py @@ -368,6 +368,7 @@ class PromptExecutor: d = self.outputs_ui.pop(x) del d + comfy.model_management.cleanup_models(keep_clone_weights_loaded=True) self.add_message("execution_cached", { "nodes": list(current_outputs) , "prompt_id": prompt_id}, broadcast=False) From 9bf6061dfcd1370ceae3ceac07765ff84686bd20 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 29 Mar 2024 03:07:13 -0400 Subject: [PATCH 122/378] Switch prints to logging in folder_paths and add some extra debug. --- folder_paths.py | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/folder_paths.py b/folder_paths.py index f1bf40f8..a84524d9 100644 --- a/folder_paths.py +++ b/folder_paths.py @@ -1,5 +1,6 @@ import os import time +import logging supported_pt_extensions = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors']) @@ -44,7 +45,7 @@ if not os.path.exists(input_directory): try: os.makedirs(input_directory) except: - print("Failed to create input directory") + logging.error("Failed to create input directory") def set_output_directory(output_dir): global output_directory @@ -146,21 +147,23 @@ def recursive_search(directory, excluded_dir_names=None): try: dirs[directory] = os.path.getmtime(directory) except FileNotFoundError: - print(f"Warning: Unable to access {directory}. Skipping this path.") - + logging.warning(f"Warning: Unable to access {directory}. Skipping this path.") + + logging.debug("recursive file list on directory {}".format(directory)) for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True): subdirs[:] = [d for d in subdirs if d not in excluded_dir_names] for file_name in filenames: relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory) result.append(relative_path) - + for d in subdirs: path = os.path.join(dirpath, d) try: dirs[path] = os.path.getmtime(path) except FileNotFoundError: - print(f"Warning: Unable to access {path}. Skipping this path.") + logging.warning(f"Warning: Unable to access {path}. Skipping this path.") continue + logging.debug("found {} files".format(len(result))) return result, dirs def filter_files_extensions(files, extensions): @@ -248,8 +251,8 @@ def get_save_image_path(filename_prefix, output_dir, image_width=0, image_height err = "**** ERROR: Saving image outside the output folder is not allowed." + \ "\n full_output_folder: " + os.path.abspath(full_output_folder) + \ "\n output_dir: " + output_dir + \ - "\n commonpath: " + os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) - print(err) + "\n commonpath: " + os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) + logging.error(err) raise Exception(err) try: From 94a5a67c32ded77efbd1b47e8d0257ddab9ac3bc Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 29 Mar 2024 14:43:24 -0400 Subject: [PATCH 123/378] Cleanup to support different types of inpaint models. --- comfy/model_base.py | 43 ++++++++++++++++++++++--------------------- 1 file changed, 22 insertions(+), 21 deletions(-) diff --git a/comfy/model_base.py b/comfy/model_base.py index bc019de5..6f530d2f 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -66,7 +66,8 @@ class BaseModel(torch.nn.Module): self.adm_channels = unet_config.get("adm_in_channels", None) if self.adm_channels is None: self.adm_channels = 0 - self.inpaint_model = False + + self.concat_keys = () logging.info("model_type {}".format(model_type.name)) logging.debug("adm {}".format(self.adm_channels)) @@ -107,8 +108,7 @@ class BaseModel(torch.nn.Module): def extra_conds(self, **kwargs): out = {} - if self.inpaint_model: - concat_keys = ("mask", "masked_image") + if len(self.concat_keys) > 0: cond_concat = [] denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) concat_latent_image = kwargs.get("concat_latent_image", None) @@ -125,24 +125,16 @@ class BaseModel(torch.nn.Module): concat_latent_image = utils.resize_to_batch_size(concat_latent_image, noise.shape[0]) - if len(denoise_mask.shape) == len(noise.shape): - denoise_mask = denoise_mask[:,:1] + if denoise_mask is not None: + if len(denoise_mask.shape) == len(noise.shape): + denoise_mask = denoise_mask[:,:1] - denoise_mask = denoise_mask.reshape((-1, 1, denoise_mask.shape[-2], denoise_mask.shape[-1])) - if denoise_mask.shape[-2:] != noise.shape[-2:]: - denoise_mask = utils.common_upscale(denoise_mask, noise.shape[-1], noise.shape[-2], "bilinear", "center") - denoise_mask = utils.resize_to_batch_size(denoise_mask.round(), noise.shape[0]) + denoise_mask = denoise_mask.reshape((-1, 1, denoise_mask.shape[-2], denoise_mask.shape[-1])) + if denoise_mask.shape[-2:] != noise.shape[-2:]: + denoise_mask = utils.common_upscale(denoise_mask, noise.shape[-1], noise.shape[-2], "bilinear", "center") + denoise_mask = utils.resize_to_batch_size(denoise_mask.round(), noise.shape[0]) - def blank_inpaint_image_like(latent_image): - blank_image = torch.ones_like(latent_image) - # these are the values for "zero" in pixel space translated to latent space - blank_image[:,0] *= 0.8223 - blank_image[:,1] *= -0.6876 - blank_image[:,2] *= 0.6364 - blank_image[:,3] *= 0.1380 - return blank_image - - for ck in concat_keys: + for ck in self.concat_keys: if denoise_mask is not None: if ck == "mask": cond_concat.append(denoise_mask.to(device)) @@ -152,7 +144,7 @@ class BaseModel(torch.nn.Module): if ck == "mask": cond_concat.append(torch.ones_like(noise)[:,:1]) elif ck == "masked_image": - cond_concat.append(blank_inpaint_image_like(noise)) + cond_concat.append(self.blank_inpaint_image_like(noise)) data = torch.cat(cond_concat, dim=1) out['c_concat'] = comfy.conds.CONDNoiseShape(data) @@ -221,7 +213,16 @@ class BaseModel(torch.nn.Module): return unet_state_dict def set_inpaint(self): - self.inpaint_model = True + self.concat_keys = ("mask", "masked_image") + def blank_inpaint_image_like(latent_image): + blank_image = torch.ones_like(latent_image) + # these are the values for "zero" in pixel space translated to latent space + blank_image[:,0] *= 0.8223 + blank_image[:,1] *= -0.6876 + blank_image[:,2] *= 0.6364 + blank_image[:,3] *= 0.1380 + return blank_image + self.blank_inpaint_image_like = blank_inpaint_image_like def memory_required(self, input_shape): if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention(): From 96b4c757cf55698d50c782cf0f9462bc40ec4c19 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 30 Mar 2024 11:52:11 -0400 Subject: [PATCH 124/378] Add log to debug custom nodes that hang when imported. --- nodes.py | 1 + 1 file changed, 1 insertion(+) diff --git a/nodes.py b/nodes.py index 6c05e3a2..5cf020df 100644 --- a/nodes.py +++ b/nodes.py @@ -1876,6 +1876,7 @@ def load_custom_node(module_path, ignore=set()): sp = os.path.splitext(module_path) module_name = sp[0] try: + logging.debug("Trying to load custom node {}".format(module_path)) if os.path.isfile(module_path): module_spec = importlib.util.spec_from_file_location(module_name, module_path) module_dir = os.path.split(module_path)[0] From 575acb69e46a4c24ddba4e0bdb895d6e85dc9354 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 31 Mar 2024 01:25:16 -0400 Subject: [PATCH 125/378] IP2P model loading support. This is the code to load the model and inference it with only a text prompt. This commit does not contain the nodes to properly use it with an image input. This supports both the original SD1 instructpix2pix model and the diffusers SDXL one. --- comfy/model_base.py | 34 ++++++++++++++++++++++++++++++++++ comfy/model_detection.py | 14 ++++++++++---- comfy/supported_models.py | 34 +++++++++++++++++++++++++++++++++- comfy/supported_models_base.py | 8 +++++++- 4 files changed, 84 insertions(+), 6 deletions(-) diff --git a/comfy/model_base.py b/comfy/model_base.py index 6f530d2f..898adb66 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -473,6 +473,40 @@ class SD_X4Upscaler(BaseModel): out['y'] = comfy.conds.CONDRegular(noise_level) return out +class IP2P: + def extra_conds(self, **kwargs): + out = {} + + image = kwargs.get("concat_latent_image", None) + noise = kwargs.get("noise", None) + device = kwargs["device"] + + if image is None: + image = torch.zeros_like(noise) + + if image.shape[1:] != noise.shape[1:]: + image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") + + image = utils.resize_to_batch_size(image, noise.shape[0]) + + out['c_concat'] = comfy.conds.CONDNoiseShape(self.process_ip2p_image_in(image)) + adm = self.encode_adm(**kwargs) + if adm is not None: + out['y'] = comfy.conds.CONDRegular(adm) + return out + +class SD15_instructpix2pix(IP2P, BaseModel): + def __init__(self, model_config, model_type=ModelType.EPS, device=None): + super().__init__(model_config, model_type, device=device) + self.process_ip2p_image_in = lambda image: image + +class SDXL_instructpix2pix(IP2P, SDXL): + def __init__(self, model_config, model_type=ModelType.EPS, device=None): + super().__init__(model_config, model_type, device=device) + # self.process_ip2p_image_in = lambda image: comfy.latent_formats.SDXL().process_in(image) + self.process_ip2p_image_in = lambda image: image + + class StableCascade_C(BaseModel): def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None): super().__init__(model_config, model_type, device=device, unet_model=StageC) diff --git a/comfy/model_detection.py b/comfy/model_detection.py index bddbe2a4..795af828 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -182,9 +182,9 @@ def detect_unet_config(state_dict, key_prefix): return unet_config -def model_config_from_unet_config(unet_config): +def model_config_from_unet_config(unet_config, state_dict=None): for model_config in comfy.supported_models.models: - if model_config.matches(unet_config): + if model_config.matches(unet_config, state_dict): return model_config(unet_config) logging.error("no match {}".format(unet_config)) @@ -192,7 +192,7 @@ def model_config_from_unet_config(unet_config): def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=False): unet_config = detect_unet_config(state_dict, unet_key_prefix) - model_config = model_config_from_unet_config(unet_config) + model_config = model_config_from_unet_config(unet_config, state_dict) if model_config is None and use_base_if_no_match: return comfy.supported_models_base.BASE(unet_config) else: @@ -321,6 +321,12 @@ def unet_config_from_diffusers_unet(state_dict, dtype=None): 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], 'use_temporal_attention': False, 'use_temporal_resblock': False} + SDXL_diffusers_ip2p = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 8, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10, + 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + SSD_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 4, 4], 'transformer_depth_output': [0, 0, 0, 1, 1, 2, 10, 4, 4], @@ -351,7 +357,7 @@ def unet_config_from_diffusers_unet(state_dict, dtype=None): 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1], 'use_temporal_attention': False, 'use_temporal_resblock': False, 'disable_self_attentions': [True, False, False]} - supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega, KOALA_700M, KOALA_1B, SD09_XS] + supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega, KOALA_700M, KOALA_1B, SD09_XS, SDXL_diffusers_ip2p] for unet_config in supported_models: matches = True diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 5b2eb73f..9bfe3ea1 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -334,6 +334,11 @@ class Stable_Zero123(supported_models_base.BASE): "num_head_channels": -1, } + required_keys = { + "cc_projection.weight": None, + "cc_projection.bias": None, + } + clip_vision_prefix = "cond_stage_model.model.visual." latent_format = latent_formats.SD15 @@ -439,6 +444,33 @@ class Stable_Cascade_B(Stable_Cascade_C): out = model_base.StableCascade_B(self, device=device) return out +class SD15_instructpix2pix(SD15): + unet_config = { + "context_dim": 768, + "model_channels": 320, + "use_linear_in_transformer": False, + "adm_in_channels": None, + "use_temporal_attention": False, + "in_channels": 8, + } + + def get_model(self, state_dict, prefix="", device=None): + return model_base.SD15_instructpix2pix(self, device=device) + +class SDXL_instructpix2pix(SDXL): + unet_config = { + "model_channels": 320, + "use_linear_in_transformer": True, + "transformer_depth": [0, 0, 2, 2, 10, 10], + "context_dim": 2048, + "adm_in_channels": 2816, + "use_temporal_attention": False, + "in_channels": 8, + } + + def get_model(self, state_dict, prefix="", device=None): + return model_base.SDXL_instructpix2pix(self, device=device) + +models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p] -models = [Stable_Zero123, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p] models += [SVD_img2vid] diff --git a/comfy/supported_models_base.py b/comfy/supported_models_base.py index 4d7e2593..6196daab 100644 --- a/comfy/supported_models_base.py +++ b/comfy/supported_models_base.py @@ -16,6 +16,8 @@ class BASE: "num_head_channels": 64, } + required_keys = {} + clip_prefix = [] clip_vision_prefix = None noise_aug_config = None @@ -28,10 +30,14 @@ class BASE: manual_cast_dtype = None @classmethod - def matches(s, unet_config): + def matches(s, unet_config, state_dict=None): for k in s.unet_config: if k not in unet_config or s.unet_config[k] != unet_config[k]: return False + if state_dict is not None: + for k in s.required_keys: + if k not in state_dict: + return False return True def model_type(self, state_dict, prefix=""): From 130646453891bea5e86c93b689fe2daf93cae35c Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 31 Mar 2024 12:50:28 -0400 Subject: [PATCH 126/378] --force-fp16 is no longer necessary on Mac. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a94a212a..ba1e844b 100644 --- a/README.md +++ b/README.md @@ -142,7 +142,7 @@ You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS ve 1. Install pytorch nightly. For instructions, read the [Accelerated PyTorch training on Mac](https://developer.apple.com/metal/pytorch/) Apple Developer guide (make sure to install the latest pytorch nightly). 1. Follow the [ComfyUI manual installation](#manual-install-windows-linux) instructions for Windows and Linux. 1. Install the ComfyUI [dependencies](#dependencies). If you have another Stable Diffusion UI [you might be able to reuse the dependencies](#i-already-have-another-ui-for-stable-diffusion-installed-do-i-really-have-to-install-all-of-these-dependencies). -1. Launch ComfyUI by running `python main.py --force-fp16`. Note that --force-fp16 will only work if you installed the latest pytorch nightly. +1. Launch ComfyUI by running `python main.py` > **Note**: Remember to add your models, VAE, LoRAs etc. to the corresponding Comfy folders, as discussed in [ComfyUI manual installation](#manual-install-windows-linux). From e6482fbbfc83cd25add0532b2e4c51d305e8a232 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 1 Apr 2024 17:23:07 -0400 Subject: [PATCH 127/378] Refactor calc_cond_uncond_batch into calc_cond_batch. calc_cond_batch can take an arbitrary amount of cond inputs. Added a calc_cond_uncond_batch wrapper with a warning so custom nodes won't break. --- comfy/samplers.py | 67 +++++++++++++++++------------------ comfy_extras/nodes_perpneg.py | 2 +- comfy_extras/nodes_sag.py | 2 +- 3 files changed, 34 insertions(+), 37 deletions(-) diff --git a/comfy/samplers.py b/comfy/samplers.py index 3678dc81..204a98f9 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -127,30 +127,23 @@ def cond_cat(c_list): return out -def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): - out_cond = torch.zeros_like(x_in) - out_count = torch.ones_like(x_in) * 1e-37 - - out_uncond = torch.zeros_like(x_in) - out_uncond_count = torch.ones_like(x_in) * 1e-37 - - COND = 0 - UNCOND = 1 - +def calc_cond_batch(model, conds, x_in, timestep, model_options): + out_conds = [] + out_counts = [] to_run = [] - for x in cond: - p = get_area_and_mult(x, x_in, timestep) - if p is None: - continue - to_run += [(p, COND)] - if uncond is not None: - for x in uncond: - p = get_area_and_mult(x, x_in, timestep) - if p is None: - continue + for i in range(len(conds)): + out_conds.append(torch.zeros_like(x_in)) + out_counts.append(torch.ones_like(x_in) * 1e-37) - to_run += [(p, UNCOND)] + cond = conds[i] + if cond is not None: + for x in cond: + p = get_area_and_mult(x, x_in, timestep) + if p is None: + continue + + to_run += [(p, i)] while len(to_run) > 0: first = to_run[0] @@ -222,22 +215,20 @@ def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): output = model_options['model_function_wrapper'](model.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks) else: output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks) - del input_x for o in range(batch_chunks): - if cond_or_uncond[o] == COND: - out_cond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o] - out_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o] - else: - out_uncond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o] - out_uncond_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o] - del mult + cond_index = cond_or_uncond[o] + out_conds[cond_index][:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o] + out_counts[cond_index][:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o] - out_cond /= out_count - del out_count - out_uncond /= out_uncond_count - del out_uncond_count - return out_cond, out_uncond + for i in range(len(out_conds)): + out_conds[i] /= out_counts[i] + + return out_conds + +def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): #TODO: remove + logging.warning("WARNING: The comfy.samplers.calc_cond_uncond_batch function is deprecated please use the calc_cond_batch one instead.") + return tuple(calc_cond_batch(model, [cond, uncond], x_in, timestep, model_options)) #The main sampling function shared by all the samplers #Returns denoised @@ -247,7 +238,13 @@ def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_option else: uncond_ = uncond - cond_pred, uncond_pred = calc_cond_uncond_batch(model, cond, uncond_, x, timestep, model_options) + + conds = [cond, uncond_] + + out = calc_cond_batch(model, conds, x, timestep, model_options) + cond_pred = out[0] + uncond_pred = out[1] + if "sampler_cfg_function" in model_options: args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep, "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options} diff --git a/comfy_extras/nodes_perpneg.py b/comfy_extras/nodes_perpneg.py index dc73c552..9e8a218f 100644 --- a/comfy_extras/nodes_perpneg.py +++ b/comfy_extras/nodes_perpneg.py @@ -31,7 +31,7 @@ class PerpNeg: model_options = args["model_options"] nocond_processed = comfy.samplers.encode_model_conds(model.extra_conds, nocond, x, x.device, "negative") - (noise_pred_nocond, _) = comfy.samplers.calc_cond_uncond_batch(model, nocond_processed, None, x, sigma, model_options) + (noise_pred_nocond,) = comfy.samplers.calc_cond_batch(model, [nocond_processed], x, sigma, model_options) pos = noise_pred_pos - noise_pred_nocond neg = noise_pred_neg - noise_pred_nocond diff --git a/comfy_extras/nodes_sag.py b/comfy_extras/nodes_sag.py index bbd38080..69084e91 100644 --- a/comfy_extras/nodes_sag.py +++ b/comfy_extras/nodes_sag.py @@ -150,7 +150,7 @@ class SelfAttentionGuidance: degraded = create_blur_map(uncond_pred, uncond_attn, sag_sigma, sag_threshold) degraded_noised = degraded + x - uncond_pred # call into the UNet - (sag, _) = comfy.samplers.calc_cond_uncond_batch(model, uncond, None, degraded_noised, sigma, model_options) + (sag,) = comfy.samplers.calc_cond_batch(model, [uncond], degraded_noised, sigma, model_options) return cfg_result + (degraded - sag) * sag_scale m.set_model_sampler_post_cfg_function(post_cfg_function, disable_cfg1_optimization=True) From 6c6a39251fe313c56a88c90d820009073b623dfe Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 2 Apr 2024 11:46:34 -0400 Subject: [PATCH 128/378] Fix saving text encoder in fp8. --- comfy/diffusers_convert.py | 19 +++++++++++++++++-- 1 file changed, 17 insertions(+), 2 deletions(-) diff --git a/comfy/diffusers_convert.py b/comfy/diffusers_convert.py index 08018c54..ed2a45fe 100644 --- a/comfy/diffusers_convert.py +++ b/comfy/diffusers_convert.py @@ -206,6 +206,21 @@ textenc_pattern = re.compile("|".join(protected.keys())) # Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp code2idx = {"q": 0, "k": 1, "v": 2} +# This function exists because at the time of writing torch.cat can't do fp8 with cuda +def cat_tensors(tensors): + x = 0 + for t in tensors: + x += t.shape[0] + + shape = [x] + list(tensors[0].shape)[1:] + out = torch.empty(shape, device=tensors[0].device, dtype=tensors[0].dtype) + + x = 0 + for t in tensors: + out[x:x + t.shape[0]] = t + x += t.shape[0] + + return out def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""): new_state_dict = {} @@ -249,13 +264,13 @@ def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""): if None in tensors: raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) - new_state_dict[relabelled_key + ".in_proj_weight"] = torch.cat(tensors) + new_state_dict[relabelled_key + ".in_proj_weight"] = cat_tensors(tensors) for k_pre, tensors in capture_qkv_bias.items(): if None in tensors: raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) - new_state_dict[relabelled_key + ".in_proj_bias"] = torch.cat(tensors) + new_state_dict[relabelled_key + ".in_proj_bias"] = cat_tensors(tensors) return new_state_dict From 57753c964affd18d2b87d2a47fe6b375bca39004 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 3 Apr 2024 16:34:19 -0400 Subject: [PATCH 129/378] Refactor sampling code for more advanced sampler nodes. --- comfy/sample.py | 38 +++++++++++++++--------- comfy/samplers.py | 74 +++++++++++++++++++++++++++-------------------- 2 files changed, 68 insertions(+), 44 deletions(-) diff --git a/comfy/sample.py b/comfy/sample.py index 5c8a7d13..3c65d0a8 100644 --- a/comfy/sample.py +++ b/comfy/sample.py @@ -52,9 +52,16 @@ def convert_cond(cond): out.append(temp) return out -def get_additional_models(positive, negative, dtype): - """loads additional models in positive and negative conditioning""" - control_nets = set(get_models_from_cond(positive, "control") + get_models_from_cond(negative, "control")) +def get_additional_models(conds, dtype): + """loads additional models in conditioning""" + cnets = [] + gligen = [] + + for i in range(len(conds)): + cnets += get_models_from_cond(conds[i], "control") + gligen += get_models_from_cond(conds[i], "gligen") + + control_nets = set(cnets) inference_memory = 0 control_models = [] @@ -62,7 +69,6 @@ def get_additional_models(positive, negative, dtype): control_models += m.get_models() inference_memory += m.inference_memory_requirements(dtype) - gligen = get_models_from_cond(positive, "gligen") + get_models_from_cond(negative, "gligen") gligen = [x[1] for x in gligen] models = control_models + gligen return models, inference_memory @@ -73,24 +79,25 @@ def cleanup_additional_models(models): if hasattr(m, 'cleanup'): m.cleanup() -def prepare_sampling(model, noise_shape, positive, negative, noise_mask): +def prepare_sampling(model, noise_shape, conds, noise_mask): device = model.load_device - positive = convert_cond(positive) - negative = convert_cond(negative) + for i in range(len(conds)): + conds[i] = convert_cond(conds[i]) if noise_mask is not None: noise_mask = prepare_mask(noise_mask, noise_shape, device) real_model = None - models, inference_memory = get_additional_models(positive, negative, model.model_dtype()) + models, inference_memory = get_additional_models(conds, model.model_dtype()) comfy.model_management.load_models_gpu([model] + models, model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory) real_model = model.model - return real_model, positive, negative, noise_mask, models + return real_model, conds, noise_mask, models def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None): - real_model, positive_copy, negative_copy, noise_mask, models = prepare_sampling(model, noise.shape, positive, negative, noise_mask) + real_model, conds_copy, noise_mask, models = prepare_sampling(model, noise.shape, [positive, negative], noise_mask) + positive_copy, negative_copy = conds_copy noise = noise.to(model.load_device) latent_image = latent_image.to(model.load_device) @@ -105,14 +112,19 @@ def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative return samples def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=None, callback=None, disable_pbar=False, seed=None): - real_model, positive_copy, negative_copy, noise_mask, models = prepare_sampling(model, noise.shape, positive, negative, noise_mask) + real_model, conds, noise_mask, models = prepare_sampling(model, noise.shape, [positive, negative], noise_mask) noise = noise.to(model.load_device) latent_image = latent_image.to(model.load_device) sigmas = sigmas.to(model.load_device) - samples = comfy.samplers.sample(real_model, noise, positive_copy, negative_copy, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) + samples = comfy.samplers.sample(real_model, noise, conds[0], conds[1], cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) samples = samples.to(comfy.model_management.intermediate_device()) cleanup_additional_models(models) - cleanup_additional_models(set(get_models_from_cond(positive_copy, "control") + get_models_from_cond(negative_copy, "control"))) + + control_cleanup = [] + for i in range(len(conds)): + control_cleanup += get_models_from_cond(conds[i], "control") + + cleanup_additional_models(set(control_cleanup)) return samples diff --git a/comfy/samplers.py b/comfy/samplers.py index 204a98f9..f18de200 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -260,11 +260,12 @@ def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_option return cfg_result class CFGNoisePredictor(torch.nn.Module): - def __init__(self, model): + def __init__(self, model, cond_scale=1.0): super().__init__() self.inner_model = model - def apply_model(self, x, timestep, cond, uncond, cond_scale, model_options={}, seed=None): - out = sampling_function(self.inner_model, x, timestep, uncond, cond, cond_scale, model_options=model_options, seed=seed) + self.cond_scale = cond_scale + def apply_model(self, x, timestep, conds, model_options={}, seed=None): + out = sampling_function(self.inner_model, x, timestep, conds.get("negative", None), conds.get("positive", None), self.cond_scale, model_options=model_options, seed=seed) return out def forward(self, *args, **kwargs): return self.apply_model(*args, **kwargs) @@ -274,13 +275,13 @@ class KSamplerX0Inpaint(torch.nn.Module): super().__init__() self.inner_model = model self.sigmas = sigmas - def forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, model_options={}, seed=None): + def forward(self, x, sigma, conds, denoise_mask, model_options={}, seed=None): if denoise_mask is not None: if "denoise_mask_function" in model_options: denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask, extra_options={"model": self.inner_model, "sigmas": self.sigmas}) latent_mask = 1. - denoise_mask x = x * denoise_mask + self.inner_model.inner_model.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1)), self.noise, self.latent_image) * latent_mask - out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, model_options=model_options, seed=seed) + out = self.inner_model(x, sigma, conds=conds, model_options=model_options, seed=seed) if denoise_mask is not None: out = out * denoise_mask + self.latent_image * latent_mask return out @@ -568,45 +569,56 @@ def ksampler(sampler_name, extra_options={}, inpaint_options={}): return KSAMPLER(sampler_function, extra_options, inpaint_options) -def wrap_model(model): - model_denoise = CFGNoisePredictor(model) - return model_denoise -def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None): - positive = positive[:] - negative = negative[:] +def process_conds(model, noise, conds, device, latent_image=None, denoise_mask=None, seed=None): + for k in conds: + conds[k] = conds[k][:] + resolve_areas_and_cond_masks(conds[k], noise.shape[2], noise.shape[3], device) - resolve_areas_and_cond_masks(positive, noise.shape[2], noise.shape[3], device) - resolve_areas_and_cond_masks(negative, noise.shape[2], noise.shape[3], device) + for k in conds: + calculate_start_end_timesteps(model, conds[k]) - model_wrap = wrap_model(model) + if hasattr(model, 'extra_conds'): + for k in conds: + conds[k] = encode_model_conds(model.extra_conds, conds[k], noise, device, k, latent_image=latent_image, denoise_mask=denoise_mask, seed=seed) - calculate_start_end_timesteps(model, negative) - calculate_start_end_timesteps(model, positive) + #make sure each cond area has an opposite one with the same area + for k in conds: + for c in conds[k]: + for kk in conds: + if k != kk: + create_cond_with_same_area_if_none(conds[kk], c) + for k in conds: + pre_run_control(model, conds[k]) + + if "positive" in conds: + positive = conds["positive"] + for k in conds: + if k != "positive": + apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), conds[k], 'control', lambda cond_cnets, x: cond_cnets[x]) + apply_empty_x_to_equal_area(positive, conds[k], 'gligen', lambda cond_cnets, x: cond_cnets[x]) + + return conds + + +def sample_advanced(model, noise, conds, guider_class, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None): if latent_image is not None and torch.count_nonzero(latent_image) > 0: #Don't shift the empty latent image. latent_image = model.process_latent_in(latent_image) - if hasattr(model, 'extra_conds'): - positive = encode_model_conds(model.extra_conds, positive, noise, device, "positive", latent_image=latent_image, denoise_mask=denoise_mask, seed=seed) - negative = encode_model_conds(model.extra_conds, negative, noise, device, "negative", latent_image=latent_image, denoise_mask=denoise_mask, seed=seed) + conds = process_conds(model, noise, conds, device, latent_image, denoise_mask, seed) + model_wrap = guider_class(model) - #make sure each cond area has an opposite one with the same area - for c in positive: - create_cond_with_same_area_if_none(negative, c) - for c in negative: - create_cond_with_same_area_if_none(positive, c) - - pre_run_control(model, negative + positive) - - apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), negative, 'control', lambda cond_cnets, x: cond_cnets[x]) - apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x]) - - extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": model_options, "seed":seed} + extra_args = {"conds": conds, "model_options": model_options, "seed":seed} samples = sampler.sample(model_wrap, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar) return model.process_latent_out(samples.to(torch.float32)) + +def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None): + return sample_advanced(model, noise, {"positive": positive, "negative": negative}, lambda a: CFGNoisePredictor(a, cfg), device, sampler, sigmas, model_options, latent_image, denoise_mask, callback, disable_pbar, seed) + + SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"] SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"] From 0542088ef895b4825df80fd3babf91513441af65 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 4 Apr 2024 00:48:42 -0400 Subject: [PATCH 130/378] Refactor sampler code for more advanced sampler nodes part 2. --- comfy/sample.py | 102 +++--------------------------- comfy/sampler_helpers.py | 76 +++++++++++++++++++++++ comfy/samplers.py | 130 ++++++++++++++++++++++++--------------- 3 files changed, 165 insertions(+), 143 deletions(-) create mode 100644 comfy/sampler_helpers.py diff --git a/comfy/sample.py b/comfy/sample.py index 3c65d0a8..e51bd67d 100644 --- a/comfy/sample.py +++ b/comfy/sample.py @@ -1,10 +1,9 @@ import torch import comfy.model_management import comfy.samplers -import comfy.conds import comfy.utils -import math import numpy as np +import logging def prepare_noise(latent_image, seed, noise_inds=None): """ @@ -25,106 +24,21 @@ def prepare_noise(latent_image, seed, noise_inds=None): noises = torch.cat(noises, axis=0) return noises -def prepare_mask(noise_mask, shape, device): - """ensures noise mask is of proper dimensions""" - noise_mask = torch.nn.functional.interpolate(noise_mask.reshape((-1, 1, noise_mask.shape[-2], noise_mask.shape[-1])), size=(shape[2], shape[3]), mode="bilinear") - noise_mask = torch.cat([noise_mask] * shape[1], dim=1) - noise_mask = comfy.utils.repeat_to_batch_size(noise_mask, shape[0]) - noise_mask = noise_mask.to(device) - return noise_mask - -def get_models_from_cond(cond, model_type): - models = [] - for c in cond: - if model_type in c: - models += [c[model_type]] - return models - -def convert_cond(cond): - out = [] - for c in cond: - temp = c[1].copy() - model_conds = temp.get("model_conds", {}) - if c[0] is not None: - model_conds["c_crossattn"] = comfy.conds.CONDCrossAttn(c[0]) #TODO: remove - temp["cross_attn"] = c[0] - temp["model_conds"] = model_conds - out.append(temp) - return out - -def get_additional_models(conds, dtype): - """loads additional models in conditioning""" - cnets = [] - gligen = [] - - for i in range(len(conds)): - cnets += get_models_from_cond(conds[i], "control") - gligen += get_models_from_cond(conds[i], "gligen") - - control_nets = set(cnets) - - inference_memory = 0 - control_models = [] - for m in control_nets: - control_models += m.get_models() - inference_memory += m.inference_memory_requirements(dtype) - - gligen = [x[1] for x in gligen] - models = control_models + gligen - return models, inference_memory +def prepare_sampling(model, noise_shape, positive, negative, noise_mask): + logging.warning("Warning: comfy.sample.prepare_sampling isn't used anymore and can be removed") + return model, positive, negative, noise_mask, [] def cleanup_additional_models(models): - """cleanup additional models that were loaded""" - for m in models: - if hasattr(m, 'cleanup'): - m.cleanup() - -def prepare_sampling(model, noise_shape, conds, noise_mask): - device = model.load_device - for i in range(len(conds)): - conds[i] = convert_cond(conds[i]) - - if noise_mask is not None: - noise_mask = prepare_mask(noise_mask, noise_shape, device) - - real_model = None - models, inference_memory = get_additional_models(conds, model.model_dtype()) - comfy.model_management.load_models_gpu([model] + models, model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory) - real_model = model.model - - return real_model, conds, noise_mask, models - + logging.warning("Warning: comfy.sample.cleanup_additional_models isn't used anymore and can be removed") def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None): - real_model, conds_copy, noise_mask, models = prepare_sampling(model, noise.shape, [positive, negative], noise_mask) - positive_copy, negative_copy = conds_copy + sampler = comfy.samplers.KSampler(model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options) - noise = noise.to(model.load_device) - latent_image = latent_image.to(model.load_device) - - sampler = comfy.samplers.KSampler(real_model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options) - - samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed) + samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed) samples = samples.to(comfy.model_management.intermediate_device()) - - cleanup_additional_models(models) - cleanup_additional_models(set(get_models_from_cond(positive_copy, "control") + get_models_from_cond(negative_copy, "control"))) return samples def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=None, callback=None, disable_pbar=False, seed=None): - real_model, conds, noise_mask, models = prepare_sampling(model, noise.shape, [positive, negative], noise_mask) - noise = noise.to(model.load_device) - latent_image = latent_image.to(model.load_device) - sigmas = sigmas.to(model.load_device) - - samples = comfy.samplers.sample(real_model, noise, conds[0], conds[1], cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) + samples = comfy.samplers.sample(model, noise, positive, negative, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) samples = samples.to(comfy.model_management.intermediate_device()) - cleanup_additional_models(models) - - control_cleanup = [] - for i in range(len(conds)): - control_cleanup += get_models_from_cond(conds[i], "control") - - cleanup_additional_models(set(control_cleanup)) return samples - diff --git a/comfy/sampler_helpers.py b/comfy/sampler_helpers.py new file mode 100644 index 00000000..a18abd9e --- /dev/null +++ b/comfy/sampler_helpers.py @@ -0,0 +1,76 @@ +import torch +import comfy.model_management +import comfy.conds + +def prepare_mask(noise_mask, shape, device): + """ensures noise mask is of proper dimensions""" + noise_mask = torch.nn.functional.interpolate(noise_mask.reshape((-1, 1, noise_mask.shape[-2], noise_mask.shape[-1])), size=(shape[2], shape[3]), mode="bilinear") + noise_mask = torch.cat([noise_mask] * shape[1], dim=1) + noise_mask = comfy.utils.repeat_to_batch_size(noise_mask, shape[0]) + noise_mask = noise_mask.to(device) + return noise_mask + +def get_models_from_cond(cond, model_type): + models = [] + for c in cond: + if model_type in c: + models += [c[model_type]] + return models + +def convert_cond(cond): + out = [] + for c in cond: + temp = c[1].copy() + model_conds = temp.get("model_conds", {}) + if c[0] is not None: + model_conds["c_crossattn"] = comfy.conds.CONDCrossAttn(c[0]) #TODO: remove + temp["cross_attn"] = c[0] + temp["model_conds"] = model_conds + out.append(temp) + return out + +def get_additional_models(conds, dtype): + """loads additional models in conditioning""" + cnets = [] + gligen = [] + + for k in conds: + cnets += get_models_from_cond(conds[k], "control") + gligen += get_models_from_cond(conds[k], "gligen") + + control_nets = set(cnets) + + inference_memory = 0 + control_models = [] + for m in control_nets: + control_models += m.get_models() + inference_memory += m.inference_memory_requirements(dtype) + + gligen = [x[1] for x in gligen] + models = control_models + gligen + return models, inference_memory + +def cleanup_additional_models(models): + """cleanup additional models that were loaded""" + for m in models: + if hasattr(m, 'cleanup'): + m.cleanup() + + +def prepare_sampling(model, noise_shape, conds): + device = model.load_device + real_model = None + models, inference_memory = get_additional_models(conds, model.model_dtype()) + comfy.model_management.load_models_gpu([model] + models, model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory) + real_model = model.model + + return real_model, conds, models + +def cleanup_models(conds, models): + cleanup_additional_models(models) + + control_cleanup = [] + for k in conds: + control_cleanup += get_models_from_cond(conds[k], "control") + + cleanup_additional_models(set(control_cleanup)) diff --git a/comfy/samplers.py b/comfy/samplers.py index f18de200..57f5632c 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -5,6 +5,7 @@ import collections from comfy import model_management import math import logging +import comfy.sampler_helpers def get_area_and_mult(conds, x_in, timestep_in): area = (x_in.shape[2], x_in.shape[3], 0, 0) @@ -230,58 +231,45 @@ def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): logging.warning("WARNING: The comfy.samplers.calc_cond_uncond_batch function is deprecated please use the calc_cond_batch one instead.") return tuple(calc_cond_batch(model, [cond, uncond], x_in, timestep, model_options)) +def cfg_function(model, cond_pred, uncond_pred, cond_scale, x, timestep, model_options={}): + if "sampler_cfg_function" in model_options: + args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep, + "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options} + cfg_result = x - model_options["sampler_cfg_function"](args) + else: + cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale + + for fn in model_options.get("sampler_post_cfg_function", []): + args = {"denoised": cfg_result, "cond": cond, "uncond": uncond, "model": model, "uncond_denoised": uncond_pred, "cond_denoised": cond_pred, + "sigma": timestep, "model_options": model_options, "input": x} + cfg_result = fn(args) + + return cfg_result + #The main sampling function shared by all the samplers #Returns denoised def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None): - if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False: - uncond_ = None - else: - uncond_ = uncond + if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False: + uncond_ = None + else: + uncond_ = uncond + + conds = [cond, uncond_] + out = calc_cond_batch(model, conds, x, timestep, model_options) + return cfg_function(model, out[0], out[1], cond_scale, x, timestep, model_options=model_options) - conds = [cond, uncond_] - - out = calc_cond_batch(model, conds, x, timestep, model_options) - cond_pred = out[0] - uncond_pred = out[1] - - if "sampler_cfg_function" in model_options: - args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep, - "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options} - cfg_result = x - model_options["sampler_cfg_function"](args) - else: - cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale - - for fn in model_options.get("sampler_post_cfg_function", []): - args = {"denoised": cfg_result, "cond": cond, "uncond": uncond, "model": model, "uncond_denoised": uncond_pred, "cond_denoised": cond_pred, - "sigma": timestep, "model_options": model_options, "input": x} - cfg_result = fn(args) - - return cfg_result - -class CFGNoisePredictor(torch.nn.Module): - def __init__(self, model, cond_scale=1.0): - super().__init__() - self.inner_model = model - self.cond_scale = cond_scale - def apply_model(self, x, timestep, conds, model_options={}, seed=None): - out = sampling_function(self.inner_model, x, timestep, conds.get("negative", None), conds.get("positive", None), self.cond_scale, model_options=model_options, seed=seed) - return out - def forward(self, *args, **kwargs): - return self.apply_model(*args, **kwargs) - -class KSamplerX0Inpaint(torch.nn.Module): +class KSamplerX0Inpaint: def __init__(self, model, sigmas): - super().__init__() self.inner_model = model self.sigmas = sigmas - def forward(self, x, sigma, conds, denoise_mask, model_options={}, seed=None): + def __call__(self, x, sigma, denoise_mask, model_options={}, seed=None): if denoise_mask is not None: if "denoise_mask_function" in model_options: denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask, extra_options={"model": self.inner_model, "sigmas": self.sigmas}) latent_mask = 1. - denoise_mask x = x * denoise_mask + self.inner_model.inner_model.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1)), self.noise, self.latent_image) * latent_mask - out = self.inner_model(x, sigma, conds=conds, model_options=model_options, seed=seed) + out = self.inner_model(x, sigma, model_options=model_options, seed=seed) if denoise_mask is not None: out = out * denoise_mask + self.latent_image * latent_mask return out @@ -601,22 +589,66 @@ def process_conds(model, noise, conds, device, latent_image=None, denoise_mask=N return conds +class CFGGuider: + def __init__(self, model_patcher): + self.model_patcher = model_patcher + self.model_options = model_patcher.model_options + self.original_conds = {} + self.cfg = 1.0 -def sample_advanced(model, noise, conds, guider_class, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None): - if latent_image is not None and torch.count_nonzero(latent_image) > 0: #Don't shift the empty latent image. - latent_image = model.process_latent_in(latent_image) + def set_conds(self, conds): + for k in conds: + self.original_conds[k] = comfy.sampler_helpers.convert_cond(conds[k]) - conds = process_conds(model, noise, conds, device, latent_image, denoise_mask, seed) - model_wrap = guider_class(model) + def set_cfg(self, cfg): + self.cfg = cfg - extra_args = {"conds": conds, "model_options": model_options, "seed":seed} + def __call__(self, *args, **kwargs): + return self.predict_noise(*args, **kwargs) - samples = sampler.sample(model_wrap, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar) - return model.process_latent_out(samples.to(torch.float32)) + def predict_noise(self, x, timestep, model_options={}, seed=None): + return sampling_function(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, model_options=model_options, seed=seed) + + def inner_sample(self, noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed): + if latent_image is not None and torch.count_nonzero(latent_image) > 0: #Don't shift the empty latent image. + latent_image = self.inner_model.process_latent_in(latent_image) + + self.conds = process_conds(self.inner_model, noise, self.conds, device, latent_image, denoise_mask, seed) + + extra_args = {"model_options": self.model_options, "seed":seed} + + samples = sampler.sample(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar) + return self.inner_model.process_latent_out(samples.to(torch.float32)) + + def sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None): + self.conds = {} + for k in self.original_conds: + self.conds[k] = list(map(lambda a: a.copy(), self.original_conds[k])) + + self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds) + device = self.model_patcher.load_device + + if denoise_mask is not None: + denoise_mask = comfy.sampler_helpers.prepare_mask(denoise_mask, noise.shape, device) + + noise = noise.to(device) + latent_image = latent_image.to(device) + sigmas = sigmas.to(device) + + output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed) + + comfy.sampler_helpers.cleanup_models(self.conds, self.loaded_models) + del self.inner_model + del self.conds + del self.loaded_models + return output def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None): - return sample_advanced(model, noise, {"positive": positive, "negative": negative}, lambda a: CFGNoisePredictor(a, cfg), device, sampler, sigmas, model_options, latent_image, denoise_mask, callback, disable_pbar, seed) + cfg_guider = CFGGuider(model) + cfg_guider.set_conds({"positive": positive, "negative": negative}) + cfg_guider.set_cfg(cfg) + return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed) SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"] @@ -676,7 +708,7 @@ class KSampler: steps += 1 discard_penultimate_sigma = True - sigmas = calculate_sigmas_scheduler(self.model, self.scheduler, steps) + sigmas = calculate_sigmas_scheduler(self.model.model, self.scheduler, steps) if discard_penultimate_sigma: sigmas = torch.cat([sigmas[:-2], sigmas[-1:]]) From f117566299edd621ea8b00b70cf81d5d96c20917 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 4 Apr 2024 01:32:25 -0400 Subject: [PATCH 131/378] SamplerCustomAdvanced node. This node enables the creation of nodes to change the guider/denoiser and the noise algorithm. --- comfy_extras/nodes_custom_sampler.py | 122 ++++++++++++++++++++++++++- 1 file changed, 119 insertions(+), 3 deletions(-) diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index 72ff7957..32b2a56d 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -310,6 +310,24 @@ class SamplerDPMAdaptative: "s_noise":s_noise }) return (sampler, ) +class Noise_EmptyNoise: + def __init__(self): + self.seed = 0 + + def generate_noise(self, input_latent): + latent_image = input_latent["samples"] + return torch.zeros(shape, dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") + + +class Noise_RandomNoise: + def __init__(self, seed): + self.seed = seed + + def generate_noise(self, input_latent): + latent_image = input_latent["samples"] + batch_inds = input_latent["batch_index"] if "batch_index" in input_latent else None + return comfy.sample.prepare_noise(latent_image, self.seed, batch_inds) + class SamplerCustom: @classmethod def INPUT_TYPES(s): @@ -337,10 +355,9 @@ class SamplerCustom: latent = latent_image latent_image = latent["samples"] if not add_noise: - noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") + noise = Noise_EmptyNoise().generate_noise(latent) else: - batch_inds = latent["batch_index"] if "batch_index" in latent else None - noise = comfy.sample.prepare_noise(latent_image, noise_seed, batch_inds) + noise = Noise_RandomNoise(noise_seed).generate_noise(latent) noise_mask = None if "noise_mask" in latent: @@ -361,6 +378,100 @@ class SamplerCustom: out_denoised = out return (out, out_denoised) + +class CFGGuider: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), + } + } + + RETURN_TYPES = ("GUIDER",) + + FUNCTION = "get_guider" + CATEGORY = "sampling/custom_sampling/guiders" + + def get_guider(self, model, positive, negative, cfg): + guider = comfy.samplers.CFGGuider(model) + guider.set_conds({"positive": positive, "negative": negative}) + guider.set_cfg(cfg) + return (guider,) + + +class DisableNoise: + @classmethod + def INPUT_TYPES(s): + return {"required":{ + } + } + + RETURN_TYPES = ("NOISE",) + FUNCTION = "get_noise" + CATEGORY = "sampling/custom_sampling/noise" + + def get_noise(self, noise_seed): + return (Noise_EmptyNoise(),) + + +class RandomNoise(DisableNoise): + @classmethod + def INPUT_TYPES(s): + return {"required":{ + "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + } + } + + def get_noise(self, noise_seed): + return (Noise_RandomNoise(noise_seed),) + + +class SamplerCustomAdvanced: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"noise": ("NOISE", ), + "guider": ("GUIDER", ), + "sampler": ("SAMPLER", ), + "sigmas": ("SIGMAS", ), + "latent_image": ("LATENT", ), + } + } + + RETURN_TYPES = ("LATENT","LATENT") + RETURN_NAMES = ("output", "denoised_output") + + FUNCTION = "sample" + + CATEGORY = "sampling/custom_sampling" + + def sample(self, noise, guider, sampler, sigmas, latent_image): + latent = latent_image + latent_image = latent["samples"] + + noise_mask = None + if "noise_mask" in latent: + noise_mask = latent["noise_mask"] + + x0_output = {} + callback = latent_preview.prepare_callback(guider.model_patcher, sigmas.shape[-1] - 1, x0_output) + + disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED + samples = guider.sample(noise.generate_noise(latent), latent_image, sampler, sigmas, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise.seed) + samples = samples.to(comfy.model_management.intermediate_device()) + + out = latent.copy() + out["samples"] = samples + if "x0" in x0_output: + out_denoised = latent.copy() + out_denoised["samples"] = guider.model_patcher.model.process_latent_out(x0_output["x0"].cpu()) + else: + out_denoised = out + return (out, out_denoised) + NODE_CLASS_MAPPINGS = { "SamplerCustom": SamplerCustom, "BasicScheduler": BasicScheduler, @@ -378,4 +489,9 @@ NODE_CLASS_MAPPINGS = { "SamplerDPMAdaptative": SamplerDPMAdaptative, "SplitSigmas": SplitSigmas, "FlipSigmas": FlipSigmas, + + "CFGGuider": CFGGuider, + "RandomNoise": RandomNoise, + "DisableNoise": DisableNoise, + "SamplerCustomAdvanced": SamplerCustomAdvanced, } From fcfd2bdf8a4fcc57ed842685bb3bfd39b6505e42 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 4 Apr 2024 11:16:49 -0400 Subject: [PATCH 132/378] Small cleanup. --- comfy/samplers.py | 11 +++++++---- comfy_extras/nodes_custom_sampler.py | 2 +- 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/comfy/samplers.py b/comfy/samplers.py index 57f5632c..a89e3a6c 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -596,13 +596,16 @@ class CFGGuider: self.original_conds = {} self.cfg = 1.0 - def set_conds(self, conds): - for k in conds: - self.original_conds[k] = comfy.sampler_helpers.convert_cond(conds[k]) + def set_conds(self, positive, negative): + self.inner_set_conds({"positive": positive, "negative": negative}) def set_cfg(self, cfg): self.cfg = cfg + def inner_set_conds(self, conds): + for k in conds: + self.original_conds[k] = comfy.sampler_helpers.convert_cond(conds[k]) + def __call__(self, *args, **kwargs): return self.predict_noise(*args, **kwargs) @@ -646,7 +649,7 @@ class CFGGuider: def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None): cfg_guider = CFGGuider(model) - cfg_guider.set_conds({"positive": positive, "negative": negative}) + cfg_guider.set_conds(positive, negative) cfg_guider.set_cfg(cfg) return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed) diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index 32b2a56d..e9dc3bd9 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -397,7 +397,7 @@ class CFGGuider: def get_guider(self, model, positive, negative, cfg): guider = comfy.samplers.CFGGuider(model) - guider.set_conds({"positive": positive, "negative": negative}) + guider.set_conds(positive, negative) guider.set_cfg(cfg) return (guider,) From c6bd456c45fd24818223bd4f61a6840e281ac82f Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 4 Apr 2024 11:38:25 -0400 Subject: [PATCH 133/378] Make zero denoise a NOP. --- comfy/samplers.py | 12 +++++++++--- comfy_extras/nodes_custom_sampler.py | 5 +++++ 2 files changed, 14 insertions(+), 3 deletions(-) diff --git a/comfy/samplers.py b/comfy/samplers.py index a89e3a6c..475b1aad 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -624,6 +624,9 @@ class CFGGuider: return self.inner_model.process_latent_out(samples.to(torch.float32)) def sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None): + if sigmas.shape[-1] == 0: + return latent_image + self.conds = {} for k in self.original_conds: self.conds[k] = list(map(lambda a: a.copy(), self.original_conds[k])) @@ -722,9 +725,12 @@ class KSampler: if denoise is None or denoise > 0.9999: self.sigmas = self.calculate_sigmas(steps).to(self.device) else: - new_steps = int(steps/denoise) - sigmas = self.calculate_sigmas(new_steps).to(self.device) - self.sigmas = sigmas[-(steps + 1):] + if denoise <= 0.0: + self.sigmas = torch.FloatTensor([]) + else: + new_steps = int(steps/denoise) + sigmas = self.calculate_sigmas(new_steps).to(self.device) + self.sigmas = sigmas[-(steps + 1):] def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None): if sigmas is None: diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index e9dc3bd9..a99dbcee 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -24,6 +24,8 @@ class BasicScheduler: def get_sigmas(self, model, scheduler, steps, denoise): total_steps = steps if denoise < 1.0: + if denoise <= 0.0: + return (torch.FloatTensor([]),) total_steps = int(steps/denoise) comfy.model_management.load_models_gpu([model]) @@ -160,6 +162,9 @@ class FlipSigmas: FUNCTION = "get_sigmas" def get_sigmas(self, sigmas): + if len(sigmas) == 0: + return (sigmas,) + sigmas = sigmas.flip(0) if sigmas[0] == 0: sigmas[0] = 0.0001 From cfbf3be54b4730d0a434115fcad2ff138a5753ba Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 4 Apr 2024 13:57:32 -0400 Subject: [PATCH 134/378] Add basic guider for models with no cfg. --- comfy_extras/nodes_custom_sampler.py | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index a99dbcee..5da437a6 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -383,6 +383,28 @@ class SamplerCustom: out_denoised = out return (out, out_denoised) +class Guider_Basic(comfy.samplers.CFGGuider): + def set_conds(self, positive): + self.inner_set_conds({"positive": positive}) + +class BasicGuider: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "conditioning": ("CONDITIONING", ), + } + } + + RETURN_TYPES = ("GUIDER",) + + FUNCTION = "get_guider" + CATEGORY = "sampling/custom_sampling/guiders" + + def get_guider(self, model, conditioning): + guider = Guider_Basic(model) + guider.set_conds(conditioning) + return (guider,) class CFGGuider: @classmethod @@ -496,6 +518,7 @@ NODE_CLASS_MAPPINGS = { "FlipSigmas": FlipSigmas, "CFGGuider": CFGGuider, + "BasicGuider": BasicGuider, "RandomNoise": RandomNoise, "DisableNoise": DisableNoise, "SamplerCustomAdvanced": SamplerCustomAdvanced, From 5272fd4b0389c6e702493d193a1f824f9fa4c7b8 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 4 Apr 2024 14:57:44 -0400 Subject: [PATCH 135/378] Add DualCFGGuider used in IP2P models for example. --- comfy_extras/nodes_custom_sampler.py | 36 ++++++++++++++++++++++++++++ 1 file changed, 36 insertions(+) diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index 5da437a6..fbd8cd25 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -428,6 +428,41 @@ class CFGGuider: guider.set_cfg(cfg) return (guider,) +class Guider_DualCFG(comfy.samplers.CFGGuider): + def set_cfg(self, cfg1, cfg2): + self.cfg1 = cfg1 + self.cfg2 = cfg2 + + def set_conds(self, positive, middle, negative): + self.inner_set_conds({"positive": positive, "middle": middle, "negative": negative}) + + def predict_noise(self, x, timestep, model_options={}, seed=None): + out = comfy.samplers.calc_cond_batch(self.inner_model, [self.conds.get("negative", None), self.conds.get("middle", None), self.conds.get("positive", None)], x, timestep, model_options) + return comfy.samplers.cfg_function(self.inner_model, out[1], out[0], self.cfg2, x, timestep, model_options=model_options) + (out[2] - out[1]) * self.cfg1 + +class DualCFGGuider: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "cond1": ("CONDITIONING", ), + "cond2": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "cfg_conds": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), + "cfg_cond2_negative": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), + } + } + + RETURN_TYPES = ("GUIDER",) + + FUNCTION = "get_guider" + CATEGORY = "sampling/custom_sampling/guiders" + + def get_guider(self, model, cond1, cond2, negative, cfg_conds, cfg_cond2_negative): + guider = Guider_DualCFG(model) + guider.set_conds(cond1, cond2, negative) + guider.set_cfg(cfg_conds, cfg_cond2_negative) + return (guider,) class DisableNoise: @classmethod @@ -518,6 +553,7 @@ NODE_CLASS_MAPPINGS = { "FlipSigmas": FlipSigmas, "CFGGuider": CFGGuider, + "DualCFGGuider": DualCFGGuider, "BasicGuider": BasicGuider, "RandomNoise": RandomNoise, "DisableNoise": DisableNoise, From 1f8d8e6c772fe462f697486626c6c1720b16a37d Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 4 Apr 2024 15:06:17 -0400 Subject: [PATCH 136/378] Add InstructPixToPixConditioning node. --- comfy_extras/nodes_ip2p.py | 45 ++++++++++++++++++++++++++++++++++++++ nodes.py | 1 + 2 files changed, 46 insertions(+) create mode 100644 comfy_extras/nodes_ip2p.py diff --git a/comfy_extras/nodes_ip2p.py b/comfy_extras/nodes_ip2p.py new file mode 100644 index 00000000..c2e70a84 --- /dev/null +++ b/comfy_extras/nodes_ip2p.py @@ -0,0 +1,45 @@ +import torch + +class InstructPixToPixConditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "vae": ("VAE", ), + "pixels": ("IMAGE", ), + }} + + RETURN_TYPES = ("CONDITIONING","CONDITIONING","LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + FUNCTION = "encode" + + CATEGORY = "conditioning/instructpix2pix" + + def encode(self, positive, negative, pixels, vae): + x = (pixels.shape[1] // 8) * 8 + y = (pixels.shape[2] // 8) * 8 + + if pixels.shape[1] != x or pixels.shape[2] != y: + x_offset = (pixels.shape[1] % 8) // 2 + y_offset = (pixels.shape[2] % 8) // 2 + pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:] + + concat_latent = vae.encode(pixels) + + out_latent = {} + out_latent["samples"] = torch.zeros_like(concat_latent) + + out = [] + for conditioning in [positive, negative]: + c = [] + for t in conditioning: + d = t[1].copy() + d["concat_latent_image"] = concat_latent + n = [t[0], d] + c.append(n) + out.append(c) + return (out[0], out[1], out_latent) + +NODE_CLASS_MAPPINGS = { + "InstructPixToPixConditioning": InstructPixToPixConditioning, +} diff --git a/nodes.py b/nodes.py index 5cf020df..cdb82f80 100644 --- a/nodes.py +++ b/nodes.py @@ -1965,6 +1965,7 @@ def init_custom_nodes(): "nodes_morphology.py", "nodes_stable_cascade.py", "nodes_differential_diffusion.py", + "nodes_ip2p.py", ] import_failed = [] From 1a0486bb96fb1ff10f4ea3c0d62eb815e9630585 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 4 Apr 2024 22:08:49 -0400 Subject: [PATCH 137/378] Fix model needing to be loaded on GPU to generate the sigmas. --- comfy/model_patcher.py | 6 ++++++ comfy/samplers.py | 28 ++++++++++++++-------------- comfy_extras/nodes_custom_sampler.py | 3 +-- 3 files changed, 21 insertions(+), 16 deletions(-) diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index 8dda84cf..97fabd4f 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -150,6 +150,12 @@ class ModelPatcher: def add_object_patch(self, name, obj): self.object_patches[name] = obj + def get_model_object(self, name): + if name in self.object_patches: + return self.object_patches[name] + else: + return comfy.utils.get_attr(self.model, name) + def model_patches_to(self, device): to = self.model_options["transformer_options"] if "patches" in to: diff --git a/comfy/samplers.py b/comfy/samplers.py index 475b1aad..08cbab75 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -274,8 +274,8 @@ class KSamplerX0Inpaint: out = out * denoise_mask + self.latent_image * latent_mask return out -def simple_scheduler(model, steps): - s = model.model_sampling +def simple_scheduler(model_sampling, steps): + s = model_sampling sigs = [] ss = len(s.sigmas) / steps for x in range(steps): @@ -283,8 +283,8 @@ def simple_scheduler(model, steps): sigs += [0.0] return torch.FloatTensor(sigs) -def ddim_scheduler(model, steps): - s = model.model_sampling +def ddim_scheduler(model_sampling, steps): + s = model_sampling sigs = [] ss = max(len(s.sigmas) // steps, 1) x = 1 @@ -295,8 +295,8 @@ def ddim_scheduler(model, steps): sigs += [0.0] return torch.FloatTensor(sigs) -def normal_scheduler(model, steps, sgm=False, floor=False): - s = model.model_sampling +def normal_scheduler(model_sampling, steps, sgm=False, floor=False): + s = model_sampling start = s.timestep(s.sigma_max) end = s.timestep(s.sigma_min) @@ -660,19 +660,19 @@ def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"] SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"] -def calculate_sigmas_scheduler(model, scheduler_name, steps): +def calculate_sigmas(model_sampling, scheduler_name, steps): if scheduler_name == "karras": - sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max)) + sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model_sampling.sigma_min), sigma_max=float(model_sampling.sigma_max)) elif scheduler_name == "exponential": - sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max)) + sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model_sampling.sigma_min), sigma_max=float(model_sampling.sigma_max)) elif scheduler_name == "normal": - sigmas = normal_scheduler(model, steps) + sigmas = normal_scheduler(model_sampling, steps) elif scheduler_name == "simple": - sigmas = simple_scheduler(model, steps) + sigmas = simple_scheduler(model_sampling, steps) elif scheduler_name == "ddim_uniform": - sigmas = ddim_scheduler(model, steps) + sigmas = ddim_scheduler(model_sampling, steps) elif scheduler_name == "sgm_uniform": - sigmas = normal_scheduler(model, steps, sgm=True) + sigmas = normal_scheduler(model_sampling, steps, sgm=True) else: logging.error("error invalid scheduler {}".format(scheduler_name)) return sigmas @@ -714,7 +714,7 @@ class KSampler: steps += 1 discard_penultimate_sigma = True - sigmas = calculate_sigmas_scheduler(self.model.model, self.scheduler, steps) + sigmas = calculate_sigmas(self.model.get_model_object("model_sampling"), self.scheduler, steps) if discard_penultimate_sigma: sigmas = torch.cat([sigmas[:-2], sigmas[-1:]]) diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index fbd8cd25..fa113192 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -28,8 +28,7 @@ class BasicScheduler: return (torch.FloatTensor([]),) total_steps = int(steps/denoise) - comfy.model_management.load_models_gpu([model]) - sigmas = comfy.samplers.calculate_sigmas_scheduler(model.model, scheduler, total_steps).cpu() + sigmas = comfy.samplers.calculate_sigmas(model.get_model_object("model_sampling"), scheduler, total_steps).cpu() sigmas = sigmas[-(steps + 1):] return (sigmas, ) From 1f4fc9ea0ccceba2e86668a22d86e63b3d262b83 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 4 Apr 2024 23:01:02 -0400 Subject: [PATCH 138/378] Fix issue with get_model_object on patched model. --- comfy/model_patcher.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index 97fabd4f..1595d4a2 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -154,7 +154,10 @@ class ModelPatcher: if name in self.object_patches: return self.object_patches[name] else: - return comfy.utils.get_attr(self.model, name) + if name in self.object_patches_backup: + return self.object_patches_backup[name] + else: + return comfy.utils.get_attr(self.model, name) def model_patches_to(self, device): to = self.model_options["transformer_options"] From 0f5768e038343a8eb96074c8c7f3911abf12461e Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 4 Apr 2024 23:38:57 -0400 Subject: [PATCH 139/378] Fix missing arguments in cfg_function. --- comfy/samplers.py | 4 ++-- comfy_extras/nodes_custom_sampler.py | 7 +++++-- 2 files changed, 7 insertions(+), 4 deletions(-) diff --git a/comfy/samplers.py b/comfy/samplers.py index 08cbab75..415a35cc 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -231,7 +231,7 @@ def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): logging.warning("WARNING: The comfy.samplers.calc_cond_uncond_batch function is deprecated please use the calc_cond_batch one instead.") return tuple(calc_cond_batch(model, [cond, uncond], x_in, timestep, model_options)) -def cfg_function(model, cond_pred, uncond_pred, cond_scale, x, timestep, model_options={}): +def cfg_function(model, cond_pred, uncond_pred, cond_scale, x, timestep, model_options={}, cond=None, uncond=None): if "sampler_cfg_function" in model_options: args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep, "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options} @@ -256,7 +256,7 @@ def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_option conds = [cond, uncond_] out = calc_cond_batch(model, conds, x, timestep, model_options) - return cfg_function(model, out[0], out[1], cond_scale, x, timestep, model_options=model_options) + return cfg_function(model, out[0], out[1], cond_scale, x, timestep, model_options=model_options, cond=cond, uncond=uncond_) class KSamplerX0Inpaint: diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index fa113192..a5d791d9 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -436,8 +436,11 @@ class Guider_DualCFG(comfy.samplers.CFGGuider): self.inner_set_conds({"positive": positive, "middle": middle, "negative": negative}) def predict_noise(self, x, timestep, model_options={}, seed=None): - out = comfy.samplers.calc_cond_batch(self.inner_model, [self.conds.get("negative", None), self.conds.get("middle", None), self.conds.get("positive", None)], x, timestep, model_options) - return comfy.samplers.cfg_function(self.inner_model, out[1], out[0], self.cfg2, x, timestep, model_options=model_options) + (out[2] - out[1]) * self.cfg1 + negative_cond = self.conds.get("negative", None) + middle_cond = self.conds.get("middle", None) + + out = comfy.samplers.calc_cond_batch(self.inner_model, [negative_cond, middle_cond, self.conds.get("positive", None)], x, timestep, model_options) + return comfy.samplers.cfg_function(self.inner_model, out[1], out[0], self.cfg2, x, timestep, model_options=model_options, cond=middle_cond, uncond=negative_cond) + (out[2] - out[1]) * self.cfg1 class DualCFGGuider: @classmethod From 41ed7e85ea4bb9e77f44bc9b2f87cca4e3dc99b2 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 5 Apr 2024 00:22:44 -0400 Subject: [PATCH 140/378] Fix object_patches_backup not being the same object across clones. --- comfy/model_patcher.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index 1595d4a2..76485ced 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -471,4 +471,4 @@ class ModelPatcher: for k in keys: comfy.utils.set_attr(self.model, k, self.object_patches_backup[k]) - self.object_patches_backup = {} + self.object_patches_backup.clear() From 1088d1850f9b13233e3cf4460ee077b15e4f712f Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 5 Apr 2024 10:40:27 -0400 Subject: [PATCH 141/378] Support for CosXL models. --- comfy/model_base.py | 6 ++++-- comfy/supported_models.py | 7 ++++++- 2 files changed, 10 insertions(+), 3 deletions(-) diff --git a/comfy/model_base.py b/comfy/model_base.py index 898adb66..8c89adf5 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -503,8 +503,10 @@ class SD15_instructpix2pix(IP2P, BaseModel): class SDXL_instructpix2pix(IP2P, SDXL): def __init__(self, model_config, model_type=ModelType.EPS, device=None): super().__init__(model_config, model_type, device=device) - # self.process_ip2p_image_in = lambda image: comfy.latent_formats.SDXL().process_in(image) - self.process_ip2p_image_in = lambda image: image + if model_type == ModelType.V_PREDICTION_EDM: + self.process_ip2p_image_in = lambda image: comfy.latent_formats.SDXL().process_in(image) #cosxl ip2p + else: + self.process_ip2p_image_in = lambda image: image #diffusers ip2p class StableCascade_C(BaseModel): diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 9bfe3ea1..b3b69e05 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -174,6 +174,11 @@ class SDXL(supported_models_base.BASE): self.sampling_settings["sigma_max"] = 80.0 self.sampling_settings["sigma_min"] = 0.002 return model_base.ModelType.EDM + elif "edm_vpred.sigma_max" in state_dict: + self.sampling_settings["sigma_max"] = float(state_dict["edm_vpred.sigma_max"].item()) + if "edm_vpred.sigma_min" in state_dict: + self.sampling_settings["sigma_min"] = float(state_dict["edm_vpred.sigma_min"].item()) + return model_base.ModelType.V_PREDICTION_EDM elif "v_pred" in state_dict: return model_base.ModelType.V_PREDICTION else: @@ -469,7 +474,7 @@ class SDXL_instructpix2pix(SDXL): } def get_model(self, state_dict, prefix="", device=None): - return model_base.SDXL_instructpix2pix(self, device=device) + return model_base.SDXL_instructpix2pix(self, model_type=self.model_type(state_dict, prefix), device=device) models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p] From ea9ac9d30beb23119e590610d3ec5dcd146a12f2 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 5 Apr 2024 11:33:36 -0400 Subject: [PATCH 142/378] Fix PerpNeg node. --- comfy_extras/nodes_perpneg.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/comfy_extras/nodes_perpneg.py b/comfy_extras/nodes_perpneg.py index 9e8a218f..992e8ab6 100644 --- a/comfy_extras/nodes_perpneg.py +++ b/comfy_extras/nodes_perpneg.py @@ -1,10 +1,11 @@ import torch import comfy.model_management -import comfy.sample +import comfy.sampler_helpers import comfy.samplers import comfy.utils +#TODO: This node should be removed and replaced with one that uses the new Guider/SamplerCustomAdvanced. class PerpNeg: @classmethod def INPUT_TYPES(s): @@ -19,7 +20,7 @@ class PerpNeg: def patch(self, model, empty_conditioning, neg_scale): m = model.clone() - nocond = comfy.sample.convert_cond(empty_conditioning) + nocond = comfy.sampler_helpers.convert_cond(empty_conditioning) def cfg_function(args): model = args["model"] From 38ed2da2ddc463a84acc08dbd2bbd5785190d487 Mon Sep 17 00:00:00 2001 From: kk-89 <31396340+kk-89@users.noreply.github.com> Date: Fri, 5 Apr 2024 09:02:13 -0700 Subject: [PATCH 143/378] Fix typo in lowvram patcher (#3209) --- comfy/model_patcher.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index 76485ced..657ddf84 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -287,7 +287,7 @@ class ModelPatcher: if weight_key in self.patches: m.weight_function = LowVramPatch(weight_key, self) if bias_key in self.patches: - m.bias_function = LowVramPatch(weight_key, self) + m.bias_function = LowVramPatch(bias_key, self) m.prev_comfy_cast_weights = m.comfy_cast_weights m.comfy_cast_weights = True From a7dd82e668bfaf7fac365a4e73a1ba1acf224fbb Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 5 Apr 2024 14:59:05 -0400 Subject: [PATCH 144/378] Fix copy paste issue with litegraph. --- web/lib/litegraph.core.js | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/web/lib/litegraph.core.js b/web/lib/litegraph.core.js index 4ff05ae8..427a62b5 100644 --- a/web/lib/litegraph.core.js +++ b/web/lib/litegraph.core.js @@ -7247,7 +7247,7 @@ LGraphNode.prototype.executeAction = function(action) //create links for (var i = 0; i < clipboard_info.links.length; ++i) { var link_info = clipboard_info.links[i]; - var origin_node; + var origin_node = undefined; var origin_node_relative_id = link_info[0]; if (origin_node_relative_id != null) { origin_node = nodes[origin_node_relative_id]; From d8dea4cdb88cae92ac4b4a4d79bbfd60b7f8923d Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 5 Apr 2024 21:36:23 -0400 Subject: [PATCH 145/378] Fix DisableNoise node. --- comfy_extras/nodes_custom_sampler.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index a5d791d9..56cb505e 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -320,7 +320,7 @@ class Noise_EmptyNoise: def generate_noise(self, input_latent): latent_image = input_latent["samples"] - return torch.zeros(shape, dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") + return torch.zeros(latent_image.shape, dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") class Noise_RandomNoise: @@ -477,7 +477,7 @@ class DisableNoise: FUNCTION = "get_noise" CATEGORY = "sampling/custom_sampling/noise" - def get_noise(self, noise_seed): + def get_noise(self): return (Noise_EmptyNoise(),) From de172f8be716ab8a28c202d8162e62a184f39acd Mon Sep 17 00:00:00 2001 From: Gorka Eguileor Date: Sat, 6 Apr 2024 18:10:17 +0200 Subject: [PATCH 146/378] Improve A1111 metadata parsing (#3216) * A1111 import: Set VAE name This patch sets the VAE name for the `VAELoader` when present in the png metadata. * A1111 import: Skip all hashes When importing from A1111 the parsing assumes that values of a key will never contain a ":", which is not correct. There are 2 cases where we can have ":" in the value: - Inside a string. E.g.: Lora hashes: "xl_more_art-full_v1: fe3b4816be83, add-detail-xl: 9c783c8ce46c" - When the value is a json dictionary. E.g.: Hashes: {"vae": "63aeecb90f", "embed:negativeXL_D": "fff5d51ab6"} This patch changes how we parse the metadata to take those 2 cases into account and also skips the following additional keys that are present in some Forge images: - Version - VAE hash - TI hashes - Lora hashes - Hashes * A1111 import: Parse Hires steps This patch parses the `Hires steps` parameter that is part of the High Resolution Upscale configuration when it is present, and fallbacks to the one from the `samplerNode` (like the code currently does) if it's not present. --- web/scripts/pnginfo.js | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/web/scripts/pnginfo.js b/web/scripts/pnginfo.js index 16960920..7132fb60 100644 --- a/web/scripts/pnginfo.js +++ b/web/scripts/pnginfo.js @@ -170,9 +170,12 @@ export async function importA1111(graph, parameters) { const opts = parameters .substr(p) .split("\n")[1] - .split(",") + .match(new RegExp("\\s*([^:]+:\\s*([^\"\\{].*?|\".*?\"|\\{.*?\\}))\\s*(,|$)", "g")) .reduce((p, n) => { const s = n.split(":"); + if (s[1].endsWith(',')) { + s[1] = s[1].substr(0, s[1].length -1); + } p[s[0].trim().toLowerCase()] = s[1].trim(); return p; }, {}); @@ -191,6 +194,7 @@ export async function importA1111(graph, parameters) { const vaeLoaderNode = LiteGraph.createNode("VAELoader"); const saveNode = LiteGraph.createNode("SaveImage"); let hrSamplerNode = null; + let hrSteps = null; const ceil64 = (v) => Math.ceil(v / 64) * 64; @@ -290,6 +294,9 @@ export async function importA1111(graph, parameters) { model(v) { setWidgetValue(ckptNode, "ckpt_name", v, true); }, + "vae"(v) { + setWidgetValue(vaeLoaderNode, "vae_name", v, true); + }, "cfg scale"(v) { setWidgetValue(samplerNode, "cfg", +v); }, @@ -316,6 +323,7 @@ export async function importA1111(graph, parameters) { const h = ceil64(+wxh[1]); const hrUp = popOpt("hires upscale"); const hrSz = popOpt("hires resize"); + hrSteps = popOpt("hires steps"); let hrMethod = popOpt("hires upscaler"); setWidgetValue(imageNode, "width", w); @@ -398,7 +406,7 @@ export async function importA1111(graph, parameters) { } if (hrSamplerNode) { - setWidgetValue(hrSamplerNode, "steps", getWidget(samplerNode, "steps").value); + setWidgetValue(hrSamplerNode, "steps", hrSteps? +hrSteps : getWidget(samplerNode, "steps").value); setWidgetValue(hrSamplerNode, "cfg", getWidget(samplerNode, "cfg").value); setWidgetValue(hrSamplerNode, "scheduler", getWidget(samplerNode, "scheduler").value); setWidgetValue(hrSamplerNode, "sampler_name", getWidget(samplerNode, "sampler_name").value); @@ -415,7 +423,7 @@ export async function importA1111(graph, parameters) { graph.arrange(); - for (const opt of ["model hash", "ensd"]) { + for (const opt of ["model hash", "ensd", "version", "vae hash", "ti hashes", "lora hashes", "hashes"]) { delete opts[opt]; } From 0a03009808a5ad13fa3a44edbabcae68576c3982 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 6 Apr 2024 18:38:39 -0400 Subject: [PATCH 147/378] Fix issue with controlnet models getting loaded multiple times. --- comfy/controlnet.py | 10 +++++++--- comfy/model_management.py | 2 ++ 2 files changed, 9 insertions(+), 3 deletions(-) diff --git a/comfy/controlnet.py b/comfy/controlnet.py index b6941d8c..8cf4a61a 100644 --- a/comfy/controlnet.py +++ b/comfy/controlnet.py @@ -138,11 +138,13 @@ class ControlBase: return out class ControlNet(ControlBase): - def __init__(self, control_model, global_average_pooling=False, device=None, load_device=None, manual_cast_dtype=None): + def __init__(self, control_model=None, global_average_pooling=False, device=None, load_device=None, manual_cast_dtype=None): super().__init__(device) self.control_model = control_model self.load_device = load_device - self.control_model_wrapped = comfy.model_patcher.ModelPatcher(self.control_model, load_device=load_device, offload_device=comfy.model_management.unet_offload_device()) + if control_model is not None: + self.control_model_wrapped = comfy.model_patcher.ModelPatcher(self.control_model, load_device=load_device, offload_device=comfy.model_management.unet_offload_device()) + self.global_average_pooling = global_average_pooling self.model_sampling_current = None self.manual_cast_dtype = manual_cast_dtype @@ -183,7 +185,9 @@ class ControlNet(ControlBase): return self.control_merge(None, control, control_prev, output_dtype) def copy(self): - c = ControlNet(self.control_model, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype) + c = ControlNet(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype) + c.control_model = self.control_model + c.control_model_wrapped = self.control_model_wrapped self.copy_to(c) return c diff --git a/comfy/model_management.py b/comfy/model_management.py index 26216432..310ec253 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -385,6 +385,8 @@ def load_models_gpu(models, memory_required=0): inference_memory = minimum_inference_memory() extra_mem = max(inference_memory, memory_required) + models = set(models) + models_to_load = [] models_already_loaded = [] for x in models: From 80bda6c16393e7af7934e791b1babedb2cf4896a Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 7 Apr 2024 14:27:40 -0400 Subject: [PATCH 148/378] Cleanup a few conditioning nodes. --- node_helpers.py | 10 ++++++++++ nodes.py | 39 ++++++++++++--------------------------- 2 files changed, 22 insertions(+), 27 deletions(-) create mode 100644 node_helpers.py diff --git a/node_helpers.py b/node_helpers.py new file mode 100644 index 00000000..8828a4ec --- /dev/null +++ b/node_helpers.py @@ -0,0 +1,10 @@ + +def conditioning_set_values(conditioning, values={}): + c = [] + for t in conditioning: + n = [t[0], t[1].copy()] + for k in values: + n[1][k] = values[k] + c.append(n) + + return c diff --git a/nodes.py b/nodes.py index cdb82f80..6d625cc2 100644 --- a/nodes.py +++ b/nodes.py @@ -34,6 +34,7 @@ import importlib import folder_paths import latent_preview +import node_helpers def before_node_execution(): comfy.model_management.throw_exception_if_processing_interrupted() @@ -151,13 +152,9 @@ class ConditioningSetArea: CATEGORY = "conditioning" def append(self, conditioning, width, height, x, y, strength): - c = [] - for t in conditioning: - n = [t[0], t[1].copy()] - n[1]['area'] = (height // 8, width // 8, y // 8, x // 8) - n[1]['strength'] = strength - n[1]['set_area_to_bounds'] = False - c.append(n) + c = node_helpers.conditioning_set_values(conditioning, {"area": (height // 8, width // 8, y // 8, x // 8), + "strength": strength, + "set_area_to_bounds": False}) return (c, ) class ConditioningSetAreaPercentage: @@ -176,13 +173,9 @@ class ConditioningSetAreaPercentage: CATEGORY = "conditioning" def append(self, conditioning, width, height, x, y, strength): - c = [] - for t in conditioning: - n = [t[0], t[1].copy()] - n[1]['area'] = ("percentage", height, width, y, x) - n[1]['strength'] = strength - n[1]['set_area_to_bounds'] = False - c.append(n) + c = node_helpers.conditioning_set_values(conditioning, {"area": ("percentage", height, width, y, x), + "strength": strength, + "set_area_to_bounds": False}) return (c, ) class ConditioningSetAreaStrength: @@ -197,11 +190,7 @@ class ConditioningSetAreaStrength: CATEGORY = "conditioning" def append(self, conditioning, strength): - c = [] - for t in conditioning: - n = [t[0], t[1].copy()] - n[1]['strength'] = strength - c.append(n) + c = node_helpers.conditioning_set_values(conditioning, {"strength": strength}) return (c, ) @@ -219,19 +208,15 @@ class ConditioningSetMask: CATEGORY = "conditioning" def append(self, conditioning, mask, set_cond_area, strength): - c = [] set_area_to_bounds = False if set_cond_area != "default": set_area_to_bounds = True if len(mask.shape) < 3: mask = mask.unsqueeze(0) - for t in conditioning: - n = [t[0], t[1].copy()] - _, h, w = mask.shape - n[1]['mask'] = mask - n[1]['set_area_to_bounds'] = set_area_to_bounds - n[1]['mask_strength'] = strength - c.append(n) + + c = node_helpers.conditioning_set_values(conditioning, {"mask": mask, + "set_area_to_bounds": set_area_to_bounds, + "mask_strength": strength}) return (c, ) class ConditioningZeroOut: From c9fc242e2ca7c471743d05648abf7785f0610590 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 7 Apr 2024 14:34:43 -0400 Subject: [PATCH 149/378] The middle prompt should be treated more as a negative prompt. --- comfy_extras/nodes_custom_sampler.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index 56cb505e..1971f2c5 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -4,6 +4,7 @@ from comfy.k_diffusion import sampling as k_diffusion_sampling import latent_preview import torch import comfy.utils +import node_helpers class BasicScheduler: @@ -433,6 +434,7 @@ class Guider_DualCFG(comfy.samplers.CFGGuider): self.cfg2 = cfg2 def set_conds(self, positive, middle, negative): + middle = node_helpers.conditioning_set_values(middle, {"prompt_type": "negative"}) self.inner_set_conds({"positive": positive, "middle": middle, "negative": negative}) def predict_noise(self, x, timestep, model_options={}, seed=None): From d644b6bcd8991357de674f72eba02d81875d7847 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 7 Apr 2024 14:40:43 -0400 Subject: [PATCH 150/378] Cleanup some more conditioning nodes. --- nodes.py | 18 ++++-------------- 1 file changed, 4 insertions(+), 14 deletions(-) diff --git a/nodes.py b/nodes.py index 6d625cc2..a1baa98a 100644 --- a/nodes.py +++ b/nodes.py @@ -251,13 +251,8 @@ class ConditioningSetTimestepRange: CATEGORY = "advanced/conditioning" def set_range(self, conditioning, start, end): - c = [] - for t in conditioning: - d = t[1].copy() - d['start_percent'] = start - d['end_percent'] = end - n = [t[0], d] - c.append(n) + c = node_helpers.conditioning_set_values(conditioning, {"start_percent": start, + "end_percent": end}) return (c, ) class VAEDecode: @@ -398,13 +393,8 @@ class InpaintModelConditioning: out = [] for conditioning in [positive, negative]: - c = [] - for t in conditioning: - d = t[1].copy() - d["concat_latent_image"] = concat_latent - d["concat_mask"] = mask - n = [t[0], d] - c.append(n) + c = node_helpers.conditioning_set_values(conditioning, {"concat_latent_image": concat_latent, + "concat_mask": mask}) out.append(c) return (out[0], out[1], out_latent) From 30abc324c2f73e6b648093ccd4741dece20be1e5 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 8 Apr 2024 00:36:22 -0400 Subject: [PATCH 151/378] Support properly saving CosXL checkpoints. --- comfy/sd.py | 5 ++++- comfy_extras/nodes_model_merging.py | 11 ++++++++++- 2 files changed, 14 insertions(+), 2 deletions(-) diff --git a/comfy/sd.py b/comfy/sd.py index 85821120..3919f4bf 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -600,7 +600,7 @@ def load_unet(unet_path): raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path)) return model -def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, metadata=None): +def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, metadata=None, extra_keys={}): clip_sd = None load_models = [model] if clip is not None: @@ -610,4 +610,7 @@ def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, m model_management.load_models_gpu(load_models) clip_vision_sd = clip_vision.get_sd() if clip_vision is not None else None sd = model.model.state_dict_for_saving(clip_sd, vae.get_sd(), clip_vision_sd) + for k in extra_keys: + sd[k] = extra_keys[k] + comfy.utils.save_torch_file(sd, output_path, metadata=metadata) diff --git a/comfy_extras/nodes_model_merging.py b/comfy_extras/nodes_model_merging.py index a25b73ca..2a431f65 100644 --- a/comfy_extras/nodes_model_merging.py +++ b/comfy_extras/nodes_model_merging.py @@ -2,7 +2,9 @@ import comfy.sd import comfy.utils import comfy.model_base import comfy.model_management +import comfy.model_sampling +import torch import folder_paths import json import os @@ -189,6 +191,13 @@ def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefi # "stable-diffusion-v2-768-v", "stable-diffusion-v2-unclip-l", "stable-diffusion-v2-unclip-h", # "v2-inpainting" + extra_keys = {} + model_sampling = model.get_model_object("model_sampling") + if isinstance(model_sampling, comfy.model_sampling.ModelSamplingContinuousEDM): + if isinstance(model_sampling, comfy.model_sampling.V_PREDICTION): + extra_keys["edm_vpred.sigma_max"] = torch.tensor(model_sampling.sigma_max).float() + extra_keys["edm_vpred.sigma_min"] = torch.tensor(model_sampling.sigma_min).float() + if model.model.model_type == comfy.model_base.ModelType.EPS: metadata["modelspec.predict_key"] = "epsilon" elif model.model.model_type == comfy.model_base.ModelType.V_PREDICTION: @@ -203,7 +212,7 @@ def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefi output_checkpoint = f"{filename}_{counter:05}_.safetensors" output_checkpoint = os.path.join(full_output_folder, output_checkpoint) - comfy.sd.save_checkpoint(output_checkpoint, model, clip, vae, clip_vision, metadata=metadata) + comfy.sd.save_checkpoint(output_checkpoint, model, clip, vae, clip_vision, metadata=metadata, extra_keys=extra_keys) class CheckpointSave: def __init__(self): From 4201181b35402e0a992b861f8d2f0e0b267f52fa Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 9 Apr 2024 04:25:45 -0400 Subject: [PATCH 152/378] Add ModelMergeSD1, ModelMergeSD2 and ModelMergeSDXL. --- .../nodes_model_merging_model_specific.py | 60 +++++++++++++++++++ nodes.py | 1 + 2 files changed, 61 insertions(+) create mode 100644 comfy_extras/nodes_model_merging_model_specific.py diff --git a/comfy_extras/nodes_model_merging_model_specific.py b/comfy_extras/nodes_model_merging_model_specific.py new file mode 100644 index 00000000..f2d008d8 --- /dev/null +++ b/comfy_extras/nodes_model_merging_model_specific.py @@ -0,0 +1,60 @@ +import comfy_extras.nodes_model_merging + +class ModelMergeSD1(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["time_embed."] = argument + arg_dict["label_emb."] = argument + + for i in range(12): + arg_dict["input_blocks.{}.".format(i)] = argument + + for i in range(3): + arg_dict["middle_block.{}.".format(i)] = argument + + for i in range(12): + arg_dict["output_blocks.{}.".format(i)] = argument + + arg_dict["out."] = argument + + return {"required": arg_dict} + + +class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["time_embed."] = argument + arg_dict["label_emb."] = argument + + for i in range(9): + arg_dict["input_blocks.{}".format(i)] = argument + + for i in range(3): + arg_dict["middle_block.{}".format(i)] = argument + + for i in range(9): + arg_dict["output_blocks.{}".format(i)] = argument + + arg_dict["out."] = argument + + return {"required": arg_dict} + + +NODE_CLASS_MAPPINGS = { + "ModelMergeSD1": ModelMergeSD1, + "ModelMergeSD2": ModelMergeSD1, #SD1 and SD2 have the same blocks + "ModelMergeSDXL": ModelMergeSDXL, +} diff --git a/nodes.py b/nodes.py index a1baa98a..78e0cf11 100644 --- a/nodes.py +++ b/nodes.py @@ -1941,6 +1941,7 @@ def init_custom_nodes(): "nodes_stable_cascade.py", "nodes_differential_diffusion.py", "nodes_ip2p.py", + "nodes_model_merging_model_specific.py", ] import_failed = [] From 831511a1eecbe271e302f2f2053f285f00614180 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 9 Apr 2024 23:20:43 -0400 Subject: [PATCH 153/378] Fix issue with sampling_settings persisting across models. --- comfy/supported_models_base.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/comfy/supported_models_base.py b/comfy/supported_models_base.py index 6196daab..cf7cdff3 100644 --- a/comfy/supported_models_base.py +++ b/comfy/supported_models_base.py @@ -47,7 +47,8 @@ class BASE: return self.unet_config["in_channels"] > 4 def __init__(self, unet_config): - self.unet_config = unet_config + self.unet_config = unet_config.copy() + self.sampling_settings = self.sampling_settings.copy() self.latent_format = self.latent_format() for x in self.unet_extra_config: self.unet_config[x] = self.unet_extra_config[x] From fd7c63668080f8c2eb56edffeef028aa3932e0fe Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 10 Apr 2024 20:29:35 -0400 Subject: [PATCH 154/378] Add an AddNoise node to add noise depending on the sigma. --- comfy_extras/nodes_custom_sampler.py | 47 ++++++++++++++++++++++++++++ 1 file changed, 47 insertions(+) diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index 1971f2c5..06238f89 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -538,6 +538,52 @@ class SamplerCustomAdvanced: out_denoised = out return (out, out_denoised) +class AddNoise: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "noise": ("NOISE", ), + "sigmas": ("SIGMAS", ), + "latent_image": ("LATENT", ), + } + } + + RETURN_TYPES = ("LATENT",) + + FUNCTION = "add_noise" + + CATEGORY = "_for_testing/custom_sampling/noise" + + def add_noise(self, model, noise, sigmas, latent_image): + if len(sigmas) == 0: + return latent_image + + latent = latent_image + latent_image = latent["samples"] + + noisy = noise.generate_noise(latent) + + model_sampling = model.get_model_object("model_sampling") + process_latent_out = model.get_model_object("process_latent_out") + process_latent_in = model.get_model_object("process_latent_in") + + if len(sigmas) > 1: + scale = torch.abs(sigmas[0] - sigmas[-1]) + else: + scale = sigmas[0] + + if torch.count_nonzero(latent_image) > 0: #Don't shift the empty latent image. + latent_image = process_latent_in(latent_image) + noisy = model_sampling.noise_scaling(scale, noisy, latent_image) + noisy = process_latent_out(noisy) + noisy = torch.nan_to_num(noisy, nan=0.0, posinf=0.0, neginf=0.0) + + out = latent.copy() + out["samples"] = noisy + return (out,) + + NODE_CLASS_MAPPINGS = { "SamplerCustom": SamplerCustom, "BasicScheduler": BasicScheduler, @@ -561,5 +607,6 @@ NODE_CLASS_MAPPINGS = { "BasicGuider": BasicGuider, "RandomNoise": RandomNoise, "DisableNoise": DisableNoise, + "AddNoise": AddNoise, "SamplerCustomAdvanced": SamplerCustomAdvanced, } From 4bd7d55b9028d79829a645edfe8259f7b7a049c0 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 11 Apr 2024 22:43:05 -0400 Subject: [PATCH 155/378] Add some colors to SamplerCustom links. If you don't like them I am open to a PR. --- web/extensions/core/colorPalette.js | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/web/extensions/core/colorPalette.js b/web/extensions/core/colorPalette.js index b8d83613..02546782 100644 --- a/web/extensions/core/colorPalette.js +++ b/web/extensions/core/colorPalette.js @@ -20,6 +20,10 @@ const colorPalettes = { "MODEL": "#B39DDB", // light lavender-purple "STYLE_MODEL": "#C2FFAE", // light green-yellow "VAE": "#FF6E6E", // bright red + "NOISE": "#B0B0B0", // gray + "GUIDER": "#66FFFF", // cyan + "SAMPLER": "#ECB4B4", // very soft red + "SIGMAS": "#CDFFCD", // soft lime green "TAESD": "#DCC274", // cheesecake }, "litegraph_base": { From 2bef134ebfe38dd1ce4d25eefe933c6748f0f35a Mon Sep 17 00:00:00 2001 From: NyaamZ <43065065+NyaamZ@users.noreply.github.com> Date: Sat, 13 Apr 2024 06:02:17 +0900 Subject: [PATCH 156/378] change Convert.. input (#3246) --- web/extensions/core/widgetInputs.js | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/web/extensions/core/widgetInputs.js b/web/extensions/core/widgetInputs.js index 23f51d81..e6db9f71 100644 --- a/web/extensions/core/widgetInputs.js +++ b/web/extensions/core/widgetInputs.js @@ -292,12 +292,23 @@ app.registerExtension({ } } } + + //Convert.. main menu if (toInput.length) { - options.push(...toInput, null); + options.push({ + content: `Convert input to 🔘..`, + submenu: { + options: toInput, + }, + }); } - if (toWidget.length) { - options.push(...toWidget, null); + options.push({ + content: `Convert 🔘 to widget..`, + submenu: { + options: toWidget, + }, + }); } } From 0256e7f7699271b114593198278c5590a2e690d4 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 12 Apr 2024 20:01:34 -0400 Subject: [PATCH 157/378] Fix tests. --- tests-ui/utils/ezgraph.js | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/tests-ui/utils/ezgraph.js b/tests-ui/utils/ezgraph.js index 8a55246e..8bf8c5d8 100644 --- a/tests-ui/utils/ezgraph.js +++ b/tests-ui/utils/ezgraph.js @@ -204,13 +204,17 @@ export class EzWidget { convertToWidget() { if (!this.isConvertedToInput) throw new Error(`Widget ${this.widget.name} cannot be converted as it is already a widget.`); - this.node.menu[`Convert ${this.widget.name} to widget`].call(); + var menu = this.node.menu["Convert 🔘 to widget.."].item.submenu.options; + var index = menu.findIndex(a => a.content == `Convert ${this.widget.name} to widget`); + menu[index].callback.call(); } convertToInput() { if (this.isConvertedToInput) throw new Error(`Widget ${this.widget.name} cannot be converted as it is already an input.`); - this.node.menu[`Convert ${this.widget.name} to input`].call(); + var menu = this.node.menu["Convert input to 🔘.."].item.submenu.options; + var index = menu.findIndex(a => a.content == `Convert ${this.widget.name} to input`); + menu[index].callback.call(); } } From 58812ab8ca601cc2dd9dbe64c1f3ffd4929fd0ca Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 12 Apr 2024 22:12:35 -0400 Subject: [PATCH 158/378] Support SDXS 512 model. --- comfy/model_detection.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/comfy/model_detection.py b/comfy/model_detection.py index 795af828..23358a2c 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -357,7 +357,14 @@ def unet_config_from_diffusers_unet(state_dict, dtype=None): 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1], 'use_temporal_attention': False, 'use_temporal_resblock': False, 'disable_self_attentions': [True, False, False]} - supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega, KOALA_700M, KOALA_1B, SD09_XS, SDXL_diffusers_ip2p] + SD_XS = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1], + 'transformer_depth': [0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': False, + 'context_dim': 768, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 1, 1, 1, 1], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + + supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega, KOALA_700M, KOALA_1B, SD09_XS, SD_XS, SDXL_diffusers_ip2p] for unet_config in supported_models: matches = True From 744ac944db24795881cf41fc4ea015e5ea07a444 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 13 Apr 2024 16:12:09 -0400 Subject: [PATCH 159/378] Don't make dynamicPrompts the default on multiline string inputs. This should be less confusing to those who want to use multiline input without them. --- comfy_extras/nodes_clip_sdxl.py | 6 +++--- comfy_extras/nodes_cond.py | 2 +- comfy_extras/nodes_photomaker.py | 2 +- nodes.py | 4 ++-- web/extensions/core/dynamicPrompts.js | 2 +- 5 files changed, 8 insertions(+), 8 deletions(-) diff --git a/comfy_extras/nodes_clip_sdxl.py b/comfy_extras/nodes_clip_sdxl.py index dcf8859f..3087b917 100644 --- a/comfy_extras/nodes_clip_sdxl.py +++ b/comfy_extras/nodes_clip_sdxl.py @@ -8,7 +8,7 @@ class CLIPTextEncodeSDXLRefiner: "ascore": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.01}), "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), - "text": ("STRING", {"multiline": True}), "clip": ("CLIP", ), + "text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "encode" @@ -30,8 +30,8 @@ class CLIPTextEncodeSDXL: "crop_h": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}), "target_width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), "target_height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), - "text_g": ("STRING", {"multiline": True, "default": "CLIP_G"}), "clip": ("CLIP", ), - "text_l": ("STRING", {"multiline": True, "default": "CLIP_L"}), "clip": ("CLIP", ), + "text_g": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ), + "text_l": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "encode" diff --git a/comfy_extras/nodes_cond.py b/comfy_extras/nodes_cond.py index 646fefa1..4c3a1d5b 100644 --- a/comfy_extras/nodes_cond.py +++ b/comfy_extras/nodes_cond.py @@ -3,7 +3,7 @@ class CLIPTextEncodeControlnet: @classmethod def INPUT_TYPES(s): - return {"required": {"clip": ("CLIP", ), "conditioning": ("CONDITIONING", ), "text": ("STRING", {"multiline": True})}} + return {"required": {"clip": ("CLIP", ), "conditioning": ("CONDITIONING", ), "text": ("STRING", {"multiline": True, "dynamicPrompts": True})}} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "encode" diff --git a/comfy_extras/nodes_photomaker.py b/comfy_extras/nodes_photomaker.py index 90130142..29d127d7 100644 --- a/comfy_extras/nodes_photomaker.py +++ b/comfy_extras/nodes_photomaker.py @@ -141,7 +141,7 @@ class PhotoMakerEncode: return {"required": { "photomaker": ("PHOTOMAKER",), "image": ("IMAGE",), "clip": ("CLIP", ), - "text": ("STRING", {"multiline": True, "default": "photograph of photomaker"}), + "text": ("STRING", {"multiline": True, "dynamicPrompts": True, "default": "photograph of photomaker"}), }} RETURN_TYPES = ("CONDITIONING",) diff --git a/nodes.py b/nodes.py index 78e0cf11..fda073cf 100644 --- a/nodes.py +++ b/nodes.py @@ -47,7 +47,7 @@ MAX_RESOLUTION=16384 class CLIPTextEncode: @classmethod def INPUT_TYPES(s): - return {"required": {"text": ("STRING", {"multiline": True}), "clip": ("CLIP", )}} + return {"required": {"text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", )}} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "encode" @@ -966,7 +966,7 @@ class GLIGENTextBoxApply: return {"required": {"conditioning_to": ("CONDITIONING", ), "clip": ("CLIP", ), "gligen_textbox_model": ("GLIGEN", ), - "text": ("STRING", {"multiline": True}), + "text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "width": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), diff --git a/web/extensions/core/dynamicPrompts.js b/web/extensions/core/dynamicPrompts.js index 599a9e68..7417361b 100644 --- a/web/extensions/core/dynamicPrompts.js +++ b/web/extensions/core/dynamicPrompts.js @@ -17,7 +17,7 @@ app.registerExtension({ // Locate dynamic prompt text widgets // Include any widgets with dynamicPrompts set to true, and customtext const widgets = node.widgets.filter( - (n) => (n.type === "customtext" && n.dynamicPrompts !== false) || n.dynamicPrompts + (n) => n.dynamicPrompts ); for (const widget of widgets) { // Override the serialization of the value to resolve dynamic prompts for all widgets supporting it in this node From 258dbc06c34ebf723ae17fa23207407e2e05fad1 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 14 Apr 2024 12:08:58 -0400 Subject: [PATCH 160/378] Fix some memory related issues. --- comfy/model_management.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index 310ec253..537df41e 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -617,7 +617,8 @@ def supports_dtype(device, dtype): #TODO def device_supports_non_blocking(device): if is_device_mps(device): return False #pytorch bug? mps doesn't support non blocking - return True + return False + # return True #TODO: figure out why this causes issues def cast_to_device(tensor, device, dtype, copy=False): device_supports_cast = False From 719fb2c81d716ce8edd7f1bdc7804ae160a71d3a Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 14 Apr 2024 23:34:25 -0400 Subject: [PATCH 161/378] Add basic PAG node. --- comfy/model_patcher.py | 31 +++++++++++++++------- comfy_extras/nodes_pag.py | 56 +++++++++++++++++++++++++++++++++++++++ nodes.py | 1 + 3 files changed, 78 insertions(+), 10 deletions(-) create mode 100644 comfy_extras/nodes_pag.py diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index 657ddf84..cf51c4ad 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -18,6 +18,26 @@ def apply_weight_decompose(dora_scale, weight): return weight * (dora_scale / weight_norm) +def set_model_options_patch_replace(model_options, patch, name, block_name, number, transformer_index=None): + to = model_options["transformer_options"].copy() + + if "patches_replace" not in to: + to["patches_replace"] = {} + else: + to["patches_replace"] = to["patches_replace"].copy() + + if name not in to["patches_replace"]: + to["patches_replace"][name] = {} + else: + to["patches_replace"][name] = to["patches_replace"][name].copy() + + if transformer_index is not None: + block = (block_name, number, transformer_index) + else: + block = (block_name, number) + to["patches_replace"][name][block] = patch + model_options["transformer_options"] = to + return model_options class ModelPatcher: def __init__(self, model, load_device, offload_device, size=0, current_device=None, weight_inplace_update=False): @@ -109,16 +129,7 @@ class ModelPatcher: to["patches"][name] = to["patches"].get(name, []) + [patch] def set_model_patch_replace(self, patch, name, block_name, number, transformer_index=None): - to = self.model_options["transformer_options"] - if "patches_replace" not in to: - to["patches_replace"] = {} - if name not in to["patches_replace"]: - to["patches_replace"][name] = {} - if transformer_index is not None: - block = (block_name, number, transformer_index) - else: - block = (block_name, number) - to["patches_replace"][name][block] = patch + self.model_options = set_model_options_patch_replace(self.model_options, patch, name, block_name, number, transformer_index=transformer_index) def set_model_attn1_patch(self, patch): self.set_model_patch(patch, "attn1_patch") diff --git a/comfy_extras/nodes_pag.py b/comfy_extras/nodes_pag.py new file mode 100644 index 00000000..c48a3958 --- /dev/null +++ b/comfy_extras/nodes_pag.py @@ -0,0 +1,56 @@ +#Modified/simplified version of the node from: https://github.com/pamparamm/sd-perturbed-attention +#If you want the one with more options see the above repo. + +#My modified one here is more basic but has less chances of breaking with ComfyUI updates. + +import comfy.model_patcher +import comfy.samplers + +class PerturbedAttentionGuidance: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}), + } + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "_for_testing" + + def patch(self, model, scale): + unet_block = "middle" + unet_block_id = 0 + m = model.clone() + + def perturbed_attention(q, k, v, extra_options, mask=None): + return v + + def post_cfg_function(args): + model = args["model"] + cond_pred = args["cond_denoised"] + cond = args["cond"] + cfg_result = args["denoised"] + sigma = args["sigma"] + model_options = args["model_options"].copy() + x = args["input"] + + if scale == 0: + return cfg_result + + # Replace Self-attention with PAG + model_options = comfy.model_patcher.set_model_options_patch_replace(model_options, perturbed_attention, "attn1", unet_block, unet_block_id) + (pag,) = comfy.samplers.calc_cond_batch(model, [cond], x, sigma, model_options) + + return cfg_result + (cond_pred - pag) * scale + + m.set_model_sampler_post_cfg_function(post_cfg_function, disable_cfg1_optimization=True) + + return (m,) + +NODE_CLASS_MAPPINGS = { + "PerturbedAttentionGuidance": PerturbedAttentionGuidance, +} diff --git a/nodes.py b/nodes.py index fda073cf..ea1e3203 100644 --- a/nodes.py +++ b/nodes.py @@ -1942,6 +1942,7 @@ def init_custom_nodes(): "nodes_differential_diffusion.py", "nodes_ip2p.py", "nodes_model_merging_model_specific.py", + "nodes_pag.py", ] import_failed = [] From 8903dce862616ddd937400ffe6a971b66409262f Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 15 Apr 2024 12:14:00 -0400 Subject: [PATCH 162/378] This can be removed since PAG doesn't use the uncond. --- comfy_extras/nodes_pag.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy_extras/nodes_pag.py b/comfy_extras/nodes_pag.py index c48a3958..63f43fd6 100644 --- a/comfy_extras/nodes_pag.py +++ b/comfy_extras/nodes_pag.py @@ -47,7 +47,7 @@ class PerturbedAttentionGuidance: return cfg_result + (cond_pred - pag) * scale - m.set_model_sampler_post_cfg_function(post_cfg_function, disable_cfg1_optimization=True) + m.set_model_sampler_post_cfg_function(post_cfg_function) return (m,) From 45ec1cbe963055798765645c4f727122a7d3e35e Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 16 Apr 2024 02:57:01 -0400 Subject: [PATCH 163/378] Implement PerpNeg as a guider. --- comfy_extras/nodes_perpneg.py | 62 ++++++++++++++++++++++++++++++----- 1 file changed, 54 insertions(+), 8 deletions(-) diff --git a/comfy_extras/nodes_perpneg.py b/comfy_extras/nodes_perpneg.py index 992e8ab6..306cf9cd 100644 --- a/comfy_extras/nodes_perpneg.py +++ b/comfy_extras/nodes_perpneg.py @@ -3,9 +3,18 @@ import comfy.model_management import comfy.sampler_helpers import comfy.samplers import comfy.utils +import node_helpers +def perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_nocond, neg_scale, cond_scale): + pos = noise_pred_pos - noise_pred_nocond + neg = noise_pred_neg - noise_pred_nocond -#TODO: This node should be removed and replaced with one that uses the new Guider/SamplerCustomAdvanced. + perp = neg - ((torch.mul(neg, pos).sum())/(torch.norm(pos)**2)) * pos + perp_neg = perp * neg_scale + cfg_result = noise_pred_nocond + cond_scale*(pos - perp_neg) + return cfg_result + +#TODO: This node should be removed, it has been replaced with PerpNegGuider class PerpNeg: @classmethod def INPUT_TYPES(s): @@ -34,12 +43,7 @@ class PerpNeg: (noise_pred_nocond,) = comfy.samplers.calc_cond_batch(model, [nocond_processed], x, sigma, model_options) - pos = noise_pred_pos - noise_pred_nocond - neg = noise_pred_neg - noise_pred_nocond - perp = neg - ((torch.mul(neg, pos).sum())/(torch.norm(pos)**2)) * pos - perp_neg = perp * neg_scale - cfg_result = noise_pred_nocond + cond_scale*(pos - perp_neg) - cfg_result = x - cfg_result + cfg_result = x - perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_nocond, neg_scale, cond_scale) return cfg_result m.set_model_sampler_cfg_function(cfg_function) @@ -47,10 +51,52 @@ class PerpNeg: return (m, ) +class Guider_PerpNeg(comfy.samplers.CFGGuider): + def set_conds(self, positive, negative, empty_negative_prompt): + empty_negative_prompt = node_helpers.conditioning_set_values(empty_negative_prompt, {"prompt_type": "negative"}) + self.inner_set_conds({"positive": positive, "empty_negative_prompt": empty_negative_prompt, "negative": negative}) + + def set_cfg(self, cfg, neg_scale): + self.cfg = cfg + self.neg_scale = neg_scale + + def predict_noise(self, x, timestep, model_options={}, seed=None): + positive_cond = self.conds.get("positive", None) + negative_cond = self.conds.get("negative", None) + empty_cond = self.conds.get("empty_negative_prompt", None) + + out = comfy.samplers.calc_cond_batch(self.inner_model, [negative_cond, positive_cond, empty_cond], x, timestep, model_options) + return perp_neg(x, out[1], out[0], out[2], self.neg_scale, self.cfg) + +class PerpNegGuider: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "empty_conditioning": ("CONDITIONING", ), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), + "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), + } + } + + RETURN_TYPES = ("GUIDER",) + + FUNCTION = "get_guider" + CATEGORY = "_for_testing" + + def get_guider(self, model, positive, negative, empty_conditioning, cfg, neg_scale): + guider = Guider_PerpNeg(model) + guider.set_conds(positive, negative, empty_conditioning) + guider.set_cfg(cfg, neg_scale) + return (guider,) + NODE_CLASS_MAPPINGS = { "PerpNeg": PerpNeg, + "PerpNegGuider": PerpNegGuider, } NODE_DISPLAY_NAME_MAPPINGS = { - "PerpNeg": "Perp-Neg", + "PerpNeg": "Perp-Neg (DEPRECATED by PerpNegGuider)", } From abc69cab4560d0c50bc757de2695cf19fe1bfc3f Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 17 Apr 2024 12:28:05 -0400 Subject: [PATCH 164/378] Add a helpful warning for links that don't point anywhere. --- comfy_extras/nodes_canny.py | 1 - folder_paths.py | 2 ++ 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/comfy_extras/nodes_canny.py b/comfy_extras/nodes_canny.py index 8138b5f7..fab2ab7a 100644 --- a/comfy_extras/nodes_canny.py +++ b/comfy_extras/nodes_canny.py @@ -1,4 +1,3 @@ -#From https://github.com/kornia/kornia import math import torch diff --git a/folder_paths.py b/folder_paths.py index a84524d9..3f9a32b4 100644 --- a/folder_paths.py +++ b/folder_paths.py @@ -181,6 +181,8 @@ def get_full_path(folder_name, filename): full_path = os.path.join(x, filename) if os.path.isfile(full_path): return full_path + elif os.path.islink(full_path): + logging.warning("WARNING path {} exists but doesn't link anywhere, skipping.".format(full_path)) return None From 072e3bd2b542eaf7f757d898f2fac67de4dea099 Mon Sep 17 00:00:00 2001 From: "Dr.Lt.Data" <128333288+ltdrdata@users.noreply.github.com> Date: Thu, 18 Apr 2024 05:36:49 +0900 Subject: [PATCH 165/378] Fixed an issue where the main menu disappears intermittently as the coordinates become negative. (#3269) --- web/scripts/ui.js | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/web/scripts/ui.js b/web/scripts/ui.js index 5ca6214e..d0fa46ef 100644 --- a/web/scripts/ui.js +++ b/web/scripts/ui.js @@ -90,12 +90,15 @@ function dragElement(dragEl, settings) { }).observe(dragEl); function ensureInBounds() { - if (dragEl.classList.contains("comfy-menu-manual-pos")) { + try { newPosX = Math.min(document.body.clientWidth - dragEl.clientWidth, Math.max(0, dragEl.offsetLeft)); newPosY = Math.min(document.body.clientHeight - dragEl.clientHeight, Math.max(0, dragEl.offsetTop)); positionElement(); } + catch(exception){ + // robust + } } function positionElement() { From d64e2174276c0f5a1db605af6f0331eb0c75b42d Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 17 Apr 2024 17:34:02 -0400 Subject: [PATCH 166/378] Fix annoying float issue causing the value to be rounded to above the max. --- web/scripts/widgets.js | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/web/scripts/widgets.js b/web/scripts/widgets.js index 678b1b8e..00c91914 100644 --- a/web/scripts/widgets.js +++ b/web/scripts/widgets.js @@ -307,7 +307,9 @@ export const ComfyWidgets = { return { widget: node.addWidget(widgetType, inputName, val, function (v) { if (config.round) { - this.value = Math.round(v/config.round)*config.round; + this.value = Math.round((v + Number.EPSILON)/config.round)*config.round; + if (this.value > config.max) this.value = config.max; + if (this.value < config.min) this.value = config.min; } else { this.value = v; } From a88b0ebc2d2f933c94e42aa689c42e836eedaf3c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Torbj=C3=B6rn=20L=C3=B6nnemark?= Date: Thu, 18 Apr 2024 22:41:23 +0200 Subject: [PATCH 167/378] Improve node input/widget conversion sub-menus (#3281) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Make input/widget conversion sub-menus optional * Improve input/widget conversion sub-menu text - Fix incorrect text for conversion from widget to input, previously it effectively said "convert input to input" - Use "input" instead of "🔘". The former is clearer and consistent with the rest of the application. - Use title case (consistent with the rest of the menu entries). - Strip the trailing periods. There is already a visual indicator for sub-menus, and no other sub-menus use trailing periods. --- tests-ui/utils/ezgraph.js | 4 +-- web/extensions/core/widgetInputs.js | 42 ++++++++++++++++++++--------- 2 files changed, 32 insertions(+), 14 deletions(-) diff --git a/tests-ui/utils/ezgraph.js b/tests-ui/utils/ezgraph.js index 8bf8c5d8..97be7aa7 100644 --- a/tests-ui/utils/ezgraph.js +++ b/tests-ui/utils/ezgraph.js @@ -204,7 +204,7 @@ export class EzWidget { convertToWidget() { if (!this.isConvertedToInput) throw new Error(`Widget ${this.widget.name} cannot be converted as it is already a widget.`); - var menu = this.node.menu["Convert 🔘 to widget.."].item.submenu.options; + var menu = this.node.menu["Convert Input to Widget"].item.submenu.options; var index = menu.findIndex(a => a.content == `Convert ${this.widget.name} to widget`); menu[index].callback.call(); } @@ -212,7 +212,7 @@ export class EzWidget { convertToInput() { if (this.isConvertedToInput) throw new Error(`Widget ${this.widget.name} cannot be converted as it is already an input.`); - var menu = this.node.menu["Convert input to 🔘.."].item.submenu.options; + var menu = this.node.menu["Convert Widget to Input"].item.submenu.options; var index = menu.findIndex(a => a.content == `Convert ${this.widget.name} to input`); menu[index].callback.call(); } diff --git a/web/extensions/core/widgetInputs.js b/web/extensions/core/widgetInputs.js index e6db9f71..f1a1d22c 100644 --- a/web/extensions/core/widgetInputs.js +++ b/web/extensions/core/widgetInputs.js @@ -256,8 +256,18 @@ export function mergeIfValid(output, config2, forceUpdate, recreateWidget, confi return { customConfig }; } +let useConversionSubmenusSetting; app.registerExtension({ name: "Comfy.WidgetInputs", + init() { + useConversionSubmenusSetting = app.ui.settings.addSetting({ + id: "Comfy.NodeInputConversionSubmenus", + name: "Node widget/input conversion sub-menus", + tooltip: "In the node context menu, place the entries that convert between input/widget in sub-menus.", + type: "boolean", + defaultValue: true, + }); + }, async beforeRegisterNodeDef(nodeType, nodeData, app) { // Add menu options to conver to/from widgets const origGetExtraMenuOptions = nodeType.prototype.getExtraMenuOptions; @@ -295,20 +305,28 @@ app.registerExtension({ //Convert.. main menu if (toInput.length) { - options.push({ - content: `Convert input to 🔘..`, - submenu: { - options: toInput, - }, - }); + if (useConversionSubmenusSetting.value) { + options.push({ + content: "Convert Widget to Input", + submenu: { + options: toInput, + }, + }); + } else { + options.push(...toInput, null); + } } if (toWidget.length) { - options.push({ - content: `Convert 🔘 to widget..`, - submenu: { - options: toWidget, - }, - }); + if (useConversionSubmenusSetting.value) { + options.push({ + content: "Convert Input to Widget", + submenu: { + options: toWidget, + }, + }); + } else { + options.push(...toWidget, null); + } } } From c59fe9f254d9e423aa3bb52ca4b6d8b4679bd11c Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 18 Apr 2024 21:05:33 -0400 Subject: [PATCH 168/378] Support VAE without quant_conv. --- comfy/sd.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/comfy/sd.py b/comfy/sd.py index 3919f4bf..57dba0b4 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -214,12 +214,18 @@ class VAE: #default SD1.x/SD2.x VAE parameters ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} - if 'encoder.down.2.downsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE + if 'encoder.down.2.downsample.conv.weight' not in sd and 'decoder.up.3.upsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE ddconfig['ch_mult'] = [1, 2, 4] self.downscale_ratio = 4 self.upscale_ratio = 4 - self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=4) + self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1] + if 'quant_conv.weight' in sd: + self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=4) + else: + self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, + encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig}, + decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig}) else: self.first_stage_model = AutoencoderKL(**(config['params'])) self.first_stage_model = self.first_stage_model.eval() From 5d08802f783a59d4fbdcfd5462f7edd9c6e0204d Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 19 Apr 2024 03:43:09 -0400 Subject: [PATCH 169/378] Sync some minor changes from the other repo. --- execution.py | 2 +- folder_paths.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/execution.py b/execution.py index 35bdb77a..47d58b9d 100644 --- a/execution.py +++ b/execution.py @@ -176,7 +176,7 @@ def recursive_execute(server, prompt, outputs, current_item, extra_data, execute for node_id, node_outputs in outputs.items(): output_data_formatted[node_id] = [[format_value(x) for x in l] for l in node_outputs] - logging.error("!!! Exception during processing !!!") + logging.error(f"!!! Exception during processing!!! {ex}") logging.error(traceback.format_exc()) error_details = { diff --git a/folder_paths.py b/folder_paths.py index 3f9a32b4..48979500 100644 --- a/folder_paths.py +++ b/folder_paths.py @@ -2,7 +2,7 @@ import os import time import logging -supported_pt_extensions = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors']) +supported_pt_extensions = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl']) folder_names_and_paths = {} From 133dc3351b3277f6ce41da7839ace9055329c64c Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 19 Apr 2024 03:52:02 -0400 Subject: [PATCH 170/378] Faster blur. --- comfy_extras/nodes_post_processing.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py index 0110b472..f0e83fc6 100644 --- a/comfy_extras/nodes_post_processing.py +++ b/comfy_extras/nodes_post_processing.py @@ -5,6 +5,7 @@ from PIL import Image import math import comfy.utils +import comfy.model_management class Blend: @@ -102,6 +103,7 @@ class Blur: if blur_radius == 0: return (image,) + image = image.to(comfy.model_management.get_torch_device()) batch_size, height, width, channels = image.shape kernel_size = blur_radius * 2 + 1 @@ -112,7 +114,7 @@ class Blur: blurred = F.conv2d(padded_image, kernel, padding=kernel_size // 2, groups=channels)[:,:,blur_radius:-blur_radius, blur_radius:-blur_radius] blurred = blurred.permute(0, 2, 3, 1) - return (blurred,) + return (blurred.to(comfy.model_management.intermediate_device()),) class Quantize: def __init__(self): From 644a3ae58d426ffbbc02ef4104034c98e8fc6513 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 20 Apr 2024 04:31:49 -0400 Subject: [PATCH 171/378] Implement Align Your Steps as a AlignYourStepsScheduler node. --- comfy_extras/nodes_align_your_steps.py | 45 ++++++++++++++++++++++++++ nodes.py | 1 + 2 files changed, 46 insertions(+) create mode 100644 comfy_extras/nodes_align_your_steps.py diff --git a/comfy_extras/nodes_align_your_steps.py b/comfy_extras/nodes_align_your_steps.py new file mode 100644 index 00000000..b59f6945 --- /dev/null +++ b/comfy_extras/nodes_align_your_steps.py @@ -0,0 +1,45 @@ +#from: https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html +import numpy as np +import torch + +def loglinear_interp(t_steps, num_steps): + """ + Performs log-linear interpolation of a given array of decreasing numbers. + """ + xs = np.linspace(0, 1, len(t_steps)) + ys = np.log(t_steps[::-1]) + + new_xs = np.linspace(0, 1, num_steps) + new_ys = np.interp(new_xs, xs, ys) + + interped_ys = np.exp(new_ys)[::-1].copy() + return interped_ys + +NOISE_LEVELS = {"SD1": [14.6146412293, 6.4745760956, 3.8636745985, 2.6946151520, 1.8841921177, 1.3943805092, 0.9642583904, 0.6523686016, 0.3977456272, 0.1515232662, 0.0291671582], + "SDXL":[14.6146412293, 6.3184485287, 3.7681790315, 2.1811480769, 1.3405244945, 0.8620721141, 0.5550693289, 0.3798540708, 0.2332364134, 0.1114188177, 0.0291671582], + "SVD": [700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002]} + +class AlignYourStepsScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model_type": (["SD1", "SDXL", "SVD"], ), + "steps": ("INT", {"default": 10, "min": 10, "max": 10000}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, model_type, steps): + sigmas = NOISE_LEVELS[model_type][:] + if (steps + 1) != len(sigmas): + sigmas = loglinear_interp(sigmas, steps + 1) + + sigmas[-1] = 0 + return (torch.FloatTensor(sigmas), ) + +NODE_CLASS_MAPPINGS = { + "AlignYourStepsScheduler": AlignYourStepsScheduler, +} diff --git a/nodes.py b/nodes.py index ea1e3203..28359e93 100644 --- a/nodes.py +++ b/nodes.py @@ -1943,6 +1943,7 @@ def init_custom_nodes(): "nodes_ip2p.py", "nodes_model_merging_model_specific.py", "nodes_pag.py", + "nodes_align_your_steps.py", ] import_failed = [] From 4ee9aad6caf4c23e69e9e166573c505e757062e5 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 21 Apr 2024 09:02:06 -0400 Subject: [PATCH 172/378] Speed up Sharpen node. --- comfy_extras/nodes_post_processing.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py index f0e83fc6..68f6ef51 100644 --- a/comfy_extras/nodes_post_processing.py +++ b/comfy_extras/nodes_post_processing.py @@ -227,6 +227,7 @@ class Sharpen: return (image,) batch_size, height, width, channels = image.shape + image = image.to(comfy.model_management.get_torch_device()) kernel_size = sharpen_radius * 2 + 1 kernel = gaussian_kernel(kernel_size, sigma, device=image.device) * -(alpha*10) @@ -241,7 +242,7 @@ class Sharpen: result = torch.clamp(sharpened, 0, 1) - return (result,) + return (result.to(comfy.model_management.intermediate_device()),) class ImageScaleToTotalPixels: upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] From d09b5ef4ef150adab31195761725eaba409f6343 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 22 Apr 2024 18:42:41 -0400 Subject: [PATCH 173/378] Free some memory before loading upscale models. --- comfy_extras/nodes_upscale_model.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/comfy_extras/nodes_upscale_model.py b/comfy_extras/nodes_upscale_model.py index 2b5e49a5..4de5d7e3 100644 --- a/comfy_extras/nodes_upscale_model.py +++ b/comfy_extras/nodes_upscale_model.py @@ -37,9 +37,14 @@ class ImageUpscaleWithModel: def upscale(self, upscale_model, image): device = model_management.get_torch_device() + + memory_required = model_management.module_size(upscale_model) + memory_required += (512 * 512 * 3) * image.element_size() * max(upscale_model.scale, 1.0) * 256.0 #The 256.0 is an estimate of how much some of these models take, TODO: make it more accurate + memory_required += image.nelement() * image.element_size() + model_management.free_memory(memory_required, device) + upscale_model.to(device) in_img = image.movedim(-1,-3).to(device) - free_memory = model_management.get_free_memory(device) tile = 512 overlap = 32 From b8218522f112be2e69fd49bbefbe68b57868baa0 Mon Sep 17 00:00:00 2001 From: Pam <42671363+pamparamm@users.noreply.github.com> Date: Tue, 23 Apr 2024 18:40:10 +0500 Subject: [PATCH 174/378] Increase sigma_min/sigma_max range for custom schedulers (#3317) --- comfy_extras/nodes_custom_sampler.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index 06238f89..7afdbf4b 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -39,8 +39,8 @@ class KarrasScheduler: def INPUT_TYPES(s): return {"required": {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), - "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), - "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), + "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), + "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), "rho": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), } } @@ -58,8 +58,8 @@ class ExponentialScheduler: def INPUT_TYPES(s): return {"required": {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), - "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), - "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), + "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), + "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), } } RETURN_TYPES = ("SIGMAS",) @@ -76,8 +76,8 @@ class PolyexponentialScheduler: def INPUT_TYPES(s): return {"required": {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), - "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), - "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), + "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), + "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), "rho": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), } } @@ -117,8 +117,8 @@ class VPScheduler: def INPUT_TYPES(s): return {"required": {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), - "beta_d": ("FLOAT", {"default": 19.9, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), #TODO: fix default values - "beta_min": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), + "beta_d": ("FLOAT", {"default": 19.9, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), #TODO: fix default values + "beta_min": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), "eps_s": ("FLOAT", {"default": 0.001, "min": 0.0, "max": 1.0, "step":0.0001, "round": False}), } } From 27d5808fc491c7174abc6f407e7dc11c6a7a1ec0 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 23 Apr 2024 13:07:39 -0400 Subject: [PATCH 175/378] Increase max lora strength to 100.0 --- nodes.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/nodes.py b/nodes.py index 28359e93..a1cfd636 100644 --- a/nodes.py +++ b/nodes.py @@ -583,8 +583,8 @@ class LoraLoader: return {"required": { "model": ("MODEL",), "clip": ("CLIP", ), "lora_name": (folder_paths.get_filename_list("loras"), ), - "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), - "strength_clip": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), + "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}), + "strength_clip": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL", "CLIP") FUNCTION = "load_lora" @@ -617,7 +617,7 @@ class LoraLoaderModelOnly(LoraLoader): def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "lora_name": (folder_paths.get_filename_list("loras"), ), - "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), + "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "load_lora_model_only" From 8dc19e40d129c8ee049be7be2657458509717ba5 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 24 Apr 2024 09:20:31 -0400 Subject: [PATCH 176/378] Don't init a VAE model when there are no VAE weights. --- comfy/sd.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/comfy/sd.py b/comfy/sd.py index 57dba0b4..16dc0b73 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -210,7 +210,7 @@ class VAE: self.first_stage_model = StageC_coder() self.downscale_ratio = 32 self.latent_channels = 16 - else: + elif "decoder.conv_in.weight" in sd: #default SD1.x/SD2.x VAE parameters ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} @@ -226,6 +226,10 @@ class VAE: self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig}, decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig}) + else: + logging.warning("WARNING: No VAE weights detected, VAE not initalized.") + self.first_stage_model = None + return else: self.first_stage_model = AutoencoderKL(**(config['params'])) self.first_stage_model = self.first_stage_model.eval() From 16eabdf70dbdb64dc4822908f0fe455c56d11ec3 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 25 Apr 2024 17:04:19 -0400 Subject: [PATCH 177/378] Free more vram for upscale models. --- comfy_extras/nodes_upscale_model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy_extras/nodes_upscale_model.py b/comfy_extras/nodes_upscale_model.py index 4de5d7e3..52c95df2 100644 --- a/comfy_extras/nodes_upscale_model.py +++ b/comfy_extras/nodes_upscale_model.py @@ -39,7 +39,7 @@ class ImageUpscaleWithModel: device = model_management.get_torch_device() memory_required = model_management.module_size(upscale_model) - memory_required += (512 * 512 * 3) * image.element_size() * max(upscale_model.scale, 1.0) * 256.0 #The 256.0 is an estimate of how much some of these models take, TODO: make it more accurate + memory_required += (512 * 512 * 3) * image.element_size() * max(upscale_model.scale, 1.0) * 384.0 #The 384.0 is an estimate of how much some of these models take, TODO: make it more accurate memory_required += image.nelement() * image.element_size() model_management.free_memory(memory_required, device) From 7990ae18c1df1dc18b0f1197c9e09551c202c829 Mon Sep 17 00:00:00 2001 From: Jedrzej Kosinski Date: Fri, 26 Apr 2024 11:51:12 -0500 Subject: [PATCH 178/378] Fix error when more cond masks passed in than batch size (#3353) --- comfy/samplers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/samplers.py b/comfy/samplers.py index 415a35cc..b12b0fd1 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -34,7 +34,7 @@ def get_area_and_mult(conds, x_in, timestep_in): mask = conds['mask'] assert(mask.shape[1] == x_in.shape[2]) assert(mask.shape[2] == x_in.shape[3]) - mask = mask[:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] * mask_strength + mask = mask[:input_x.shape[0],area[2]:area[0] + area[2],area[3]:area[1] + area[3]] * mask_strength mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1) else: mask = torch.ones_like(input_x) From 8cab3be67351a6185945bfecab21ccdfa60d80cd Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 26 Apr 2024 15:44:12 -0400 Subject: [PATCH 179/378] Update command for AMD stable pytorch install in README. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index ba1e844b..8ca7e437 100644 --- a/README.md +++ b/README.md @@ -99,7 +99,7 @@ Put your VAE in: models/vae ### AMD GPUs (Linux only) AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version: -```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.7``` +```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.0``` This is the command to install the nightly with ROCm 6.0 which might have some performance improvements: From 10fcd09f4af5de62aa662dab03320cfca46b0edb Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 27 Apr 2024 00:14:06 -0400 Subject: [PATCH 180/378] Add a denoise value to AlignYourStepsScheduler. --- comfy_extras/nodes_align_your_steps.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/comfy_extras/nodes_align_your_steps.py b/comfy_extras/nodes_align_your_steps.py index b59f6945..3ffe5318 100644 --- a/comfy_extras/nodes_align_your_steps.py +++ b/comfy_extras/nodes_align_your_steps.py @@ -25,6 +25,7 @@ class AlignYourStepsScheduler: return {"required": {"model_type": (["SD1", "SDXL", "SVD"], ), "steps": ("INT", {"default": 10, "min": 10, "max": 10000}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), } } RETURN_TYPES = ("SIGMAS",) @@ -32,11 +33,18 @@ class AlignYourStepsScheduler: FUNCTION = "get_sigmas" - def get_sigmas(self, model_type, steps): + def get_sigmas(self, model_type, steps, denoise): + total_steps = steps + if denoise < 1.0: + if denoise <= 0.0: + return (torch.FloatTensor([]),) + total_steps = round(steps * denoise) + sigmas = NOISE_LEVELS[model_type][:] if (steps + 1) != len(sigmas): sigmas = loglinear_interp(sigmas, steps + 1) + sigmas = sigmas[-(total_steps + 1):] sigmas[-1] = 0 return (torch.FloatTensor(sigmas), ) From 059773a6df310d44026be12140310688a16e3735 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 28 Apr 2024 12:50:22 -0400 Subject: [PATCH 181/378] Add some nodes to multiply the attention in UNet and Clip models. --- comfy_extras/nodes_attention_multiply.py | 120 +++++++++++++++++++++++ nodes.py | 1 + 2 files changed, 121 insertions(+) create mode 100644 comfy_extras/nodes_attention_multiply.py diff --git a/comfy_extras/nodes_attention_multiply.py b/comfy_extras/nodes_attention_multiply.py new file mode 100644 index 00000000..4747eb39 --- /dev/null +++ b/comfy_extras/nodes_attention_multiply.py @@ -0,0 +1,120 @@ + +def attention_multiply(attn, model, q, k, v, out): + m = model.clone() + sd = model.model_state_dict() + + for key in sd: + if key.endswith("{}.to_q.bias".format(attn)) or key.endswith("{}.to_q.weight".format(attn)): + m.add_patches({key: (None,)}, 0.0, q) + if key.endswith("{}.to_k.bias".format(attn)) or key.endswith("{}.to_k.weight".format(attn)): + m.add_patches({key: (None,)}, 0.0, k) + if key.endswith("{}.to_v.bias".format(attn)) or key.endswith("{}.to_v.weight".format(attn)): + m.add_patches({key: (None,)}, 0.0, v) + if key.endswith("{}.to_out.0.bias".format(attn)) or key.endswith("{}.to_out.0.weight".format(attn)): + m.add_patches({key: (None,)}, 0.0, out) + + return m + + +class UNetSelfAttentionMultiply: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "q": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "k": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "v": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "_for_testing/attention_experiments" + + def patch(self, model, q, k, v, out): + m = attention_multiply("attn1", model, q, k, v, out) + return (m, ) + +class UNetCrossAttentionMultiply: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "q": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "k": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "v": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "_for_testing/attention_experiments" + + def patch(self, model, q, k, v, out): + m = attention_multiply("attn2", model, q, k, v, out) + return (m, ) + +class CLIPAttentionMultiply: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip": ("CLIP",), + "q": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "k": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "v": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "patch" + + CATEGORY = "_for_testing/attention_experiments" + + def patch(self, clip, q, k, v, out): + m = clip.clone() + sd = m.patcher.model_state_dict() + + for key in sd: + if key.endswith("self_attn.q_proj.weight") or key.endswith("self_attn.q_proj.bias"): + m.add_patches({key: (None,)}, 0.0, q) + if key.endswith("self_attn.k_proj.weight") or key.endswith("self_attn.k_proj.bias"): + m.add_patches({key: (None,)}, 0.0, k) + if key.endswith("self_attn.v_proj.weight") or key.endswith("self_attn.v_proj.bias"): + m.add_patches({key: (None,)}, 0.0, v) + if key.endswith("self_attn.out_proj.weight") or key.endswith("self_attn.out_proj.bias"): + m.add_patches({key: (None,)}, 0.0, out) + return (m, ) + +class UNetTemporalAttentionMultiply: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "self_structural": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "self_temporal": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "cross_structural": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "cross_temporal": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "_for_testing/attention_experiments" + + def patch(self, model, self_structural, self_temporal, cross_structural, cross_temporal): + m = model.clone() + sd = model.model_state_dict() + + for k in sd: + if (k.endswith("attn1.to_out.0.bias") or k.endswith("attn1.to_out.0.weight")): + if '.time_stack.' in k: + m.add_patches({k: (None,)}, 0.0, self_temporal) + else: + m.add_patches({k: (None,)}, 0.0, self_structural) + elif (k.endswith("attn2.to_out.0.bias") or k.endswith("attn2.to_out.0.weight")): + if '.time_stack.' in k: + m.add_patches({k: (None,)}, 0.0, cross_temporal) + else: + m.add_patches({k: (None,)}, 0.0, cross_structural) + return (m, ) + +NODE_CLASS_MAPPINGS = { + "UNetSelfAttentionMultiply": UNetSelfAttentionMultiply, + "UNetCrossAttentionMultiply": UNetCrossAttentionMultiply, + "CLIPAttentionMultiply": CLIPAttentionMultiply, + "UNetTemporalAttentionMultiply": UNetTemporalAttentionMultiply, +} diff --git a/nodes.py b/nodes.py index a1cfd636..1651a71c 100644 --- a/nodes.py +++ b/nodes.py @@ -1944,6 +1944,7 @@ def init_custom_nodes(): "nodes_model_merging_model_specific.py", "nodes_pag.py", "nodes_align_your_steps.py", + "nodes_attention_multiply.py", ] import_failed = [] From eecd69b53a896343775bcb02a4f8349e7442ffd1 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 29 Apr 2024 20:00:47 -0400 Subject: [PATCH 182/378] Add a SamplerLCMUpscale node. This sampler is an LCM sampler that upscales the latent during sampling. It can be used to generate at a higher resolution with an LCM model very quickly. To try it use it with a basic 5 step LCM workflow with scale_ratio 1.5 or 2.0 --- comfy_extras/nodes_advanced_samplers.py | 61 +++++++++++++++++++++++++ nodes.py | 1 + 2 files changed, 62 insertions(+) create mode 100644 comfy_extras/nodes_advanced_samplers.py diff --git a/comfy_extras/nodes_advanced_samplers.py b/comfy_extras/nodes_advanced_samplers.py new file mode 100644 index 00000000..d973def8 --- /dev/null +++ b/comfy_extras/nodes_advanced_samplers.py @@ -0,0 +1,61 @@ +import comfy.samplers +import comfy.utils +import torch +import numpy as np +from tqdm.auto import trange, tqdm +import math + + +@torch.no_grad() +def sample_lcm_upscale(model, x, sigmas, extra_args=None, callback=None, disable=None, total_upscale=2.0, upscale_method="bislerp", upscale_steps=None): + extra_args = {} if extra_args is None else extra_args + + if upscale_steps is None: + upscale_steps = max(len(sigmas) // 2 + 1, 2) + else: + upscale_steps += 1 + upscale_steps = min(upscale_steps, len(sigmas) + 1) + + upscales = np.linspace(1.0, total_upscale, upscale_steps)[1:] + + orig_shape = x.size() + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + + x = denoised + if i < len(upscales): + x = comfy.utils.common_upscale(x, round(orig_shape[-1] * upscales[i]), round(orig_shape[-2] * upscales[i]), upscale_method, "disabled") + + if sigmas[i + 1] > 0: + x += sigmas[i + 1] * torch.randn_like(x) + return x + + +class SamplerLCMUpscale: + upscale_methods = ["bislerp", "nearest-exact", "bilinear", "area", "bicubic"] + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"scale_ratio": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 20.0, "step": 0.01}), + "scale_steps": ("INT", {"default": -1, "min": -1, "max": 1000, "step": 1}), + "upscale_method": (s.upscale_methods,), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, scale_ratio, scale_steps, upscale_method): + if scale_steps < 0: + scale_steps = None + sampler = comfy.samplers.KSAMPLER(sample_lcm_upscale, extra_options={"total_upscale": scale_ratio, "upscale_steps": scale_steps, "upscale_method": upscale_method}) + return (sampler, ) + +NODE_CLASS_MAPPINGS = { + "SamplerLCMUpscale": SamplerLCMUpscale, +} diff --git a/nodes.py b/nodes.py index 1651a71c..acad256f 100644 --- a/nodes.py +++ b/nodes.py @@ -1945,6 +1945,7 @@ def init_custom_nodes(): "nodes_pag.py", "nodes_align_your_steps.py", "nodes_attention_multiply.py", + "nodes_advanced_samplers.py", ] import_failed = [] From bb8b48a260256bd7461f45de0397d15df822e5a5 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 30 Apr 2024 20:11:34 -0400 Subject: [PATCH 183/378] Update Readme. --- README.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/README.md b/README.md index 8ca7e437..d392cd80 100644 --- a/README.md +++ b/README.md @@ -197,9 +197,7 @@ To use a textual inversion concepts/embeddings in a text prompt put them in the ## How to increase generation speed? -Make sure you use the regular loaders/Load Checkpoint node to load checkpoints. It will auto pick the right settings depending on your GPU. - -You can set this command line setting to disable the upcasting to fp32 in some cross attention operations which will increase your speed. Note that this will very likely give you black images on SD2.x models. If you use xformers or pytorch attention this option does not do anything. +On non Nvidia hardware you can set this command line setting to disable the upcasting to fp32 in some cross attention operations which will increase your speed. Note that this will very likely give you black images on SD2.x models. If you use xformers or pytorch attention this option does not do anything. ```--dont-upcast-attention``` From bacce529fbedeafc826731f4281f325f4eae3f85 Mon Sep 17 00:00:00 2001 From: Garrett Sutula Date: Tue, 30 Apr 2024 20:17:02 -0400 Subject: [PATCH 184/378] Add TLS Support (#3312) * Add TLS Support * Add to readme * Add guidance for windows users on generating certificates * Add guidance for windows users on generating certificates * Fix typo --- README.md | 8 ++++++++ comfy/cli_args.py | 2 ++ main.py | 4 ++-- server.py | 15 ++++++++++++--- 4 files changed, 24 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index d392cd80..eb07540c 100644 --- a/README.md +++ b/README.md @@ -207,6 +207,14 @@ Use ```--preview-method auto``` to enable previews. The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with [TAESD](https://github.com/madebyollin/taesd), download the [taesd_decoder.pth](https://github.com/madebyollin/taesd/raw/main/taesd_decoder.pth) (for SD1.x and SD2.x) and [taesdxl_decoder.pth](https://github.com/madebyollin/taesd/raw/main/taesdxl_decoder.pth) (for SDXL) models and place them in the `models/vae_approx` folder. Once they're installed, restart ComfyUI to enable high-quality previews. +## How to use TLS/SSL? +Generate a self-signed certificate (not appropriate for shared/production use) and key by running the command: `openssl req -x509 -newkey rsa:4096 -keyout key.pem -out cert.pem -sha256 -days 3650 -nodes -subj "/C=XX/ST=StateName/L=CityName/O=CompanyName/OU=CompanySectionName/CN=CommonNameOrHostname"` + +Use `--tls-keyfile key.pem --tls-certfile cert.pem` to enable TLS/SSL, the app will now be accessible with `https://...` instead of `http://...`. + +> Note: Windows users can use [alexisrolland/docker-openssl](https://github.com/alexisrolland/docker-openssl) or one of the [3rd party binary distributions](https://wiki.openssl.org/index.php/Binaries) to run the command example above. +

If you use a container, note that the volume mount `-v` can be a relative path so `... -v ".\:/openssl-certs" ...` would create the key & cert files in the current directory of your command prompt or powershell terminal. + ## Support and dev channel [Matrix space: #comfyui_space:matrix.org](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) (it's like discord but open source). diff --git a/comfy/cli_args.py b/comfy/cli_args.py index 353bb51e..569c7938 100644 --- a/comfy/cli_args.py +++ b/comfy/cli_args.py @@ -35,6 +35,8 @@ parser = argparse.ArgumentParser() parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0", help="Specify the IP address to listen on (default: 127.0.0.1). If --listen is provided without an argument, it defaults to 0.0.0.0. (listens on all)") parser.add_argument("--port", type=int, default=8188, help="Set the listen port.") +parser.add_argument("--tls-keyfile", type=str, help="Path to TLS (SSL) key file. Enables TLS, makes app accessible at https://... requires --tls-certfile to function") +parser.add_argument("--tls-certfile", type=str, help="Path to TLS (SSL) certificate file. Enables TLS, makes app accessible at https://... requires --tls-keyfile to function") parser.add_argument("--enable-cors-header", type=str, default=None, metavar="ORIGIN", nargs="?", const="*", help="Enable CORS (Cross-Origin Resource Sharing) with optional origin or allow all with default '*'.") parser.add_argument("--max-upload-size", type=float, default=100, help="Set the maximum upload size in MB.") diff --git a/main.py b/main.py index b3a3ebea..a374f2b1 100644 --- a/main.py +++ b/main.py @@ -243,11 +243,11 @@ if __name__ == "__main__": call_on_start = None if args.auto_launch: - def startup_server(address, port): + def startup_server(scheme, address, port): import webbrowser if os.name == 'nt' and address == '0.0.0.0': address = '127.0.0.1' - webbrowser.open(f"http://{address}:{port}") + webbrowser.open(f"{scheme}://{address}:{port}") call_on_start = startup_server try: diff --git a/server.py b/server.py index 5642bd5e..bab3b060 100644 --- a/server.py +++ b/server.py @@ -11,6 +11,7 @@ import urllib import json import glob import struct +import ssl from PIL import Image, ImageOps from PIL.PngImagePlugin import PngInfo from io import BytesIO @@ -623,14 +624,22 @@ class PromptServer(): async def start(self, address, port, verbose=True, call_on_start=None): runner = web.AppRunner(self.app, access_log=None) await runner.setup() - site = web.TCPSite(runner, address, port) + ssl_ctx = None + scheme = "http" + if args.tls_keyfile and args.tls_certfile: + ssl_ctx = ssl.SSLContext(protocol=ssl.PROTOCOL_TLS_SERVER, verify_mode=ssl.CERT_NONE) + ssl_ctx.load_cert_chain(certfile=args.tls_certfile, + keyfile=args.tls_keyfile) + scheme = "https" + + site = web.TCPSite(runner, address, port, ssl_context=ssl_ctx) await site.start() if verbose: logging.info("Starting server\n") - logging.info("To see the GUI go to: http://{}:{}".format(address, port)) + logging.info("To see the GUI go to: {}://{}:{}".format(scheme, address, port)) if call_on_start is not None: - call_on_start(address, port) + call_on_start(scheme, address, port) def add_on_prompt_handler(self, handler): self.on_prompt_handlers.append(handler) From 2aed53c4ac78d842a2e984d23343334a29ed8562 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 30 Apr 2024 21:23:40 -0400 Subject: [PATCH 185/378] Workaround xformers bug. --- comfy/ldm/modules/attention.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py index f116efee..d51a2fae 100644 --- a/comfy/ldm/modules/attention.py +++ b/comfy/ldm/modules/attention.py @@ -297,8 +297,8 @@ def attention_split(q, k, v, heads, mask=None): BROKEN_XFORMERS = False try: x_vers = xformers.__version__ - #I think 0.0.23 is also broken (q with bs bigger than 65535 gives CUDA error) - BROKEN_XFORMERS = x_vers.startswith("0.0.21") or x_vers.startswith("0.0.22") or x_vers.startswith("0.0.23") + # XFormers bug confirmed on all versions from 0.0.21 to 0.0.26 (q with bs bigger than 65535 gives CUDA error) + BROKEN_XFORMERS = x_vers.startswith("0.0.2") and not x_vers.startswith("0.0.20") except: pass From 94d5a128010f5956d0e6d1ea905b60890bd3bad7 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 1 May 2024 16:57:10 -0400 Subject: [PATCH 186/378] Don't load the model in SDTurboScheduler --- comfy_extras/nodes_custom_sampler.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index 7afdbf4b..f3dff000 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -107,8 +107,7 @@ class SDTurboScheduler: def get_sigmas(self, model, steps, denoise): start_step = 10 - int(10 * denoise) timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[start_step:start_step + steps] - comfy.model_management.load_models_gpu([model]) - sigmas = model.model.model_sampling.sigma(timesteps) + sigmas = model.get_model_object("model_sampling").sigma(timesteps) sigmas = torch.cat([sigmas, sigmas.new_zeros([1])]) return (sigmas, ) From f81a6fade86bfaec91a115284745d9f95b74e265 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 1 May 2024 17:05:30 -0400 Subject: [PATCH 187/378] Fix some edge cases with samplers and arrays with a single sigma. --- comfy/k_diffusion/sampling.py | 17 +++++++++++++++++ comfy/samplers.py | 6 ++++++ 2 files changed, 23 insertions(+) diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py index 7af01682..f9b28189 100644 --- a/comfy/k_diffusion/sampling.py +++ b/comfy/k_diffusion/sampling.py @@ -527,6 +527,9 @@ def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None, @torch.no_grad() def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2): """DPM-Solver++ (stochastic).""" + if len(sigmas) <= 1: + return x + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() seed = extra_args.get("seed", None) noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler @@ -595,6 +598,8 @@ def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=No @torch.no_grad() def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'): """DPM-Solver++(2M) SDE.""" + if len(sigmas) <= 1: + return x if solver_type not in {'heun', 'midpoint'}: raise ValueError('solver_type must be \'heun\' or \'midpoint\'') @@ -642,6 +647,9 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): """DPM-Solver++(3M) SDE.""" + if len(sigmas) <= 1: + return x + seed = extra_args.get("seed", None) sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler @@ -690,18 +698,27 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl @torch.no_grad() def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + if len(sigmas) <= 1: + return x + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler) @torch.no_grad() def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'): + if len(sigmas) <= 1: + return x + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type) @torch.no_grad() def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2): + if len(sigmas) <= 1: + return x + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler return sample_dpmpp_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=r) diff --git a/comfy/samplers.py b/comfy/samplers.py index b12b0fd1..29962a91 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -539,6 +539,9 @@ class KSAMPLER(Sampler): def ksampler(sampler_name, extra_options={}, inpaint_options={}): if sampler_name == "dpm_fast": def dpm_fast_function(model, noise, sigmas, extra_args, callback, disable): + if len(sigmas) <= 1: + return noise + sigma_min = sigmas[-1] if sigma_min == 0: sigma_min = sigmas[-2] @@ -547,6 +550,9 @@ def ksampler(sampler_name, extra_options={}, inpaint_options={}): sampler_function = dpm_fast_function elif sampler_name == "dpm_adaptive": def dpm_adaptive_function(model, noise, sigmas, extra_args, callback, disable, **extra_options): + if len(sigmas) <= 1: + return noise + sigma_min = sigmas[-1] if sigma_min == 0: sigma_min = sigmas[-2] From a56d02efc7fc111f00ba9be9d01f2945d41552d6 Mon Sep 17 00:00:00 2001 From: Simon Lui <502929+simonlui@users.noreply.github.com> Date: Thu, 2 May 2024 00:26:50 -0700 Subject: [PATCH 188/378] Change torch.xpu to ipex.optimize, xpu device initialization and remove workaround for text node issue from older IPEX. (#3388) --- comfy/model_management.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index 537df41e..913b6844 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -83,7 +83,7 @@ def get_torch_device(): return torch.device("cpu") else: if is_intel_xpu(): - return torch.device("xpu") + return torch.device("xpu", torch.xpu.current_device()) else: return torch.device(torch.cuda.current_device()) @@ -304,7 +304,7 @@ class LoadedModel: raise e if is_intel_xpu() and not args.disable_ipex_optimize: - self.real_model = torch.xpu.optimize(self.real_model.eval(), inplace=True, auto_kernel_selection=True, graph_mode=True) + self.real_model = ipex.optimize(self.real_model.eval(), graph_mode=True, concat_linear=True) self.weights_loaded = True return self.real_model @@ -552,8 +552,6 @@ def text_encoder_device(): if args.gpu_only: return get_torch_device() elif vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.NORMAL_VRAM: - if is_intel_xpu(): - return torch.device("cpu") if should_use_fp16(prioritize_performance=False): return get_torch_device() else: From 89d0e9abeb31e44cccef46537cd10d8812130ef3 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 2 May 2024 03:34:19 -0400 Subject: [PATCH 189/378] Fix int widgets rounding. --- web/scripts/widgets.js | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/web/scripts/widgets.js b/web/scripts/widgets.js index 00c91914..8f160325 100644 --- a/web/scripts/widgets.js +++ b/web/scripts/widgets.js @@ -229,7 +229,7 @@ function createIntWidget(node, inputName, inputData, app, isSeedInput) { val, function (v) { const s = this.options.step / 10; - this.value = Math.round(v / s) * s; + this.value = Math.round((v - this.options.min) / s) * s + this.options.min; }, config ), From daa92a8ff4d3e75a3b17bb1a6b6c508b27264ff5 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 3 May 2024 05:49:21 -0400 Subject: [PATCH 190/378] Fix potential issues with the int rounding fix. --- web/scripts/widgets.js | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/web/scripts/widgets.js b/web/scripts/widgets.js index 8f160325..6a689970 100644 --- a/web/scripts/widgets.js +++ b/web/scripts/widgets.js @@ -229,7 +229,11 @@ function createIntWidget(node, inputName, inputData, app, isSeedInput) { val, function (v) { const s = this.options.step / 10; - this.value = Math.round((v - this.options.min) / s) * s + this.options.min; + let sh = this.options.min % s; + if (isNaN(sh)) { + sh = 0; + } + this.value = Math.round((v - sh) / s) * s + sh; }, config ), From 0d45efb7d6809fae272a9ba68c3aac5f713347e7 Mon Sep 17 00:00:00 2001 From: shawnington <88048838+shawnington@users.noreply.github.com> Date: Sat, 4 May 2024 02:32:41 -0500 Subject: [PATCH 191/378] Fixed Issue with LoadImage node when loading PNG files with embedded ICC profiles. (#3316) * Fix issue with how PIL loads small PNG files nodes.py Added flag to prevent ValueError: Decompressed Data Too Large when loading PNG images with large meta data such as large embedded color profiles * Update LoadImage node to fix error when loading PNG's in nodes.py Fixed Value Error: Decompressed Data Too Large thrown by PIL when attempting to opening PNG files with large embedded ICC colorspaces by setting the follow flag to true when loading png images: ImageFile.LOAD_TRUNCATED_IMAGES = True * Update node_helpers.py to include open_image helper function open_image includes try except to catch Pillow Value Errors that occur when large ICC profiles are embedded in images. * Update LoadImage node to use open_image helper function inplace of Image.open open_image helper function in node_helpers.py fixes a Pillow error when attempting to open images with large embedded ICC profiles by adding an exception handler to load the image with truncated meta data if regular loading is not possible. --- node_helpers.py | 14 ++++++++++++++ nodes.py | 6 ++++-- 2 files changed, 18 insertions(+), 2 deletions(-) diff --git a/node_helpers.py b/node_helpers.py index 8828a4ec..264bd4d5 100644 --- a/node_helpers.py +++ b/node_helpers.py @@ -1,3 +1,4 @@ +from PIL import Image, ImageFile def conditioning_set_values(conditioning, values={}): c = [] @@ -8,3 +9,16 @@ def conditioning_set_values(conditioning, values={}): c.append(n) return c + +def open_image(path): + try : + ImageFile.LOAD_TRUNCATED_IMAGES = False + img = Image.open(path) + + except: + ImageFile.LOAD_TRUNCATED_IMAGES = True + img = Image.open(path) + + finally: + ImageFile.LOAD_TRUNCATED_IMAGES = False + return img diff --git a/nodes.py b/nodes.py index acad256f..aa6d6fa9 100644 --- a/nodes.py +++ b/nodes.py @@ -12,12 +12,12 @@ import logging from PIL import Image, ImageOps, ImageSequence from PIL.PngImagePlugin import PngInfo + import numpy as np import safetensors.torch sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy")) - import comfy.diffusers_load import comfy.samplers import comfy.sample @@ -1456,7 +1456,9 @@ class LoadImage: FUNCTION = "load_image" def load_image(self, image): image_path = folder_paths.get_annotated_filepath(image) - img = Image.open(image_path) + + img = node_helpers.open_image(image_path) + output_images = [] output_masks = [] for i in ImageSequence.Iterator(img): From 72508a8d19121e2814ea4dfbce8a5311f37dcd61 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 4 May 2024 03:39:37 -0400 Subject: [PATCH 192/378] Only set LOAD_TRUNCATED_IMAGES when if the Image open fails. Document which PIL issues this works around. --- node_helpers.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/node_helpers.py b/node_helpers.py index 264bd4d5..60f8fa41 100644 --- a/node_helpers.py +++ b/node_helpers.py @@ -1,4 +1,4 @@ -from PIL import Image, ImageFile +from PIL import Image, ImageFile, UnidentifiedImageError def conditioning_set_values(conditioning, values={}): c = [] @@ -11,14 +11,15 @@ def conditioning_set_values(conditioning, values={}): return c def open_image(path): - try : - ImageFile.LOAD_TRUNCATED_IMAGES = False + prev_value = None + + try: img = Image.open(path) - - except: + except (UnidentifiedImageError, ValueError): #PIL issues #4472 and #2445 + prev_value = ImageFile.LOAD_TRUNCATED_IMAGES ImageFile.LOAD_TRUNCATED_IMAGES = True img = Image.open(path) - finally: - ImageFile.LOAD_TRUNCATED_IMAGES = False + if prev_value is not None: + ImageFile.LOAD_TRUNCATED_IMAGES = prev_value return img From 9a70b70de4b98e02dfd8b6e1747387b52a0d5903 Mon Sep 17 00:00:00 2001 From: vilanele <73059775+vilanele@users.noreply.github.com> Date: Sun, 5 May 2024 11:01:06 +0200 Subject: [PATCH 193/378] add opacity slider in maskeditor (#3404) Co-authored-by: vilanele --- web/extensions/core/maskeditor.js | 46 ++++++++++++++++++++++++++++++- 1 file changed, 45 insertions(+), 1 deletion(-) diff --git a/web/extensions/core/maskeditor.js b/web/extensions/core/maskeditor.js index 4f69ac76..36f7496e 100644 --- a/web/extensions/core/maskeditor.js +++ b/web/extensions/core/maskeditor.js @@ -164,6 +164,41 @@ class MaskEditorDialog extends ComfyDialog { return divElement; } + createOpacitySlider(self, name, callback) { + const divElement = document.createElement('div'); + divElement.id = "maskeditor-opacity-slider"; + divElement.style.cssFloat = "left"; + divElement.style.fontFamily = "sans-serif"; + divElement.style.marginRight = "4px"; + divElement.style.color = "var(--input-text)"; + divElement.style.backgroundColor = "var(--comfy-input-bg)"; + divElement.style.borderRadius = "8px"; + divElement.style.borderColor = "var(--border-color)"; + divElement.style.borderStyle = "solid"; + divElement.style.fontSize = "15px"; + divElement.style.height = "21px"; + divElement.style.padding = "1px 6px"; + divElement.style.display = "flex"; + divElement.style.position = "relative"; + divElement.style.top = "2px"; + divElement.style.pointerEvents = "auto"; + self.opacity_slider_input = document.createElement('input'); + self.opacity_slider_input.setAttribute('type', 'range'); + self.opacity_slider_input.setAttribute('min', '0.1'); + self.opacity_slider_input.setAttribute('max', '1.0'); + self.opacity_slider_input.setAttribute('step', '0.01') + self.opacity_slider_input.setAttribute('value', '0.7'); + const labelElement = document.createElement("label"); + labelElement.textContent = name; + + divElement.appendChild(labelElement); + divElement.appendChild(self.opacity_slider_input); + + self.opacity_slider_input.addEventListener("input", callback); + + return divElement; + } + setlayout(imgCanvas, maskCanvas) { const self = this; @@ -203,6 +238,13 @@ class MaskEditorDialog extends ComfyDialog { self.updateBrushPreview(self, null, null); }); + this.brush_opacity_slider = this.createOpacitySlider(self, "Opacity", (event) => { + self.brush_opacity = event.target.value; + if (self.brush_color_mode !== "negative") { + self.maskCanvas.style.opacity = self.brush_opacity; + } + }); + this.colorButton = this.createLeftButton(this.getColorButtonText(), () => { if (self.brush_color_mode === "black") { self.brush_color_mode = "white"; @@ -237,6 +279,7 @@ class MaskEditorDialog extends ComfyDialog { bottom_panel.appendChild(this.saveButton); bottom_panel.appendChild(cancelButton); bottom_panel.appendChild(this.brush_size_slider); + bottom_panel.appendChild(this.brush_opacity_slider); bottom_panel.appendChild(this.colorButton); imgCanvas.style.position = "absolute"; @@ -472,7 +515,7 @@ class MaskEditorDialog extends ComfyDialog { else { return { mixBlendMode: "initial", - opacity: "0.7", + opacity: this.brush_opacity, }; } } @@ -538,6 +581,7 @@ class MaskEditorDialog extends ComfyDialog { this.maskCtx.putImageData(maskData, 0, 0); } + brush_opacity = 0.7; brush_size = 10; brush_color_mode = "black"; drawing_mode = false; From 565eb6d176d2c1c25382585379c4007436aba438 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 5 May 2024 05:24:36 -0400 Subject: [PATCH 194/378] Add a SplitSigmasDenoise node as an alternative to SplitSigmas. --- comfy_extras/nodes_custom_sampler.py | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index f3dff000..47f08bf6 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -139,6 +139,7 @@ class SplitSigmas: } } RETURN_TYPES = ("SIGMAS","SIGMAS") + RETURN_NAMES = ("high_sigmas", "low_sigmas") CATEGORY = "sampling/custom_sampling/sigmas" FUNCTION = "get_sigmas" @@ -148,6 +149,27 @@ class SplitSigmas: sigmas2 = sigmas[step:] return (sigmas1, sigmas2) +class SplitSigmasDenoise: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"sigmas": ("SIGMAS", ), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + } + } + RETURN_TYPES = ("SIGMAS","SIGMAS") + RETURN_NAMES = ("high_sigmas", "low_sigmas") + CATEGORY = "sampling/custom_sampling/sigmas" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, sigmas, denoise): + steps = max(sigmas.shape[-1] - 1, 0) + total_steps = round(steps * denoise) + sigmas1 = sigmas[:-(total_steps)] + sigmas2 = sigmas[-(total_steps + 1):] + return (sigmas1, sigmas2) + class FlipSigmas: @classmethod def INPUT_TYPES(s): @@ -599,6 +621,7 @@ NODE_CLASS_MAPPINGS = { "SamplerDPMPP_SDE": SamplerDPMPP_SDE, "SamplerDPMAdaptative": SamplerDPMAdaptative, "SplitSigmas": SplitSigmas, + "SplitSigmasDenoise": SplitSigmasDenoise, "FlipSigmas": FlipSigmas, "CFGGuider": CFGGuider, From 3787b4f246e05302c4502be116a2bc1a15d03ab1 Mon Sep 17 00:00:00 2001 From: Pam <42671363+pamparamm@users.noreply.github.com> Date: Tue, 7 May 2024 03:39:39 +0500 Subject: [PATCH 195/378] Use get_model_object in Deep Shrink node (#3408) --- comfy_extras/nodes_model_downscale.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/comfy_extras/nodes_model_downscale.py b/comfy_extras/nodes_model_downscale.py index 48bcc689..58b5073e 100644 --- a/comfy_extras/nodes_model_downscale.py +++ b/comfy_extras/nodes_model_downscale.py @@ -20,8 +20,9 @@ class PatchModelAddDownscale: CATEGORY = "_for_testing" def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method): - sigma_start = model.model.model_sampling.percent_to_sigma(start_percent) - sigma_end = model.model.model_sampling.percent_to_sigma(end_percent) + model_sampling = model.get_model_object("model_sampling") + sigma_start = model_sampling.percent_to_sigma(start_percent) + sigma_end = model_sampling.percent_to_sigma(end_percent) def input_block_patch(h, transformer_options): if transformer_options["block"][1] == block_number: From c61eadf69a3ba4033dcf22e2e190fd54f779fc5b Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 6 May 2024 20:04:39 -0400 Subject: [PATCH 196/378] Make the load checkpoint with config function call the regular one. I was going to completely remove this function because it is unmaintainable but I think this is the best compromise. The clip skip and v_prediction parts of the configs should still work but not the fp16 vs fp32. --- comfy/sd.py | 81 ++++++++--------------------------------------------- 1 file changed, 11 insertions(+), 70 deletions(-) diff --git a/comfy/sd.py b/comfy/sd.py index 16dc0b73..ceb080b3 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -418,6 +418,8 @@ def load_gligen(ckpt_path): return comfy.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device()) def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None): + logging.warning("Warning: The load checkpoint with config function is deprecated and will eventually be removed, please use the other one.") + model, clip, vae, _ = load_checkpoint_guess_config(ckpt_path, output_vae=output_vae, output_clip=output_clip, output_clipvision=False, embedding_directory=embedding_directory, output_model=True) #TODO: this function is a mess and should be removed eventually if config is None: with open(config_path, 'r') as stream: @@ -425,81 +427,20 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl model_config_params = config['model']['params'] clip_config = model_config_params['cond_stage_config'] scale_factor = model_config_params['scale_factor'] - vae_config = model_config_params['first_stage_config'] - - fp16 = False - if "unet_config" in model_config_params: - if "params" in model_config_params["unet_config"]: - unet_config = model_config_params["unet_config"]["params"] - if "use_fp16" in unet_config: - fp16 = unet_config.pop("use_fp16") - if fp16: - unet_config["dtype"] = torch.float16 - - noise_aug_config = None - if "noise_aug_config" in model_config_params: - noise_aug_config = model_config_params["noise_aug_config"] - - model_type = model_base.ModelType.EPS if "parameterization" in model_config_params: if model_config_params["parameterization"] == "v": - model_type = model_base.ModelType.V_PREDICTION + m = model.clone() + class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingDiscrete, comfy.model_sampling.V_PREDICTION): + pass + m.add_object_patch("model_sampling", ModelSamplingAdvanced(model.model.model_config)) + model = m - clip = None - vae = None + layer_idx = clip_config.get("params", {}).get("layer_idx", None) + if layer_idx is not None: + clip.clip_layer(layer_idx) - class WeightsLoader(torch.nn.Module): - pass - - if state_dict is None: - state_dict = comfy.utils.load_torch_file(ckpt_path) - - class EmptyClass: - pass - - model_config = comfy.supported_models_base.BASE({}) - - from . import latent_formats - model_config.latent_format = latent_formats.SD15(scale_factor=scale_factor) - model_config.unet_config = model_detection.convert_config(unet_config) - - if config['model']["target"].endswith("ImageEmbeddingConditionedLatentDiffusion"): - model = model_base.SD21UNCLIP(model_config, noise_aug_config["params"], model_type=model_type) - else: - model = model_base.BaseModel(model_config, model_type=model_type) - - if config['model']["target"].endswith("LatentInpaintDiffusion"): - model.set_inpaint() - - if fp16: - model = model.half() - - offload_device = model_management.unet_offload_device() - model = model.to(offload_device) - model.load_model_weights(state_dict, "model.diffusion_model.") - - if output_vae: - vae_sd = comfy.utils.state_dict_prefix_replace(state_dict, {"first_stage_model.": ""}, filter_keys=True) - vae = VAE(sd=vae_sd, config=vae_config) - - if output_clip: - w = WeightsLoader() - clip_target = EmptyClass() - clip_target.params = clip_config.get("params", {}) - if clip_config["target"].endswith("FrozenOpenCLIPEmbedder"): - clip_target.clip = sd2_clip.SD2ClipModel - clip_target.tokenizer = sd2_clip.SD2Tokenizer - clip = CLIP(clip_target, embedding_directory=embedding_directory) - w.cond_stage_model = clip.cond_stage_model.clip_h - elif clip_config["target"].endswith("FrozenCLIPEmbedder"): - clip_target.clip = sd1_clip.SD1ClipModel - clip_target.tokenizer = sd1_clip.SD1Tokenizer - clip = CLIP(clip_target, embedding_directory=embedding_directory) - w.cond_stage_model = clip.cond_stage_model.clip_l - load_clip_weights(w, state_dict) - - return (comfy.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae) + return (model, clip, vae) def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True): sd = comfy.utils.load_torch_file(ckpt_path) From d7fa417bfa24f98fb20495c221faff818ebf5988 Mon Sep 17 00:00:00 2001 From: "Dr.Lt.Data" <128333288+ltdrdata@users.noreply.github.com> Date: Tue, 7 May 2024 17:40:56 +0900 Subject: [PATCH 197/378] feat: shortcuts for zoom in/out (#3410) * feat: shortcuts for zoom in/out * feat: pen support for canvas zoom ctrl + LMB + vertical drag * Ctrl+LMB+Drag -> ctrl+Shift+LMB+Drag --------- Co-authored-by: Lt.Dr.Data --- README.md | 49 ++++++++++++++++++++++++---------------------- web/scripts/app.js | 40 +++++++++++++++++++++++++++++++++++++ 2 files changed, 66 insertions(+), 23 deletions(-) diff --git a/README.md b/README.md index eb07540c..2636ce14 100644 --- a/README.md +++ b/README.md @@ -41,29 +41,32 @@ Workflow examples can be found on the [Examples page](https://comfyanonymous.git ## Shortcuts -| Keybind | Explanation | -|---------------------------|--------------------------------------------------------------------------------------------------------------------| -| Ctrl + Enter | Queue up current graph for generation | -| Ctrl + Shift + Enter | Queue up current graph as first for generation | -| Ctrl + Z/Ctrl + Y | Undo/Redo | -| Ctrl + S | Save workflow | -| Ctrl + O | Load workflow | -| Ctrl + A | Select all nodes | -| Alt + C | Collapse/uncollapse selected nodes | -| Ctrl + M | Mute/unmute selected nodes | -| Ctrl + B | Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through) | -| Delete/Backspace | Delete selected nodes | -| Ctrl + Delete/Backspace | Delete the current graph | -| Space | Move the canvas around when held and moving the cursor | -| Ctrl/Shift + Click | Add clicked node to selection | -| Ctrl + C/Ctrl + V | Copy and paste selected nodes (without maintaining connections to outputs of unselected nodes) | -| Ctrl + C/Ctrl + Shift + V | Copy and paste selected nodes (maintaining connections from outputs of unselected nodes to inputs of pasted nodes) | -| Shift + Drag | Move multiple selected nodes at the same time | -| Ctrl + D | Load default graph | -| Q | Toggle visibility of the queue | -| H | Toggle visibility of history | -| R | Refresh graph | -| Double-Click LMB | Open node quick search palette | +| Keybind | Explanation | +|------------------------------------|--------------------------------------------------------------------------------------------------------------------| +| Ctrl + Enter | Queue up current graph for generation | +| Ctrl + Shift + Enter | Queue up current graph as first for generation | +| Ctrl + Z/Ctrl + Y | Undo/Redo | +| Ctrl + S | Save workflow | +| Ctrl + O | Load workflow | +| Ctrl + A | Select all nodes | +| Alt + C | Collapse/uncollapse selected nodes | +| Ctrl + M | Mute/unmute selected nodes | +| Ctrl + B | Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through) | +| Delete/Backspace | Delete selected nodes | +| Ctrl + Delete/Backspace | Delete the current graph | +| Space | Move the canvas around when held and moving the cursor | +| Ctrl/Shift + Click | Add clicked node to selection | +| Ctrl + C/Ctrl + V | Copy and paste selected nodes (without maintaining connections to outputs of unselected nodes) | +| Ctrl + C/Ctrl + Shift + V | Copy and paste selected nodes (maintaining connections from outputs of unselected nodes to inputs of pasted nodes) | +| Shift + Drag | Move multiple selected nodes at the same time | +| Ctrl + D | Load default graph | +| Alt + `+` | Canvas Zoom in | +| Alt + `-` | Canvas Zoom out | +| Ctrl + Shift + LMB + Vertical drag | Canvas Zoom in/out | +| Q | Toggle visibility of the queue | +| H | Toggle visibility of history | +| R | Refresh graph | +| Double-Click LMB | Open node quick search palette | Ctrl can also be replaced with Cmd instead for macOS users diff --git a/web/scripts/app.js b/web/scripts/app.js index 77f29b8e..a3105c27 100644 --- a/web/scripts/app.js +++ b/web/scripts/app.js @@ -953,6 +953,12 @@ export class ComfyApp { const origProcessMouseDown = LGraphCanvas.prototype.processMouseDown; LGraphCanvas.prototype.processMouseDown = function(e) { + // prepare for ctrl+shift drag: zoom start + if(e.ctrlKey && e.shiftKey && e.buttons) { + self.zoom_drag_start = [e.x, e.y, this.ds.scale]; + return; + } + const res = origProcessMouseDown.apply(this, arguments); this.selected_group_moving = false; @@ -973,6 +979,26 @@ export class ComfyApp { const origProcessMouseMove = LGraphCanvas.prototype.processMouseMove; LGraphCanvas.prototype.processMouseMove = function(e) { + // handle ctrl+shift drag + if(e.ctrlKey && e.shiftKey && self.zoom_drag_start) { + // stop canvas zoom action + if(!e.buttons) { + self.zoom_drag_start = null; + return; + } + + // calculate delta + let deltaY = e.y - self.zoom_drag_start[1]; + let startScale = self.zoom_drag_start[2]; + + let scale = startScale - deltaY/100; + + this.ds.changeScale(scale, [this.ds.element.width/2, this.ds.element.height/2]); + this.graph.change(); + + return; + } + const orig_selected_group = this.selected_group; if (this.selected_group && !this.selected_group_resizing && !this.selected_group_moving) { @@ -1059,6 +1085,20 @@ export class ComfyApp { // Trigger onPaste return true; } + + if((e.key === '+') && e.altKey) { + block_default = true; + let scale = this.ds.scale * 1.1; + this.ds.changeScale(scale, [this.ds.element.width/2, this.ds.element.height/2]); + this.graph.change(); + } + + if((e.key === '-') && e.altKey) { + block_default = true; + let scale = this.ds.scale * 1 / 1.1; + this.ds.changeScale(scale, [this.ds.element.width/2, this.ds.element.height/2]); + this.graph.change(); + } } this.graph.change(); From c33412288fbdcd132265c9029a38001fd9696aa5 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 7 May 2024 05:41:06 -0400 Subject: [PATCH 198/378] Fix issue with loading some JPG: #3416 --- nodes.py | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/nodes.py b/nodes.py index aa6d6fa9..4d3171b8 100644 --- a/nodes.py +++ b/nodes.py @@ -10,7 +10,7 @@ import time import random import logging -from PIL import Image, ImageOps, ImageSequence +from PIL import Image, ImageOps, ImageSequence, ImageFile from PIL.PngImagePlugin import PngInfo import numpy as np @@ -1462,7 +1462,17 @@ class LoadImage: output_images = [] output_masks = [] for i in ImageSequence.Iterator(img): - i = ImageOps.exif_transpose(i) + prev_value = None + try: + i = ImageOps.exif_transpose(i) + except OSError: + prev_value = ImageFile.LOAD_TRUNCATED_IMAGES + ImageFile.LOAD_TRUNCATED_IMAGES = True + i = ImageOps.exif_transpose(i) + finally: + if prev_value is not None: + ImageFile.LOAD_TRUNCATED_IMAGES = prev_value + if i.mode == 'I': i = i.point(lambda i: i * (1 / 255)) image = i.convert("RGB") From cd07340d96e26319d4fae5ea54b16001fe55e772 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 8 May 2024 18:36:56 -0400 Subject: [PATCH 199/378] Typo fix. --- comfy/model_base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/model_base.py b/comfy/model_base.py index 8c89adf5..841598b7 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -162,7 +162,7 @@ class BaseModel(torch.nn.Module): c_concat = kwargs.get("noise_concat", None) if c_concat is not None: - out['c_concat'] = comfy.conds.CONDNoiseShape(data) + out['c_concat'] = comfy.conds.CONDNoiseShape(c_concat) return out From 93e876a3bed0cff640ba922f36786957ed68ef6e Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 9 May 2024 04:39:46 -0400 Subject: [PATCH 200/378] Remove warnings that confuse people. --- comfy/sd.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/comfy/sd.py b/comfy/sd.py index ceb080b3..9671e4ae 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -486,7 +486,11 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o clip = CLIP(clip_target, embedding_directory=embedding_directory) m, u = clip.load_sd(clip_sd, full_model=True) if len(m) > 0: - logging.warning("clip missing: {}".format(m)) + m_filter = list(filter(lambda a: ".logit_scale" not in a and ".transformer.text_projection.weight" not in a, m)) + if len(m_filter) > 0: + logging.warning("clip missing: {}".format(m)) + else: + logging.debug("clip missing: {}".format(m)) if len(u) > 0: logging.debug("clip unexpected {}:".format(u)) From 0fecfd2b1a2794b77277c7e256c84de54a63d860 Mon Sep 17 00:00:00 2001 From: shawnington <88048838+shawnington@users.noreply.github.com> Date: Thu, 9 May 2024 02:38:00 -0700 Subject: [PATCH 201/378] Added generic wrapper function node_helpers.pillow to fix PIL issues #4472 and #2445 (#3422) * Update node_helpers.py to use generic pillow wrapper to resolve multiple meta-data related issues. replaced open_image function with a generic pillow function that takes Pil functions as a dependency injection and applies the ImageFile.LOAD_TRUNCATED_IMAGES try except fix to them. This provides an extensible function to handle related errors that can wrap offending functions when discovered without the need to repeat code. * Update a few Pil functions to use node_helpers.pillow wrapper Update a Pil function calls in a few locations to use the generic node_helpers.pillow wrapper that takes the function as a dependency injection and uses the try except method with ImageFIle.LOAD_TRUNCATED_IMAGES solution * Corrected comment in issue #s fixed. * Update node_helpers.py to remove import of Image from PIL import of Image is no longer required as functions are Injected --- node_helpers.py | 13 ++++++------- nodes.py | 17 ++++------------- 2 files changed, 10 insertions(+), 20 deletions(-) diff --git a/node_helpers.py b/node_helpers.py index 60f8fa41..43b9e829 100644 --- a/node_helpers.py +++ b/node_helpers.py @@ -1,4 +1,4 @@ -from PIL import Image, ImageFile, UnidentifiedImageError +from PIL import ImageFile, UnidentifiedImageError def conditioning_set_values(conditioning, values={}): c = [] @@ -10,16 +10,15 @@ def conditioning_set_values(conditioning, values={}): return c -def open_image(path): +def pillow(fn, arg): prev_value = None - try: - img = Image.open(path) - except (UnidentifiedImageError, ValueError): #PIL issues #4472 and #2445 + x = fn(arg) + except (OSError, UnidentifiedImageError, ValueError): #PIL issues #4472 and #2445, also fixes ComfyUI issue #3416 prev_value = ImageFile.LOAD_TRUNCATED_IMAGES ImageFile.LOAD_TRUNCATED_IMAGES = True - img = Image.open(path) + x = fn(arg) finally: if prev_value is not None: ImageFile.LOAD_TRUNCATED_IMAGES = prev_value - return img + return x diff --git a/nodes.py b/nodes.py index 4d3171b8..488afd57 100644 --- a/nodes.py +++ b/nodes.py @@ -1457,21 +1457,12 @@ class LoadImage: def load_image(self, image): image_path = folder_paths.get_annotated_filepath(image) - img = node_helpers.open_image(image_path) + img = node_helpers.pillow(Image.open, image_path) output_images = [] output_masks = [] for i in ImageSequence.Iterator(img): - prev_value = None - try: - i = ImageOps.exif_transpose(i) - except OSError: - prev_value = ImageFile.LOAD_TRUNCATED_IMAGES - ImageFile.LOAD_TRUNCATED_IMAGES = True - i = ImageOps.exif_transpose(i) - finally: - if prev_value is not None: - ImageFile.LOAD_TRUNCATED_IMAGES = prev_value + i = node_helpers.pillow(ImageOps.exif_transpose, i) if i.mode == 'I': i = i.point(lambda i: i * (1 / 255)) @@ -1527,8 +1518,8 @@ class LoadImageMask: FUNCTION = "load_image" def load_image(self, image, channel): image_path = folder_paths.get_annotated_filepath(image) - i = Image.open(image_path) - i = ImageOps.exif_transpose(i) + i = node_helpers.pillow(Image.open, image_path) + i = node_helpers.pillow(ImageOps.exif_transpose, i) if i.getbands() != ("R", "G", "B", "A"): if i.mode == 'I': i = i.point(lambda i: i * (1 / 255)) From f374ea714d8c32c2f214e818146b06754c963028 Mon Sep 17 00:00:00 2001 From: pythongosssss <125205205+pythongosssss@users.noreply.github.com> Date: Fri, 10 May 2024 22:07:46 +0100 Subject: [PATCH 202/378] Setting for saving and restoring canvas position and zoom level (#3437) --- web/scripts/app.js | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) diff --git a/web/scripts/app.js b/web/scripts/app.js index a3105c27..a0fe8b29 100644 --- a/web/scripts/app.js +++ b/web/scripts/app.js @@ -262,6 +262,36 @@ export class ComfyApp { }) ); } + + #addRestoreWorkflowView() { + const serialize = LGraph.prototype.serialize; + const self = this; + LGraph.prototype.serialize = function() { + const workflow = serialize.apply(this, arguments); + + // Store the drag & scale info in the serialized workflow if the setting is enabled + if (self.enableWorkflowViewRestore.value) { + if (!workflow.extra) { + workflow.extra = {}; + } + workflow.extra.ds = { + scale: self.canvas.ds.scale, + offset: self.canvas.ds.offset, + }; + } else if (workflow.extra?.ds) { + // Clear any old view data + delete workflow.extra.ds; + } + + return workflow; + } + this.enableWorkflowViewRestore = this.ui.settings.addSetting({ + id: "Comfy.EnableWorkflowViewRestore", + name: "Save and restore canvas position and zoom level in workflows", + type: "boolean", + defaultValue: true + }); + } /** * Adds special context menu handling for nodes @@ -1505,6 +1535,7 @@ export class ComfyApp { this.#addProcessKeyHandler(); this.#addConfigureHandler(); this.#addApiUpdateHandlers(); + this.#addRestoreWorkflowView(); this.graph = new LGraph(); @@ -1805,6 +1836,10 @@ export class ComfyApp { try { this.graph.configure(graphData); + if (this.enableWorkflowViewRestore.value && graphData.extra?.ds) { + this.canvas.ds.offset = graphData.extra.ds.offset; + this.canvas.ds.scale = graphData.extra.ds.scale; + } } catch (error) { let errorHint = []; // Try extracting filename to see if it was caused by an extension script From 4f63ee99f185db316a017520570310d094efffba Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 10 May 2024 17:30:52 -0400 Subject: [PATCH 203/378] Add a button to reset the view. --- web/scripts/app.js | 6 ++++++ web/scripts/ui.js | 7 +++++++ 2 files changed, 13 insertions(+) diff --git a/web/scripts/app.js b/web/scripts/app.js index a0fe8b29..7ed262cb 100644 --- a/web/scripts/app.js +++ b/web/scripts/app.js @@ -2313,6 +2313,12 @@ export class ComfyApp { await this.#invokeExtensionsAsync("refreshComboInNodes", defs); } + resetView() { + app.canvas.ds.scale = 1; + app.canvas.ds.offset = [0, 0] + app.graph.setDirtyCanvas(true, true); + } + /** * Clean current state */ diff --git a/web/scripts/ui.js b/web/scripts/ui.js index d0fa46ef..36fed323 100644 --- a/web/scripts/ui.js +++ b/web/scripts/ui.js @@ -597,16 +597,23 @@ export class ComfyUI { if (!confirmClear.value || confirm("Clear workflow?")) { app.clean(); app.graph.clear(); + app.resetView(); } } }), $el("button", { id: "comfy-load-default-button", textContent: "Load Default", onclick: async () => { if (!confirmClear.value || confirm("Load default workflow?")) { + app.resetView(); await app.loadGraphData() } } }), + $el("button", { + id: "comfy-reset-view-button", textContent: "Reset View", onclick: async () => { + app.resetView(); + } + }), ]); const devMode = this.settings.addSetting({ From e1489ad2576651a1384e9b82fd1991ac2f8764e0 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 11 May 2024 21:46:05 -0400 Subject: [PATCH 204/378] Fix issue with lowvram mode breaking model saving. --- comfy/model_management.py | 8 ++++---- comfy/model_patcher.py | 12 +++++++++--- comfy/sd.py | 2 +- comfy_extras/nodes_model_merging.py | 2 +- 4 files changed, 15 insertions(+), 9 deletions(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index 913b6844..15dd73a6 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -285,7 +285,7 @@ class LoadedModel: else: return self.model_memory() - def model_load(self, lowvram_model_memory=0): + def model_load(self, lowvram_model_memory=0, force_patch_weights=False): patch_model_to = self.device self.model.model_patches_to(self.device) @@ -295,7 +295,7 @@ class LoadedModel: try: if lowvram_model_memory > 0 and load_weights: - self.real_model = self.model.patch_model_lowvram(device_to=patch_model_to, lowvram_model_memory=lowvram_model_memory) + self.real_model = self.model.patch_model_lowvram(device_to=patch_model_to, lowvram_model_memory=lowvram_model_memory, force_patch_weights=force_patch_weights) else: self.real_model = self.model.patch_model(device_to=patch_model_to, patch_weights=load_weights) except Exception as e: @@ -379,7 +379,7 @@ def free_memory(memory_required, device, keep_loaded=[]): if mem_free_torch > mem_free_total * 0.25: soft_empty_cache() -def load_models_gpu(models, memory_required=0): +def load_models_gpu(models, memory_required=0, force_patch_weights=False): global vram_state inference_memory = minimum_inference_memory() @@ -444,7 +444,7 @@ def load_models_gpu(models, memory_required=0): if vram_set_state == VRAMState.NO_VRAM: lowvram_model_memory = 64 * 1024 * 1024 - cur_loaded_model = loaded_model.model_load(lowvram_model_memory) + cur_loaded_model = loaded_model.model_load(lowvram_model_memory, force_patch_weights=force_patch_weights) current_loaded_models.insert(0, loaded_model) return diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index cf51c4ad..48e5be31 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -272,7 +272,7 @@ class ModelPatcher: return self.model - def patch_model_lowvram(self, device_to=None, lowvram_model_memory=0): + def patch_model_lowvram(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False): self.patch_model(device_to, patch_weights=False) logging.info("loading in lowvram mode {}".format(lowvram_model_memory/(1024 * 1024))) @@ -296,9 +296,15 @@ class ModelPatcher: if lowvram_weight: if weight_key in self.patches: - m.weight_function = LowVramPatch(weight_key, self) + if force_patch_weights: + self.patch_weight_to_device(weight_key) + else: + m.weight_function = LowVramPatch(weight_key, self) if bias_key in self.patches: - m.bias_function = LowVramPatch(bias_key, self) + if force_patch_weights: + self.patch_weight_to_device(bias_key) + else: + m.bias_function = LowVramPatch(bias_key, self) m.prev_comfy_cast_weights = m.comfy_cast_weights m.comfy_cast_weights = True diff --git a/comfy/sd.py b/comfy/sd.py index 9671e4ae..8044c184 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -562,7 +562,7 @@ def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, m load_models.append(clip.load_model()) clip_sd = clip.get_sd() - model_management.load_models_gpu(load_models) + model_management.load_models_gpu(load_models, force_patch_weights=True) clip_vision_sd = clip_vision.get_sd() if clip_vision is not None else None sd = model.model.state_dict_for_saving(clip_sd, vae.get_sd(), clip_vision_sd) for k in extra_keys: diff --git a/comfy_extras/nodes_model_merging.py b/comfy_extras/nodes_model_merging.py index 2a431f65..8c5dc985 100644 --- a/comfy_extras/nodes_model_merging.py +++ b/comfy_extras/nodes_model_merging.py @@ -262,7 +262,7 @@ class CLIPSave: for x in extra_pnginfo: metadata[x] = json.dumps(extra_pnginfo[x]) - comfy.model_management.load_models_gpu([clip.load_model()]) + comfy.model_management.load_models_gpu([clip.load_model()], force_patch_weights=True) clip_sd = clip.get_sd() for prefix in ["clip_l.", "clip_g.", ""]: From 49c20cdc70149b2f18586d7de5c99b1013044ab5 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 12 May 2024 05:34:43 -0400 Subject: [PATCH 205/378] No longer necessary. --- comfy/model_management.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index 15dd73a6..5a66a383 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -393,8 +393,6 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False): loaded_model = LoadedModel(x) if loaded_model in current_loaded_models: - index = current_loaded_models.index(loaded_model) - current_loaded_models.insert(0, current_loaded_models.pop(index)) models_already_loaded.append(loaded_model) else: if hasattr(x, "model"): From fa6dd7e5bbee031defa640534b0924313757676f Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 12 May 2024 06:13:45 -0400 Subject: [PATCH 206/378] Fix lowvram issue with saving checkpoints. The previous fix didn't cover the case where the model was loaded in lowvram mode right before. --- comfy/model_management.py | 23 ++++++++++++++++++++--- comfy/model_patcher.py | 6 ++++++ 2 files changed, 26 insertions(+), 3 deletions(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index 5a66a383..3d01e8a2 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -309,6 +309,11 @@ class LoadedModel: self.weights_loaded = True return self.real_model + def should_reload_model(self, force_patch_weights=False): + if force_patch_weights and self.model.lowvram_patch_counter > 0: + return True + return False + def model_unload(self, unpatch_weights=True): self.model.unpatch_model(self.model.offload_device, unpatch_weights=unpatch_weights) self.model.model_patches_to(self.model.offload_device) @@ -391,10 +396,22 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False): models_already_loaded = [] for x in models: loaded_model = LoadedModel(x) + loaded = None - if loaded_model in current_loaded_models: - models_already_loaded.append(loaded_model) - else: + try: + loaded_model_index = current_loaded_models.index(loaded_model) + except: + loaded_model_index = None + + if loaded_model_index is not None: + loaded = current_loaded_models[loaded_model_index] + if loaded.should_reload_model(force_patch_weights=force_patch_weights): #TODO: cleanup this model reload logic + current_loaded_models.pop(loaded_model_index).model_unload(unpatch_weights=True) + loaded = None + else: + models_already_loaded.append(loaded) + + if loaded is None: if hasattr(x, "model"): logging.info(f"Requested to load {x.model.__class__.__name__}") models_to_load.append(loaded_model) diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index 48e5be31..c38b2f79 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -58,6 +58,7 @@ class ModelPatcher: self.weight_inplace_update = weight_inplace_update self.model_lowvram = False + self.lowvram_patch_counter = 0 self.patches_uuid = uuid.uuid4() def model_size(self): @@ -284,6 +285,7 @@ class ModelPatcher: return self.model_patcher.calculate_weight(self.model_patcher.patches[self.key], weight, self.key) mem_counter = 0 + patch_counter = 0 for n, m in self.model.named_modules(): lowvram_weight = False if hasattr(m, "comfy_cast_weights"): @@ -300,11 +302,13 @@ class ModelPatcher: self.patch_weight_to_device(weight_key) else: m.weight_function = LowVramPatch(weight_key, self) + patch_counter += 1 if bias_key in self.patches: if force_patch_weights: self.patch_weight_to_device(bias_key) else: m.bias_function = LowVramPatch(bias_key, self) + patch_counter += 1 m.prev_comfy_cast_weights = m.comfy_cast_weights m.comfy_cast_weights = True @@ -317,6 +321,7 @@ class ModelPatcher: logging.debug("lowvram: loaded module regularly {}".format(m)) self.model_lowvram = True + self.lowvram_patch_counter = patch_counter return self.model def calculate_weight(self, patches, weight, key): @@ -468,6 +473,7 @@ class ModelPatcher: m.bias_function = None self.model_lowvram = False + self.lowvram_patch_counter = 0 keys = list(self.backup.keys()) From f509c6fe21179db585372ec4443c80179fa6c659 Mon Sep 17 00:00:00 2001 From: Simon Lui <502929+simonlui@users.noreply.github.com> Date: Sun, 12 May 2024 03:36:30 -0700 Subject: [PATCH 207/378] Fix Intel GPU memory allocation accuracy and documentation update. (#3459) * Change calculation of memory total to be more accurate, allocated is actually smaller than reserved. * Update README.md install documentation for Intel GPUs. --- README.md | 11 ++++++++++- comfy/model_management.py | 6 +++--- 2 files changed, 13 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 2636ce14..312468a9 100644 --- a/README.md +++ b/README.md @@ -136,7 +136,16 @@ After this you should have everything installed and can proceed to running Comfy ### Others: -#### [Intel Arc](https://github.com/comfyanonymous/ComfyUI/discussions/476) +#### Intel GPUs + +Intel GPU support is available for all Intel GPUs supported by Intel's Extension for Pytorch (IPEX) with the support requirements listed in the [Installation](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu) page. Choose your platform and method of install and follow the instructions. The steps are as follows: + +1. Start by installing the drivers or kernel listed or newer in the Installation page of IPEX linked above for Windows and Linux if needed. +1. Follow the instructions to install [Intel's oneAPI Basekit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html) for your platform. +1. Install the packages for IPEX using the instructions provided in the Installation page for your platform. +1. Follow the [ComfyUI manual installation](#manual-install-windows-linux) instructions for Windows and Linux and run ComfyUI normally as described above after everything is installed. + +Additional discussion and help can be found [here](https://github.com/comfyanonymous/ComfyUI/discussions/476). #### Apple Mac silicon diff --git a/comfy/model_management.py b/comfy/model_management.py index 3d01e8a2..7b54b256 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -102,8 +102,8 @@ def get_total_memory(dev=None, torch_total_too=False): elif is_intel_xpu(): stats = torch.xpu.memory_stats(dev) mem_reserved = stats['reserved_bytes.all.current'] - mem_total = torch.xpu.get_device_properties(dev).total_memory mem_total_torch = mem_reserved + mem_total = torch.xpu.get_device_properties(dev).total_memory else: stats = torch.cuda.memory_stats(dev) mem_reserved = stats['reserved_bytes.all.current'] @@ -701,10 +701,10 @@ def get_free_memory(dev=None, torch_free_too=False): elif is_intel_xpu(): stats = torch.xpu.memory_stats(dev) mem_active = stats['active_bytes.all.current'] - mem_allocated = stats['allocated_bytes.all.current'] mem_reserved = stats['reserved_bytes.all.current'] mem_free_torch = mem_reserved - mem_active - mem_free_total = torch.xpu.get_device_properties(dev).total_memory - mem_allocated + mem_free_xpu = torch.xpu.get_device_properties(dev).total_memory - mem_reserved + mem_free_total = mem_free_xpu + mem_free_torch else: stats = torch.cuda.memory_stats(dev) mem_active = stats['active_bytes.all.current'] From 22edd3add541cdb956b50a3c6412ce2bb36c1090 Mon Sep 17 00:00:00 2001 From: shawnington <88048838+shawnington@users.noreply.github.com> Date: Sun, 12 May 2024 04:07:38 -0700 Subject: [PATCH 208/378] =?UTF-8?q?Fix=20to=20LoadImage=20Node=20for=20#34?= =?UTF-8?q?16=20HDR=20images=20loading=20additional=20smaller=E2=80=A6=20(?= =?UTF-8?q?#3454)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Fix to LoadImage Node for #3416 HDR images loading additional smaller images. Added a blocking if statement in the ImageSequence.Iterator that checks if subsequent images after the first match dimensionally, and prevent them from being appended to output_images if they do not match. This does not fix or change current behavior for PIL 10.2.0 where the images are loaded at the same size, but it does for 10.3.0 where they are loaded at their correct smaller sizes. * added list of excluded formats that should return 1 image added an explicit check for the image format so that additional formats can be added to the list that have problematic behavior. --- nodes.py | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/nodes.py b/nodes.py index 488afd57..37e7d734 100644 --- a/nodes.py +++ b/nodes.py @@ -1461,12 +1461,24 @@ class LoadImage: output_images = [] output_masks = [] + w, h = None, None + + excluded_formats = ['MPO'] + for i in ImageSequence.Iterator(img): i = node_helpers.pillow(ImageOps.exif_transpose, i) if i.mode == 'I': i = i.point(lambda i: i * (1 / 255)) image = i.convert("RGB") + + if len(output_images) == 0: + w = image.size[0] + h = image.size[1] + + if image.size[0] != w or image.size[1] != h: + continue + image = np.array(image).astype(np.float32) / 255.0 image = torch.from_numpy(image)[None,] if 'A' in i.getbands(): @@ -1477,7 +1489,7 @@ class LoadImage: output_images.append(image) output_masks.append(mask.unsqueeze(0)) - if len(output_images) > 1: + if len(output_images) > 1 and img.format not in excluded_formats: output_image = torch.cat(output_images, dim=0) output_mask = torch.cat(output_masks, dim=0) else: From 794a357f7a4bd40c6e2dafab9d93142be9933a56 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 12 May 2024 07:24:12 -0400 Subject: [PATCH 209/378] Update the nightly workflow. --- .github/workflows/windows_release_nightly_pytorch.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/windows_release_nightly_pytorch.yml b/.github/workflows/windows_release_nightly_pytorch.yml index 672a7f22..1434b0a2 100644 --- a/.github/workflows/windows_release_nightly_pytorch.yml +++ b/.github/workflows/windows_release_nightly_pytorch.yml @@ -7,7 +7,7 @@ on: description: 'cuda version' required: true type: string - default: "121" + default: "124" python_minor: description: 'python minor version' @@ -19,7 +19,7 @@ on: description: 'python patch version' required: true type: string - default: "2" + default: "3" # push: # branches: # - master From ece5acb8e8025d8ca26aa880f604d971d245475d Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 12 May 2024 16:05:10 -0400 Subject: [PATCH 210/378] Fix nightly package workflow. --- .github/workflows/windows_release_nightly_pytorch.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/windows_release_nightly_pytorch.yml b/.github/workflows/windows_release_nightly_pytorch.yml index 1434b0a2..fa24a985 100644 --- a/.github/workflows/windows_release_nightly_pytorch.yml +++ b/.github/workflows/windows_release_nightly_pytorch.yml @@ -49,7 +49,7 @@ jobs: echo 'import site' >> ./python3${{ inputs.python_minor }}._pth curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py ./python.exe get-pip.py - python -m pip wheel torch torchvision torchaudio mpmath==1.3.0 --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2 -w ../temp_wheel_dir + python -m pip wheel torch torchvision torchaudio mpmath==1.3.0 numpy==1.26.4 --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2 -w ../temp_wheel_dir ls ../temp_wheel_dir ./python.exe -s -m pip install --pre ../temp_wheel_dir/* sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth From cf6e1efb69a17cdaad509e665ad53b0e031b4807 Mon Sep 17 00:00:00 2001 From: freakabcd Date: Tue, 14 May 2024 05:22:22 +1000 Subject: [PATCH 211/378] Show message on error when loading wf from file (works on drag and drop) (#3466) --- web/scripts/app.js | 48 +++++++++++++++++++++++++++++----------------- 1 file changed, 30 insertions(+), 18 deletions(-) diff --git a/web/scripts/app.js b/web/scripts/app.js index 7ed262cb..a516be70 100644 --- a/web/scripts/app.js +++ b/web/scripts/app.js @@ -2157,6 +2157,14 @@ export class ComfyApp { api.dispatchEvent(new CustomEvent("promptQueued", { detail: { number, batchCount } })); } + showErrorOnFileLoad(file) { + this.ui.dialog.show( + $el("div", [ + $el("p", {textContent: `Unable to find workflow in ${file.name}`}) + ]).outerHTML + ); + } + /** * Loads workflow data from the specified file * @param {File} file @@ -2164,27 +2172,27 @@ export class ComfyApp { async handleFile(file) { if (file.type === "image/png") { const pngInfo = await getPngMetadata(file); - if (pngInfo) { - if (pngInfo.workflow) { - await this.loadGraphData(JSON.parse(pngInfo.workflow)); - } else if (pngInfo.prompt) { - this.loadApiJson(JSON.parse(pngInfo.prompt)); - } else if (pngInfo.parameters) { - importA1111(this.graph, pngInfo.parameters); - } + if (pngInfo?.workflow) { + await this.loadGraphData(JSON.parse(pngInfo.workflow)); + } else if (pngInfo?.prompt) { + this.loadApiJson(JSON.parse(pngInfo.prompt)); + } else if (pngInfo?.parameters) { + importA1111(this.graph, pngInfo.parameters); + } else { + this.showErrorOnFileLoad(file); } } else if (file.type === "image/webp") { const pngInfo = await getWebpMetadata(file); - if (pngInfo) { - if (pngInfo.workflow) { - this.loadGraphData(JSON.parse(pngInfo.workflow)); - } else if (pngInfo.Workflow) { - this.loadGraphData(JSON.parse(pngInfo.Workflow)); // Support loading workflows from that webp custom node. - } else if (pngInfo.prompt) { - this.loadApiJson(JSON.parse(pngInfo.prompt)); - } else if (pngInfo.Prompt) { - this.loadApiJson(JSON.parse(pngInfo.Prompt)); // Support loading prompts from that webp custom node. - } + // Support loading workflows from that webp custom node. + const workflow = pngInfo?.workflow || pngInfo?.Workflow; + const prompt = pngInfo?.prompt || pngInfo?.Prompt; + + if (workflow) { + this.loadGraphData(JSON.parse(workflow)); + } else if (prompt) { + this.loadApiJson(JSON.parse(prompt)); + } else { + this.showErrorOnFileLoad(file); } } else if (file.type === "application/json" || file.name?.endsWith(".json")) { const reader = new FileReader(); @@ -2205,7 +2213,11 @@ export class ComfyApp { await this.loadGraphData(JSON.parse(info.workflow)); } else if (info.prompt) { this.loadApiJson(JSON.parse(info.prompt)); + } else { + this.showErrorOnFileLoad(file); } + } else { + this.showErrorOnFileLoad(file); } } From 2de3b69b3074028fd0f850f801f4ca023489c692 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 13 May 2024 21:54:11 -0400 Subject: [PATCH 212/378] Support saving some more modelspec types. --- comfy_extras/nodes_model_merging.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/comfy_extras/nodes_model_merging.py b/comfy_extras/nodes_model_merging.py index 8c5dc985..bb15112f 100644 --- a/comfy_extras/nodes_model_merging.py +++ b/comfy_extras/nodes_model_merging.py @@ -175,9 +175,14 @@ def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefi enable_modelspec = True if isinstance(model.model, comfy.model_base.SDXL): - metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-base" + if isinstance(model.model, comfy.model_base.SDXL_instructpix2pix): + metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-edit" + else: + metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-base" elif isinstance(model.model, comfy.model_base.SDXLRefiner): metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-refiner" + elif isinstance(model.model, comfy.model_base.SVD_img2vid): + metadata["modelspec.architecture"] = "stable-video-diffusion-img2vid-v1" else: enable_modelspec = False From b0ab31d06c5df98b094d8f38db5cda4e5aec47eb Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 14 May 2024 12:47:31 -0400 Subject: [PATCH 213/378] Refactor attention upcasting code part 1. --- comfy/ldm/modules/attention.py | 28 +++++++++++++++------------- 1 file changed, 15 insertions(+), 13 deletions(-) diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py index d51a2fae..de66db4f 100644 --- a/comfy/ldm/modules/attention.py +++ b/comfy/ldm/modules/attention.py @@ -22,9 +22,9 @@ ops = comfy.ops.disable_weight_init # CrossAttn precision handling if args.dont_upcast_attention: logging.info("disabling upcasting of attention") - _ATTN_PRECISION = "fp16" + _ATTN_PRECISION = None else: - _ATTN_PRECISION = "fp32" + _ATTN_PRECISION = torch.float32 def exists(val): @@ -85,7 +85,7 @@ class FeedForward(nn.Module): def Normalize(in_channels, dtype=None, device=None): return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device) -def attention_basic(q, k, v, heads, mask=None): +def attention_basic(q, k, v, heads, mask=None, attn_precision=None): b, _, dim_head = q.shape dim_head //= heads scale = dim_head ** -0.5 @@ -101,7 +101,7 @@ def attention_basic(q, k, v, heads, mask=None): ) # force cast to fp32 to avoid overflowing - if _ATTN_PRECISION =="fp32": + if attn_precision == torch.float32: sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale else: sim = einsum('b i d, b j d -> b i j', q, k) * scale @@ -135,7 +135,7 @@ def attention_basic(q, k, v, heads, mask=None): return out -def attention_sub_quad(query, key, value, heads, mask=None): +def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None): b, _, dim_head = query.shape dim_head //= heads @@ -146,7 +146,7 @@ def attention_sub_quad(query, key, value, heads, mask=None): key = key.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1) dtype = query.dtype - upcast_attention = _ATTN_PRECISION =="fp32" and query.dtype != torch.float32 + upcast_attention = attn_precision == torch.float32 and query.dtype != torch.float32 if upcast_attention: bytes_per_token = torch.finfo(torch.float32).bits//8 else: @@ -195,7 +195,7 @@ def attention_sub_quad(query, key, value, heads, mask=None): hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2) return hidden_states -def attention_split(q, k, v, heads, mask=None): +def attention_split(q, k, v, heads, mask=None, attn_precision=None): b, _, dim_head = q.shape dim_head //= heads scale = dim_head ** -0.5 @@ -214,10 +214,12 @@ def attention_split(q, k, v, heads, mask=None): mem_free_total = model_management.get_free_memory(q.device) - if _ATTN_PRECISION =="fp32": + if attn_precision == torch.float32: element_size = 4 + upcast = True else: element_size = q.element_size() + upcast = False gb = 1024 ** 3 tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * element_size @@ -251,7 +253,7 @@ def attention_split(q, k, v, heads, mask=None): slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1] for i in range(0, q.shape[1], slice_size): end = i + slice_size - if _ATTN_PRECISION =="fp32": + if upcast: with torch.autocast(enabled=False, device_type = 'cuda'): s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * scale else: @@ -302,7 +304,7 @@ try: except: pass -def attention_xformers(q, k, v, heads, mask=None): +def attention_xformers(q, k, v, heads, mask=None, attn_precision=None): b, _, dim_head = q.shape dim_head //= heads if BROKEN_XFORMERS: @@ -334,7 +336,7 @@ def attention_xformers(q, k, v, heads, mask=None): ) return out -def attention_pytorch(q, k, v, heads, mask=None): +def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None): b, _, dim_head = q.shape dim_head //= heads q, k, v = map( @@ -409,9 +411,9 @@ class CrossAttention(nn.Module): v = self.to_v(context) if mask is None: - out = optimized_attention(q, k, v, self.heads) + out = optimized_attention(q, k, v, self.heads, attn_precision=_ATTN_PRECISION) else: - out = optimized_attention_masked(q, k, v, self.heads, mask) + out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=_ATTN_PRECISION) return self.to_out(out) From bb4940d837f0cfd338ff64776b084303be066c67 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 14 May 2024 15:18:00 -0400 Subject: [PATCH 214/378] Only enable attention upcasting on models that actually need it. --- README.md | 6 ---- comfy/cli_args.py | 1 - comfy/ldm/modules/attention.py | 28 ++++++++----------- .../modules/diffusionmodules/openaimodel.py | 4 ++- comfy/supported_models.py | 12 ++++++++ 5 files changed, 27 insertions(+), 24 deletions(-) diff --git a/README.md b/README.md index 312468a9..80de21bc 100644 --- a/README.md +++ b/README.md @@ -207,12 +207,6 @@ To use a textual inversion concepts/embeddings in a text prompt put them in the ```embedding:embedding_filename.pt``` -## How to increase generation speed? - -On non Nvidia hardware you can set this command line setting to disable the upcasting to fp32 in some cross attention operations which will increase your speed. Note that this will very likely give you black images on SD2.x models. If you use xformers or pytorch attention this option does not do anything. - -```--dont-upcast-attention``` - ## How to show high-quality previews? Use ```--preview-method auto``` to enable previews. diff --git a/comfy/cli_args.py b/comfy/cli_args.py index 569c7938..2759f4e9 100644 --- a/comfy/cli_args.py +++ b/comfy/cli_args.py @@ -51,7 +51,6 @@ cm_group = parser.add_mutually_exclusive_group() cm_group.add_argument("--cuda-malloc", action="store_true", help="Enable cudaMallocAsync (enabled by default for torch 2.0 and up).") cm_group.add_argument("--disable-cuda-malloc", action="store_true", help="Disable cudaMallocAsync.") -parser.add_argument("--dont-upcast-attention", action="store_true", help="Disable upcasting of attention. Can boost speed but increase the chances of black images.") fp_group = parser.add_mutually_exclusive_group() fp_group.add_argument("--force-fp32", action="store_true", help="Force fp32 (If this makes your GPU work better please report it).") diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py index de66db4f..2515bac5 100644 --- a/comfy/ldm/modules/attention.py +++ b/comfy/ldm/modules/attention.py @@ -19,14 +19,6 @@ from comfy.cli_args import args import comfy.ops ops = comfy.ops.disable_weight_init -# CrossAttn precision handling -if args.dont_upcast_attention: - logging.info("disabling upcasting of attention") - _ATTN_PRECISION = None -else: - _ATTN_PRECISION = torch.float32 - - def exists(val): return val is not None @@ -386,10 +378,11 @@ def optimized_attention_for_device(device, mask=False, small_input=False): class CrossAttention(nn.Module): - def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=ops): + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., attn_precision=None, dtype=None, device=None, operations=ops): super().__init__() inner_dim = dim_head * heads context_dim = default(context_dim, query_dim) + self.attn_precision = attn_precision self.heads = heads self.dim_head = dim_head @@ -411,15 +404,15 @@ class CrossAttention(nn.Module): v = self.to_v(context) if mask is None: - out = optimized_attention(q, k, v, self.heads, attn_precision=_ATTN_PRECISION) + out = optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision) else: - out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=_ATTN_PRECISION) + out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision) return self.to_out(out) class BasicTransformerBlock(nn.Module): def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, ff_in=False, inner_dim=None, - disable_self_attn=False, disable_temporal_crossattention=False, switch_temporal_ca_to_sa=False, dtype=None, device=None, operations=ops): + disable_self_attn=False, disable_temporal_crossattention=False, switch_temporal_ca_to_sa=False, attn_precision=None, dtype=None, device=None, operations=ops): super().__init__() self.ff_in = ff_in or inner_dim is not None @@ -434,7 +427,7 @@ class BasicTransformerBlock(nn.Module): self.disable_self_attn = disable_self_attn self.attn1 = CrossAttention(query_dim=inner_dim, heads=n_heads, dim_head=d_head, dropout=dropout, - context_dim=context_dim if self.disable_self_attn else None, dtype=dtype, device=device, operations=operations) # is a self-attention if not self.disable_self_attn + context_dim=context_dim if self.disable_self_attn else None, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations) # is a self-attention if not self.disable_self_attn self.ff = FeedForward(inner_dim, dim_out=dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations) if disable_temporal_crossattention: @@ -448,7 +441,7 @@ class BasicTransformerBlock(nn.Module): context_dim_attn2 = context_dim self.attn2 = CrossAttention(query_dim=inner_dim, context_dim=context_dim_attn2, - heads=n_heads, dim_head=d_head, dropout=dropout, dtype=dtype, device=device, operations=operations) # is self-attn if context is none + heads=n_heads, dim_head=d_head, dropout=dropout, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations) # is self-attn if context is none self.norm2 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) self.norm1 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) @@ -588,7 +581,7 @@ class SpatialTransformer(nn.Module): def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., context_dim=None, disable_self_attn=False, use_linear=False, - use_checkpoint=True, dtype=None, device=None, operations=ops): + use_checkpoint=True, attn_precision=None, dtype=None, device=None, operations=ops): super().__init__() if exists(context_dim) and not isinstance(context_dim, list): context_dim = [context_dim] * depth @@ -606,7 +599,7 @@ class SpatialTransformer(nn.Module): self.transformer_blocks = nn.ModuleList( [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d], - disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, dtype=dtype, device=device, operations=operations) + disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations) for d in range(depth)] ) if not use_linear: @@ -662,6 +655,7 @@ class SpatialVideoTransformer(SpatialTransformer): disable_self_attn=False, disable_temporal_crossattention=False, max_time_embed_period: int = 10000, + attn_precision=None, dtype=None, device=None, operations=ops ): super().__init__( @@ -674,6 +668,7 @@ class SpatialVideoTransformer(SpatialTransformer): context_dim=context_dim, use_linear=use_linear, disable_self_attn=disable_self_attn, + attn_precision=attn_precision, dtype=dtype, device=device, operations=operations ) self.time_depth = time_depth @@ -703,6 +698,7 @@ class SpatialVideoTransformer(SpatialTransformer): inner_dim=time_mix_inner_dim, disable_self_attn=disable_self_attn, disable_temporal_crossattention=disable_temporal_crossattention, + attn_precision=attn_precision, dtype=dtype, device=device, operations=operations ) for _ in range(self.depth) diff --git a/comfy/ldm/modules/diffusionmodules/openaimodel.py b/comfy/ldm/modules/diffusionmodules/openaimodel.py index d782eff3..1f5a4ded 100644 --- a/comfy/ldm/modules/diffusionmodules/openaimodel.py +++ b/comfy/ldm/modules/diffusionmodules/openaimodel.py @@ -431,6 +431,7 @@ class UNetModel(nn.Module): video_kernel_size=None, disable_temporal_crossattention=False, max_ddpm_temb_period=10000, + attn_precision=None, device=None, operations=ops, ): @@ -550,13 +551,14 @@ class UNetModel(nn.Module): disable_self_attn=disable_self_attn, disable_temporal_crossattention=disable_temporal_crossattention, max_time_embed_period=max_ddpm_temb_period, + attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations ) else: return SpatialTransformer( ch, num_heads, dim_head, depth=depth, context_dim=context_dim, disable_self_attn=disable_self_attn, use_linear=use_linear_in_transformer, - use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations + use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations ) def get_resblock( diff --git a/comfy/supported_models.py b/comfy/supported_models.py index b3b69e05..6ca32e8e 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -65,6 +65,12 @@ class SD20(supported_models_base.BASE): "use_temporal_attention": False, } + unet_extra_config = { + "num_heads": -1, + "num_head_channels": 64, + "attn_precision": torch.float32, + } + latent_format = latent_formats.SD15 def model_type(self, state_dict, prefix=""): @@ -276,6 +282,12 @@ class SVD_img2vid(supported_models_base.BASE): "use_temporal_resblock": True } + unet_extra_config = { + "num_heads": -1, + "num_head_channels": 64, + "attn_precision": torch.float32, + } + clip_vision_prefix = "conditioner.embedders.0.open_clip.model.visual." latent_format = latent_formats.SD15 From ec6f16adb607fa8d14b26670106e1a09d8401e20 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 14 May 2024 18:02:27 -0400 Subject: [PATCH 215/378] Fix SAG. --- comfy/ldm/modules/attention.py | 6 ++++-- comfy_extras/nodes_sag.py | 10 +++++----- 2 files changed, 9 insertions(+), 7 deletions(-) diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py index 2515bac5..1d5cf0da 100644 --- a/comfy/ldm/modules/attention.py +++ b/comfy/ldm/modules/attention.py @@ -420,6 +420,7 @@ class BasicTransformerBlock(nn.Module): inner_dim = dim self.is_res = inner_dim == dim + self.attn_precision = attn_precision if self.ff_in: self.norm_in = operations.LayerNorm(dim, dtype=dtype, device=device) @@ -427,7 +428,7 @@ class BasicTransformerBlock(nn.Module): self.disable_self_attn = disable_self_attn self.attn1 = CrossAttention(query_dim=inner_dim, heads=n_heads, dim_head=d_head, dropout=dropout, - context_dim=context_dim if self.disable_self_attn else None, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations) # is a self-attention if not self.disable_self_attn + context_dim=context_dim if self.disable_self_attn else None, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations) # is a self-attention if not self.disable_self_attn self.ff = FeedForward(inner_dim, dim_out=dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations) if disable_temporal_crossattention: @@ -441,7 +442,7 @@ class BasicTransformerBlock(nn.Module): context_dim_attn2 = context_dim self.attn2 = CrossAttention(query_dim=inner_dim, context_dim=context_dim_attn2, - heads=n_heads, dim_head=d_head, dropout=dropout, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations) # is self-attn if context is none + heads=n_heads, dim_head=d_head, dropout=dropout, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations) # is self-attn if context is none self.norm2 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) self.norm1 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) @@ -471,6 +472,7 @@ class BasicTransformerBlock(nn.Module): extra_options["n_heads"] = self.n_heads extra_options["dim_head"] = self.d_head + extra_options["attn_precision"] = self.attn_precision if self.ff_in: x_skip = x diff --git a/comfy_extras/nodes_sag.py b/comfy_extras/nodes_sag.py index 69084e91..8d786db5 100644 --- a/comfy_extras/nodes_sag.py +++ b/comfy_extras/nodes_sag.py @@ -5,12 +5,12 @@ import math from einops import rearrange, repeat import os -from comfy.ldm.modules.attention import optimized_attention, _ATTN_PRECISION +from comfy.ldm.modules.attention import optimized_attention import comfy.samplers # from comfy/ldm/modules/attention.py # but modified to return attention scores as well as output -def attention_basic_with_sim(q, k, v, heads, mask=None): +def attention_basic_with_sim(q, k, v, heads, mask=None, attn_precision=None): b, _, dim_head = q.shape dim_head //= heads scale = dim_head ** -0.5 @@ -26,7 +26,7 @@ def attention_basic_with_sim(q, k, v, heads, mask=None): ) # force cast to fp32 to avoid overflowing - if _ATTN_PRECISION =="fp32": + if attn_precision == torch.float32: sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale else: sim = einsum('b i d, b j d -> b i j', q, k) * scale @@ -121,13 +121,13 @@ class SelfAttentionGuidance: if 1 in cond_or_uncond: uncond_index = cond_or_uncond.index(1) # do the entire attention operation, but save the attention scores to attn_scores - (out, sim) = attention_basic_with_sim(q, k, v, heads=heads) + (out, sim) = attention_basic_with_sim(q, k, v, heads=heads, attn_precision=extra_options["attn_precision"]) # when using a higher batch size, I BELIEVE the result batch dimension is [uc1, ... ucn, c1, ... cn] n_slices = heads * b attn_scores = sim[n_slices * uncond_index:n_slices * (uncond_index+1)] return out else: - return optimized_attention(q, k, v, heads=heads) + return optimized_attention(q, k, v, heads=heads, attn_precision=extra_options["attn_precision"]) def post_cfg_function(args): nonlocal attn_scores From 2d4164271634476627aae31fbec251ca748a0ae0 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 15 May 2024 02:40:06 -0400 Subject: [PATCH 216/378] Fix lowvram dora issue. --- comfy/model_patcher.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index c38b2f79..35ede5ee 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -16,7 +16,7 @@ def apply_weight_decompose(dora_scale, weight): .transpose(0, 1) ) - return weight * (dora_scale / weight_norm) + return weight * (dora_scale / weight_norm).type(weight.dtype) def set_model_options_patch_replace(model_options, patch, name, block_name, number, transformer_index=None): to = model_options["transformer_options"].copy() From 58f8388020ba6ab5a913beb742a6312914d640b2 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 16 May 2024 00:11:01 -0400 Subject: [PATCH 217/378] More proper fix for #3484. --- folder_paths.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/folder_paths.py b/folder_paths.py index 48979500..234b7340 100644 --- a/folder_paths.py +++ b/folder_paths.py @@ -258,7 +258,7 @@ def get_save_image_path(filename_prefix, output_dir, image_width=0, image_height raise Exception(err) try: - counter = max(filter(lambda a: a[1][:-1] == filename and a[1][-1] == "_", map(map_filename, os.listdir(full_output_folder))))[0] + 1 + counter = max(filter(lambda a: os.path.normcase(a[1][:-1]) == os.path.normcase(filename) and a[1][-1] == "_", map(map_filename, os.listdir(full_output_folder))))[0] + 1 except ValueError: counter = 1 except FileNotFoundError: From 46daf0a9a7c02e7652e60f3a7af898e2f6594c2e Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 16 May 2024 04:09:41 -0400 Subject: [PATCH 218/378] Add debug options to force on and off attention upcasting. --- comfy/cli_args.py | 5 +++++ comfy/ldm/modules/attention.py | 14 ++++++++++++++ 2 files changed, 19 insertions(+) diff --git a/comfy/cli_args.py b/comfy/cli_args.py index 2759f4e9..b8ac9bc6 100644 --- a/comfy/cli_args.py +++ b/comfy/cli_args.py @@ -95,6 +95,11 @@ attn_group.add_argument("--use-pytorch-cross-attention", action="store_true", he parser.add_argument("--disable-xformers", action="store_true", help="Disable xformers.") +upcast = parser.add_mutually_exclusive_group() +upcast.add_argument("--force-upcast-attention", action="store_true", help="Force enable attention upcasting, please report if it fixes black images.") +upcast.add_argument("--dont-upcast-attention", action="store_true", help="Disable all upcasting of attention. Should be unnecessary except for debugging.") + + vram_group = parser.add_mutually_exclusive_group() vram_group.add_argument("--gpu-only", action="store_true", help="Store and run everything (text encoders/CLIP models, etc... on the GPU).") vram_group.add_argument("--highvram", action="store_true", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.") diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py index 1d5cf0da..42653086 100644 --- a/comfy/ldm/modules/attention.py +++ b/comfy/ldm/modules/attention.py @@ -19,6 +19,14 @@ from comfy.cli_args import args import comfy.ops ops = comfy.ops.disable_weight_init + +def get_attn_precision(attn_precision): + if args.dont_upcast_attention: + return None + if attn_precision is None and args.force_upcast_attention: + return torch.float32 + return attn_precision + def exists(val): return val is not None @@ -78,6 +86,8 @@ def Normalize(in_channels, dtype=None, device=None): return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device) def attention_basic(q, k, v, heads, mask=None, attn_precision=None): + attn_precision = get_attn_precision(attn_precision) + b, _, dim_head = q.shape dim_head //= heads scale = dim_head ** -0.5 @@ -128,6 +138,8 @@ def attention_basic(q, k, v, heads, mask=None, attn_precision=None): def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None): + attn_precision = get_attn_precision(attn_precision) + b, _, dim_head = query.shape dim_head //= heads @@ -188,6 +200,8 @@ def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None) return hidden_states def attention_split(q, k, v, heads, mask=None, attn_precision=None): + attn_precision = get_attn_precision(attn_precision) + b, _, dim_head = q.shape dim_head //= heads scale = dim_head ** -0.5 From 19300655ddaeb1287a2ecf427cf64c0766e8a999 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 17 May 2024 00:31:32 -0400 Subject: [PATCH 219/378] Don't automatically switch to lowvram mode on GPUs with low memory. --- comfy/model_management.py | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index 7b54b256..21ae8d29 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -119,10 +119,6 @@ def get_total_memory(dev=None, torch_total_too=False): total_vram = get_total_memory(get_torch_device()) / (1024 * 1024) total_ram = psutil.virtual_memory().total / (1024 * 1024) logging.info("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram)) -if not args.normalvram and not args.cpu: - if lowvram_available and total_vram <= 4096: - logging.warning("Trying to enable lowvram mode because your GPU seems to have 4GB or less. If you don't want this use: --normalvram") - set_vram_to = VRAMState.LOW_VRAM try: OOM_EXCEPTION = torch.cuda.OutOfMemoryError @@ -451,9 +447,7 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False): model_size = loaded_model.model_memory_required(torch_dev) current_free_mem = get_free_memory(torch_dev) lowvram_model_memory = int(max(64 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3 )) - if model_size > (current_free_mem - inference_memory): #only switch to lowvram if really necessary - vram_set_state = VRAMState.LOW_VRAM - else: + if model_size <= (current_free_mem - inference_memory): #only switch to lowvram if really necessary lowvram_model_memory = 0 if vram_set_state == VRAMState.NO_VRAM: From 91590adf04e644304267a7a296710b4f7ae80bb2 Mon Sep 17 00:00:00 2001 From: pythongosssss <125205205+pythongosssss@users.noreply.github.com> Date: Fri, 17 May 2024 18:16:08 +0100 Subject: [PATCH 220/378] Add webcam node (#3497) * Add webcam node * unused import --- comfy_extras/nodes_webcam.py | 33 ++++++++ nodes.py | 1 + web/extensions/core/webcamCapture.js | 120 +++++++++++++++++++++++++++ 3 files changed, 154 insertions(+) create mode 100644 comfy_extras/nodes_webcam.py create mode 100644 web/extensions/core/webcamCapture.js diff --git a/comfy_extras/nodes_webcam.py b/comfy_extras/nodes_webcam.py new file mode 100644 index 00000000..32a0ba2f --- /dev/null +++ b/comfy_extras/nodes_webcam.py @@ -0,0 +1,33 @@ +import nodes +import folder_paths + +MAX_RESOLUTION = nodes.MAX_RESOLUTION + + +class WebcamCapture(nodes.LoadImage): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("WEBCAM", {}), + "width": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "height": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "capture_on_queue": ("BOOLEAN", {"default": True}), + } + } + RETURN_TYPES = ("IMAGE",) + FUNCTION = "load_capture" + + CATEGORY = "image" + + def load_capture(s, image, **kwargs): + return super().load_image(folder_paths.get_annotated_filepath(image)) + + +NODE_CLASS_MAPPINGS = { + "WebcamCapture": WebcamCapture, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "WebcamCapture": "Webcam Capture", +} \ No newline at end of file diff --git a/nodes.py b/nodes.py index 37e7d734..34821ca3 100644 --- a/nodes.py +++ b/nodes.py @@ -1961,6 +1961,7 @@ def init_custom_nodes(): "nodes_align_your_steps.py", "nodes_attention_multiply.py", "nodes_advanced_samplers.py", + "nodes_webcam.py", ] import_failed = [] diff --git a/web/extensions/core/webcamCapture.js b/web/extensions/core/webcamCapture.js new file mode 100644 index 00000000..ea1f597e --- /dev/null +++ b/web/extensions/core/webcamCapture.js @@ -0,0 +1,120 @@ +import { app } from "../../scripts/app.js"; +import { api } from "../../scripts/api.js"; + +const WEBCAM_READY = Symbol(); + +app.registerExtension({ + name: "Comfy.WebcamCapture", + getCustomWidgets(app) { + return { + WEBCAM(node, inputName) { + let res; + node[WEBCAM_READY] = new Promise((resolve) => (res = resolve)); + + const container = document.createElement("div"); + container.style.background = "rgba(0,0,0,0.25)"; + container.style.textAlign = "center"; + + const video = document.createElement("video"); + video.style.height = video.style.width = "100%"; + + const loadVideo = async () => { + try { + const stream = await navigator.mediaDevices.getUserMedia({ video: true, audio: false }); + container.replaceChildren(video); + + setTimeout(() => res(video), 500); // Fallback as loadedmetadata doesnt fire sometimes? + video.addEventListener("loadedmetadata", () => res(video), false); + video.srcObject = stream; + video.play(); + } catch (error) { + const label = document.createElement("div"); + label.style.color = "red"; + label.style.overflow = "auto"; + label.style.maxHeight = "100%"; + label.style.whiteSpace = "pre-wrap"; + label.textContent = "Unable to load webcam, please ensure access is granted:\n" + error.message; + container.replaceChildren(label); + } + }; + + loadVideo(); + + return { widget: node.addDOMWidget(inputName, "WEBCAM", container) }; + }, + }; + }, + nodeCreated(node) { + if ((node.type, node.constructor.comfyClass !== "WebcamCapture")) return; + + let video; + const camera = node.widgets.find((w) => w.name === "image"); + const w = node.widgets.find((w) => w.name === "width"); + const h = node.widgets.find((w) => w.name === "height"); + const captureOnQueue = node.widgets.find((w) => w.name === "capture_on_queue"); + + const canvas = document.createElement("canvas"); + + const capture = () => { + canvas.width = w.value; + canvas.height = h.value; + const ctx = canvas.getContext("2d"); + ctx.drawImage(video, 0, 0, w.value, h.value); + const data = canvas.toDataURL("image/png"); + + const img = new Image(); + img.onload = () => { + node.imgs = [img]; + app.graph.setDirtyCanvas(true); + requestAnimationFrame(() => { + node.setSizeForImage?.(); + }); + }; + img.src = data; + }; + + const btn = node.addWidget("button", "waiting for camera...", "capture", capture); + btn.disabled = true; + btn.serializeValue = () => undefined; + + camera.serializeValue = async () => { + if (captureOnQueue.value) { + capture(); + } else if (!node.imgs?.length) { + const err = `No webcam image captured`; + alert(err); + throw new Error(err); + } + + // Upload image to temp storage + const blob = await new Promise((r) => canvas.toBlob(r)); + const name = `${+new Date()}.png`; + const file = new File([blob], name); + const body = new FormData(); + body.append("image", file); + body.append("subfolder", "webcam"); + body.append("type", "temp"); + const resp = await api.fetchApi("/upload/image", { + method: "POST", + body, + }); + if (resp.status !== 200) { + const err = `Error uploading camera image: ${resp.status} - ${resp.statusText}`; + alert(err); + throw new Error(err); + } + return `webcam/${name} [temp]`; + }; + + node[WEBCAM_READY].then((v) => { + video = v; + // If width isnt specified then use video output resolution + if (!w.value) { + w.value = video.videoWidth || 640; + h.value = video.videoHeight || 480; + } + btn.disabled = false; + btn.label = "capture"; + }); + }, +}); From 1c4af5918a5ffb022606e56ab2d31e510dcccec2 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 17 May 2024 14:02:09 -0400 Subject: [PATCH 221/378] Better error message if the webcam node doesn't work. --- web/extensions/core/webcamCapture.js | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/web/extensions/core/webcamCapture.js b/web/extensions/core/webcamCapture.js index ea1f597e..dd5725bd 100644 --- a/web/extensions/core/webcamCapture.js +++ b/web/extensions/core/webcamCapture.js @@ -33,7 +33,13 @@ app.registerExtension({ label.style.overflow = "auto"; label.style.maxHeight = "100%"; label.style.whiteSpace = "pre-wrap"; - label.textContent = "Unable to load webcam, please ensure access is granted:\n" + error.message; + + if (window.isSecureContext) { + label.textContent = "Unable to load webcam, please ensure access is granted:\n" + error.message; + } else { + label.textContent = "Unable to load webcam. A secure context is required, if you are not accessing ComfyUI on localhost (127.0.0.1) you will have to enable TLS (https)\n\n" + error.message; + } + container.replaceChildren(label); } }; From 98f828fad98643d4f8f31e355cc79a1fd42e1bb8 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 18 May 2024 09:36:26 -0400 Subject: [PATCH 222/378] Remove unnecessary code. --- comfy/ldm/modules/attention.py | 11 ++--------- comfy/ldm/modules/diffusionmodules/model.py | 1 - 2 files changed, 2 insertions(+), 10 deletions(-) diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py index 42653086..88ee2f32 100644 --- a/comfy/ldm/modules/attention.py +++ b/comfy/ldm/modules/attention.py @@ -318,11 +318,7 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None): return attention_pytorch(q, k, v, heads, mask) q, k, v = map( - lambda t: t.unsqueeze(3) - .reshape(b, -1, heads, dim_head) - .permute(0, 2, 1, 3) - .reshape(b * heads, -1, dim_head) - .contiguous(), + lambda t: t.reshape(b, -1, heads, dim_head), (q, k, v), ) @@ -335,10 +331,7 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None): out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask) out = ( - out.unsqueeze(0) - .reshape(b, heads, -1, dim_head) - .permute(0, 2, 1, 3) - .reshape(b, -1, heads * dim_head) + out.reshape(b, -1, heads * dim_head) ) return out diff --git a/comfy/ldm/modules/diffusionmodules/model.py b/comfy/ldm/modules/diffusionmodules/model.py index fabc5c5e..04eb83b2 100644 --- a/comfy/ldm/modules/diffusionmodules/model.py +++ b/comfy/ldm/modules/diffusionmodules/model.py @@ -3,7 +3,6 @@ import math import torch import torch.nn as nn import numpy as np -from einops import rearrange from typing import Optional, Any import logging From 0bdc2b15c75426af75a326d5966ad47aab5b76d3 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 18 May 2024 10:11:44 -0400 Subject: [PATCH 223/378] Cleanup. --- comfy/ldm/modules/attention.py | 10 +++------- comfy/ldm/modules/diffusionmodules/openaimodel.py | 2 +- 2 files changed, 4 insertions(+), 8 deletions(-) diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py index 88ee2f32..aa74b632 100644 --- a/comfy/ldm/modules/attention.py +++ b/comfy/ldm/modules/attention.py @@ -6,7 +6,7 @@ from einops import rearrange, repeat from typing import Optional, Any import logging -from .diffusionmodules.util import checkpoint, AlphaBlender, timestep_embedding +from .diffusionmodules.util import AlphaBlender, timestep_embedding from .sub_quadratic_attention import efficient_dot_product_attention from comfy import model_management @@ -454,15 +454,11 @@ class BasicTransformerBlock(nn.Module): self.norm1 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) self.norm3 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) - self.checkpoint = checkpoint self.n_heads = n_heads self.d_head = d_head self.switch_temporal_ca_to_sa = switch_temporal_ca_to_sa def forward(self, x, context=None, transformer_options={}): - return checkpoint(self._forward, (x, context, transformer_options), self.parameters(), self.checkpoint) - - def _forward(self, x, context=None, transformer_options={}): extra_options = {} block = transformer_options.get("block", None) block_index = transformer_options.get("block_index", 0) @@ -629,7 +625,7 @@ class SpatialTransformer(nn.Module): x = self.norm(x) if not self.use_linear: x = self.proj_in(x) - x = rearrange(x, 'b c h w -> b (h w) c').contiguous() + x = x.movedim(1, -1).flatten(1, 2).contiguous() if self.use_linear: x = self.proj_in(x) for i, block in enumerate(self.transformer_blocks): @@ -637,7 +633,7 @@ class SpatialTransformer(nn.Module): x = block(x, context=context[i], transformer_options=transformer_options) if self.use_linear: x = self.proj_out(x) - x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous() + x = x.reshape(x.shape[0], h, w, x.shape[-1]).movedim(-1, 1).contiguous() if not self.use_linear: x = self.proj_out(x) return x + x_in diff --git a/comfy/ldm/modules/diffusionmodules/openaimodel.py b/comfy/ldm/modules/diffusionmodules/openaimodel.py index 1f5a4ded..ba8fc2c4 100644 --- a/comfy/ldm/modules/diffusionmodules/openaimodel.py +++ b/comfy/ldm/modules/diffusionmodules/openaimodel.py @@ -258,7 +258,7 @@ class ResBlock(TimestepBlock): else: if emb_out is not None: if self.exchange_temb_dims: - emb_out = rearrange(emb_out, "b t c ... -> b c t ...") + emb_out = emb_out.movedim(1, 2) h = h + emb_out h = self.out_layers(h) return self.skip_connection(x) + h From f37a47110bef97186b8392830d5c49148a06ecef Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 19 May 2024 11:45:36 -0400 Subject: [PATCH 224/378] Make --preview-method auto default to the fast latent2rgb previews. --- latent_preview.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/latent_preview.py b/latent_preview.py index 4dbcbf45..12927647 100644 --- a/latent_preview.py +++ b/latent_preview.py @@ -63,8 +63,6 @@ def get_previewer(device, latent_format): if method == LatentPreviewMethod.Auto: method = LatentPreviewMethod.Latent2RGB - if taesd_decoder_path: - method = LatentPreviewMethod.TAESD if method == LatentPreviewMethod.TAESD: if taesd_decoder_path: From 4ae1515f14d338a347fdf8f647e062f5bc17d196 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 19 May 2024 17:42:35 -0400 Subject: [PATCH 225/378] Slightly faster latent2rgb previews. --- latent_preview.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/latent_preview.py b/latent_preview.py index 12927647..05d750e2 100644 --- a/latent_preview.py +++ b/latent_preview.py @@ -37,12 +37,13 @@ class Latent2RGBPreviewer(LatentPreviewer): self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu") def decode_latent_to_preview(self, x0): - latent_image = x0[0].permute(1, 2, 0).cpu() @ self.latent_rgb_factors + self.latent_rgb_factors = self.latent_rgb_factors.to(dtype=x0.dtype, device=x0.device) + latent_image = x0[0].permute(1, 2, 0) @ self.latent_rgb_factors latents_ubyte = (((latent_image + 1) / 2) .clamp(0, 1) # change scale from -1..1 to 0..1 .mul(0xFF) # to 0..255 - .byte()).cpu() + ).to(device="cpu", dtype=torch.uint8, non_blocking=True) return Image.fromarray(latents_ubyte.numpy()) From 11a2ad5110a96ac5895e889d455c227d1b80dd30 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 19 May 2024 17:58:03 -0400 Subject: [PATCH 226/378] Fix controlnet not upcasting on models that have it enabled. --- comfy/cldm/cldm.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/comfy/cldm/cldm.py b/comfy/cldm/cldm.py index 5eee5a51..28076dd9 100644 --- a/comfy/cldm/cldm.py +++ b/comfy/cldm/cldm.py @@ -52,6 +52,7 @@ class ControlNet(nn.Module): adm_in_channels=None, transformer_depth_middle=None, transformer_depth_output=None, + attn_precision=None, device=None, operations=comfy.ops.disable_weight_init, **kwargs, @@ -202,7 +203,7 @@ class ControlNet(nn.Module): SpatialTransformer( ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim, disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, - use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations + use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations ) ) self.input_blocks.append(TimestepEmbedSequential(*layers)) @@ -262,7 +263,7 @@ class ControlNet(nn.Module): mid_block += [SpatialTransformer( # always uses a self-attn ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim, disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer, - use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations + use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations ), ResBlock( ch, From 09e069ae6ca700cabb885662a83a05227b4f79a5 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 20 May 2024 06:22:29 -0400 Subject: [PATCH 227/378] Log the pytorch version. --- comfy/model_management.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/comfy/model_management.py b/comfy/model_management.py index 21ae8d29..b2192862 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -120,6 +120,11 @@ total_vram = get_total_memory(get_torch_device()) / (1024 * 1024) total_ram = psutil.virtual_memory().total / (1024 * 1024) logging.info("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram)) +try: + logging.info("pytorch version: {}".format(torch.version.__version__)) +except: + pass + try: OOM_EXCEPTION = torch.cuda.OutOfMemoryError except: From 4bc1884478a14dea2d64a34f9beb1c52a2d16b09 Mon Sep 17 00:00:00 2001 From: "Dr.Lt.Data" <128333288+ltdrdata@users.noreply.github.com> Date: Mon, 20 May 2024 19:58:46 +0900 Subject: [PATCH 228/378] Provide a better error message when attempting to execute the workflow with a missing node. (#3517) --- execution.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/execution.py b/execution.py index 47d58b9d..86bde1c4 100644 --- a/execution.py +++ b/execution.py @@ -622,8 +622,17 @@ def full_type_name(klass): def validate_prompt(prompt): outputs = set() for x in prompt: + if 'class_type' not in prompt[x]: + error = { + "type": "invalid_prompt", + "message": f"Cannot execute due to a missing node", + "details": f"Node ID '#{x}'", + "extra_info": {} + } + return (False, error, [], []) + class_ = nodes.NODE_CLASS_MAPPINGS[prompt[x]['class_type']] - if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE == True: + if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE is True: outputs.add(x) if len(outputs) == 0: From 276f8fce9f5a80b500947fb5745a4dde9e84622d Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 20 May 2024 07:03:06 -0400 Subject: [PATCH 229/378] Print error when node is missing. --- execution.py | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/execution.py b/execution.py index 86bde1c4..76225a96 100644 --- a/execution.py +++ b/execution.py @@ -625,13 +625,23 @@ def validate_prompt(prompt): if 'class_type' not in prompt[x]: error = { "type": "invalid_prompt", - "message": f"Cannot execute due to a missing node", + "message": f"Cannot execute because a node is missing the class_type property.", + "details": f"Node ID '#{x}'", + "extra_info": {} + } + return (False, error, [], []) + + class_type = prompt[x]['class_type'] + class_ = nodes.NODE_CLASS_MAPPINGS.get(class_type, None) + if class_ is None: + error = { + "type": "invalid_prompt", + "message": f"Cannot execute because node {class_type} does not exist.", "details": f"Node ID '#{x}'", "extra_info": {} } return (False, error, [], []) - class_ = nodes.NODE_CLASS_MAPPINGS[prompt[x]['class_type']] if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE is True: outputs.add(x) From 1900e5119f70d6db0677fe91194050be3c4476c4 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 20 May 2024 08:19:54 -0400 Subject: [PATCH 230/378] Fix potential issue. --- comfy/ldm/modules/attention.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py index aa74b632..74a2fd99 100644 --- a/comfy/ldm/modules/attention.py +++ b/comfy/ldm/modules/attention.py @@ -625,7 +625,7 @@ class SpatialTransformer(nn.Module): x = self.norm(x) if not self.use_linear: x = self.proj_in(x) - x = x.movedim(1, -1).flatten(1, 2).contiguous() + x = x.movedim(1, 3).flatten(1, 2).contiguous() if self.use_linear: x = self.proj_in(x) for i, block in enumerate(self.transformer_blocks): @@ -633,7 +633,7 @@ class SpatialTransformer(nn.Module): x = block(x, context=context[i], transformer_options=transformer_options) if self.use_linear: x = self.proj_out(x) - x = x.reshape(x.shape[0], h, w, x.shape[-1]).movedim(-1, 1).contiguous() + x = x.reshape(x.shape[0], h, w, x.shape[-1]).movedim(3, 1).contiguous() if not self.use_linear: x = self.proj_out(x) return x + x_in From 83d969e3975d340ef980db59b07954a67d08ce6f Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 21 May 2024 13:55:49 -0400 Subject: [PATCH 231/378] Disable xformers when tracing model. --- comfy/ldm/modules/attention.py | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py index 74a2fd99..2ce99d46 100644 --- a/comfy/ldm/modules/attention.py +++ b/comfy/ldm/modules/attention.py @@ -313,9 +313,19 @@ except: def attention_xformers(q, k, v, heads, mask=None, attn_precision=None): b, _, dim_head = q.shape dim_head //= heads + + disabled_xformers = False + if BROKEN_XFORMERS: if b * heads > 65535: - return attention_pytorch(q, k, v, heads, mask) + disabled_xformers = True + + if not disabled_xformers: + if torch.jit.is_tracing() or torch.jit.is_scripting(): + disabled_xformers = True + + if disabled_xformers: + return attention_pytorch(q, k, v, heads, mask) q, k, v = map( lambda t: t.reshape(b, -1, heads, dim_head), From 8508df25691b0c9213049ab0d723610d3d8f9136 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 21 May 2024 16:56:33 -0400 Subject: [PATCH 232/378] Work around black image bug on Mac 14.5 by forcing attention upcasting. --- comfy/ldm/modules/attention.py | 5 +++-- comfy/model_management.py | 13 +++++++++++++ 2 files changed, 16 insertions(+), 2 deletions(-) diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py index 2ce99d46..93c94458 100644 --- a/comfy/ldm/modules/attention.py +++ b/comfy/ldm/modules/attention.py @@ -19,12 +19,13 @@ from comfy.cli_args import args import comfy.ops ops = comfy.ops.disable_weight_init +FORCE_UPCAST_ATTENTION_DTYPE = model_management.force_upcast_attention_dtype() def get_attn_precision(attn_precision): if args.dont_upcast_attention: return None - if attn_precision is None and args.force_upcast_attention: - return torch.float32 + if FORCE_UPCAST_ATTENTION_DTYPE is not None: + return FORCE_UPCAST_ATTENTION_DTYPE return attn_precision def exists(val): diff --git a/comfy/model_management.py b/comfy/model_management.py index b2192862..fbfbb7e4 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -5,6 +5,7 @@ from comfy.cli_args import args import comfy.utils import torch import sys +import platform class VRAMState(Enum): DISABLED = 0 #No vram present: no need to move models to vram @@ -685,6 +686,18 @@ def pytorch_attention_flash_attention(): return True return False +def force_upcast_attention_dtype(): + upcast = args.force_upcast_attention + try: + if platform.mac_ver()[0] in ['14.5']: #black image bug on OSX Sonoma 14.5 + upcast = True + except: + pass + if upcast: + return torch.float32 + else: + return None + def get_free_memory(dev=None, torch_free_too=False): global directml_enabled if dev is None: From 7718ada4eddf101d088b69e159011e4108286b5b Mon Sep 17 00:00:00 2001 From: Chenlei Hu Date: Wed, 22 May 2024 02:07:27 -0400 Subject: [PATCH 233/378] Add type annotation UnetWrapperFunction (#3531) * Add type annotation UnetWrapperFunction * nit * Add types.py --- comfy/model_patcher.py | 4 +++- comfy/types.py | 32 ++++++++++++++++++++++++++++++++ 2 files changed, 35 insertions(+), 1 deletion(-) create mode 100644 comfy/types.py diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index 35ede5ee..c397ee51 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -6,6 +6,8 @@ import uuid import comfy.utils import comfy.model_management +from comfy.types import UnetWrapperFunction + def apply_weight_decompose(dora_scale, weight): weight_norm = ( @@ -117,7 +119,7 @@ class ModelPatcher: if disable_cfg1_optimization: self.model_options["disable_cfg1_optimization"] = True - def set_model_unet_function_wrapper(self, unet_wrapper_function): + def set_model_unet_function_wrapper(self, unet_wrapper_function: UnetWrapperFunction): self.model_options["model_function_wrapper"] = unet_wrapper_function def set_model_denoise_mask_function(self, denoise_mask_function): diff --git a/comfy/types.py b/comfy/types.py new file mode 100644 index 00000000..a8a3d29f --- /dev/null +++ b/comfy/types.py @@ -0,0 +1,32 @@ +import torch +from typing import Callable, Protocol, TypedDict, Optional, List + + +class UnetApplyFunction(Protocol): + """Function signature protocol on comfy.model_base.BaseModel.apply_model""" + + def __call__(self, x: torch.Tensor, t: torch.Tensor, **kwargs) -> torch.Tensor: + pass + + +class UnetApplyConds(TypedDict): + """Optional conditions for unet apply function.""" + + c_concat: Optional[torch.Tensor] + c_crossattn: Optional[torch.Tensor] + control: Optional[torch.Tensor] + transformer_options: Optional[dict] + + +class UnetParams(TypedDict): + # Tensor of shape [B, C, H, W] + input: torch.Tensor + # Tensor of shape [B] + timestep: torch.Tensor + c: UnetApplyConds + # List of [0, 1], [0], [1], ... + # 0 means unconditional, 1 means conditional + cond_or_uncond: List[int] + + +UnetWrapperFunction = Callable[[UnetApplyFunction, UnetParams], torch.Tensor] From 6c23854f54edb51643026b7b10d1ae9e34cfd3d6 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 22 May 2024 13:56:28 -0400 Subject: [PATCH 234/378] Fix OSX latent2rgb previews. --- comfy/model_management.py | 10 ++++++++-- comfy/ops.py | 2 +- latent_preview.py | 3 ++- 3 files changed, 11 insertions(+), 4 deletions(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index fbfbb7e4..ef36a2c4 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -630,8 +630,14 @@ def supports_dtype(device, dtype): #TODO def device_supports_non_blocking(device): if is_device_mps(device): return False #pytorch bug? mps doesn't support non blocking + return True + +def device_should_use_non_blocking(device): + if not device_supports_non_blocking(device): + return False return False - # return True #TODO: figure out why this causes issues + # return True #TODO: figure out why this causes memory issues on Nvidia and possibly others + def cast_to_device(tensor, device, dtype, copy=False): device_supports_cast = False @@ -643,7 +649,7 @@ def cast_to_device(tensor, device, dtype, copy=False): elif is_intel_xpu(): device_supports_cast = True - non_blocking = device_supports_non_blocking(device) + non_blocking = device_should_use_non_blocking(device) if device_supports_cast: if copy: diff --git a/comfy/ops.py b/comfy/ops.py index eb650768..7ebb3dd2 100644 --- a/comfy/ops.py +++ b/comfy/ops.py @@ -21,7 +21,7 @@ import comfy.model_management def cast_bias_weight(s, input): bias = None - non_blocking = comfy.model_management.device_supports_non_blocking(input.device) + non_blocking = comfy.model_management.device_should_use_non_blocking(input.device) if s.bias is not None: bias = s.bias.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking) if s.bias_function is not None: diff --git a/latent_preview.py b/latent_preview.py index 05d750e2..dae9beb6 100644 --- a/latent_preview.py +++ b/latent_preview.py @@ -4,6 +4,7 @@ import struct import numpy as np from comfy.cli_args import args, LatentPreviewMethod from comfy.taesd.taesd import TAESD +import comfy.model_management import folder_paths import comfy.utils import logging @@ -43,7 +44,7 @@ class Latent2RGBPreviewer(LatentPreviewer): latents_ubyte = (((latent_image + 1) / 2) .clamp(0, 1) # change scale from -1..1 to 0..1 .mul(0xFF) # to 0..255 - ).to(device="cpu", dtype=torch.uint8, non_blocking=True) + ).to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(latent_image.device)) return Image.fromarray(latents_ubyte.numpy()) From 6507a9c71691102fcba3d3a6adcf68303cb33895 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 23 May 2024 01:29:22 -0400 Subject: [PATCH 235/378] Remove the CTRL-Delete keybind. On some keyboards it's apparently too easy to accidentally do CTRL-Delete when pressing CTRL-Enter repeatedly. CTRL-Backspace can still be used to clear the workflow. --- README.md | 2 +- web/extensions/core/keybinds.js | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/README.md b/README.md index 80de21bc..cf32014b 100644 --- a/README.md +++ b/README.md @@ -53,7 +53,7 @@ Workflow examples can be found on the [Examples page](https://comfyanonymous.git | Ctrl + M | Mute/unmute selected nodes | | Ctrl + B | Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through) | | Delete/Backspace | Delete selected nodes | -| Ctrl + Delete/Backspace | Delete the current graph | +| Ctrl + Backspace | Delete the current graph | | Space | Move the canvas around when held and moving the cursor | | Ctrl/Shift + Click | Add clicked node to selection | | Ctrl + C/Ctrl + V | Copy and paste selected nodes (without maintaining connections to outputs of unselected nodes) | diff --git a/web/extensions/core/keybinds.js b/web/extensions/core/keybinds.js index cf698ea5..ac367c11 100644 --- a/web/extensions/core/keybinds.js +++ b/web/extensions/core/keybinds.js @@ -21,7 +21,6 @@ app.registerExtension({ s: "#comfy-save-button", o: "#comfy-file-input", Backspace: "#comfy-clear-button", - Delete: "#comfy-clear-button", d: "#comfy-load-default-button", }; From b02bcced05dcc2d09a0358eff6393c5641485878 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 23 May 2024 11:47:43 -0400 Subject: [PATCH 236/378] Fix FreeU not working when shape is tensor. --- comfy_extras/nodes_freelunch.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comfy_extras/nodes_freelunch.py b/comfy_extras/nodes_freelunch.py index 6f1d87bf..c5ebcf26 100644 --- a/comfy_extras/nodes_freelunch.py +++ b/comfy_extras/nodes_freelunch.py @@ -42,7 +42,7 @@ class FreeU: on_cpu_devices = {} def output_block_patch(h, hsp, transformer_options): - scale = scale_dict.get(h.shape[1], None) + scale = scale_dict.get(int(h.shape[1]), None) if scale is not None: h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * scale[0] if hsp.device not in on_cpu_devices: @@ -81,7 +81,7 @@ class FreeU_V2: on_cpu_devices = {} def output_block_patch(h, hsp, transformer_options): - scale = scale_dict.get(h.shape[1], None) + scale = scale_dict.get(int(h.shape[1]), None) if scale is not None: hidden_mean = h.mean(1).unsqueeze(1) B = hidden_mean.shape[0] From 58c9838274f53c6aa8912992db9f73e9a0721227 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 24 May 2024 02:37:57 -0400 Subject: [PATCH 237/378] Speed up TAESD preview. --- latent_preview.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/latent_preview.py b/latent_preview.py index dae9beb6..b258fcf2 100644 --- a/latent_preview.py +++ b/latent_preview.py @@ -25,9 +25,8 @@ class TAESDPreviewerImpl(LatentPreviewer): def decode_latent_to_preview(self, x0): x_sample = self.taesd.decode(x0[:1])[0].detach() - x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0) - x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) - x_sample = x_sample.astype(np.uint8) + x_sample = 255. * torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0) + x_sample = np.moveaxis(x_sample.to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(x_sample.device)).numpy(), 0, 2) preview_image = Image.fromarray(x_sample) return preview_image From efa5a711b20c4544fe7377cc85d9f40d7d180b37 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 24 May 2024 23:36:48 -0400 Subject: [PATCH 238/378] Reduce memory usage when applying DORA: #3557 --- comfy/model_patcher.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index c397ee51..78982d79 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -9,7 +9,7 @@ import comfy.model_management from comfy.types import UnetWrapperFunction -def apply_weight_decompose(dora_scale, weight): +def weight_decompose_scale(dora_scale, weight): weight_norm = ( weight.transpose(0, 1) .reshape(weight.shape[1], -1) @@ -18,7 +18,7 @@ def apply_weight_decompose(dora_scale, weight): .transpose(0, 1) ) - return weight * (dora_scale / weight_norm).type(weight.dtype) + return (dora_scale / weight_norm).type(weight.dtype) def set_model_options_patch_replace(model_options, patch, name, block_name, number, transformer_index=None): to = model_options["transformer_options"].copy() @@ -365,7 +365,7 @@ class ModelPatcher: try: weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(weight.shape).type(weight.dtype) if dora_scale is not None: - weight = apply_weight_decompose(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight) + weight *= weight_decompose_scale(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight) except Exception as e: logging.error("ERROR {} {} {}".format(patch_type, key, e)) elif patch_type == "lokr": @@ -407,7 +407,7 @@ class ModelPatcher: try: weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype) if dora_scale is not None: - weight = apply_weight_decompose(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight) + weight *= weight_decompose_scale(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight) except Exception as e: logging.error("ERROR {} {} {}".format(patch_type, key, e)) elif patch_type == "loha": @@ -439,7 +439,7 @@ class ModelPatcher: try: weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype) if dora_scale is not None: - weight = apply_weight_decompose(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight) + weight *= weight_decompose_scale(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight) except Exception as e: logging.error("ERROR {} {} {}".format(patch_type, key, e)) elif patch_type == "glora": @@ -456,7 +456,7 @@ class ModelPatcher: try: weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype) if dora_scale is not None: - weight = apply_weight_decompose(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight) + weight *= weight_decompose_scale(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight) except Exception as e: logging.error("ERROR {} {} {}".format(patch_type, key, e)) else: From 5b8736947490ea94e219a24743ce5460816696ee Mon Sep 17 00:00:00 2001 From: DLohn <41272253+DLohn@users.noreply.github.com> Date: Fri, 24 May 2024 20:53:15 -0700 Subject: [PATCH 239/378] Load titles from API format JSON (#3563) --- web/scripts/app.js | 1 + 1 file changed, 1 insertion(+) diff --git a/web/scripts/app.js b/web/scripts/app.js index a516be70..4dc011b9 100644 --- a/web/scripts/app.js +++ b/web/scripts/app.js @@ -2238,6 +2238,7 @@ export class ComfyApp { const data = apiData[id]; const node = LiteGraph.createNode(data.class_type); node.id = isNaN(+id) ? id : +id; + node.title = data._meta?.title ?? node.title graph.add(node); } From ffc4b7c30e35eb2773ace52a0b00e0ca5c1f4362 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 25 May 2024 02:31:23 -0400 Subject: [PATCH 240/378] Fix DORA strength. This is a different version of #3298 with more correct behavior. --- comfy/model_patcher.py | 68 +++++++++++++++++++++++++++++------------- 1 file changed, 48 insertions(+), 20 deletions(-) diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index 78982d79..2e746d8a 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -9,16 +9,26 @@ import comfy.model_management from comfy.types import UnetWrapperFunction -def weight_decompose_scale(dora_scale, weight): +def weight_decompose(dora_scale, weight, lora_diff, alpha, strength): + dora_scale = comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32) + lora_diff *= alpha + weight_calc = weight + lora_diff.type(weight.dtype) weight_norm = ( - weight.transpose(0, 1) - .reshape(weight.shape[1], -1) + weight_calc.transpose(0, 1) + .reshape(weight_calc.shape[1], -1) .norm(dim=1, keepdim=True) - .reshape(weight.shape[1], *[1] * (weight.dim() - 1)) + .reshape(weight_calc.shape[1], *[1] * (weight_calc.dim() - 1)) .transpose(0, 1) ) - return (dora_scale / weight_norm).type(weight.dtype) + weight_calc *= (dora_scale / weight_norm).type(weight.dtype) + if strength != 1.0: + weight_calc -= weight + weight += strength * (weight_calc) + else: + weight[:] = weight_calc + return weight + def set_model_options_patch_replace(model_options, patch, name, block_name, number, transformer_index=None): to = model_options["transformer_options"].copy() @@ -328,7 +338,7 @@ class ModelPatcher: def calculate_weight(self, patches, weight, key): for p in patches: - alpha = p[0] + strength = p[0] v = p[1] strength_model = p[2] @@ -346,26 +356,31 @@ class ModelPatcher: if patch_type == "diff": w1 = v[0] - if alpha != 0.0: + if strength != 0.0: if w1.shape != weight.shape: logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape)) else: - weight += alpha * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype) + weight += strength * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype) elif patch_type == "lora": #lora/locon mat1 = comfy.model_management.cast_to_device(v[0], weight.device, torch.float32) mat2 = comfy.model_management.cast_to_device(v[1], weight.device, torch.float32) dora_scale = v[4] if v[2] is not None: - alpha *= v[2] / mat2.shape[0] + alpha = v[2] / mat2.shape[0] + else: + alpha = 1.0 + if v[3] is not None: #locon mid weights, hopefully the math is fine because I didn't properly test it mat3 = comfy.model_management.cast_to_device(v[3], weight.device, torch.float32) final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]] mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1) try: - weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(weight.shape).type(weight.dtype) + lora_diff = torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1)).reshape(weight.shape) if dora_scale is not None: - weight *= weight_decompose_scale(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight) + weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength) + else: + weight += ((strength * alpha) * lora_diff).type(weight.dtype) except Exception as e: logging.error("ERROR {} {} {}".format(patch_type, key, e)) elif patch_type == "lokr": @@ -402,19 +417,26 @@ class ModelPatcher: if len(w2.shape) == 4: w1 = w1.unsqueeze(2).unsqueeze(2) if v[2] is not None and dim is not None: - alpha *= v[2] / dim + alpha = v[2] / dim + else: + alpha = 1.0 try: - weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype) + lora_diff = torch.kron(w1, w2).reshape(weight.shape) if dora_scale is not None: - weight *= weight_decompose_scale(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight) + weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength) + else: + weight += ((strength * alpha) * lora_diff).type(weight.dtype) except Exception as e: logging.error("ERROR {} {} {}".format(patch_type, key, e)) elif patch_type == "loha": w1a = v[0] w1b = v[1] if v[2] is not None: - alpha *= v[2] / w1b.shape[0] + alpha = v[2] / w1b.shape[0] + else: + alpha = 1.0 + w2a = v[3] w2b = v[4] dora_scale = v[7] @@ -437,14 +459,18 @@ class ModelPatcher: comfy.model_management.cast_to_device(w2b, weight.device, torch.float32)) try: - weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype) + lora_diff = (m1 * m2).reshape(weight.shape) if dora_scale is not None: - weight *= weight_decompose_scale(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight) + weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength) + else: + weight += ((strength * alpha) * lora_diff).type(weight.dtype) except Exception as e: logging.error("ERROR {} {} {}".format(patch_type, key, e)) elif patch_type == "glora": if v[4] is not None: - alpha *= v[4] / v[0].shape[0] + alpha = v[4] / v[0].shape[0] + else: + alpha = 1.0 dora_scale = v[5] @@ -454,9 +480,11 @@ class ModelPatcher: b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, torch.float32) try: - weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype) + lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)).reshape(weight.shape) if dora_scale is not None: - weight *= weight_decompose_scale(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight) + weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength) + else: + weight += ((strength * alpha) * lora_diff).type(weight.dtype) except Exception as e: logging.error("ERROR {} {} {}".format(patch_type, key, e)) else: From 8cfd677cc0add9048ca9c085a8b79afbbc70bd91 Mon Sep 17 00:00:00 2001 From: Joey Ballentine <34788790+joeyballentine@users.noreply.github.com> Date: Sun, 26 May 2024 13:44:17 -0400 Subject: [PATCH 241/378] Replace chainner_models with Spandrel package (#2146) * Replace chainner_models with Spandrel * Update to latest spandrel * Use spandrel_foss instead * update spandrel to new FOSS-compliant version --- comfy_extras/chainner_models/__init__.py | 0 .../chainner_models/architecture/DAT.py | 1182 -------------- .../chainner_models/architecture/HAT.py | 1277 --------------- .../chainner_models/architecture/LICENSE-DAT | 201 --- .../architecture/LICENSE-ESRGAN | 201 --- .../chainner_models/architecture/LICENSE-HAT | 21 - .../architecture/LICENSE-RealESRGAN | 29 - .../architecture/LICENSE-SCUNet | 201 --- .../chainner_models/architecture/LICENSE-SPSR | 201 --- .../architecture/LICENSE-SwiftSRGAN | 121 -- .../architecture/LICENSE-Swin2SR | 201 --- .../architecture/LICENSE-SwinIR | 201 --- .../chainner_models/architecture/LICENSE-lama | 201 --- .../chainner_models/architecture/LaMa.py | 694 --------- .../architecture/OmniSR/ChannelAttention.py | 110 -- .../architecture/OmniSR/LICENSE | 201 --- .../architecture/OmniSR/OSA.py | 577 ------- .../architecture/OmniSR/OSAG.py | 60 - .../architecture/OmniSR/OmniSR.py | 143 -- .../architecture/OmniSR/esa.py | 294 ---- .../architecture/OmniSR/layernorm.py | 70 - .../architecture/OmniSR/pixelshuffle.py | 31 - .../chainner_models/architecture/RRDB.py | 296 ---- .../chainner_models/architecture/SCUNet.py | 455 ------ .../chainner_models/architecture/SPSR.py | 383 ----- .../chainner_models/architecture/SRVGG.py | 114 -- .../architecture/SwiftSRGAN.py | 161 -- .../chainner_models/architecture/Swin2SR.py | 1377 ----------------- .../chainner_models/architecture/SwinIR.py | 1224 --------------- .../chainner_models/architecture/__init__.py | 0 .../chainner_models/architecture/block.py | 546 ------- .../architecture/face/LICENSE-GFPGAN | 351 ----- .../architecture/face/LICENSE-RestoreFormer | 351 ----- .../architecture/face/LICENSE-codeformer | 35 - .../architecture/face/arcface_arch.py | 265 ---- .../architecture/face/codeformer.py | 790 ---------- .../architecture/face/fused_act.py | 81 - .../architecture/face/gfpgan_bilinear_arch.py | 389 ----- .../architecture/face/gfpganv1_arch.py | 566 ------- .../architecture/face/gfpganv1_clean_arch.py | 370 ----- .../architecture/face/restoreformer_arch.py | 776 ---------- .../architecture/face/stylegan2_arch.py | 865 ----------- .../face/stylegan2_bilinear_arch.py | 709 --------- .../architecture/face/stylegan2_clean_arch.py | 453 ------ .../architecture/face/upfirdn2d.py | 194 --- .../chainner_models/architecture/timm/LICENSE | 201 --- .../chainner_models/architecture/timm/drop.py | 223 --- .../architecture/timm/helpers.py | 31 - .../architecture/timm/weight_init.py | 128 -- comfy_extras/chainner_models/model_loading.py | 99 -- comfy_extras/chainner_models/types.py | 69 - comfy_extras/nodes_upscale_model.py | 10 +- requirements.txt | 1 + 53 files changed, 8 insertions(+), 17722 deletions(-) delete mode 100644 comfy_extras/chainner_models/__init__.py delete mode 100644 comfy_extras/chainner_models/architecture/DAT.py delete mode 100644 comfy_extras/chainner_models/architecture/HAT.py delete mode 100644 comfy_extras/chainner_models/architecture/LICENSE-DAT delete mode 100644 comfy_extras/chainner_models/architecture/LICENSE-ESRGAN delete mode 100644 comfy_extras/chainner_models/architecture/LICENSE-HAT delete mode 100644 comfy_extras/chainner_models/architecture/LICENSE-RealESRGAN delete mode 100644 comfy_extras/chainner_models/architecture/LICENSE-SCUNet delete mode 100644 comfy_extras/chainner_models/architecture/LICENSE-SPSR delete mode 100644 comfy_extras/chainner_models/architecture/LICENSE-SwiftSRGAN delete mode 100644 comfy_extras/chainner_models/architecture/LICENSE-Swin2SR delete mode 100644 comfy_extras/chainner_models/architecture/LICENSE-SwinIR delete mode 100644 comfy_extras/chainner_models/architecture/LICENSE-lama delete mode 100644 comfy_extras/chainner_models/architecture/LaMa.py delete mode 100644 comfy_extras/chainner_models/architecture/OmniSR/ChannelAttention.py delete mode 100644 comfy_extras/chainner_models/architecture/OmniSR/LICENSE delete mode 100644 comfy_extras/chainner_models/architecture/OmniSR/OSA.py delete mode 100644 comfy_extras/chainner_models/architecture/OmniSR/OSAG.py delete mode 100644 comfy_extras/chainner_models/architecture/OmniSR/OmniSR.py delete mode 100644 comfy_extras/chainner_models/architecture/OmniSR/esa.py delete mode 100644 comfy_extras/chainner_models/architecture/OmniSR/layernorm.py delete mode 100644 comfy_extras/chainner_models/architecture/OmniSR/pixelshuffle.py delete mode 100644 comfy_extras/chainner_models/architecture/RRDB.py delete mode 100644 comfy_extras/chainner_models/architecture/SCUNet.py delete mode 100644 comfy_extras/chainner_models/architecture/SPSR.py delete mode 100644 comfy_extras/chainner_models/architecture/SRVGG.py delete mode 100644 comfy_extras/chainner_models/architecture/SwiftSRGAN.py delete mode 100644 comfy_extras/chainner_models/architecture/Swin2SR.py delete mode 100644 comfy_extras/chainner_models/architecture/SwinIR.py delete mode 100644 comfy_extras/chainner_models/architecture/__init__.py delete mode 100644 comfy_extras/chainner_models/architecture/block.py delete mode 100644 comfy_extras/chainner_models/architecture/face/LICENSE-GFPGAN delete mode 100644 comfy_extras/chainner_models/architecture/face/LICENSE-RestoreFormer delete mode 100644 comfy_extras/chainner_models/architecture/face/LICENSE-codeformer delete mode 100644 comfy_extras/chainner_models/architecture/face/arcface_arch.py delete mode 100644 comfy_extras/chainner_models/architecture/face/codeformer.py delete mode 100644 comfy_extras/chainner_models/architecture/face/fused_act.py delete mode 100644 comfy_extras/chainner_models/architecture/face/gfpgan_bilinear_arch.py delete mode 100644 comfy_extras/chainner_models/architecture/face/gfpganv1_arch.py delete mode 100644 comfy_extras/chainner_models/architecture/face/gfpganv1_clean_arch.py delete mode 100644 comfy_extras/chainner_models/architecture/face/restoreformer_arch.py delete mode 100644 comfy_extras/chainner_models/architecture/face/stylegan2_arch.py delete mode 100644 comfy_extras/chainner_models/architecture/face/stylegan2_bilinear_arch.py delete mode 100644 comfy_extras/chainner_models/architecture/face/stylegan2_clean_arch.py delete mode 100644 comfy_extras/chainner_models/architecture/face/upfirdn2d.py delete mode 100644 comfy_extras/chainner_models/architecture/timm/LICENSE delete mode 100644 comfy_extras/chainner_models/architecture/timm/drop.py delete mode 100644 comfy_extras/chainner_models/architecture/timm/helpers.py delete mode 100644 comfy_extras/chainner_models/architecture/timm/weight_init.py delete mode 100644 comfy_extras/chainner_models/model_loading.py delete mode 100644 comfy_extras/chainner_models/types.py diff --git a/comfy_extras/chainner_models/__init__.py b/comfy_extras/chainner_models/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/comfy_extras/chainner_models/architecture/DAT.py b/comfy_extras/chainner_models/architecture/DAT.py deleted file mode 100644 index 0bcc26ef..00000000 --- a/comfy_extras/chainner_models/architecture/DAT.py +++ /dev/null @@ -1,1182 +0,0 @@ -# pylint: skip-file -import math -import re - -import numpy as np -import torch -import torch.nn as nn -import torch.utils.checkpoint as checkpoint -from einops import rearrange -from einops.layers.torch import Rearrange -from torch import Tensor -from torch.nn import functional as F - -from .timm.drop import DropPath -from .timm.weight_init import trunc_normal_ - - -def img2windows(img, H_sp, W_sp): - """ - Input: Image (B, C, H, W) - Output: Window Partition (B', N, C) - """ - B, C, H, W = img.shape - img_reshape = img.view(B, C, H // H_sp, H_sp, W // W_sp, W_sp) - img_perm = ( - img_reshape.permute(0, 2, 4, 3, 5, 1).contiguous().reshape(-1, H_sp * W_sp, C) - ) - return img_perm - - -def windows2img(img_splits_hw, H_sp, W_sp, H, W): - """ - Input: Window Partition (B', N, C) - Output: Image (B, H, W, C) - """ - B = int(img_splits_hw.shape[0] / (H * W / H_sp / W_sp)) - - img = img_splits_hw.view(B, H // H_sp, W // W_sp, H_sp, W_sp, -1) - img = img.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) - return img - - -class SpatialGate(nn.Module): - """Spatial-Gate. - Args: - dim (int): Half of input channels. - """ - - def __init__(self, dim): - super().__init__() - self.norm = nn.LayerNorm(dim) - self.conv = nn.Conv2d( - dim, dim, kernel_size=3, stride=1, padding=1, groups=dim - ) # DW Conv - - def forward(self, x, H, W): - # Split - x1, x2 = x.chunk(2, dim=-1) - B, N, C = x.shape - x2 = ( - self.conv(self.norm(x2).transpose(1, 2).contiguous().view(B, C // 2, H, W)) - .flatten(2) - .transpose(-1, -2) - .contiguous() - ) - - return x1 * x2 - - -class SGFN(nn.Module): - """Spatial-Gate Feed-Forward Network. - Args: - in_features (int): Number of input channels. - hidden_features (int | None): Number of hidden channels. Default: None - out_features (int | None): Number of output channels. Default: None - act_layer (nn.Module): Activation layer. Default: nn.GELU - drop (float): Dropout rate. Default: 0.0 - """ - - def __init__( - self, - in_features, - hidden_features=None, - out_features=None, - act_layer=nn.GELU, - drop=0.0, - ): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.act = act_layer() - self.sg = SpatialGate(hidden_features // 2) - self.fc2 = nn.Linear(hidden_features // 2, out_features) - self.drop = nn.Dropout(drop) - - def forward(self, x, H, W): - """ - Input: x: (B, H*W, C), H, W - Output: x: (B, H*W, C) - """ - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - - x = self.sg(x, H, W) - x = self.drop(x) - - x = self.fc2(x) - x = self.drop(x) - return x - - -class DynamicPosBias(nn.Module): - # The implementation builds on Crossformer code https://github.com/cheerss/CrossFormer/blob/main/models/crossformer.py - """Dynamic Relative Position Bias. - Args: - dim (int): Number of input channels. - num_heads (int): Number of attention heads. - residual (bool): If True, use residual strage to connect conv. - """ - - def __init__(self, dim, num_heads, residual): - super().__init__() - self.residual = residual - self.num_heads = num_heads - self.pos_dim = dim // 4 - self.pos_proj = nn.Linear(2, self.pos_dim) - self.pos1 = nn.Sequential( - nn.LayerNorm(self.pos_dim), - nn.ReLU(inplace=True), - nn.Linear(self.pos_dim, self.pos_dim), - ) - self.pos2 = nn.Sequential( - nn.LayerNorm(self.pos_dim), - nn.ReLU(inplace=True), - nn.Linear(self.pos_dim, self.pos_dim), - ) - self.pos3 = nn.Sequential( - nn.LayerNorm(self.pos_dim), - nn.ReLU(inplace=True), - nn.Linear(self.pos_dim, self.num_heads), - ) - - def forward(self, biases): - if self.residual: - pos = self.pos_proj(biases) # 2Gh-1 * 2Gw-1, heads - pos = pos + self.pos1(pos) - pos = pos + self.pos2(pos) - pos = self.pos3(pos) - else: - pos = self.pos3(self.pos2(self.pos1(self.pos_proj(biases)))) - return pos - - -class Spatial_Attention(nn.Module): - """Spatial Window Self-Attention. - It supports rectangle window (containing square window). - Args: - dim (int): Number of input channels. - idx (int): The indentix of window. (0/1) - split_size (tuple(int)): Height and Width of spatial window. - dim_out (int | None): The dimension of the attention output. Default: None - num_heads (int): Number of attention heads. Default: 6 - attn_drop (float): Dropout ratio of attention weight. Default: 0.0 - proj_drop (float): Dropout ratio of output. Default: 0.0 - qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set - position_bias (bool): The dynamic relative position bias. Default: True - """ - - def __init__( - self, - dim, - idx, - split_size=[8, 8], - dim_out=None, - num_heads=6, - attn_drop=0.0, - proj_drop=0.0, - qk_scale=None, - position_bias=True, - ): - super().__init__() - self.dim = dim - self.dim_out = dim_out or dim - self.split_size = split_size - self.num_heads = num_heads - self.idx = idx - self.position_bias = position_bias - - head_dim = dim // num_heads - self.scale = qk_scale or head_dim**-0.5 - - if idx == 0: - H_sp, W_sp = self.split_size[0], self.split_size[1] - elif idx == 1: - W_sp, H_sp = self.split_size[0], self.split_size[1] - else: - print("ERROR MODE", idx) - exit(0) - self.H_sp = H_sp - self.W_sp = W_sp - - if self.position_bias: - self.pos = DynamicPosBias(self.dim // 4, self.num_heads, residual=False) - # generate mother-set - position_bias_h = torch.arange(1 - self.H_sp, self.H_sp) - position_bias_w = torch.arange(1 - self.W_sp, self.W_sp) - biases = torch.stack(torch.meshgrid([position_bias_h, position_bias_w])) - biases = biases.flatten(1).transpose(0, 1).contiguous().float() - self.register_buffer("rpe_biases", biases) - - # get pair-wise relative position index for each token inside the window - coords_h = torch.arange(self.H_sp) - coords_w = torch.arange(self.W_sp) - coords = torch.stack(torch.meshgrid([coords_h, coords_w])) - coords_flatten = torch.flatten(coords, 1) - relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] - relative_coords = relative_coords.permute(1, 2, 0).contiguous() - relative_coords[:, :, 0] += self.H_sp - 1 - relative_coords[:, :, 1] += self.W_sp - 1 - relative_coords[:, :, 0] *= 2 * self.W_sp - 1 - relative_position_index = relative_coords.sum(-1) - self.register_buffer("relative_position_index", relative_position_index) - - self.attn_drop = nn.Dropout(attn_drop) - - def im2win(self, x, H, W): - B, N, C = x.shape - x = x.transpose(-2, -1).contiguous().view(B, C, H, W) - x = img2windows(x, self.H_sp, self.W_sp) - x = ( - x.reshape(-1, self.H_sp * self.W_sp, self.num_heads, C // self.num_heads) - .permute(0, 2, 1, 3) - .contiguous() - ) - return x - - def forward(self, qkv, H, W, mask=None): - """ - Input: qkv: (B, 3*L, C), H, W, mask: (B, N, N), N is the window size - Output: x (B, H, W, C) - """ - q, k, v = qkv[0], qkv[1], qkv[2] - - B, L, C = q.shape - assert L == H * W, "flatten img_tokens has wrong size" - - # partition the q,k,v, image to window - q = self.im2win(q, H, W) - k = self.im2win(k, H, W) - v = self.im2win(v, H, W) - - q = q * self.scale - attn = q @ k.transpose(-2, -1) # B head N C @ B head C N --> B head N N - - # calculate drpe - if self.position_bias: - pos = self.pos(self.rpe_biases) - # select position bias - relative_position_bias = pos[self.relative_position_index.view(-1)].view( - self.H_sp * self.W_sp, self.H_sp * self.W_sp, -1 - ) - relative_position_bias = relative_position_bias.permute( - 2, 0, 1 - ).contiguous() - attn = attn + relative_position_bias.unsqueeze(0) - - N = attn.shape[3] - - # use mask for shift window - if mask is not None: - nW = mask.shape[0] - attn = attn.view(B, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze( - 0 - ) - attn = attn.view(-1, self.num_heads, N, N) - - attn = nn.functional.softmax(attn, dim=-1, dtype=attn.dtype) - attn = self.attn_drop(attn) - - x = attn @ v - x = x.transpose(1, 2).reshape( - -1, self.H_sp * self.W_sp, C - ) # B head N N @ B head N C - - # merge the window, window to image - x = windows2img(x, self.H_sp, self.W_sp, H, W) # B H' W' C - - return x - - -class Adaptive_Spatial_Attention(nn.Module): - # The implementation builds on CAT code https://github.com/Zhengchen1999/CAT - """Adaptive Spatial Self-Attention - Args: - dim (int): Number of input channels. - num_heads (int): Number of attention heads. Default: 6 - split_size (tuple(int)): Height and Width of spatial window. - shift_size (tuple(int)): Shift size for spatial window. - qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. - drop (float): Dropout rate. Default: 0.0 - attn_drop (float): Attention dropout rate. Default: 0.0 - rg_idx (int): The indentix of Residual Group (RG) - b_idx (int): The indentix of Block in each RG - """ - - def __init__( - self, - dim, - num_heads, - reso=64, - split_size=[8, 8], - shift_size=[1, 2], - qkv_bias=False, - qk_scale=None, - drop=0.0, - attn_drop=0.0, - rg_idx=0, - b_idx=0, - ): - super().__init__() - self.dim = dim - self.num_heads = num_heads - self.split_size = split_size - self.shift_size = shift_size - self.b_idx = b_idx - self.rg_idx = rg_idx - self.patches_resolution = reso - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - - assert ( - 0 <= self.shift_size[0] < self.split_size[0] - ), "shift_size must in 0-split_size0" - assert ( - 0 <= self.shift_size[1] < self.split_size[1] - ), "shift_size must in 0-split_size1" - - self.branch_num = 2 - - self.proj = nn.Linear(dim, dim) - self.proj_drop = nn.Dropout(drop) - - self.attns = nn.ModuleList( - [ - Spatial_Attention( - dim // 2, - idx=i, - split_size=split_size, - num_heads=num_heads // 2, - dim_out=dim // 2, - qk_scale=qk_scale, - attn_drop=attn_drop, - proj_drop=drop, - position_bias=True, - ) - for i in range(self.branch_num) - ] - ) - - if (self.rg_idx % 2 == 0 and self.b_idx > 0 and (self.b_idx - 2) % 4 == 0) or ( - self.rg_idx % 2 != 0 and self.b_idx % 4 == 0 - ): - attn_mask = self.calculate_mask( - self.patches_resolution, self.patches_resolution - ) - self.register_buffer("attn_mask_0", attn_mask[0]) - self.register_buffer("attn_mask_1", attn_mask[1]) - else: - attn_mask = None - self.register_buffer("attn_mask_0", None) - self.register_buffer("attn_mask_1", None) - - self.dwconv = nn.Sequential( - nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim), - nn.BatchNorm2d(dim), - nn.GELU(), - ) - self.channel_interaction = nn.Sequential( - nn.AdaptiveAvgPool2d(1), - nn.Conv2d(dim, dim // 8, kernel_size=1), - nn.BatchNorm2d(dim // 8), - nn.GELU(), - nn.Conv2d(dim // 8, dim, kernel_size=1), - ) - self.spatial_interaction = nn.Sequential( - nn.Conv2d(dim, dim // 16, kernel_size=1), - nn.BatchNorm2d(dim // 16), - nn.GELU(), - nn.Conv2d(dim // 16, 1, kernel_size=1), - ) - - def calculate_mask(self, H, W): - # The implementation builds on Swin Transformer code https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py - # calculate attention mask for shift window - img_mask_0 = torch.zeros((1, H, W, 1)) # 1 H W 1 idx=0 - img_mask_1 = torch.zeros((1, H, W, 1)) # 1 H W 1 idx=1 - h_slices_0 = ( - slice(0, -self.split_size[0]), - slice(-self.split_size[0], -self.shift_size[0]), - slice(-self.shift_size[0], None), - ) - w_slices_0 = ( - slice(0, -self.split_size[1]), - slice(-self.split_size[1], -self.shift_size[1]), - slice(-self.shift_size[1], None), - ) - - h_slices_1 = ( - slice(0, -self.split_size[1]), - slice(-self.split_size[1], -self.shift_size[1]), - slice(-self.shift_size[1], None), - ) - w_slices_1 = ( - slice(0, -self.split_size[0]), - slice(-self.split_size[0], -self.shift_size[0]), - slice(-self.shift_size[0], None), - ) - cnt = 0 - for h in h_slices_0: - for w in w_slices_0: - img_mask_0[:, h, w, :] = cnt - cnt += 1 - cnt = 0 - for h in h_slices_1: - for w in w_slices_1: - img_mask_1[:, h, w, :] = cnt - cnt += 1 - - # calculate mask for window-0 - img_mask_0 = img_mask_0.view( - 1, - H // self.split_size[0], - self.split_size[0], - W // self.split_size[1], - self.split_size[1], - 1, - ) - img_mask_0 = ( - img_mask_0.permute(0, 1, 3, 2, 4, 5) - .contiguous() - .view(-1, self.split_size[0], self.split_size[1], 1) - ) # nW, sw[0], sw[1], 1 - mask_windows_0 = img_mask_0.view(-1, self.split_size[0] * self.split_size[1]) - attn_mask_0 = mask_windows_0.unsqueeze(1) - mask_windows_0.unsqueeze(2) - attn_mask_0 = attn_mask_0.masked_fill( - attn_mask_0 != 0, float(-100.0) - ).masked_fill(attn_mask_0 == 0, float(0.0)) - - # calculate mask for window-1 - img_mask_1 = img_mask_1.view( - 1, - H // self.split_size[1], - self.split_size[1], - W // self.split_size[0], - self.split_size[0], - 1, - ) - img_mask_1 = ( - img_mask_1.permute(0, 1, 3, 2, 4, 5) - .contiguous() - .view(-1, self.split_size[1], self.split_size[0], 1) - ) # nW, sw[1], sw[0], 1 - mask_windows_1 = img_mask_1.view(-1, self.split_size[1] * self.split_size[0]) - attn_mask_1 = mask_windows_1.unsqueeze(1) - mask_windows_1.unsqueeze(2) - attn_mask_1 = attn_mask_1.masked_fill( - attn_mask_1 != 0, float(-100.0) - ).masked_fill(attn_mask_1 == 0, float(0.0)) - - return attn_mask_0, attn_mask_1 - - def forward(self, x, H, W): - """ - Input: x: (B, H*W, C), H, W - Output: x: (B, H*W, C) - """ - B, L, C = x.shape - assert L == H * W, "flatten img_tokens has wrong size" - - qkv = self.qkv(x).reshape(B, -1, 3, C).permute(2, 0, 1, 3) # 3, B, HW, C - # V without partition - v = qkv[2].transpose(-2, -1).contiguous().view(B, C, H, W) - - # image padding - max_split_size = max(self.split_size[0], self.split_size[1]) - pad_l = pad_t = 0 - pad_r = (max_split_size - W % max_split_size) % max_split_size - pad_b = (max_split_size - H % max_split_size) % max_split_size - - qkv = qkv.reshape(3 * B, H, W, C).permute(0, 3, 1, 2) # 3B C H W - qkv = ( - F.pad(qkv, (pad_l, pad_r, pad_t, pad_b)) - .reshape(3, B, C, -1) - .transpose(-2, -1) - ) # l r t b - _H = pad_b + H - _W = pad_r + W - _L = _H * _W - - # window-0 and window-1 on split channels [C/2, C/2]; for square windows (e.g., 8x8), window-0 and window-1 can be merged - # shift in block: (0, 4, 8, ...), (2, 6, 10, ...), (0, 4, 8, ...), (2, 6, 10, ...), ... - if (self.rg_idx % 2 == 0 and self.b_idx > 0 and (self.b_idx - 2) % 4 == 0) or ( - self.rg_idx % 2 != 0 and self.b_idx % 4 == 0 - ): - qkv = qkv.view(3, B, _H, _W, C) - qkv_0 = torch.roll( - qkv[:, :, :, :, : C // 2], - shifts=(-self.shift_size[0], -self.shift_size[1]), - dims=(2, 3), - ) - qkv_0 = qkv_0.view(3, B, _L, C // 2) - qkv_1 = torch.roll( - qkv[:, :, :, :, C // 2 :], - shifts=(-self.shift_size[1], -self.shift_size[0]), - dims=(2, 3), - ) - qkv_1 = qkv_1.view(3, B, _L, C // 2) - - if self.patches_resolution != _H or self.patches_resolution != _W: - mask_tmp = self.calculate_mask(_H, _W) - x1_shift = self.attns[0](qkv_0, _H, _W, mask=mask_tmp[0].to(x.device)) - x2_shift = self.attns[1](qkv_1, _H, _W, mask=mask_tmp[1].to(x.device)) - else: - x1_shift = self.attns[0](qkv_0, _H, _W, mask=self.attn_mask_0) - x2_shift = self.attns[1](qkv_1, _H, _W, mask=self.attn_mask_1) - - x1 = torch.roll( - x1_shift, shifts=(self.shift_size[0], self.shift_size[1]), dims=(1, 2) - ) - x2 = torch.roll( - x2_shift, shifts=(self.shift_size[1], self.shift_size[0]), dims=(1, 2) - ) - x1 = x1[:, :H, :W, :].reshape(B, L, C // 2) - x2 = x2[:, :H, :W, :].reshape(B, L, C // 2) - # attention output - attened_x = torch.cat([x1, x2], dim=2) - - else: - x1 = self.attns[0](qkv[:, :, :, : C // 2], _H, _W)[:, :H, :W, :].reshape( - B, L, C // 2 - ) - x2 = self.attns[1](qkv[:, :, :, C // 2 :], _H, _W)[:, :H, :W, :].reshape( - B, L, C // 2 - ) - # attention output - attened_x = torch.cat([x1, x2], dim=2) - - # convolution output - conv_x = self.dwconv(v) - - # Adaptive Interaction Module (AIM) - # C-Map (before sigmoid) - channel_map = ( - self.channel_interaction(conv_x) - .permute(0, 2, 3, 1) - .contiguous() - .view(B, 1, C) - ) - # S-Map (before sigmoid) - attention_reshape = attened_x.transpose(-2, -1).contiguous().view(B, C, H, W) - spatial_map = self.spatial_interaction(attention_reshape) - - # C-I - attened_x = attened_x * torch.sigmoid(channel_map) - # S-I - conv_x = torch.sigmoid(spatial_map) * conv_x - conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(B, L, C) - - x = attened_x + conv_x - - x = self.proj(x) - x = self.proj_drop(x) - - return x - - -class Adaptive_Channel_Attention(nn.Module): - # The implementation builds on XCiT code https://github.com/facebookresearch/xcit - """Adaptive Channel Self-Attention - Args: - dim (int): Number of input channels. - num_heads (int): Number of attention heads. Default: 6 - qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. - attn_drop (float): Attention dropout rate. Default: 0.0 - drop_path (float): Stochastic depth rate. Default: 0.0 - """ - - def __init__( - self, - dim, - num_heads=8, - qkv_bias=False, - qk_scale=None, - attn_drop=0.0, - proj_drop=0.0, - ): - super().__init__() - self.num_heads = num_heads - self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1)) - - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - self.proj_drop = nn.Dropout(proj_drop) - - self.dwconv = nn.Sequential( - nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim), - nn.BatchNorm2d(dim), - nn.GELU(), - ) - self.channel_interaction = nn.Sequential( - nn.AdaptiveAvgPool2d(1), - nn.Conv2d(dim, dim // 8, kernel_size=1), - nn.BatchNorm2d(dim // 8), - nn.GELU(), - nn.Conv2d(dim // 8, dim, kernel_size=1), - ) - self.spatial_interaction = nn.Sequential( - nn.Conv2d(dim, dim // 16, kernel_size=1), - nn.BatchNorm2d(dim // 16), - nn.GELU(), - nn.Conv2d(dim // 16, 1, kernel_size=1), - ) - - def forward(self, x, H, W): - """ - Input: x: (B, H*W, C), H, W - Output: x: (B, H*W, C) - """ - B, N, C = x.shape - qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads) - qkv = qkv.permute(2, 0, 3, 1, 4) - q, k, v = qkv[0], qkv[1], qkv[2] - - q = q.transpose(-2, -1) - k = k.transpose(-2, -1) - v = v.transpose(-2, -1) - - v_ = v.reshape(B, C, N).contiguous().view(B, C, H, W) - - q = torch.nn.functional.normalize(q, dim=-1) - k = torch.nn.functional.normalize(k, dim=-1) - - attn = (q @ k.transpose(-2, -1)) * self.temperature - attn = attn.softmax(dim=-1) - attn = self.attn_drop(attn) - - # attention output - attened_x = (attn @ v).permute(0, 3, 1, 2).reshape(B, N, C) - - # convolution output - conv_x = self.dwconv(v_) - - # Adaptive Interaction Module (AIM) - # C-Map (before sigmoid) - attention_reshape = attened_x.transpose(-2, -1).contiguous().view(B, C, H, W) - channel_map = self.channel_interaction(attention_reshape) - # S-Map (before sigmoid) - spatial_map = ( - self.spatial_interaction(conv_x) - .permute(0, 2, 3, 1) - .contiguous() - .view(B, N, 1) - ) - - # S-I - attened_x = attened_x * torch.sigmoid(spatial_map) - # C-I - conv_x = conv_x * torch.sigmoid(channel_map) - conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(B, N, C) - - x = attened_x + conv_x - - x = self.proj(x) - x = self.proj_drop(x) - - return x - - -class DATB(nn.Module): - def __init__( - self, - dim, - num_heads, - reso=64, - split_size=[2, 4], - shift_size=[1, 2], - expansion_factor=4.0, - qkv_bias=False, - qk_scale=None, - drop=0.0, - attn_drop=0.0, - drop_path=0.0, - act_layer=nn.GELU, - norm_layer=nn.LayerNorm, - rg_idx=0, - b_idx=0, - ): - super().__init__() - - self.norm1 = norm_layer(dim) - - if b_idx % 2 == 0: - # DSTB - self.attn = Adaptive_Spatial_Attention( - dim, - num_heads=num_heads, - reso=reso, - split_size=split_size, - shift_size=shift_size, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop, - attn_drop=attn_drop, - rg_idx=rg_idx, - b_idx=b_idx, - ) - else: - # DCTB - self.attn = Adaptive_Channel_Attention( - dim, - num_heads=num_heads, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - attn_drop=attn_drop, - proj_drop=drop, - ) - self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() - - ffn_hidden_dim = int(dim * expansion_factor) - self.ffn = SGFN( - in_features=dim, - hidden_features=ffn_hidden_dim, - out_features=dim, - act_layer=act_layer, - ) - self.norm2 = norm_layer(dim) - - def forward(self, x, x_size): - """ - Input: x: (B, H*W, C), x_size: (H, W) - Output: x: (B, H*W, C) - """ - H, W = x_size - x = x + self.drop_path(self.attn(self.norm1(x), H, W)) - x = x + self.drop_path(self.ffn(self.norm2(x), H, W)) - - return x - - -class ResidualGroup(nn.Module): - """ResidualGroup - Args: - dim (int): Number of input channels. - reso (int): Input resolution. - num_heads (int): Number of attention heads. - split_size (tuple(int)): Height and Width of spatial window. - expansion_factor (float): Ratio of ffn hidden dim to embedding dim. - qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. Default: None - drop (float): Dropout rate. Default: 0 - attn_drop(float): Attention dropout rate. Default: 0 - drop_paths (float | None): Stochastic depth rate. - act_layer (nn.Module): Activation layer. Default: nn.GELU - norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm - depth (int): Number of dual aggregation Transformer blocks in residual group. - use_chk (bool): Whether to use checkpointing to save memory. - resi_connection: The convolutional block before residual connection. '1conv'/'3conv' - """ - - def __init__( - self, - dim, - reso, - num_heads, - split_size=[2, 4], - expansion_factor=4.0, - qkv_bias=False, - qk_scale=None, - drop=0.0, - attn_drop=0.0, - drop_paths=None, - act_layer=nn.GELU, - norm_layer=nn.LayerNorm, - depth=2, - use_chk=False, - resi_connection="1conv", - rg_idx=0, - ): - super().__init__() - self.use_chk = use_chk - self.reso = reso - - self.blocks = nn.ModuleList( - [ - DATB( - dim=dim, - num_heads=num_heads, - reso=reso, - split_size=split_size, - shift_size=[split_size[0] // 2, split_size[1] // 2], - expansion_factor=expansion_factor, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop, - attn_drop=attn_drop, - drop_path=drop_paths[i], - act_layer=act_layer, - norm_layer=norm_layer, - rg_idx=rg_idx, - b_idx=i, - ) - for i in range(depth) - ] - ) - - if resi_connection == "1conv": - self.conv = nn.Conv2d(dim, dim, 3, 1, 1) - elif resi_connection == "3conv": - self.conv = nn.Sequential( - nn.Conv2d(dim, dim // 4, 3, 1, 1), - nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), - nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(dim // 4, dim, 3, 1, 1), - ) - - def forward(self, x, x_size): - """ - Input: x: (B, H*W, C), x_size: (H, W) - Output: x: (B, H*W, C) - """ - H, W = x_size - res = x - for blk in self.blocks: - if self.use_chk: - x = checkpoint.checkpoint(blk, x, x_size) - else: - x = blk(x, x_size) - x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W) - x = self.conv(x) - x = rearrange(x, "b c h w -> b (h w) c") - x = res + x - - return x - - -class Upsample(nn.Sequential): - """Upsample module. - Args: - scale (int): Scale factor. Supported scales: 2^n and 3. - num_feat (int): Channel number of intermediate features. - """ - - def __init__(self, scale, num_feat): - m = [] - if (scale & (scale - 1)) == 0: # scale = 2^n - for _ in range(int(math.log(scale, 2))): - m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) - m.append(nn.PixelShuffle(2)) - elif scale == 3: - m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) - m.append(nn.PixelShuffle(3)) - else: - raise ValueError( - f"scale {scale} is not supported. " "Supported scales: 2^n and 3." - ) - super(Upsample, self).__init__(*m) - - -class UpsampleOneStep(nn.Sequential): - """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) - Used in lightweight SR to save parameters. - - Args: - scale (int): Scale factor. Supported scales: 2^n and 3. - num_feat (int): Channel number of intermediate features. - - """ - - def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): - self.num_feat = num_feat - self.input_resolution = input_resolution - m = [] - m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1)) - m.append(nn.PixelShuffle(scale)) - super(UpsampleOneStep, self).__init__(*m) - - def flops(self): - h, w = self.input_resolution - flops = h * w * self.num_feat * 3 * 9 - return flops - - -class DAT(nn.Module): - """Dual Aggregation Transformer - Args: - img_size (int): Input image size. Default: 64 - in_chans (int): Number of input image channels. Default: 3 - embed_dim (int): Patch embedding dimension. Default: 180 - depths (tuple(int)): Depth of each residual group (number of DATB in each RG). - split_size (tuple(int)): Height and Width of spatial window. - num_heads (tuple(int)): Number of attention heads in different residual groups. - expansion_factor (float): Ratio of ffn hidden dim to embedding dim. Default: 4 - qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. Default: None - drop_rate (float): Dropout rate. Default: 0 - attn_drop_rate (float): Attention dropout rate. Default: 0 - drop_path_rate (float): Stochastic depth rate. Default: 0.1 - act_layer (nn.Module): Activation layer. Default: nn.GELU - norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm - use_chk (bool): Whether to use checkpointing to save memory. - upscale: Upscale factor. 2/3/4 for image SR - img_range: Image range. 1. or 255. - resi_connection: The convolutional block before residual connection. '1conv'/'3conv' - """ - - def __init__(self, state_dict): - super().__init__() - - # defaults - img_size = 64 - in_chans = 3 - embed_dim = 180 - split_size = [2, 4] - depth = [2, 2, 2, 2] - num_heads = [2, 2, 2, 2] - expansion_factor = 4.0 - qkv_bias = True - qk_scale = None - drop_rate = 0.0 - attn_drop_rate = 0.0 - drop_path_rate = 0.1 - act_layer = nn.GELU - norm_layer = nn.LayerNorm - use_chk = False - upscale = 2 - img_range = 1.0 - resi_connection = "1conv" - upsampler = "pixelshuffle" - - self.model_arch = "DAT" - self.sub_type = "SR" - self.state = state_dict - - state_keys = state_dict.keys() - if "conv_before_upsample.0.weight" in state_keys: - if "conv_up1.weight" in state_keys: - upsampler = "nearest+conv" - else: - upsampler = "pixelshuffle" - supports_fp16 = False - elif "upsample.0.weight" in state_keys: - upsampler = "pixelshuffledirect" - else: - upsampler = "" - - num_feat = ( - state_dict.get("conv_before_upsample.0.weight", None).shape[1] - if state_dict.get("conv_before_upsample.weight", None) - else 64 - ) - - num_in_ch = state_dict["conv_first.weight"].shape[1] - in_chans = num_in_ch - if "conv_last.weight" in state_keys: - num_out_ch = state_dict["conv_last.weight"].shape[0] - else: - num_out_ch = num_in_ch - - upscale = 1 - if upsampler == "nearest+conv": - upsample_keys = [ - x for x in state_keys if "conv_up" in x and "bias" not in x - ] - - for upsample_key in upsample_keys: - upscale *= 2 - elif upsampler == "pixelshuffle": - upsample_keys = [ - x - for x in state_keys - if "upsample" in x and "conv" not in x and "bias" not in x - ] - for upsample_key in upsample_keys: - shape = state_dict[upsample_key].shape[0] - upscale *= math.sqrt(shape // num_feat) - upscale = int(upscale) - elif upsampler == "pixelshuffledirect": - upscale = int( - math.sqrt(state_dict["upsample.0.bias"].shape[0] // num_out_ch) - ) - - max_layer_num = 0 - max_block_num = 0 - for key in state_keys: - result = re.match(r"layers.(\d*).blocks.(\d*).norm1.weight", key) - if result: - layer_num, block_num = result.groups() - max_layer_num = max(max_layer_num, int(layer_num)) - max_block_num = max(max_block_num, int(block_num)) - - depth = [max_block_num + 1 for _ in range(max_layer_num + 1)] - - if "layers.0.blocks.1.attn.temperature" in state_keys: - num_heads_num = state_dict["layers.0.blocks.1.attn.temperature"].shape[0] - num_heads = [num_heads_num for _ in range(max_layer_num + 1)] - else: - num_heads = depth - - embed_dim = state_dict["conv_first.weight"].shape[0] - expansion_factor = float( - state_dict["layers.0.blocks.0.ffn.fc1.weight"].shape[0] / embed_dim - ) - - # TODO: could actually count the layers, but this should do - if "layers.0.conv.4.weight" in state_keys: - resi_connection = "3conv" - else: - resi_connection = "1conv" - - if "layers.0.blocks.2.attn.attn_mask_0" in state_keys: - attn_mask_0_x, attn_mask_0_y, attn_mask_0_z = state_dict[ - "layers.0.blocks.2.attn.attn_mask_0" - ].shape - - img_size = int(math.sqrt(attn_mask_0_x * attn_mask_0_y)) - - if "layers.0.blocks.0.attn.attns.0.rpe_biases" in state_keys: - split_sizes = ( - state_dict["layers.0.blocks.0.attn.attns.0.rpe_biases"][-1] + 1 - ) - split_size = [int(x) for x in split_sizes] - - self.in_nc = num_in_ch - self.out_nc = num_out_ch - self.num_feat = num_feat - self.embed_dim = embed_dim - self.num_heads = num_heads - self.depth = depth - self.scale = upscale - self.upsampler = upsampler - self.img_size = img_size - self.img_range = img_range - self.expansion_factor = expansion_factor - self.resi_connection = resi_connection - self.split_size = split_size - - self.supports_fp16 = False # Too much weirdness to support this at the moment - self.supports_bfp16 = True - self.min_size_restriction = 16 - - num_in_ch = in_chans - num_out_ch = in_chans - num_feat = 64 - self.img_range = img_range - if in_chans == 3: - rgb_mean = (0.4488, 0.4371, 0.4040) - self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) - else: - self.mean = torch.zeros(1, 1, 1, 1) - self.upscale = upscale - self.upsampler = upsampler - - # ------------------------- 1, Shallow Feature Extraction ------------------------- # - self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) - - # ------------------------- 2, Deep Feature Extraction ------------------------- # - self.num_layers = len(depth) - self.use_chk = use_chk - self.num_features = ( - self.embed_dim - ) = embed_dim # num_features for consistency with other models - heads = num_heads - - self.before_RG = nn.Sequential( - Rearrange("b c h w -> b (h w) c"), nn.LayerNorm(embed_dim) - ) - - curr_dim = embed_dim - dpr = [ - x.item() for x in torch.linspace(0, drop_path_rate, np.sum(depth)) - ] # stochastic depth decay rule - - self.layers = nn.ModuleList() - for i in range(self.num_layers): - layer = ResidualGroup( - dim=embed_dim, - num_heads=heads[i], - reso=img_size, - split_size=split_size, - expansion_factor=expansion_factor, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop_rate, - attn_drop=attn_drop_rate, - drop_paths=dpr[sum(depth[:i]) : sum(depth[: i + 1])], - act_layer=act_layer, - norm_layer=norm_layer, - depth=depth[i], - use_chk=use_chk, - resi_connection=resi_connection, - rg_idx=i, - ) - self.layers.append(layer) - - self.norm = norm_layer(curr_dim) - # build the last conv layer in deep feature extraction - if resi_connection == "1conv": - self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) - elif resi_connection == "3conv": - # to save parameters and memory - self.conv_after_body = nn.Sequential( - nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), - nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), - nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1), - ) - - # ------------------------- 3, Reconstruction ------------------------- # - if self.upsampler == "pixelshuffle": - # for classical SR - self.conv_before_upsample = nn.Sequential( - nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) - ) - self.upsample = Upsample(upscale, num_feat) - self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - elif self.upsampler == "pixelshuffledirect": - # for lightweight SR (to save parameters) - self.upsample = UpsampleOneStep( - upscale, embed_dim, num_out_ch, (img_size, img_size) - ) - - self.apply(self._init_weights) - self.load_state_dict(state_dict, strict=True) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=0.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance( - m, (nn.LayerNorm, nn.BatchNorm2d, nn.GroupNorm, nn.InstanceNorm2d) - ): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - def forward_features(self, x): - _, _, H, W = x.shape - x_size = [H, W] - x = self.before_RG(x) - for layer in self.layers: - x = layer(x, x_size) - x = self.norm(x) - x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W) - - return x - - def forward(self, x): - """ - Input: x: (B, C, H, W) - """ - self.mean = self.mean.type_as(x) - x = (x - self.mean) * self.img_range - - if self.upsampler == "pixelshuffle": - # for image SR - x = self.conv_first(x) - x = self.conv_after_body(self.forward_features(x)) + x - x = self.conv_before_upsample(x) - x = self.conv_last(self.upsample(x)) - elif self.upsampler == "pixelshuffledirect": - # for lightweight SR - x = self.conv_first(x) - x = self.conv_after_body(self.forward_features(x)) + x - x = self.upsample(x) - - x = x / self.img_range + self.mean - return x diff --git a/comfy_extras/chainner_models/architecture/HAT.py b/comfy_extras/chainner_models/architecture/HAT.py deleted file mode 100644 index 66947421..00000000 --- a/comfy_extras/chainner_models/architecture/HAT.py +++ /dev/null @@ -1,1277 +0,0 @@ -# pylint: skip-file -# HAT from https://github.com/XPixelGroup/HAT/blob/main/hat/archs/hat_arch.py -import math -import re - -import torch -import torch.nn as nn -import torch.nn.functional as F -from einops import rearrange - -from .timm.helpers import to_2tuple -from .timm.weight_init import trunc_normal_ - - -def drop_path(x, drop_prob: float = 0.0, training: bool = False): - """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). - From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py - """ - if drop_prob == 0.0 or not training: - return x - keep_prob = 1 - drop_prob - shape = (x.shape[0],) + (1,) * ( - x.ndim - 1 - ) # work with diff dim tensors, not just 2D ConvNets - random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) - random_tensor.floor_() # binarize - output = x.div(keep_prob) * random_tensor - return output - - -class DropPath(nn.Module): - """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). - From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py - """ - - def __init__(self, drop_prob=None): - super(DropPath, self).__init__() - self.drop_prob = drop_prob - - def forward(self, x): - return drop_path(x, self.drop_prob, self.training) # type: ignore - - -class ChannelAttention(nn.Module): - """Channel attention used in RCAN. - Args: - num_feat (int): Channel number of intermediate features. - squeeze_factor (int): Channel squeeze factor. Default: 16. - """ - - def __init__(self, num_feat, squeeze_factor=16): - super(ChannelAttention, self).__init__() - self.attention = nn.Sequential( - nn.AdaptiveAvgPool2d(1), - nn.Conv2d(num_feat, num_feat // squeeze_factor, 1, padding=0), - nn.ReLU(inplace=True), - nn.Conv2d(num_feat // squeeze_factor, num_feat, 1, padding=0), - nn.Sigmoid(), - ) - - def forward(self, x): - y = self.attention(x) - return x * y - - -class CAB(nn.Module): - def __init__(self, num_feat, compress_ratio=3, squeeze_factor=30): - super(CAB, self).__init__() - - self.cab = nn.Sequential( - nn.Conv2d(num_feat, num_feat // compress_ratio, 3, 1, 1), - nn.GELU(), - nn.Conv2d(num_feat // compress_ratio, num_feat, 3, 1, 1), - ChannelAttention(num_feat, squeeze_factor), - ) - - def forward(self, x): - return self.cab(x) - - -class Mlp(nn.Module): - def __init__( - self, - in_features, - hidden_features=None, - out_features=None, - act_layer=nn.GELU, - drop=0.0, - ): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - - -def window_partition(x, window_size): - """ - Args: - x: (b, h, w, c) - window_size (int): window size - Returns: - windows: (num_windows*b, window_size, window_size, c) - """ - b, h, w, c = x.shape - x = x.view(b, h // window_size, window_size, w // window_size, window_size, c) - windows = ( - x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, c) - ) - return windows - - -def window_reverse(windows, window_size, h, w): - """ - Args: - windows: (num_windows*b, window_size, window_size, c) - window_size (int): Window size - h (int): Height of image - w (int): Width of image - Returns: - x: (b, h, w, c) - """ - b = int(windows.shape[0] / (h * w / window_size / window_size)) - x = windows.view( - b, h // window_size, w // window_size, window_size, window_size, -1 - ) - x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(b, h, w, -1) - return x - - -class WindowAttention(nn.Module): - r"""Window based multi-head self attention (W-MSA) module with relative position bias. - It supports both of shifted and non-shifted window. - Args: - dim (int): Number of input channels. - window_size (tuple[int]): The height and width of the window. - num_heads (int): Number of attention heads. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set - attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 - proj_drop (float, optional): Dropout ratio of output. Default: 0.0 - """ - - def __init__( - self, - dim, - window_size, - num_heads, - qkv_bias=True, - qk_scale=None, - attn_drop=0.0, - proj_drop=0.0, - ): - super().__init__() - self.dim = dim - self.window_size = window_size # Wh, Ww - self.num_heads = num_heads - head_dim = dim // num_heads - self.scale = qk_scale or head_dim**-0.5 - - # define a parameter table of relative position bias - self.relative_position_bias_table = nn.Parameter( # type: ignore - torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads) - ) # 2*Wh-1 * 2*Ww-1, nH - - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - - self.proj_drop = nn.Dropout(proj_drop) - - trunc_normal_(self.relative_position_bias_table, std=0.02) - self.softmax = nn.Softmax(dim=-1) - - def forward(self, x, rpi, mask=None): - """ - Args: - x: input features with shape of (num_windows*b, n, c) - mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None - """ - b_, n, c = x.shape - qkv = ( - self.qkv(x) - .reshape(b_, n, 3, self.num_heads, c // self.num_heads) - .permute(2, 0, 3, 1, 4) - ) - q, k, v = ( - qkv[0], - qkv[1], - qkv[2], - ) # make torchscript happy (cannot use tensor as tuple) - - q = q * self.scale - attn = q @ k.transpose(-2, -1) - - relative_position_bias = self.relative_position_bias_table[rpi.view(-1)].view( - self.window_size[0] * self.window_size[1], - self.window_size[0] * self.window_size[1], - -1, - ) # Wh*Ww,Wh*Ww,nH - relative_position_bias = relative_position_bias.permute( - 2, 0, 1 - ).contiguous() # nH, Wh*Ww, Wh*Ww - attn = attn + relative_position_bias.unsqueeze(0) - - if mask is not None: - nw = mask.shape[0] - attn = attn.view(b_ // nw, nw, self.num_heads, n, n) + mask.unsqueeze( - 1 - ).unsqueeze(0) - attn = attn.view(-1, self.num_heads, n, n) - attn = self.softmax(attn) - else: - attn = self.softmax(attn) - - attn = self.attn_drop(attn) - - x = (attn @ v).transpose(1, 2).reshape(b_, n, c) - x = self.proj(x) - x = self.proj_drop(x) - return x - - -class HAB(nn.Module): - r"""Hybrid Attention Block. - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resolution. - num_heads (int): Number of attention heads. - window_size (int): Window size. - shift_size (int): Shift size for SW-MSA. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float, optional): Stochastic depth rate. Default: 0.0 - act_layer (nn.Module, optional): Activation layer. Default: nn.GELU - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - - def __init__( - self, - dim, - input_resolution, - num_heads, - window_size=7, - shift_size=0, - compress_ratio=3, - squeeze_factor=30, - conv_scale=0.01, - mlp_ratio=4.0, - qkv_bias=True, - qk_scale=None, - drop=0.0, - attn_drop=0.0, - drop_path=0.0, - act_layer=nn.GELU, - norm_layer=nn.LayerNorm, - ): - super().__init__() - self.dim = dim - self.input_resolution = input_resolution - self.num_heads = num_heads - self.window_size = window_size - self.shift_size = shift_size - self.mlp_ratio = mlp_ratio - if min(self.input_resolution) <= self.window_size: - # if window size is larger than input resolution, we don't partition windows - self.shift_size = 0 - self.window_size = min(self.input_resolution) - assert ( - 0 <= self.shift_size < self.window_size - ), "shift_size must in 0-window_size" - - self.norm1 = norm_layer(dim) - self.attn = WindowAttention( - dim, - window_size=to_2tuple(self.window_size), - num_heads=num_heads, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - attn_drop=attn_drop, - proj_drop=drop, - ) - - self.conv_scale = conv_scale - self.conv_block = CAB( - num_feat=dim, compress_ratio=compress_ratio, squeeze_factor=squeeze_factor - ) - - self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp( - in_features=dim, - hidden_features=mlp_hidden_dim, - act_layer=act_layer, - drop=drop, - ) - - def forward(self, x, x_size, rpi_sa, attn_mask): - h, w = x_size - b, _, c = x.shape - # assert seq_len == h * w, "input feature has wrong size" - - shortcut = x - x = self.norm1(x) - x = x.view(b, h, w, c) - - # Conv_X - conv_x = self.conv_block(x.permute(0, 3, 1, 2)) - conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(b, h * w, c) - - # cyclic shift - if self.shift_size > 0: - shifted_x = torch.roll( - x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2) - ) - attn_mask = attn_mask - else: - shifted_x = x - attn_mask = None - - # partition windows - x_windows = window_partition( - shifted_x, self.window_size - ) # nw*b, window_size, window_size, c - x_windows = x_windows.view( - -1, self.window_size * self.window_size, c - ) # nw*b, window_size*window_size, c - - # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size - attn_windows = self.attn(x_windows, rpi=rpi_sa, mask=attn_mask) - - # merge windows - attn_windows = attn_windows.view(-1, self.window_size, self.window_size, c) - shifted_x = window_reverse(attn_windows, self.window_size, h, w) # b h' w' c - - # reverse cyclic shift - if self.shift_size > 0: - attn_x = torch.roll( - shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2) - ) - else: - attn_x = shifted_x - attn_x = attn_x.view(b, h * w, c) - - # FFN - x = shortcut + self.drop_path(attn_x) + conv_x * self.conv_scale - x = x + self.drop_path(self.mlp(self.norm2(x))) - - return x - - -class PatchMerging(nn.Module): - r"""Patch Merging Layer. - Args: - input_resolution (tuple[int]): Resolution of input feature. - dim (int): Number of input channels. - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - - def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): - super().__init__() - self.input_resolution = input_resolution - self.dim = dim - self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) - self.norm = norm_layer(4 * dim) - - def forward(self, x): - """ - x: b, h*w, c - """ - h, w = self.input_resolution - b, seq_len, c = x.shape - assert seq_len == h * w, "input feature has wrong size" - assert h % 2 == 0 and w % 2 == 0, f"x size ({h}*{w}) are not even." - - x = x.view(b, h, w, c) - - x0 = x[:, 0::2, 0::2, :] # b h/2 w/2 c - x1 = x[:, 1::2, 0::2, :] # b h/2 w/2 c - x2 = x[:, 0::2, 1::2, :] # b h/2 w/2 c - x3 = x[:, 1::2, 1::2, :] # b h/2 w/2 c - x = torch.cat([x0, x1, x2, x3], -1) # b h/2 w/2 4*c - x = x.view(b, -1, 4 * c) # b h/2*w/2 4*c - - x = self.norm(x) - x = self.reduction(x) - - return x - - -class OCAB(nn.Module): - # overlapping cross-attention block - - def __init__( - self, - dim, - input_resolution, - window_size, - overlap_ratio, - num_heads, - qkv_bias=True, - qk_scale=None, - mlp_ratio=2, - norm_layer=nn.LayerNorm, - ): - super().__init__() - self.dim = dim - self.input_resolution = input_resolution - self.window_size = window_size - self.num_heads = num_heads - head_dim = dim // num_heads - self.scale = qk_scale or head_dim**-0.5 - self.overlap_win_size = int(window_size * overlap_ratio) + window_size - - self.norm1 = norm_layer(dim) - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - self.unfold = nn.Unfold( - kernel_size=(self.overlap_win_size, self.overlap_win_size), - stride=window_size, - padding=(self.overlap_win_size - window_size) // 2, - ) - - # define a parameter table of relative position bias - self.relative_position_bias_table = nn.Parameter( # type: ignore - torch.zeros( - (window_size + self.overlap_win_size - 1) - * (window_size + self.overlap_win_size - 1), - num_heads, - ) - ) # 2*Wh-1 * 2*Ww-1, nH - - trunc_normal_(self.relative_position_bias_table, std=0.02) - self.softmax = nn.Softmax(dim=-1) - - self.proj = nn.Linear(dim, dim) - - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp( - in_features=dim, hidden_features=mlp_hidden_dim, act_layer=nn.GELU - ) - - def forward(self, x, x_size, rpi): - h, w = x_size - b, _, c = x.shape - - shortcut = x - x = self.norm1(x) - x = x.view(b, h, w, c) - - qkv = self.qkv(x).reshape(b, h, w, 3, c).permute(3, 0, 4, 1, 2) # 3, b, c, h, w - q = qkv[0].permute(0, 2, 3, 1) # b, h, w, c - kv = torch.cat((qkv[1], qkv[2]), dim=1) # b, 2*c, h, w - - # partition windows - q_windows = window_partition( - q, self.window_size - ) # nw*b, window_size, window_size, c - q_windows = q_windows.view( - -1, self.window_size * self.window_size, c - ) # nw*b, window_size*window_size, c - - kv_windows = self.unfold(kv) # b, c*w*w, nw - kv_windows = rearrange( - kv_windows, - "b (nc ch owh oww) nw -> nc (b nw) (owh oww) ch", - nc=2, - ch=c, - owh=self.overlap_win_size, - oww=self.overlap_win_size, - ).contiguous() # 2, nw*b, ow*ow, c - # Do the above rearrangement without the rearrange function - # kv_windows = kv_windows.view( - # 2, b, self.overlap_win_size, self.overlap_win_size, c, -1 - # ) - # kv_windows = kv_windows.permute(0, 5, 1, 2, 3, 4).contiguous() - # kv_windows = kv_windows.view( - # 2, -1, self.overlap_win_size * self.overlap_win_size, c - # ) - - k_windows, v_windows = kv_windows[0], kv_windows[1] # nw*b, ow*ow, c - - b_, nq, _ = q_windows.shape - _, n, _ = k_windows.shape - d = self.dim // self.num_heads - q = q_windows.reshape(b_, nq, self.num_heads, d).permute( - 0, 2, 1, 3 - ) # nw*b, nH, nq, d - k = k_windows.reshape(b_, n, self.num_heads, d).permute( - 0, 2, 1, 3 - ) # nw*b, nH, n, d - v = v_windows.reshape(b_, n, self.num_heads, d).permute( - 0, 2, 1, 3 - ) # nw*b, nH, n, d - - q = q * self.scale - attn = q @ k.transpose(-2, -1) - - relative_position_bias = self.relative_position_bias_table[rpi.view(-1)].view( - self.window_size * self.window_size, - self.overlap_win_size * self.overlap_win_size, - -1, - ) # ws*ws, wse*wse, nH - relative_position_bias = relative_position_bias.permute( - 2, 0, 1 - ).contiguous() # nH, ws*ws, wse*wse - attn = attn + relative_position_bias.unsqueeze(0) - - attn = self.softmax(attn) - attn_windows = (attn @ v).transpose(1, 2).reshape(b_, nq, self.dim) - - # merge windows - attn_windows = attn_windows.view( - -1, self.window_size, self.window_size, self.dim - ) - x = window_reverse(attn_windows, self.window_size, h, w) # b h w c - x = x.view(b, h * w, self.dim) - - x = self.proj(x) + shortcut - - x = x + self.mlp(self.norm2(x)) - return x - - -class AttenBlocks(nn.Module): - """A series of attention blocks for one RHAG. - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resolution. - depth (int): Number of blocks. - num_heads (int): Number of attention heads. - window_size (int): Local window size. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. - """ - - def __init__( - self, - dim, - input_resolution, - depth, - num_heads, - window_size, - compress_ratio, - squeeze_factor, - conv_scale, - overlap_ratio, - mlp_ratio=4.0, - qkv_bias=True, - qk_scale=None, - drop=0.0, - attn_drop=0.0, - drop_path=0.0, - norm_layer=nn.LayerNorm, - downsample=None, - use_checkpoint=False, - ): - super().__init__() - self.dim = dim - self.input_resolution = input_resolution - self.depth = depth - self.use_checkpoint = use_checkpoint - - # build blocks - self.blocks = nn.ModuleList( - [ - HAB( - dim=dim, - input_resolution=input_resolution, - num_heads=num_heads, - window_size=window_size, - shift_size=0 if (i % 2 == 0) else window_size // 2, - compress_ratio=compress_ratio, - squeeze_factor=squeeze_factor, - conv_scale=conv_scale, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop, - attn_drop=attn_drop, - drop_path=drop_path[i] - if isinstance(drop_path, list) - else drop_path, - norm_layer=norm_layer, - ) - for i in range(depth) - ] - ) - - # OCAB - self.overlap_attn = OCAB( - dim=dim, - input_resolution=input_resolution, - window_size=window_size, - overlap_ratio=overlap_ratio, - num_heads=num_heads, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - mlp_ratio=mlp_ratio, # type: ignore - norm_layer=norm_layer, - ) - - # patch merging layer - if downsample is not None: - self.downsample = downsample( - input_resolution, dim=dim, norm_layer=norm_layer - ) - else: - self.downsample = None - - def forward(self, x, x_size, params): - for blk in self.blocks: - x = blk(x, x_size, params["rpi_sa"], params["attn_mask"]) - - x = self.overlap_attn(x, x_size, params["rpi_oca"]) - - if self.downsample is not None: - x = self.downsample(x) - return x - - -class RHAG(nn.Module): - """Residual Hybrid Attention Group (RHAG). - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resolution. - depth (int): Number of blocks. - num_heads (int): Number of attention heads. - window_size (int): Local window size. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. - img_size: Input image size. - patch_size: Patch size. - resi_connection: The convolutional block before residual connection. - """ - - def __init__( - self, - dim, - input_resolution, - depth, - num_heads, - window_size, - compress_ratio, - squeeze_factor, - conv_scale, - overlap_ratio, - mlp_ratio=4.0, - qkv_bias=True, - qk_scale=None, - drop=0.0, - attn_drop=0.0, - drop_path=0.0, - norm_layer=nn.LayerNorm, - downsample=None, - use_checkpoint=False, - img_size=224, - patch_size=4, - resi_connection="1conv", - ): - super(RHAG, self).__init__() - - self.dim = dim - self.input_resolution = input_resolution - - self.residual_group = AttenBlocks( - dim=dim, - input_resolution=input_resolution, - depth=depth, - num_heads=num_heads, - window_size=window_size, - compress_ratio=compress_ratio, - squeeze_factor=squeeze_factor, - conv_scale=conv_scale, - overlap_ratio=overlap_ratio, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop, - attn_drop=attn_drop, - drop_path=drop_path, - norm_layer=norm_layer, - downsample=downsample, - use_checkpoint=use_checkpoint, - ) - - if resi_connection == "1conv": - self.conv = nn.Conv2d(dim, dim, 3, 1, 1) - elif resi_connection == "identity": - self.conv = nn.Identity() - - self.patch_embed = PatchEmbed( - img_size=img_size, - patch_size=patch_size, - in_chans=0, - embed_dim=dim, - norm_layer=None, - ) - - self.patch_unembed = PatchUnEmbed( - img_size=img_size, - patch_size=patch_size, - in_chans=0, - embed_dim=dim, - norm_layer=None, - ) - - def forward(self, x, x_size, params): - return ( - self.patch_embed( - self.conv( - self.patch_unembed(self.residual_group(x, x_size, params), x_size) - ) - ) - + x - ) - - -class PatchEmbed(nn.Module): - r"""Image to Patch Embedding - Args: - img_size (int): Image size. Default: 224. - patch_size (int): Patch token size. Default: 4. - in_chans (int): Number of input image channels. Default: 3. - embed_dim (int): Number of linear projection output channels. Default: 96. - norm_layer (nn.Module, optional): Normalization layer. Default: None - """ - - def __init__( - self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None - ): - super().__init__() - img_size = to_2tuple(img_size) - patch_size = to_2tuple(patch_size) - patches_resolution = [ - img_size[0] // patch_size[0], # type: ignore - img_size[1] // patch_size[1], # type: ignore - ] - self.img_size = img_size - self.patch_size = patch_size - self.patches_resolution = patches_resolution - self.num_patches = patches_resolution[0] * patches_resolution[1] - - self.in_chans = in_chans - self.embed_dim = embed_dim - - if norm_layer is not None: - self.norm = norm_layer(embed_dim) - else: - self.norm = None - - def forward(self, x): - x = x.flatten(2).transpose(1, 2) # b Ph*Pw c - if self.norm is not None: - x = self.norm(x) - return x - - -class PatchUnEmbed(nn.Module): - r"""Image to Patch Unembedding - Args: - img_size (int): Image size. Default: 224. - patch_size (int): Patch token size. Default: 4. - in_chans (int): Number of input image channels. Default: 3. - embed_dim (int): Number of linear projection output channels. Default: 96. - norm_layer (nn.Module, optional): Normalization layer. Default: None - """ - - def __init__( - self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None - ): - super().__init__() - img_size = to_2tuple(img_size) - patch_size = to_2tuple(patch_size) - patches_resolution = [ - img_size[0] // patch_size[0], # type: ignore - img_size[1] // patch_size[1], # type: ignore - ] - self.img_size = img_size - self.patch_size = patch_size - self.patches_resolution = patches_resolution - self.num_patches = patches_resolution[0] * patches_resolution[1] - - self.in_chans = in_chans - self.embed_dim = embed_dim - - def forward(self, x, x_size): - x = ( - x.transpose(1, 2) - .contiguous() - .view(x.shape[0], self.embed_dim, x_size[0], x_size[1]) - ) # b Ph*Pw c - return x - - -class Upsample(nn.Sequential): - """Upsample module. - Args: - scale (int): Scale factor. Supported scales: 2^n and 3. - num_feat (int): Channel number of intermediate features. - """ - - def __init__(self, scale, num_feat): - m = [] - if (scale & (scale - 1)) == 0: # scale = 2^n - for _ in range(int(math.log(scale, 2))): - m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) - m.append(nn.PixelShuffle(2)) - elif scale == 3: - m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) - m.append(nn.PixelShuffle(3)) - else: - raise ValueError( - f"scale {scale} is not supported. " "Supported scales: 2^n and 3." - ) - super(Upsample, self).__init__(*m) - - -class HAT(nn.Module): - r"""Hybrid Attention Transformer - A PyTorch implementation of : `Activating More Pixels in Image Super-Resolution Transformer`. - Some codes are based on SwinIR. - Args: - img_size (int | tuple(int)): Input image size. Default 64 - patch_size (int | tuple(int)): Patch size. Default: 1 - in_chans (int): Number of input image channels. Default: 3 - embed_dim (int): Patch embedding dimension. Default: 96 - depths (tuple(int)): Depth of each Swin Transformer layer. - num_heads (tuple(int)): Number of attention heads in different layers. - window_size (int): Window size. Default: 7 - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 - qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None - drop_rate (float): Dropout rate. Default: 0 - attn_drop_rate (float): Attention dropout rate. Default: 0 - drop_path_rate (float): Stochastic depth rate. Default: 0.1 - norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. - ape (bool): If True, add absolute position embedding to the patch embedding. Default: False - patch_norm (bool): If True, add normalization after patch embedding. Default: True - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False - upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction - img_range: Image range. 1. or 255. - upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None - resi_connection: The convolutional block before residual connection. '1conv'/'3conv' - """ - - def __init__( - self, - state_dict, - **kwargs, - ): - super(HAT, self).__init__() - - # Defaults - img_size = 64 - patch_size = 1 - in_chans = 3 - embed_dim = 96 - depths = (6, 6, 6, 6) - num_heads = (6, 6, 6, 6) - window_size = 7 - compress_ratio = 3 - squeeze_factor = 30 - conv_scale = 0.01 - overlap_ratio = 0.5 - mlp_ratio = 4.0 - qkv_bias = True - qk_scale = None - drop_rate = 0.0 - attn_drop_rate = 0.0 - drop_path_rate = 0.1 - norm_layer = nn.LayerNorm - ape = False - patch_norm = True - use_checkpoint = False - upscale = 2 - img_range = 1.0 - upsampler = "" - resi_connection = "1conv" - - self.state = state_dict - self.model_arch = "HAT" - self.sub_type = "SR" - self.supports_fp16 = False - self.support_bf16 = True - self.min_size_restriction = 16 - - state_keys = list(state_dict.keys()) - - num_feat = state_dict["conv_last.weight"].shape[1] - in_chans = state_dict["conv_first.weight"].shape[1] - num_out_ch = state_dict["conv_last.weight"].shape[0] - embed_dim = state_dict["conv_first.weight"].shape[0] - - if "conv_before_upsample.0.weight" in state_keys: - if "conv_up1.weight" in state_keys: - upsampler = "nearest+conv" - else: - upsampler = "pixelshuffle" - supports_fp16 = False - elif "upsample.0.weight" in state_keys: - upsampler = "pixelshuffledirect" - else: - upsampler = "" - upscale = 1 - if upsampler == "nearest+conv": - upsample_keys = [ - x for x in state_keys if "conv_up" in x and "bias" not in x - ] - - for upsample_key in upsample_keys: - upscale *= 2 - elif upsampler == "pixelshuffle": - upsample_keys = [ - x - for x in state_keys - if "upsample" in x and "conv" not in x and "bias" not in x - ] - for upsample_key in upsample_keys: - shape = self.state[upsample_key].shape[0] - upscale *= math.sqrt(shape // num_feat) - upscale = int(upscale) - elif upsampler == "pixelshuffledirect": - upscale = int( - math.sqrt(self.state["upsample.0.bias"].shape[0] // num_out_ch) - ) - - max_layer_num = 0 - max_block_num = 0 - for key in state_keys: - result = re.match( - r"layers.(\d*).residual_group.blocks.(\d*).conv_block.cab.0.weight", key - ) - if result: - layer_num, block_num = result.groups() - max_layer_num = max(max_layer_num, int(layer_num)) - max_block_num = max(max_block_num, int(block_num)) - - depths = [max_block_num + 1 for _ in range(max_layer_num + 1)] - - if ( - "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" - in state_keys - ): - num_heads_num = self.state[ - "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" - ].shape[-1] - num_heads = [num_heads_num for _ in range(max_layer_num + 1)] - else: - num_heads = depths - - mlp_ratio = float( - self.state["layers.0.residual_group.blocks.0.mlp.fc1.bias"].shape[0] - / embed_dim - ) - - # TODO: could actually count the layers, but this should do - if "layers.0.conv.4.weight" in state_keys: - resi_connection = "3conv" - else: - resi_connection = "1conv" - - window_size = int(math.sqrt(self.state["relative_position_index_SA"].shape[0])) - - # Not sure if this is needed or used at all anywhere in HAT's config - if "layers.0.residual_group.blocks.1.attn_mask" in state_keys: - img_size = int( - math.sqrt( - self.state["layers.0.residual_group.blocks.1.attn_mask"].shape[0] - ) - * window_size - ) - - self.window_size = window_size - self.shift_size = window_size // 2 - self.overlap_ratio = overlap_ratio - - self.in_nc = in_chans - self.out_nc = num_out_ch - self.num_feat = num_feat - self.embed_dim = embed_dim - self.num_heads = num_heads - self.depths = depths - self.window_size = window_size - self.mlp_ratio = mlp_ratio - self.scale = upscale - self.upsampler = upsampler - self.img_size = img_size - self.img_range = img_range - self.resi_connection = resi_connection - - num_in_ch = in_chans - # num_out_ch = in_chans - # num_feat = 64 - self.img_range = img_range - if in_chans == 3: - rgb_mean = (0.4488, 0.4371, 0.4040) - self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) - else: - self.mean = torch.zeros(1, 1, 1, 1) - self.upscale = upscale - self.upsampler = upsampler - - # relative position index - relative_position_index_SA = self.calculate_rpi_sa() - relative_position_index_OCA = self.calculate_rpi_oca() - self.register_buffer("relative_position_index_SA", relative_position_index_SA) - self.register_buffer("relative_position_index_OCA", relative_position_index_OCA) - - # ------------------------- 1, shallow feature extraction ------------------------- # - self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) - - # ------------------------- 2, deep feature extraction ------------------------- # - self.num_layers = len(depths) - self.embed_dim = embed_dim - self.ape = ape - self.patch_norm = patch_norm - self.num_features = embed_dim - self.mlp_ratio = mlp_ratio - - # split image into non-overlapping patches - self.patch_embed = PatchEmbed( - img_size=img_size, - patch_size=patch_size, - in_chans=embed_dim, - embed_dim=embed_dim, - norm_layer=norm_layer if self.patch_norm else None, - ) - num_patches = self.patch_embed.num_patches - patches_resolution = self.patch_embed.patches_resolution - self.patches_resolution = patches_resolution - - # merge non-overlapping patches into image - self.patch_unembed = PatchUnEmbed( - img_size=img_size, - patch_size=patch_size, - in_chans=embed_dim, - embed_dim=embed_dim, - norm_layer=norm_layer if self.patch_norm else None, - ) - - # absolute position embedding - if self.ape: - self.absolute_pos_embed = nn.Parameter( # type: ignore[arg-type] - torch.zeros(1, num_patches, embed_dim) - ) - trunc_normal_(self.absolute_pos_embed, std=0.02) - - self.pos_drop = nn.Dropout(p=drop_rate) - - # stochastic depth - dpr = [ - x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) - ] # stochastic depth decay rule - - # build Residual Hybrid Attention Groups (RHAG) - self.layers = nn.ModuleList() - for i_layer in range(self.num_layers): - layer = RHAG( - dim=embed_dim, - input_resolution=(patches_resolution[0], patches_resolution[1]), - depth=depths[i_layer], - num_heads=num_heads[i_layer], - window_size=window_size, - compress_ratio=compress_ratio, - squeeze_factor=squeeze_factor, - conv_scale=conv_scale, - overlap_ratio=overlap_ratio, - mlp_ratio=self.mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop_rate, - attn_drop=attn_drop_rate, - drop_path=dpr[ - sum(depths[:i_layer]) : sum(depths[: i_layer + 1]) # type: ignore - ], # no impact on SR results - norm_layer=norm_layer, - downsample=None, - use_checkpoint=use_checkpoint, - img_size=img_size, - patch_size=patch_size, - resi_connection=resi_connection, - ) - self.layers.append(layer) - self.norm = norm_layer(self.num_features) - - # build the last conv layer in deep feature extraction - if resi_connection == "1conv": - self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) - elif resi_connection == "identity": - self.conv_after_body = nn.Identity() - - # ------------------------- 3, high quality image reconstruction ------------------------- # - if self.upsampler == "pixelshuffle": - # for classical SR - self.conv_before_upsample = nn.Sequential( - nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) - ) - self.upsample = Upsample(upscale, num_feat) - self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - - self.apply(self._init_weights) - self.load_state_dict(self.state, strict=False) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=0.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - def calculate_rpi_sa(self): - # calculate relative position index for SA - coords_h = torch.arange(self.window_size) - coords_w = torch.arange(self.window_size) - coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww - coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww - relative_coords = ( - coords_flatten[:, :, None] - coords_flatten[:, None, :] - ) # 2, Wh*Ww, Wh*Ww - relative_coords = relative_coords.permute( - 1, 2, 0 - ).contiguous() # Wh*Ww, Wh*Ww, 2 - relative_coords[:, :, 0] += self.window_size - 1 # shift to start from 0 - relative_coords[:, :, 1] += self.window_size - 1 - relative_coords[:, :, 0] *= 2 * self.window_size - 1 - relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww - return relative_position_index - - def calculate_rpi_oca(self): - # calculate relative position index for OCA - window_size_ori = self.window_size - window_size_ext = self.window_size + int(self.overlap_ratio * self.window_size) - - coords_h = torch.arange(window_size_ori) - coords_w = torch.arange(window_size_ori) - coords_ori = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, ws, ws - coords_ori_flatten = torch.flatten(coords_ori, 1) # 2, ws*ws - - coords_h = torch.arange(window_size_ext) - coords_w = torch.arange(window_size_ext) - coords_ext = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, wse, wse - coords_ext_flatten = torch.flatten(coords_ext, 1) # 2, wse*wse - - relative_coords = ( - coords_ext_flatten[:, None, :] - coords_ori_flatten[:, :, None] - ) # 2, ws*ws, wse*wse - - relative_coords = relative_coords.permute( - 1, 2, 0 - ).contiguous() # ws*ws, wse*wse, 2 - relative_coords[:, :, 0] += ( - window_size_ori - window_size_ext + 1 - ) # shift to start from 0 - relative_coords[:, :, 1] += window_size_ori - window_size_ext + 1 - - relative_coords[:, :, 0] *= window_size_ori + window_size_ext - 1 - relative_position_index = relative_coords.sum(-1) - return relative_position_index - - def calculate_mask(self, x_size): - # calculate attention mask for SW-MSA - h, w = x_size - img_mask = torch.zeros((1, h, w, 1)) # 1 h w 1 - h_slices = ( - slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None), - ) - w_slices = ( - slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None), - ) - cnt = 0 - for h in h_slices: - for w in w_slices: - img_mask[:, h, w, :] = cnt - cnt += 1 - - mask_windows = window_partition( - img_mask, self.window_size - ) # nw, window_size, window_size, 1 - mask_windows = mask_windows.view(-1, self.window_size * self.window_size) - attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) - attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill( - attn_mask == 0, float(0.0) - ) - - return attn_mask - - @torch.jit.ignore # type: ignore - def no_weight_decay(self): - return {"absolute_pos_embed"} - - @torch.jit.ignore # type: ignore - def no_weight_decay_keywords(self): - return {"relative_position_bias_table"} - - def check_image_size(self, x): - _, _, h, w = x.size() - mod_pad_h = (self.window_size - h % self.window_size) % self.window_size - mod_pad_w = (self.window_size - w % self.window_size) % self.window_size - x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") - return x - - def forward_features(self, x): - x_size = (x.shape[2], x.shape[3]) - - # Calculate attention mask and relative position index in advance to speed up inference. - # The original code is very time-cosuming for large window size. - attn_mask = self.calculate_mask(x_size).to(x.device) - params = { - "attn_mask": attn_mask, - "rpi_sa": self.relative_position_index_SA, - "rpi_oca": self.relative_position_index_OCA, - } - - x = self.patch_embed(x) - if self.ape: - x = x + self.absolute_pos_embed - x = self.pos_drop(x) - - for layer in self.layers: - x = layer(x, x_size, params) - - x = self.norm(x) # b seq_len c - x = self.patch_unembed(x, x_size) - - return x - - def forward(self, x): - H, W = x.shape[2:] - self.mean = self.mean.type_as(x) - x = (x - self.mean) * self.img_range - x = self.check_image_size(x) - - if self.upsampler == "pixelshuffle": - # for classical SR - x = self.conv_first(x) - x = self.conv_after_body(self.forward_features(x)) + x - x = self.conv_before_upsample(x) - x = self.conv_last(self.upsample(x)) - - x = x / self.img_range + self.mean - - return x[:, :, : H * self.upscale, : W * self.upscale] diff --git a/comfy_extras/chainner_models/architecture/LICENSE-DAT b/comfy_extras/chainner_models/architecture/LICENSE-DAT deleted file mode 100644 index 261eeb9e..00000000 --- a/comfy_extras/chainner_models/architecture/LICENSE-DAT +++ /dev/null @@ -1,201 +0,0 @@ - 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We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright [yyyy] [name of copyright owner] - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. diff --git a/comfy_extras/chainner_models/architecture/LICENSE-ESRGAN b/comfy_extras/chainner_models/architecture/LICENSE-ESRGAN deleted file mode 100644 index 261eeb9e..00000000 --- a/comfy_extras/chainner_models/architecture/LICENSE-ESRGAN +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. Definitions. - - "License" shall mean the terms and conditions for use, reproduction, - and distribution as defined by Sections 1 through 9 of this document. - - "Licensor" shall mean the copyright owner or entity authorized by - the copyright owner that is granting the License. - - "Legal Entity" shall mean the union of the acting entity and all - other entities that control, are controlled by, or are under common - control with that entity. For the purposes of this definition, - "control" means (i) the power, direct or indirect, to cause the - direction or management of such entity, whether by contract or - otherwise, or (ii) ownership of fifty percent (50%) or more of the - outstanding shares, or (iii) beneficial ownership of such entity. - - "You" (or "Your") shall mean an individual or Legal Entity - exercising permissions granted by this License. - - "Source" form shall mean the preferred form for making modifications, - including but not limited to software source code, documentation - source, and configuration files. - - "Object" form shall mean any form resulting from mechanical - transformation or translation of a Source form, including but - not limited to compiled object code, generated documentation, - and conversions to other media types. - - "Work" shall mean the work of authorship, whether in Source or - Object form, made available under the License, as indicated by a - copyright notice that is included in or attached to the work - (an example is provided in the Appendix below). - - "Derivative Works" shall mean any work, whether in Source or Object - form, that is based on (or derived from) the Work and for which the - editorial revisions, annotations, elaborations, or other modifications - represent, as a whole, an original work of authorship. For the purposes - of this License, Derivative Works shall not include works that remain - separable from, or merely link (or bind by name) to the interfaces of, - the Work and Derivative Works thereof. - - "Contribution" shall mean any work of authorship, including - the original version of the Work and any modifications or additions - to that Work or Derivative Works thereof, that is intentionally - submitted to Licensor for inclusion in the Work by the copyright owner - or by an individual or Legal Entity authorized to submit on behalf of - the copyright owner. For the purposes of this definition, "submitted" - means any form of electronic, verbal, or written communication sent - to the Licensor or its representatives, including but not limited to - communication on electronic mailing lists, source code control systems, - and issue tracking systems that are managed by, or on behalf of, the - Licensor for the purpose of discussing and improving the Work, but - excluding communication that is conspicuously marked or otherwise - designated in writing by the copyright owner as "Not a Contribution." - - "Contributor" shall mean Licensor and any individual or Legal Entity - on behalf of whom a Contribution has been received by Licensor and - subsequently incorporated within the Work. - - 2. Grant of Copyright License. Subject to the terms and conditions of - this License, each Contributor hereby grants to You a perpetual, - worldwide, non-exclusive, no-charge, royalty-free, irrevocable - copyright license to reproduce, prepare Derivative Works of, - publicly display, publicly perform, sublicense, and distribute the - Work and such Derivative Works in Source or Object form. - - 3. Grant of Patent License. Subject to the terms and conditions of - this License, each Contributor hereby grants to You a perpetual, - worldwide, non-exclusive, no-charge, royalty-free, irrevocable - (except as stated in this section) patent license to make, have made, - use, offer to sell, sell, import, and otherwise transfer the Work, - where such license applies only to those patent claims licensable - by such Contributor that are necessarily infringed by their - Contribution(s) alone or by combination of their Contribution(s) - with the Work to which such Contribution(s) was submitted. If You - institute patent litigation against any entity (including a - cross-claim or counterclaim in a lawsuit) alleging that the Work - or a Contribution incorporated within the Work constitutes direct - or contributory patent infringement, then any patent licenses - granted to You under this License for that Work shall terminate - as of the date such litigation is filed. - - 4. Redistribution. You may reproduce and distribute copies of the - Work or Derivative Works thereof in any medium, with or without - modifications, and in Source or Object form, provided that You - meet the following conditions: - - (a) You must give any other recipients of the Work or - Derivative Works a copy of this License; and - - (b) You must cause any modified files to carry prominent notices - stating that You changed the files; and - - (c) You must retain, in the Source form of any Derivative Works - that You distribute, all copyright, patent, trademark, and - attribution notices from the Source form of the Work, - excluding those notices that do not pertain to any part of - the Derivative Works; and - - (d) If the Work includes a "NOTICE" text file as part of its - distribution, then any Derivative Works that You distribute must - include a readable copy of the attribution notices contained - within such NOTICE file, excluding those notices that do not - pertain to any part of the Derivative Works, in at least one - of the following places: within a NOTICE text file distributed - as part of the Derivative Works; within the Source form or - documentation, if provided along with the Derivative Works; or, - within a display generated by the Derivative Works, if and - wherever such third-party notices normally appear. The contents - of the NOTICE file are for informational purposes only and - do not modify the License. You may add Your own attribution - notices within Derivative Works that You distribute, alongside - or as an addendum to the NOTICE text from the Work, provided - that such additional attribution notices cannot be construed - as modifying the License. - - You may add Your own copyright statement to Your modifications and - may provide additional or different license terms and conditions - for use, reproduction, or distribution of Your modifications, or - for any such Derivative Works as a whole, provided Your use, - reproduction, and distribution of the Work otherwise complies with - the conditions stated in this License. - - 5. Submission of Contributions. Unless You explicitly state otherwise, - any Contribution intentionally submitted for inclusion in the Work - by You to the Licensor shall be under the terms and conditions of - this License, without any additional terms or conditions. - Notwithstanding the above, nothing herein shall supersede or modify - the terms of any separate license agreement you may have executed - with Licensor regarding such Contributions. - - 6. Trademarks. This License does not grant permission to use the trade - names, trademarks, service marks, or product names of the Licensor, - except as required for reasonable and customary use in describing the - origin of the Work and reproducing the content of the NOTICE file. - - 7. Disclaimer of Warranty. Unless required by applicable law or - agreed to in writing, Licensor provides the Work (and each - Contributor provides its Contributions) on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or - implied, including, without limitation, any warranties or conditions - of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A - PARTICULAR PURPOSE. You are solely responsible for determining the - appropriateness of using or redistributing the Work and assume any - risks associated with Your exercise of permissions under this License. - - 8. Limitation of Liability. In no event and under no legal theory, - whether in tort (including negligence), contract, or otherwise, - unless required by applicable law (such as deliberate and grossly - negligent acts) or agreed to in writing, shall any Contributor be - liable to You for damages, including any direct, indirect, special, - incidental, or consequential damages of any character arising as a - result of this License or out of the use or inability to use the - Work (including but not limited to damages for loss of goodwill, - work stoppage, computer failure or malfunction, or any and all - other commercial damages or losses), even if such Contributor - has been advised of the possibility of such damages. - - 9. Accepting Warranty or Additional Liability. While redistributing - the Work or Derivative Works thereof, You may choose to offer, - and charge a fee for, acceptance of support, warranty, indemnity, - or other liability obligations and/or rights consistent with this - License. However, in accepting such obligations, You may act only - on Your own behalf and on Your sole responsibility, not on behalf - of any other Contributor, and only if You agree to indemnify, - defend, and hold each Contributor harmless for any liability - incurred by, or claims asserted against, such Contributor by reason - of your accepting any such warranty or additional liability. - - END OF TERMS AND CONDITIONS - - APPENDIX: How to apply the Apache License to your work. - - To apply the Apache License to your work, attach the following - boilerplate notice, with the fields enclosed by brackets "[]" - replaced with your own identifying information. (Don't include - the brackets!) The text should be enclosed in the appropriate - comment syntax for the file format. We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright [yyyy] [name of copyright owner] - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. diff --git a/comfy_extras/chainner_models/architecture/LICENSE-HAT b/comfy_extras/chainner_models/architecture/LICENSE-HAT deleted file mode 100644 index 003e97e9..00000000 --- a/comfy_extras/chainner_models/architecture/LICENSE-HAT +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2022 Xiangyu Chen - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/comfy_extras/chainner_models/architecture/LICENSE-RealESRGAN b/comfy_extras/chainner_models/architecture/LICENSE-RealESRGAN deleted file mode 100644 index 552a1eea..00000000 --- a/comfy_extras/chainner_models/architecture/LICENSE-RealESRGAN +++ /dev/null @@ -1,29 +0,0 @@ -BSD 3-Clause License - -Copyright (c) 2021, Xintao Wang -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - -3. Neither the name of the copyright holder nor the names of its - contributors may be used to endorse or promote products derived from - this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE -FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL -DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/comfy_extras/chainner_models/architecture/LICENSE-SCUNet b/comfy_extras/chainner_models/architecture/LICENSE-SCUNet deleted file mode 100644 index ff75c988..00000000 --- a/comfy_extras/chainner_models/architecture/LICENSE-SCUNet +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. Definitions. - - "License" shall mean the terms and conditions for use, reproduction, - and distribution as defined by Sections 1 through 9 of this document. - - "Licensor" shall mean the copyright owner or entity authorized by - the copyright owner that is granting the License. - - "Legal Entity" shall mean the union of the acting entity and all - other entities that control, are controlled by, or are under common - control with that entity. For the purposes of this definition, - "control" means (i) the power, direct or indirect, to cause the - direction or management of such entity, whether by contract or - otherwise, or (ii) ownership of fifty percent (50%) or more of the - outstanding shares, or (iii) beneficial ownership of such entity. - - "You" (or "Your") shall mean an individual or Legal Entity - exercising permissions granted by this License. - - "Source" form shall mean the preferred form for making modifications, - including but not limited to software source code, documentation - source, and configuration files. - - "Object" form shall mean any form resulting from mechanical - transformation or translation of a Source form, including but - not limited to compiled object code, generated documentation, - and conversions to other media types. - - "Work" shall mean the work of authorship, whether in Source or - Object form, made available under the License, as indicated by a - copyright notice that is included in or attached to the work - (an example is provided in the Appendix below). - - "Derivative Works" shall mean any work, whether in Source or Object - form, that is based on (or derived from) the Work and for which the - editorial revisions, annotations, elaborations, or other modifications - represent, as a whole, an original work of authorship. For the purposes - of this License, Derivative Works shall not include works that remain - separable from, or merely link (or bind by name) to the interfaces of, - the Work and Derivative Works thereof. - - "Contribution" shall mean any work of authorship, including - the original version of the Work and any modifications or additions - to that Work or Derivative Works thereof, that is intentionally - submitted to Licensor for inclusion in the Work by the copyright owner - or by an individual or Legal Entity authorized to submit on behalf of - the copyright owner. For the purposes of this definition, "submitted" - means any form of electronic, verbal, or written communication sent - to the Licensor or its representatives, including but not limited to - communication on electronic mailing lists, source code control systems, - and issue tracking systems that are managed by, or on behalf of, the - Licensor for the purpose of discussing and improving the Work, but - excluding communication that is conspicuously marked or otherwise - designated in writing by the copyright owner as "Not a Contribution." - - "Contributor" shall mean Licensor and any individual or Legal Entity - on behalf of whom a Contribution has been received by Licensor and - subsequently incorporated within the Work. - - 2. Grant of Copyright License. Subject to the terms and conditions of - this License, each Contributor hereby grants to You a perpetual, - worldwide, non-exclusive, no-charge, royalty-free, irrevocable - copyright license to reproduce, prepare Derivative Works of, - publicly display, publicly perform, sublicense, and distribute the - Work and such Derivative Works in Source or Object form. - - 3. Grant of Patent License. Subject to the terms and conditions of - this License, each Contributor hereby grants to You a perpetual, - worldwide, non-exclusive, no-charge, royalty-free, irrevocable - (except as stated in this section) patent license to make, have made, - use, offer to sell, sell, import, and otherwise transfer the Work, - where such license applies only to those patent claims licensable - by such Contributor that are necessarily infringed by their - Contribution(s) alone or by combination of their Contribution(s) - with the Work to which such Contribution(s) was submitted. If You - institute patent litigation against any entity (including a - cross-claim or counterclaim in a lawsuit) alleging that the Work - or a Contribution incorporated within the Work constitutes direct - or contributory patent infringement, then any patent licenses - granted to You under this License for that Work shall terminate - as of the date such litigation is filed. - - 4. Redistribution. You may reproduce and distribute copies of the - Work or Derivative Works thereof in any medium, with or without - modifications, and in Source or Object form, provided that You - meet the following conditions: - - (a) You must give any other recipients of the Work or - Derivative Works a copy of this License; and - - (b) You must cause any modified files to carry prominent notices - stating that You changed the files; and - - (c) You must retain, in the Source form of any Derivative Works - that You distribute, all copyright, patent, trademark, and - attribution notices from the Source form of the Work, - excluding those notices that do not pertain to any part of - the Derivative Works; and - - (d) If the Work includes a "NOTICE" text file as part of its - distribution, then any Derivative Works that You distribute must - include a readable copy of the attribution notices contained - within such NOTICE file, excluding those notices that do not - pertain to any part of the Derivative Works, in at least one - of the following places: within a NOTICE text file distributed - as part of the Derivative Works; within the Source form or - documentation, if provided along with the Derivative Works; or, - within a display generated by the Derivative Works, if and - wherever such third-party notices normally appear. The contents - of the NOTICE file are for informational purposes only and - do not modify the License. You may add Your own attribution - notices within Derivative Works that You distribute, alongside - or as an addendum to the NOTICE text from the Work, provided - that such additional attribution notices cannot be construed - as modifying the License. - - You may add Your own copyright statement to Your modifications and - may provide additional or different license terms and conditions - for use, reproduction, or distribution of Your modifications, or - for any such Derivative Works as a whole, provided Your use, - reproduction, and distribution of the Work otherwise complies with - the conditions stated in this License. - - 5. Submission of Contributions. Unless You explicitly state otherwise, - any Contribution intentionally submitted for inclusion in the Work - by You to the Licensor shall be under the terms and conditions of - this License, without any additional terms or conditions. - Notwithstanding the above, nothing herein shall supersede or modify - the terms of any separate license agreement you may have executed - with Licensor regarding such Contributions. - - 6. Trademarks. This License does not grant permission to use the trade - names, trademarks, service marks, or product names of the Licensor, - except as required for reasonable and customary use in describing the - origin of the Work and reproducing the content of the NOTICE file. - - 7. Disclaimer of Warranty. Unless required by applicable law or - agreed to in writing, Licensor provides the Work (and each - Contributor provides its Contributions) on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or - implied, including, without limitation, any warranties or conditions - of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A - PARTICULAR PURPOSE. You are solely responsible for determining the - appropriateness of using or redistributing the Work and assume any - risks associated with Your exercise of permissions under this License. - - 8. Limitation of Liability. In no event and under no legal theory, - whether in tort (including negligence), contract, or otherwise, - unless required by applicable law (such as deliberate and grossly - negligent acts) or agreed to in writing, shall any Contributor be - liable to You for damages, including any direct, indirect, special, - incidental, or consequential damages of any character arising as a - result of this License or out of the use or inability to use the - Work (including but not limited to damages for loss of goodwill, - work stoppage, computer failure or malfunction, or any and all - other commercial damages or losses), even if such Contributor - has been advised of the possibility of such damages. - - 9. Accepting Warranty or Additional Liability. While redistributing - the Work or Derivative Works thereof, You may choose to offer, - and charge a fee for, acceptance of support, warranty, indemnity, - or other liability obligations and/or rights consistent with this - License. However, in accepting such obligations, You may act only - on Your own behalf and on Your sole responsibility, not on behalf - of any other Contributor, and only if You agree to indemnify, - defend, and hold each Contributor harmless for any liability - incurred by, or claims asserted against, such Contributor by reason - of your accepting any such warranty or additional liability. - - END OF TERMS AND CONDITIONS - - APPENDIX: How to apply the Apache License to your work. - - To apply the Apache License to your work, attach the following - boilerplate notice, with the fields enclosed by brackets "[]" - replaced with your own identifying information. (Don't include - the brackets!) The text should be enclosed in the appropriate - comment syntax for the file format. We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright 2022 Kai Zhang (cskaizhang@gmail.com, https://cszn.github.io/). All rights reserved. - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. diff --git a/comfy_extras/chainner_models/architecture/LICENSE-SPSR b/comfy_extras/chainner_models/architecture/LICENSE-SPSR deleted file mode 100644 index 3245f3f9..00000000 --- a/comfy_extras/chainner_models/architecture/LICENSE-SPSR +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. Definitions. - - "License" shall mean the terms and conditions for use, reproduction, - and distribution as defined by Sections 1 through 9 of this document. - - "Licensor" shall mean the copyright owner or entity authorized by - the copyright owner that is granting the License. - - "Legal Entity" shall mean the union of the acting entity and all - other entities that control, are controlled by, or are under common - control with that entity. For the purposes of this definition, - "control" means (i) the power, direct or indirect, to cause the - direction or management of such entity, whether by contract or - otherwise, or (ii) ownership of fifty percent (50%) or more of the - outstanding shares, or (iii) beneficial ownership of such entity. - - "You" (or "Your") shall mean an individual or Legal Entity - exercising permissions granted by this License. - - "Source" form shall mean the preferred form for making modifications, - including but not limited to software source code, documentation - source, and configuration files. - - "Object" form shall mean any form resulting from mechanical - transformation or translation of a Source form, including but - not limited to compiled object code, generated documentation, - and conversions to other media types. - - "Work" shall mean the work of authorship, whether in Source or - Object form, made available under the License, as indicated by a - copyright notice that is included in or attached to the work - (an example is provided in the Appendix below). - - "Derivative Works" shall mean any work, whether in Source or Object - form, that is based on (or derived from) the Work and for which the - editorial revisions, annotations, elaborations, or other modifications - represent, as a whole, an original work of authorship. For the purposes - of this License, Derivative Works shall not include works that remain - separable from, or merely link (or bind by name) to the interfaces of, - the Work and Derivative Works thereof. - - "Contribution" shall mean any work of authorship, including - the original version of the Work and any modifications or additions - to that Work or Derivative Works thereof, that is intentionally - submitted to Licensor for inclusion in the Work by the copyright owner - or by an individual or Legal Entity authorized to submit on behalf of - the copyright owner. For the purposes of this definition, "submitted" - means any form of electronic, verbal, or written communication sent - to the Licensor or its representatives, including but not limited to - communication on electronic mailing lists, source code control systems, - and issue tracking systems that are managed by, or on behalf of, the - Licensor for the purpose of discussing and improving the Work, but - excluding communication that is conspicuously marked or otherwise - designated in writing by the copyright owner as "Not a Contribution." - - "Contributor" shall mean Licensor and any individual or Legal Entity - on behalf of whom a Contribution has been received by Licensor and - subsequently incorporated within the Work. - - 2. Grant of Copyright License. Subject to the terms and conditions of - this License, each Contributor hereby grants to You a perpetual, - worldwide, non-exclusive, no-charge, royalty-free, irrevocable - copyright license to reproduce, prepare Derivative Works of, - publicly display, publicly perform, sublicense, and distribute the - Work and such Derivative Works in Source or Object form. - - 3. Grant of Patent License. Subject to the terms and conditions of - this License, each Contributor hereby grants to You a perpetual, - worldwide, non-exclusive, no-charge, royalty-free, irrevocable - (except as stated in this section) patent license to make, have made, - use, offer to sell, sell, import, and otherwise transfer the Work, - where such license applies only to those patent claims licensable - by such Contributor that are necessarily infringed by their - Contribution(s) alone or by combination of their Contribution(s) - with the Work to which such Contribution(s) was submitted. If You - institute patent litigation against any entity (including a - cross-claim or counterclaim in a lawsuit) alleging that the Work - or a Contribution incorporated within the Work constitutes direct - or contributory patent infringement, then any patent licenses - granted to You under this License for that Work shall terminate - as of the date such litigation is filed. - - 4. Redistribution. You may reproduce and distribute copies of the - Work or Derivative Works thereof in any medium, with or without - modifications, and in Source or Object form, provided that You - meet the following conditions: - - (a) You must give any other recipients of the Work or - Derivative Works a copy of this License; and - - (b) You must cause any modified files to carry prominent notices - stating that You changed the files; and - - (c) You must retain, in the Source form of any Derivative Works - that You distribute, all copyright, patent, trademark, and - attribution notices from the Source form of the Work, - excluding those notices that do not pertain to any part of - the Derivative Works; and - - (d) If the Work includes a "NOTICE" text file as part of its - distribution, then any Derivative Works that You distribute must - include a readable copy of the attribution notices contained - within such NOTICE file, excluding those notices that do not - pertain to any part of the Derivative Works, in at least one - of the following places: within a NOTICE text file distributed - as part of the Derivative Works; within the Source form or - documentation, if provided along with the Derivative Works; or, - within a display generated by the Derivative Works, if and - wherever such third-party notices normally appear. The contents - of the NOTICE file are for informational purposes only and - do not modify the License. You may add Your own attribution - notices within Derivative Works that You distribute, alongside - or as an addendum to the NOTICE text from the Work, provided - that such additional attribution notices cannot be construed - as modifying the License. - - You may add Your own copyright statement to Your modifications and - may provide additional or different license terms and conditions - for use, reproduction, or distribution of Your modifications, or - for any such Derivative Works as a whole, provided Your use, - reproduction, and distribution of the Work otherwise complies with - the conditions stated in this License. - - 5. Submission of Contributions. Unless You explicitly state otherwise, - any Contribution intentionally submitted for inclusion in the Work - by You to the Licensor shall be under the terms and conditions of - this License, without any additional terms or conditions. - Notwithstanding the above, nothing herein shall supersede or modify - the terms of any separate license agreement you may have executed - with Licensor regarding such Contributions. - - 6. Trademarks. This License does not grant permission to use the trade - names, trademarks, service marks, or product names of the Licensor, - except as required for reasonable and customary use in describing the - origin of the Work and reproducing the content of the NOTICE file. - - 7. Disclaimer of Warranty. Unless required by applicable law or - agreed to in writing, Licensor provides the Work (and each - Contributor provides its Contributions) on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or - implied, including, without limitation, any warranties or conditions - of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A - PARTICULAR PURPOSE. You are solely responsible for determining the - appropriateness of using or redistributing the Work and assume any - risks associated with Your exercise of permissions under this License. - - 8. Limitation of Liability. In no event and under no legal theory, - whether in tort (including negligence), contract, or otherwise, - unless required by applicable law (such as deliberate and grossly - negligent acts) or agreed to in writing, shall any Contributor be - liable to You for damages, including any direct, indirect, special, - incidental, or consequential damages of any character arising as a - result of this License or out of the use or inability to use the - Work (including but not limited to damages for loss of goodwill, - work stoppage, computer failure or malfunction, or any and all - other commercial damages or losses), even if such Contributor - has been advised of the possibility of such damages. - - 9. Accepting Warranty or Additional Liability. While redistributing - the Work or Derivative Works thereof, You may choose to offer, - and charge a fee for, acceptance of support, warranty, indemnity, - or other liability obligations and/or rights consistent with this - License. However, in accepting such obligations, You may act only - on Your own behalf and on Your sole responsibility, not on behalf - of any other Contributor, and only if You agree to indemnify, - defend, and hold each Contributor harmless for any liability - incurred by, or claims asserted against, such Contributor by reason - of your accepting any such warranty or additional liability. - - END OF TERMS AND CONDITIONS - - APPENDIX: How to apply the Apache License to your work. - - To apply the Apache License to your work, attach the following - boilerplate notice, with the fields enclosed by brackets "[]" - replaced with your own identifying information. (Don't include - the brackets!) The text should be enclosed in the appropriate - comment syntax for the file format. We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright 2018-2022 BasicSR Authors - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. diff --git a/comfy_extras/chainner_models/architecture/LICENSE-SwiftSRGAN b/comfy_extras/chainner_models/architecture/LICENSE-SwiftSRGAN deleted file mode 100644 index 0e259d42..00000000 --- a/comfy_extras/chainner_models/architecture/LICENSE-SwiftSRGAN +++ /dev/null @@ -1,121 +0,0 @@ -Creative Commons Legal Code - -CC0 1.0 Universal - - CREATIVE COMMONS CORPORATION IS NOT A LAW FIRM AND DOES NOT PROVIDE - LEGAL SERVICES. DISTRIBUTION OF THIS DOCUMENT DOES NOT CREATE AN - ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES THIS - INFORMATION ON AN "AS-IS" BASIS. CREATIVE COMMONS MAKES NO WARRANTIES - REGARDING THE USE OF THIS DOCUMENT OR THE INFORMATION OR WORKS - PROVIDED HEREUNDER, AND DISCLAIMS LIABILITY FOR DAMAGES RESULTING FROM - THE USE OF THIS DOCUMENT OR THE INFORMATION OR WORKS PROVIDED - HEREUNDER. - -Statement of Purpose - -The laws of most jurisdictions throughout the world automatically confer -exclusive Copyright and Related Rights (defined below) upon the creator -and subsequent owner(s) (each and all, an "owner") of an original work of -authorship and/or a database (each, a "Work"). - -Certain owners wish to permanently relinquish those rights to a Work for -the purpose of contributing to a commons of creative, cultural and -scientific works ("Commons") that the public can reliably and without fear -of later claims of infringement build upon, modify, incorporate in other -works, reuse and redistribute as freely as possible in any form whatsoever -and for any purposes, including without limitation commercial purposes. -These owners may contribute to the Commons to promote the ideal of a free -culture and the further production of creative, cultural and scientific -works, or to gain reputation or greater distribution for their Work in -part through the use and efforts of others. - -For these and/or other purposes and motivations, and without any -expectation of additional consideration or compensation, the person -associating CC0 with a Work (the "Affirmer"), to the extent that he or she -is an owner of Copyright and Related Rights in the Work, voluntarily -elects to apply CC0 to the Work and publicly distribute the Work under its -terms, with knowledge of his or her Copyright and Related Rights in the -Work and the meaning and intended legal effect of CC0 on those rights. - -1. 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Should any part of the License for any -reason be judged legally invalid or ineffective under applicable law, such -partial invalidity or ineffectiveness shall not invalidate the remainder -of the License, and in such case Affirmer hereby affirms that he or she -will not (i) exercise any of his or her remaining Copyright and Related -Rights in the Work or (ii) assert any associated claims and causes of -action with respect to the Work, in either case contrary to Affirmer's -express Statement of Purpose. - -4. Limitations and Disclaimers. - - a. No trademark or patent rights held by Affirmer are waived, abandoned, - surrendered, licensed or otherwise affected by this document. - b. 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Affirmer understands and acknowledges that Creative Commons is not a - party to this document and has no duty or obligation with respect to - this CC0 or use of the Work. diff --git a/comfy_extras/chainner_models/architecture/LICENSE-Swin2SR b/comfy_extras/chainner_models/architecture/LICENSE-Swin2SR deleted file mode 100644 index e5e4ee06..00000000 --- a/comfy_extras/chainner_models/architecture/LICENSE-Swin2SR +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. Definitions. - - "License" shall mean the terms and conditions for use, reproduction, - and distribution as defined by Sections 1 through 9 of this document. - - "Licensor" shall mean the copyright owner or entity authorized by - the copyright owner that is granting the License. - - "Legal Entity" shall mean the union of the acting entity and all - other entities that control, are controlled by, or are under common - control with that entity. For the purposes of this definition, - "control" means (i) the power, direct or indirect, to cause the - direction or management of such entity, whether by contract or - otherwise, or (ii) ownership of fifty percent (50%) or more of the - outstanding shares, or (iii) beneficial ownership of such entity. - - "You" (or "Your") shall mean an individual or Legal Entity - exercising permissions granted by this License. - - "Source" form shall mean the preferred form for making modifications, - including but not limited to software source code, documentation - source, and configuration files. - - "Object" form shall mean any form resulting from mechanical - transformation or translation of a Source form, including but - not limited to compiled object code, generated documentation, - and conversions to other media types. - - "Work" shall mean the work of authorship, whether in Source or - Object form, made available under the License, as indicated by a - copyright notice that is included in or attached to the work - (an example is provided in the Appendix below). - - "Derivative Works" shall mean any work, whether in Source or Object - form, that is based on (or derived from) the Work and for which the - editorial revisions, annotations, elaborations, or other modifications - represent, as a whole, an original work of authorship. For the purposes - of this License, Derivative Works shall not include works that remain - separable from, or merely link (or bind by name) to the interfaces of, - the Work and Derivative Works thereof. - - "Contribution" shall mean any work of authorship, including - the original version of the Work and any modifications or additions - to that Work or Derivative Works thereof, that is intentionally - submitted to Licensor for inclusion in the Work by the copyright owner - or by an individual or Legal Entity authorized to submit on behalf of - the copyright owner. For the purposes of this definition, "submitted" - means any form of electronic, verbal, or written communication sent - to the Licensor or its representatives, including but not limited to - communication on electronic mailing lists, source code control systems, - and issue tracking systems that are managed by, or on behalf of, the - Licensor for the purpose of discussing and improving the Work, but - excluding communication that is conspicuously marked or otherwise - designated in writing by the copyright owner as "Not a Contribution." - - "Contributor" shall mean Licensor and any individual or Legal Entity - on behalf of whom a Contribution has been received by Licensor and - subsequently incorporated within the Work. - - 2. Grant of Copyright License. Subject to the terms and conditions of - this License, each Contributor hereby grants to You a perpetual, - worldwide, non-exclusive, no-charge, royalty-free, irrevocable - copyright license to reproduce, prepare Derivative Works of, - publicly display, publicly perform, sublicense, and distribute the - Work and such Derivative Works in Source or Object form. - - 3. Grant of Patent License. Subject to the terms and conditions of - this License, each Contributor hereby grants to You a perpetual, - worldwide, non-exclusive, no-charge, royalty-free, irrevocable - (except as stated in this section) patent license to make, have made, - use, offer to sell, sell, import, and otherwise transfer the Work, - where such license applies only to those patent claims licensable - by such Contributor that are necessarily infringed by their - Contribution(s) alone or by combination of their Contribution(s) - with the Work to which such Contribution(s) was submitted. If You - institute patent litigation against any entity (including a - cross-claim or counterclaim in a lawsuit) alleging that the Work - or a Contribution incorporated within the Work constitutes direct - or contributory patent infringement, then any patent licenses - granted to You under this License for that Work shall terminate - as of the date such litigation is filed. - - 4. Redistribution. You may reproduce and distribute copies of the - Work or Derivative Works thereof in any medium, with or without - modifications, and in Source or Object form, provided that You - meet the following conditions: - - (a) You must give any other recipients of the Work or - Derivative Works a copy of this License; and - - (b) You must cause any modified files to carry prominent notices - stating that You changed the files; and - - (c) You must retain, in the Source form of any Derivative Works - that You distribute, all copyright, patent, trademark, and - attribution notices from the Source form of the Work, - excluding those notices that do not pertain to any part of - the Derivative Works; and - - (d) If the Work includes a "NOTICE" text file as part of its - distribution, then any Derivative Works that You distribute must - include a readable copy of the attribution notices contained - within such NOTICE file, excluding those notices that do not - pertain to any part of the Derivative Works, in at least one - of the following places: within a NOTICE text file distributed - as part of the Derivative Works; within the Source form or - documentation, if provided along with the Derivative Works; or, - within a display generated by the Derivative Works, if and - wherever such third-party notices normally appear. The contents - of the NOTICE file are for informational purposes only and - do not modify the License. You may add Your own attribution - notices within Derivative Works that You distribute, alongside - or as an addendum to the NOTICE text from the Work, provided - that such additional attribution notices cannot be construed - as modifying the License. - - You may add Your own copyright statement to Your modifications and - may provide additional or different license terms and conditions - for use, reproduction, or distribution of Your modifications, or - for any such Derivative Works as a whole, provided Your use, - reproduction, and distribution of the Work otherwise complies with - the conditions stated in this License. - - 5. Submission of Contributions. Unless You explicitly state otherwise, - any Contribution intentionally submitted for inclusion in the Work - by You to the Licensor shall be under the terms and conditions of - this License, without any additional terms or conditions. - Notwithstanding the above, nothing herein shall supersede or modify - the terms of any separate license agreement you may have executed - with Licensor regarding such Contributions. - - 6. Trademarks. This License does not grant permission to use the trade - names, trademarks, service marks, or product names of the Licensor, - except as required for reasonable and customary use in describing the - origin of the Work and reproducing the content of the NOTICE file. - - 7. Disclaimer of Warranty. Unless required by applicable law or - agreed to in writing, Licensor provides the Work (and each - Contributor provides its Contributions) on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or - implied, including, without limitation, any warranties or conditions - of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A - PARTICULAR PURPOSE. You are solely responsible for determining the - appropriateness of using or redistributing the Work and assume any - risks associated with Your exercise of permissions under this License. - - 8. Limitation of Liability. In no event and under no legal theory, - whether in tort (including negligence), contract, or otherwise, - unless required by applicable law (such as deliberate and grossly - negligent acts) or agreed to in writing, shall any Contributor be - liable to You for damages, including any direct, indirect, special, - incidental, or consequential damages of any character arising as a - result of this License or out of the use or inability to use the - Work (including but not limited to damages for loss of goodwill, - work stoppage, computer failure or malfunction, or any and all - other commercial damages or losses), even if such Contributor - has been advised of the possibility of such damages. - - 9. Accepting Warranty or Additional Liability. While redistributing - the Work or Derivative Works thereof, You may choose to offer, - and charge a fee for, acceptance of support, warranty, indemnity, - or other liability obligations and/or rights consistent with this - License. However, in accepting such obligations, You may act only - on Your own behalf and on Your sole responsibility, not on behalf - of any other Contributor, and only if You agree to indemnify, - defend, and hold each Contributor harmless for any liability - incurred by, or claims asserted against, such Contributor by reason - of your accepting any such warranty or additional liability. - - END OF TERMS AND CONDITIONS - - APPENDIX: How to apply the Apache License to your work. - - To apply the Apache License to your work, attach the following - boilerplate notice, with the fields enclosed by brackets "[]" - replaced with your own identifying information. (Don't include - the brackets!) The text should be enclosed in the appropriate - comment syntax for the file format. We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright [2021] [SwinIR Authors] - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. diff --git a/comfy_extras/chainner_models/architecture/LICENSE-SwinIR b/comfy_extras/chainner_models/architecture/LICENSE-SwinIR deleted file mode 100644 index e5e4ee06..00000000 --- a/comfy_extras/chainner_models/architecture/LICENSE-SwinIR +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. Definitions. - - "License" shall mean the terms and conditions for use, reproduction, - and distribution as defined by Sections 1 through 9 of this document. - - "Licensor" shall mean the copyright owner or entity authorized by - the copyright owner that is granting the License. - - "Legal Entity" shall mean the union of the acting entity and all - other entities that control, are controlled by, or are under common - control with that entity. For the purposes of this definition, - "control" means (i) the power, direct or indirect, to cause the - direction or management of such entity, whether by contract or - otherwise, or (ii) ownership of fifty percent (50%) or more of the - outstanding shares, or (iii) beneficial ownership of such entity. - - "You" (or "Your") shall mean an individual or Legal Entity - exercising permissions granted by this License. - - "Source" form shall mean the preferred form for making modifications, - including but not limited to software source code, documentation - source, and configuration files. - - "Object" form shall mean any form resulting from mechanical - transformation or translation of a Source form, including but - not limited to compiled object code, generated documentation, - and conversions to other media types. - - "Work" shall mean the work of authorship, whether in Source or - Object form, made available under the License, as indicated by a - copyright notice that is included in or attached to the work - (an example is provided in the Appendix below). - - "Derivative Works" shall mean any work, whether in Source or Object - form, that is based on (or derived from) the Work and for which the - editorial revisions, annotations, elaborations, or other modifications - represent, as a whole, an original work of authorship. For the purposes - of this License, Derivative Works shall not include works that remain - separable from, or merely link (or bind by name) to the interfaces of, - the Work and Derivative Works thereof. - - "Contribution" shall mean any work of authorship, including - the original version of the Work and any modifications or additions - to that Work or Derivative Works thereof, that is intentionally - submitted to Licensor for inclusion in the Work by the copyright owner - or by an individual or Legal Entity authorized to submit on behalf of - the copyright owner. For the purposes of this definition, "submitted" - means any form of electronic, verbal, or written communication sent - to the Licensor or its representatives, including but not limited to - communication on electronic mailing lists, source code control systems, - and issue tracking systems that are managed by, or on behalf of, the - Licensor for the purpose of discussing and improving the Work, but - excluding communication that is conspicuously marked or otherwise - designated in writing by the copyright owner as "Not a Contribution." - - "Contributor" shall mean Licensor and any individual or Legal Entity - on behalf of whom a Contribution has been received by Licensor and - subsequently incorporated within the Work. - - 2. Grant of Copyright License. Subject to the terms and conditions of - this License, each Contributor hereby grants to You a perpetual, - worldwide, non-exclusive, no-charge, royalty-free, irrevocable - copyright license to reproduce, prepare Derivative Works of, - publicly display, publicly perform, sublicense, and distribute the - Work and such Derivative Works in Source or Object form. - - 3. Grant of Patent License. Subject to the terms and conditions of - this License, each Contributor hereby grants to You a perpetual, - worldwide, non-exclusive, no-charge, royalty-free, irrevocable - (except as stated in this section) patent license to make, have made, - use, offer to sell, sell, import, and otherwise transfer the Work, - where such license applies only to those patent claims licensable - by such Contributor that are necessarily infringed by their - Contribution(s) alone or by combination of their Contribution(s) - with the Work to which such Contribution(s) was submitted. If You - institute patent litigation against any entity (including a - cross-claim or counterclaim in a lawsuit) alleging that the Work - or a Contribution incorporated within the Work constitutes direct - or contributory patent infringement, then any patent licenses - granted to You under this License for that Work shall terminate - as of the date such litigation is filed. - - 4. Redistribution. You may reproduce and distribute copies of the - Work or Derivative Works thereof in any medium, with or without - modifications, and in Source or Object form, provided that You - meet the following conditions: - - (a) You must give any other recipients of the Work or - Derivative Works a copy of this License; and - - (b) You must cause any modified files to carry prominent notices - stating that You changed the files; and - - (c) You must retain, in the Source form of any Derivative Works - that You distribute, all copyright, patent, trademark, and - attribution notices from the Source form of the Work, - excluding those notices that do not pertain to any part of - the Derivative Works; and - - (d) If the Work includes a "NOTICE" text file as part of its - distribution, then any Derivative Works that You distribute must - include a readable copy of the attribution notices contained - within such NOTICE file, excluding those notices that do not - pertain to any part of the Derivative Works, in at least one - of the following places: within a NOTICE text file distributed - as part of the Derivative Works; within the Source form or - documentation, if provided along with the Derivative Works; or, - within a display generated by the Derivative Works, if and - wherever such third-party notices normally appear. The contents - of the NOTICE file are for informational purposes only and - do not modify the License. You may add Your own attribution - notices within Derivative Works that You distribute, alongside - or as an addendum to the NOTICE text from the Work, provided - that such additional attribution notices cannot be construed - as modifying the License. - - You may add Your own copyright statement to Your modifications and - may provide additional or different license terms and conditions - for use, reproduction, or distribution of Your modifications, or - for any such Derivative Works as a whole, provided Your use, - reproduction, and distribution of the Work otherwise complies with - the conditions stated in this License. - - 5. Submission of Contributions. Unless You explicitly state otherwise, - any Contribution intentionally submitted for inclusion in the Work - by You to the Licensor shall be under the terms and conditions of - this License, without any additional terms or conditions. - Notwithstanding the above, nothing herein shall supersede or modify - the terms of any separate license agreement you may have executed - with Licensor regarding such Contributions. - - 6. Trademarks. This License does not grant permission to use the trade - names, trademarks, service marks, or product names of the Licensor, - except as required for reasonable and customary use in describing the - origin of the Work and reproducing the content of the NOTICE file. - - 7. Disclaimer of Warranty. Unless required by applicable law or - agreed to in writing, Licensor provides the Work (and each - Contributor provides its Contributions) on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or - implied, including, without limitation, any warranties or conditions - of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A - PARTICULAR PURPOSE. You are solely responsible for determining the - appropriateness of using or redistributing the Work and assume any - risks associated with Your exercise of permissions under this License. - - 8. Limitation of Liability. In no event and under no legal theory, - whether in tort (including negligence), contract, or otherwise, - unless required by applicable law (such as deliberate and grossly - negligent acts) or agreed to in writing, shall any Contributor be - liable to You for damages, including any direct, indirect, special, - incidental, or consequential damages of any character arising as a - result of this License or out of the use or inability to use the - Work (including but not limited to damages for loss of goodwill, - work stoppage, computer failure or malfunction, or any and all - other commercial damages or losses), even if such Contributor - has been advised of the possibility of such damages. - - 9. Accepting Warranty or Additional Liability. While redistributing - the Work or Derivative Works thereof, You may choose to offer, - and charge a fee for, acceptance of support, warranty, indemnity, - or other liability obligations and/or rights consistent with this - License. However, in accepting such obligations, You may act only - on Your own behalf and on Your sole responsibility, not on behalf - of any other Contributor, and only if You agree to indemnify, - defend, and hold each Contributor harmless for any liability - incurred by, or claims asserted against, such Contributor by reason - of your accepting any such warranty or additional liability. - - END OF TERMS AND CONDITIONS - - APPENDIX: How to apply the Apache License to your work. - - To apply the Apache License to your work, attach the following - boilerplate notice, with the fields enclosed by brackets "[]" - replaced with your own identifying information. (Don't include - the brackets!) The text should be enclosed in the appropriate - comment syntax for the file format. We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright [2021] [SwinIR Authors] - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. diff --git a/comfy_extras/chainner_models/architecture/LICENSE-lama b/comfy_extras/chainner_models/architecture/LICENSE-lama deleted file mode 100644 index ca822bb5..00000000 --- a/comfy_extras/chainner_models/architecture/LICENSE-lama +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. Definitions. - - "License" shall mean the terms and conditions for use, reproduction, - and distribution as defined by Sections 1 through 9 of this document. - - "Licensor" shall mean the copyright owner or entity authorized by - the copyright owner that is granting the License. - - "Legal Entity" shall mean the union of the acting entity and all - other entities that control, are controlled by, or are under common - control with that entity. For the purposes of this definition, - "control" means (i) the power, direct or indirect, to cause the - direction or management of such entity, whether by contract or - otherwise, or (ii) ownership of fifty percent (50%) or more of the - outstanding shares, or (iii) beneficial ownership of such entity. - - "You" (or "Your") shall mean an individual or Legal Entity - exercising permissions granted by this License. - - "Source" form shall mean the preferred form for making modifications, - including but not limited to software source code, documentation - source, and configuration files. - - "Object" form shall mean any form resulting from mechanical - transformation or translation of a Source form, including but - not limited to compiled object code, generated documentation, - and conversions to other media types. - - "Work" shall mean the work of authorship, whether in Source or - Object form, made available under the License, as indicated by a - copyright notice that is included in or attached to the work - (an example is provided in the Appendix below). - - "Derivative Works" shall mean any work, whether in Source or Object - form, that is based on (or derived from) the Work and for which the - editorial revisions, annotations, elaborations, or other modifications - represent, as a whole, an original work of authorship. For the purposes - of this License, Derivative Works shall not include works that remain - separable from, or merely link (or bind by name) to the interfaces of, - the Work and Derivative Works thereof. - - "Contribution" shall mean any work of authorship, including - the original version of the Work and any modifications or additions - to that Work or Derivative Works thereof, that is intentionally - submitted to Licensor for inclusion in the Work by the copyright owner - or by an individual or Legal Entity authorized to submit on behalf of - the copyright owner. For the purposes of this definition, "submitted" - means any form of electronic, verbal, or written communication sent - to the Licensor or its representatives, including but not limited to - communication on electronic mailing lists, source code control systems, - and issue tracking systems that are managed by, or on behalf of, the - Licensor for the purpose of discussing and improving the Work, but - excluding communication that is conspicuously marked or otherwise - designated in writing by the copyright owner as "Not a Contribution." - - "Contributor" shall mean Licensor and any individual or Legal Entity - on behalf of whom a Contribution has been received by Licensor and - subsequently incorporated within the Work. - - 2. Grant of Copyright License. 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-# Fast Fourier Convolution NeurIPS 2020 -# original implementation https://github.com/pkumivision/FFC/blob/main/model_zoo/ffc.py -# paper https://proceedings.neurips.cc/paper/2020/file/2fd5d41ec6cfab47e32164d5624269b1-Paper.pdf - -from typing import List - -import torch -import torch.nn as nn -import torch.nn.functional as F -from torchvision.transforms.functional import InterpolationMode, rotate - - -class LearnableSpatialTransformWrapper(nn.Module): - def __init__(self, impl, pad_coef=0.5, angle_init_range=80, train_angle=True): - super().__init__() - self.impl = impl - self.angle = torch.rand(1) * angle_init_range - if train_angle: - self.angle = nn.Parameter(self.angle, requires_grad=True) - self.pad_coef = pad_coef - - def forward(self, x): - if torch.is_tensor(x): - return self.inverse_transform(self.impl(self.transform(x)), x) - elif isinstance(x, tuple): - x_trans = tuple(self.transform(elem) for elem in x) - y_trans = self.impl(x_trans) - return tuple( - self.inverse_transform(elem, orig_x) for elem, orig_x in zip(y_trans, x) - ) - else: - raise ValueError(f"Unexpected input type {type(x)}") - - def transform(self, x): - height, width = x.shape[2:] - pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef) - x_padded = F.pad(x, [pad_w, pad_w, pad_h, pad_h], mode="reflect") - x_padded_rotated = rotate( - x_padded, self.angle.to(x_padded), InterpolationMode.BILINEAR, fill=0 - ) - - return x_padded_rotated - - def inverse_transform(self, y_padded_rotated, orig_x): - height, width = orig_x.shape[2:] - pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef) - - y_padded = rotate( - y_padded_rotated, - -self.angle.to(y_padded_rotated), - InterpolationMode.BILINEAR, - fill=0, - ) - y_height, y_width = y_padded.shape[2:] - y = y_padded[:, :, pad_h : y_height - pad_h, pad_w : y_width - pad_w] - return y - - -class SELayer(nn.Module): - def __init__(self, channel, reduction=16): - super(SELayer, self).__init__() - self.avg_pool = nn.AdaptiveAvgPool2d(1) - self.fc = nn.Sequential( - nn.Linear(channel, channel // reduction, bias=False), - nn.ReLU(inplace=True), - nn.Linear(channel // reduction, channel, bias=False), - nn.Sigmoid(), - ) - - def forward(self, x): - b, c, _, _ = x.size() - y = self.avg_pool(x).view(b, c) - y = self.fc(y).view(b, c, 1, 1) - res = x * y.expand_as(x) - return res - - -class FourierUnit(nn.Module): - def __init__( - self, - in_channels, - out_channels, - groups=1, - spatial_scale_factor=None, - spatial_scale_mode="bilinear", - spectral_pos_encoding=False, - use_se=False, - se_kwargs=None, - ffc3d=False, - fft_norm="ortho", - ): - # bn_layer not used - super(FourierUnit, self).__init__() - self.groups = groups - - self.conv_layer = torch.nn.Conv2d( - in_channels=in_channels * 2 + (2 if spectral_pos_encoding else 0), - out_channels=out_channels * 2, - kernel_size=1, - stride=1, - padding=0, - groups=self.groups, - bias=False, - ) - self.bn = torch.nn.BatchNorm2d(out_channels * 2) - self.relu = torch.nn.ReLU(inplace=True) - - # squeeze and excitation block - self.use_se = use_se - if use_se: - if se_kwargs is None: - se_kwargs = {} - self.se = SELayer(self.conv_layer.in_channels, **se_kwargs) - - self.spatial_scale_factor = spatial_scale_factor - self.spatial_scale_mode = spatial_scale_mode - self.spectral_pos_encoding = spectral_pos_encoding - self.ffc3d = ffc3d - self.fft_norm = fft_norm - - def forward(self, x): - half_check = False - if x.type() == "torch.cuda.HalfTensor": - # half only works on gpu anyway - half_check = True - - batch = x.shape[0] - - if self.spatial_scale_factor is not None: - orig_size = x.shape[-2:] - x = F.interpolate( - x, - scale_factor=self.spatial_scale_factor, - mode=self.spatial_scale_mode, - align_corners=False, - ) - - # (batch, c, h, w/2+1, 2) - fft_dim = (-3, -2, -1) if self.ffc3d else (-2, -1) - if half_check == True: - ffted = torch.fft.rfftn( - x.float(), dim=fft_dim, norm=self.fft_norm - ) # .type(torch.cuda.HalfTensor) - else: - ffted = torch.fft.rfftn(x, dim=fft_dim, norm=self.fft_norm) - - ffted = torch.stack((ffted.real, ffted.imag), dim=-1) - ffted = ffted.permute(0, 1, 4, 2, 3).contiguous() # (batch, c, 2, h, w/2+1) - ffted = ffted.view( - ( - batch, - -1, - ) - + ffted.size()[3:] - ) - - if self.spectral_pos_encoding: - height, width = ffted.shape[-2:] - coords_vert = ( - torch.linspace(0, 1, height)[None, None, :, None] - .expand(batch, 1, height, width) - .to(ffted) - ) - coords_hor = ( - torch.linspace(0, 1, width)[None, None, None, :] - .expand(batch, 1, height, width) - .to(ffted) - ) - ffted = torch.cat((coords_vert, coords_hor, ffted), dim=1) - - if self.use_se: - ffted = self.se(ffted) - - if half_check == True: - ffted = self.conv_layer(ffted.half()) # (batch, c*2, h, w/2+1) - else: - ffted = self.conv_layer( - ffted - ) # .type(torch.cuda.FloatTensor) # (batch, c*2, h, w/2+1) - - ffted = self.relu(self.bn(ffted)) - # forcing to be always float - ffted = ffted.float() - - ffted = ( - ffted.view( - ( - batch, - -1, - 2, - ) - + ffted.size()[2:] - ) - .permute(0, 1, 3, 4, 2) - .contiguous() - ) # (batch,c, t, h, w/2+1, 2) - - ffted = torch.complex(ffted[..., 0], ffted[..., 1]) - - ifft_shape_slice = x.shape[-3:] if self.ffc3d else x.shape[-2:] - output = torch.fft.irfftn( - ffted, s=ifft_shape_slice, dim=fft_dim, norm=self.fft_norm - ) - - if half_check == True: - output = output.half() - - if self.spatial_scale_factor is not None: - output = F.interpolate( - output, - size=orig_size, - mode=self.spatial_scale_mode, - align_corners=False, - ) - - return output - - -class SpectralTransform(nn.Module): - def __init__( - self, - in_channels, - out_channels, - stride=1, - groups=1, - enable_lfu=True, - separable_fu=False, - **fu_kwargs, - ): - # bn_layer not used - super(SpectralTransform, self).__init__() - self.enable_lfu = enable_lfu - if stride == 2: - self.downsample = nn.AvgPool2d(kernel_size=(2, 2), stride=2) - else: - self.downsample = nn.Identity() - - self.stride = stride - self.conv1 = nn.Sequential( - nn.Conv2d( - in_channels, out_channels // 2, kernel_size=1, groups=groups, bias=False - ), - nn.BatchNorm2d(out_channels // 2), - nn.ReLU(inplace=True), - ) - fu_class = FourierUnit - self.fu = fu_class(out_channels // 2, out_channels // 2, groups, **fu_kwargs) - if self.enable_lfu: - self.lfu = fu_class(out_channels // 2, out_channels // 2, groups) - self.conv2 = torch.nn.Conv2d( - out_channels // 2, out_channels, kernel_size=1, groups=groups, bias=False - ) - - def forward(self, x): - x = self.downsample(x) - x = self.conv1(x) - output = self.fu(x) - - if self.enable_lfu: - _, c, h, _ = x.shape - split_no = 2 - split_s = h // split_no - xs = torch.cat( - torch.split(x[:, : c // 4], split_s, dim=-2), dim=1 - ).contiguous() - xs = torch.cat(torch.split(xs, split_s, dim=-1), dim=1).contiguous() - xs = self.lfu(xs) - xs = xs.repeat(1, 1, split_no, split_no).contiguous() - else: - xs = 0 - - output = self.conv2(x + output + xs) - - return output - - -class FFC(nn.Module): - def __init__( - self, - in_channels, - out_channels, - kernel_size, - ratio_gin, - ratio_gout, - stride=1, - padding=0, - dilation=1, - groups=1, - bias=False, - enable_lfu=True, - padding_type="reflect", - gated=False, - **spectral_kwargs, - ): - super(FFC, self).__init__() - - assert stride == 1 or stride == 2, "Stride should be 1 or 2." - self.stride = stride - - in_cg = int(in_channels * ratio_gin) - in_cl = in_channels - in_cg - out_cg = int(out_channels * ratio_gout) - out_cl = out_channels - out_cg - # groups_g = 1 if groups == 1 else int(groups * ratio_gout) - # groups_l = 1 if groups == 1 else groups - groups_g - - self.ratio_gin = ratio_gin - self.ratio_gout = ratio_gout - self.global_in_num = in_cg - - module = nn.Identity if in_cl == 0 or out_cl == 0 else nn.Conv2d - self.convl2l = module( - in_cl, - out_cl, - kernel_size, - stride, - padding, - dilation, - groups, - bias, - padding_mode=padding_type, - ) - module = nn.Identity if in_cl == 0 or out_cg == 0 else nn.Conv2d - self.convl2g = module( - in_cl, - out_cg, - kernel_size, - stride, - padding, - dilation, - groups, - bias, - padding_mode=padding_type, - ) - module = nn.Identity if in_cg == 0 or out_cl == 0 else nn.Conv2d - self.convg2l = module( - in_cg, - out_cl, - kernel_size, - stride, - padding, - dilation, - groups, - bias, - padding_mode=padding_type, - ) - module = nn.Identity if in_cg == 0 or out_cg == 0 else SpectralTransform - self.convg2g = module( - in_cg, - out_cg, - stride, - 1 if groups == 1 else groups // 2, - enable_lfu, - **spectral_kwargs, - ) - - self.gated = gated - module = ( - nn.Identity if in_cg == 0 or out_cl == 0 or not self.gated else nn.Conv2d - ) - self.gate = module(in_channels, 2, 1) - - def forward(self, x): - x_l, x_g = x if type(x) is tuple else (x, 0) - out_xl, out_xg = 0, 0 - - if self.gated: - total_input_parts = [x_l] - if torch.is_tensor(x_g): - total_input_parts.append(x_g) - total_input = torch.cat(total_input_parts, dim=1) - - gates = torch.sigmoid(self.gate(total_input)) - g2l_gate, l2g_gate = gates.chunk(2, dim=1) - else: - g2l_gate, l2g_gate = 1, 1 - - if self.ratio_gout != 1: - out_xl = self.convl2l(x_l) + self.convg2l(x_g) * g2l_gate - if self.ratio_gout != 0: - out_xg = self.convl2g(x_l) * l2g_gate + self.convg2g(x_g) - - return out_xl, out_xg - - -class FFC_BN_ACT(nn.Module): - def __init__( - self, - in_channels, - out_channels, - kernel_size, - ratio_gin, - ratio_gout, - stride=1, - padding=0, - dilation=1, - groups=1, - bias=False, - norm_layer=nn.BatchNorm2d, - activation_layer=nn.Identity, - padding_type="reflect", - enable_lfu=True, - **kwargs, - ): - super(FFC_BN_ACT, self).__init__() - self.ffc = FFC( - in_channels, - out_channels, - kernel_size, - ratio_gin, - ratio_gout, - stride, - padding, - dilation, - groups, - bias, - enable_lfu, - padding_type=padding_type, - **kwargs, - ) - lnorm = nn.Identity if ratio_gout == 1 else norm_layer - gnorm = nn.Identity if ratio_gout == 0 else norm_layer - global_channels = int(out_channels * ratio_gout) - self.bn_l = lnorm(out_channels - global_channels) - self.bn_g = gnorm(global_channels) - - lact = nn.Identity if ratio_gout == 1 else activation_layer - gact = nn.Identity if ratio_gout == 0 else activation_layer - self.act_l = lact(inplace=True) - self.act_g = gact(inplace=True) - - def forward(self, x): - x_l, x_g = self.ffc(x) - x_l = self.act_l(self.bn_l(x_l)) - x_g = self.act_g(self.bn_g(x_g)) - return x_l, x_g - - -class FFCResnetBlock(nn.Module): - def __init__( - self, - dim, - padding_type, - norm_layer, - activation_layer=nn.ReLU, - dilation=1, - spatial_transform_kwargs=None, - inline=False, - **conv_kwargs, - ): - super().__init__() - self.conv1 = FFC_BN_ACT( - dim, - dim, - kernel_size=3, - padding=dilation, - dilation=dilation, - norm_layer=norm_layer, - activation_layer=activation_layer, - padding_type=padding_type, - **conv_kwargs, - ) - self.conv2 = FFC_BN_ACT( - dim, - dim, - kernel_size=3, - padding=dilation, - dilation=dilation, - norm_layer=norm_layer, - activation_layer=activation_layer, - padding_type=padding_type, - **conv_kwargs, - ) - if spatial_transform_kwargs is not None: - self.conv1 = LearnableSpatialTransformWrapper( - self.conv1, **spatial_transform_kwargs - ) - self.conv2 = LearnableSpatialTransformWrapper( - self.conv2, **spatial_transform_kwargs - ) - self.inline = inline - - def forward(self, x): - if self.inline: - x_l, x_g = ( - x[:, : -self.conv1.ffc.global_in_num], - x[:, -self.conv1.ffc.global_in_num :], - ) - else: - x_l, x_g = x if type(x) is tuple else (x, 0) - - id_l, id_g = x_l, x_g - - x_l, x_g = self.conv1((x_l, x_g)) - x_l, x_g = self.conv2((x_l, x_g)) - - x_l, x_g = id_l + x_l, id_g + x_g - out = x_l, x_g - if self.inline: - out = torch.cat(out, dim=1) - return out - - -class ConcatTupleLayer(nn.Module): - def forward(self, x): - assert isinstance(x, tuple) - x_l, x_g = x - assert torch.is_tensor(x_l) or torch.is_tensor(x_g) - if not torch.is_tensor(x_g): - return x_l - return torch.cat(x, dim=1) - - -class FFCResNetGenerator(nn.Module): - def __init__( - self, - input_nc, - output_nc, - ngf=64, - n_downsampling=3, - n_blocks=18, - norm_layer=nn.BatchNorm2d, - padding_type="reflect", - activation_layer=nn.ReLU, - up_norm_layer=nn.BatchNorm2d, - up_activation=nn.ReLU(True), - init_conv_kwargs={}, - downsample_conv_kwargs={}, - resnet_conv_kwargs={}, - spatial_transform_layers=None, - spatial_transform_kwargs={}, - max_features=1024, - out_ffc=False, - out_ffc_kwargs={}, - ): - assert n_blocks >= 0 - super().__init__() - """ - init_conv_kwargs = {'ratio_gin': 0, 'ratio_gout': 0, 'enable_lfu': False} - downsample_conv_kwargs = {'ratio_gin': '${generator.init_conv_kwargs.ratio_gout}', 'ratio_gout': '${generator.downsample_conv_kwargs.ratio_gin}', 'enable_lfu': False} - resnet_conv_kwargs = {'ratio_gin': 0.75, 'ratio_gout': '${generator.resnet_conv_kwargs.ratio_gin}', 'enable_lfu': False} - spatial_transform_kwargs = {} - out_ffc_kwargs = {} - """ - """ - print(input_nc, output_nc, ngf, n_downsampling, n_blocks, norm_layer, - padding_type, activation_layer, - up_norm_layer, up_activation, - spatial_transform_layers, - add_out_act, max_features, out_ffc, file=sys.stderr) - - 4 3 64 3 18 - reflect - - ReLU(inplace=True) - None sigmoid 1024 False - """ - init_conv_kwargs = {"ratio_gin": 0, "ratio_gout": 0, "enable_lfu": False} - downsample_conv_kwargs = {"ratio_gin": 0, "ratio_gout": 0, "enable_lfu": False} - resnet_conv_kwargs = { - "ratio_gin": 0.75, - "ratio_gout": 0.75, - "enable_lfu": False, - } - spatial_transform_kwargs = {} - out_ffc_kwargs = {} - - model = [ - nn.ReflectionPad2d(3), - FFC_BN_ACT( - input_nc, - ngf, - kernel_size=7, - padding=0, - norm_layer=norm_layer, - activation_layer=activation_layer, - **init_conv_kwargs, - ), - ] - - ### downsample - for i in range(n_downsampling): - mult = 2**i - if i == n_downsampling - 1: - cur_conv_kwargs = dict(downsample_conv_kwargs) - cur_conv_kwargs["ratio_gout"] = resnet_conv_kwargs.get("ratio_gin", 0) - else: - cur_conv_kwargs = downsample_conv_kwargs - model += [ - FFC_BN_ACT( - min(max_features, ngf * mult), - min(max_features, ngf * mult * 2), - kernel_size=3, - stride=2, - padding=1, - norm_layer=norm_layer, - activation_layer=activation_layer, - **cur_conv_kwargs, - ) - ] - - mult = 2**n_downsampling - feats_num_bottleneck = min(max_features, ngf * mult) - - ### resnet blocks - for i in range(n_blocks): - cur_resblock = FFCResnetBlock( - feats_num_bottleneck, - padding_type=padding_type, - activation_layer=activation_layer, - norm_layer=norm_layer, - **resnet_conv_kwargs, - ) - if spatial_transform_layers is not None and i in spatial_transform_layers: - cur_resblock = LearnableSpatialTransformWrapper( - cur_resblock, **spatial_transform_kwargs - ) - model += [cur_resblock] - - model += [ConcatTupleLayer()] - - ### upsample - for i in range(n_downsampling): - mult = 2 ** (n_downsampling - i) - model += [ - nn.ConvTranspose2d( - min(max_features, ngf * mult), - min(max_features, int(ngf * mult / 2)), - kernel_size=3, - stride=2, - padding=1, - output_padding=1, - ), - up_norm_layer(min(max_features, int(ngf * mult / 2))), - up_activation, - ] - - if out_ffc: - model += [ - FFCResnetBlock( - ngf, - padding_type=padding_type, - activation_layer=activation_layer, - norm_layer=norm_layer, - inline=True, - **out_ffc_kwargs, - ) - ] - - model += [ - nn.ReflectionPad2d(3), - nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), - ] - model.append(nn.Sigmoid()) - self.model = nn.Sequential(*model) - - def forward(self, image, mask): - return self.model(torch.cat([image, mask], dim=1)) - - -class LaMa(nn.Module): - def __init__(self, state_dict) -> None: - super(LaMa, self).__init__() - self.model_arch = "LaMa" - self.sub_type = "Inpaint" - self.in_nc = 4 - self.out_nc = 3 - self.scale = 1 - - self.min_size = None - self.pad_mod = 8 - self.pad_to_square = False - - self.model = FFCResNetGenerator(self.in_nc, self.out_nc) - self.state = { - k.replace("generator.model", "model.model"): v - for k, v in state_dict.items() - } - - self.supports_fp16 = False - self.support_bf16 = True - - self.load_state_dict(self.state, strict=False) - - def forward(self, img, mask): - masked_img = img * (1 - mask) - inpainted_mask = mask * self.model.forward(masked_img, mask) - result = inpainted_mask + (1 - mask) * img - return result diff --git a/comfy_extras/chainner_models/architecture/OmniSR/ChannelAttention.py b/comfy_extras/chainner_models/architecture/OmniSR/ChannelAttention.py deleted file mode 100644 index f4d52aa1..00000000 --- a/comfy_extras/chainner_models/architecture/OmniSR/ChannelAttention.py +++ /dev/null @@ -1,110 +0,0 @@ -import math - -import torch.nn as nn - - -class CA_layer(nn.Module): - def __init__(self, channel, reduction=16): - super(CA_layer, self).__init__() - # global average pooling - self.gap = nn.AdaptiveAvgPool2d(1) - self.fc = nn.Sequential( - nn.Conv2d(channel, channel // reduction, kernel_size=(1, 1), bias=False), - nn.GELU(), - nn.Conv2d(channel // reduction, channel, kernel_size=(1, 1), bias=False), - # nn.Sigmoid() - ) - - def forward(self, x): - y = self.fc(self.gap(x)) - return x * y.expand_as(x) - - -class Simple_CA_layer(nn.Module): - def __init__(self, channel): - super(Simple_CA_layer, self).__init__() - self.gap = nn.AdaptiveAvgPool2d(1) - self.fc = nn.Conv2d( - in_channels=channel, - out_channels=channel, - kernel_size=1, - padding=0, - stride=1, - groups=1, - bias=True, - ) - - def forward(self, x): - return x * self.fc(self.gap(x)) - - -class ECA_layer(nn.Module): - """Constructs a ECA module. - Args: - channel: Number of channels of the input feature map - k_size: Adaptive selection of kernel size - """ - - def __init__(self, channel): - super(ECA_layer, self).__init__() - - b = 1 - gamma = 2 - k_size = int(abs(math.log(channel, 2) + b) / gamma) - k_size = k_size if k_size % 2 else k_size + 1 - self.avg_pool = nn.AdaptiveAvgPool2d(1) - self.conv = nn.Conv1d( - 1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False - ) - # self.sigmoid = nn.Sigmoid() - - def forward(self, x): - # x: input features with shape [b, c, h, w] - # b, c, h, w = x.size() - - # feature descriptor on the global spatial information - y = self.avg_pool(x) - - # Two different branches of ECA module - y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1) - - # Multi-scale information fusion - # y = self.sigmoid(y) - - return x * y.expand_as(x) - - -class ECA_MaxPool_layer(nn.Module): - """Constructs a ECA module. - Args: - channel: Number of channels of the input feature map - k_size: Adaptive selection of kernel size - """ - - def __init__(self, channel): - super(ECA_MaxPool_layer, self).__init__() - - b = 1 - gamma = 2 - k_size = int(abs(math.log(channel, 2) + b) / gamma) - k_size = k_size if k_size % 2 else k_size + 1 - self.max_pool = nn.AdaptiveMaxPool2d(1) - self.conv = nn.Conv1d( - 1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False - ) - # self.sigmoid = nn.Sigmoid() - - def forward(self, x): - # x: input features with shape [b, c, h, w] - # b, c, h, w = x.size() - - # feature descriptor on the global spatial information - y = self.max_pool(x) - - # Two different branches of ECA module - y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1) - - # Multi-scale information fusion - # y = self.sigmoid(y) - - return x * y.expand_as(x) diff --git a/comfy_extras/chainner_models/architecture/OmniSR/LICENSE b/comfy_extras/chainner_models/architecture/OmniSR/LICENSE deleted file mode 100644 index 261eeb9e..00000000 --- a/comfy_extras/chainner_models/architecture/OmniSR/LICENSE +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - 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-import torch -import torch.nn.functional as F -from einops import rearrange, repeat -from einops.layers.torch import Rearrange, Reduce -from torch import einsum, nn - -from .layernorm import LayerNorm2d - -# helpers - - -def exists(val): - return val is not None - - -def default(val, d): - return val if exists(val) else d - - -def cast_tuple(val, length=1): - return val if isinstance(val, tuple) else ((val,) * length) - - -# helper classes - - -class PreNormResidual(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.norm = nn.LayerNorm(dim) - self.fn = fn - - def forward(self, x): - return self.fn(self.norm(x)) + x - - -class Conv_PreNormResidual(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.norm = LayerNorm2d(dim) - self.fn = fn - - def forward(self, x): - return self.fn(self.norm(x)) + x - - -class FeedForward(nn.Module): - def __init__(self, dim, mult=2, dropout=0.0): - super().__init__() - inner_dim = int(dim * mult) - self.net = nn.Sequential( - nn.Linear(dim, inner_dim), - nn.GELU(), - nn.Dropout(dropout), - nn.Linear(inner_dim, dim), - nn.Dropout(dropout), - ) - - def forward(self, x): - return self.net(x) - - -class Conv_FeedForward(nn.Module): - def __init__(self, dim, mult=2, dropout=0.0): - super().__init__() - inner_dim = int(dim * mult) - self.net = nn.Sequential( - nn.Conv2d(dim, inner_dim, 1, 1, 0), - nn.GELU(), - nn.Dropout(dropout), - nn.Conv2d(inner_dim, dim, 1, 1, 0), - nn.Dropout(dropout), - ) - - def forward(self, x): - return self.net(x) - - -class Gated_Conv_FeedForward(nn.Module): - def __init__(self, dim, mult=1, bias=False, dropout=0.0): - super().__init__() - - hidden_features = int(dim * mult) - - self.project_in = nn.Conv2d(dim, hidden_features * 2, kernel_size=1, bias=bias) - - self.dwconv = nn.Conv2d( - hidden_features * 2, - hidden_features * 2, - kernel_size=3, - stride=1, - padding=1, - groups=hidden_features * 2, - bias=bias, - ) - - self.project_out = nn.Conv2d(hidden_features, dim, kernel_size=1, bias=bias) - - def forward(self, x): - x = self.project_in(x) - x1, x2 = self.dwconv(x).chunk(2, dim=1) - x = F.gelu(x1) * x2 - x = self.project_out(x) - return x - - -# MBConv - - -class SqueezeExcitation(nn.Module): - def __init__(self, dim, shrinkage_rate=0.25): - super().__init__() - hidden_dim = int(dim * shrinkage_rate) - - self.gate = nn.Sequential( - Reduce("b c h w -> b c", "mean"), - nn.Linear(dim, hidden_dim, bias=False), - nn.SiLU(), - nn.Linear(hidden_dim, dim, bias=False), - nn.Sigmoid(), - Rearrange("b c -> b c 1 1"), - ) - - def forward(self, x): - return x * self.gate(x) - - -class MBConvResidual(nn.Module): - def __init__(self, fn, dropout=0.0): - super().__init__() - self.fn = fn - self.dropsample = Dropsample(dropout) - - def forward(self, x): - out = self.fn(x) - out = self.dropsample(out) - return out + x - - -class Dropsample(nn.Module): - def __init__(self, prob=0): - super().__init__() - self.prob = prob - - def forward(self, x): - device = x.device - - if self.prob == 0.0 or (not self.training): - return x - - keep_mask = ( - torch.FloatTensor((x.shape[0], 1, 1, 1), device=device).uniform_() - > self.prob - ) - return x * keep_mask / (1 - self.prob) - - -def MBConv( - dim_in, dim_out, *, downsample, expansion_rate=4, shrinkage_rate=0.25, dropout=0.0 -): - hidden_dim = int(expansion_rate * dim_out) - stride = 2 if downsample else 1 - - net = nn.Sequential( - nn.Conv2d(dim_in, hidden_dim, 1), - # nn.BatchNorm2d(hidden_dim), - nn.GELU(), - nn.Conv2d( - hidden_dim, hidden_dim, 3, stride=stride, padding=1, groups=hidden_dim - ), - # nn.BatchNorm2d(hidden_dim), - nn.GELU(), - SqueezeExcitation(hidden_dim, shrinkage_rate=shrinkage_rate), - nn.Conv2d(hidden_dim, dim_out, 1), - # nn.BatchNorm2d(dim_out) - ) - - if dim_in == dim_out and not downsample: - net = MBConvResidual(net, dropout=dropout) - - return net - - -# attention related classes -class Attention(nn.Module): - def __init__( - self, - dim, - dim_head=32, - dropout=0.0, - window_size=7, - with_pe=True, - ): - super().__init__() - assert ( - dim % dim_head - ) == 0, "dimension should be divisible by dimension per head" - - self.heads = dim // dim_head - self.scale = dim_head**-0.5 - self.with_pe = with_pe - - self.to_qkv = nn.Linear(dim, dim * 3, bias=False) - - self.attend = nn.Sequential(nn.Softmax(dim=-1), nn.Dropout(dropout)) - - self.to_out = nn.Sequential( - nn.Linear(dim, dim, bias=False), nn.Dropout(dropout) - ) - - # relative positional bias - if self.with_pe: - self.rel_pos_bias = nn.Embedding((2 * window_size - 1) ** 2, self.heads) - - pos = torch.arange(window_size) - grid = torch.stack(torch.meshgrid(pos, pos)) - grid = rearrange(grid, "c i j -> (i j) c") - rel_pos = rearrange(grid, "i ... -> i 1 ...") - rearrange( - grid, "j ... -> 1 j ..." - ) - rel_pos += window_size - 1 - rel_pos_indices = (rel_pos * torch.tensor([2 * window_size - 1, 1])).sum( - dim=-1 - ) - - self.register_buffer("rel_pos_indices", rel_pos_indices, persistent=False) - - def forward(self, x): - batch, height, width, window_height, window_width, _, device, h = ( - *x.shape, - x.device, - self.heads, - ) - - # flatten - - x = rearrange(x, "b x y w1 w2 d -> (b x y) (w1 w2) d") - - # project for queries, keys, values - - q, k, v = self.to_qkv(x).chunk(3, dim=-1) - - # split heads - - q, k, v = map(lambda t: rearrange(t, "b n (h d ) -> b h n d", h=h), (q, k, v)) - - # scale - - q = q * self.scale - - # sim - - sim = einsum("b h i d, b h j d -> b h i j", q, k) - - # add positional bias - if self.with_pe: - bias = self.rel_pos_bias(self.rel_pos_indices) - sim = sim + rearrange(bias, "i j h -> h i j") - - # attention - - attn = self.attend(sim) - - # aggregate - - out = einsum("b h i j, b h j d -> b h i d", attn, v) - - # merge heads - - out = rearrange( - out, "b h (w1 w2) d -> b w1 w2 (h d)", w1=window_height, w2=window_width - ) - - # combine heads out - - out = self.to_out(out) - return rearrange(out, "(b x y) ... -> b x y ...", x=height, y=width) - - -class Block_Attention(nn.Module): - def __init__( - self, - dim, - dim_head=32, - bias=False, - dropout=0.0, - window_size=7, - with_pe=True, - ): - super().__init__() - assert ( - dim % dim_head - ) == 0, "dimension should be divisible by dimension per head" - - self.heads = dim // dim_head - self.ps = window_size - self.scale = dim_head**-0.5 - self.with_pe = with_pe - - self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias) - self.qkv_dwconv = nn.Conv2d( - dim * 3, - dim * 3, - kernel_size=3, - stride=1, - padding=1, - groups=dim * 3, - bias=bias, - ) - - self.attend = nn.Sequential(nn.Softmax(dim=-1), nn.Dropout(dropout)) - - self.to_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias) - - def forward(self, x): - # project for queries, keys, values - b, c, h, w = x.shape - - qkv = self.qkv_dwconv(self.qkv(x)) - q, k, v = qkv.chunk(3, dim=1) - - # split heads - - q, k, v = map( - lambda t: rearrange( - t, - "b (h d) (x w1) (y w2) -> (b x y) h (w1 w2) d", - h=self.heads, - w1=self.ps, - w2=self.ps, - ), - (q, k, v), - ) - - # scale - - q = q * self.scale - - # sim - - sim = einsum("b h i d, b h j d -> b h i j", q, k) - - # attention - attn = self.attend(sim) - - # aggregate - - out = einsum("b h i j, b h j d -> b h i d", attn, v) - - # merge heads - out = rearrange( - out, - "(b x y) head (w1 w2) d -> b (head d) (x w1) (y w2)", - x=h // self.ps, - y=w // self.ps, - head=self.heads, - w1=self.ps, - w2=self.ps, - ) - - out = self.to_out(out) - return out - - -class Channel_Attention(nn.Module): - def __init__(self, dim, heads, bias=False, dropout=0.0, window_size=7): - super(Channel_Attention, self).__init__() - self.heads = heads - - self.temperature = nn.Parameter(torch.ones(heads, 1, 1)) - - self.ps = window_size - - self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias) - self.qkv_dwconv = nn.Conv2d( - dim * 3, - dim * 3, - kernel_size=3, - stride=1, - padding=1, - groups=dim * 3, - bias=bias, - ) - self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias) - - def forward(self, x): - b, c, h, w = x.shape - - qkv = self.qkv_dwconv(self.qkv(x)) - qkv = qkv.chunk(3, dim=1) - - q, k, v = map( - lambda t: rearrange( - t, - "b (head d) (h ph) (w pw) -> b (h w) head d (ph pw)", - ph=self.ps, - pw=self.ps, - head=self.heads, - ), - qkv, - ) - - q = F.normalize(q, dim=-1) - k = F.normalize(k, dim=-1) - - attn = (q @ k.transpose(-2, -1)) * self.temperature - attn = attn.softmax(dim=-1) - out = attn @ v - - out = rearrange( - out, - "b (h w) head d (ph pw) -> b (head d) (h ph) (w pw)", - h=h // self.ps, - w=w // self.ps, - ph=self.ps, - pw=self.ps, - head=self.heads, - ) - - out = self.project_out(out) - - return out - - -class Channel_Attention_grid(nn.Module): - def __init__(self, dim, heads, bias=False, dropout=0.0, window_size=7): - super(Channel_Attention_grid, self).__init__() - self.heads = heads - - self.temperature = nn.Parameter(torch.ones(heads, 1, 1)) - - self.ps = window_size - - self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias) - self.qkv_dwconv = nn.Conv2d( - dim * 3, - dim * 3, - kernel_size=3, - stride=1, - padding=1, - groups=dim * 3, - bias=bias, - ) - self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias) - - def forward(self, x): - b, c, h, w = x.shape - - qkv = self.qkv_dwconv(self.qkv(x)) - qkv = qkv.chunk(3, dim=1) - - q, k, v = map( - lambda t: rearrange( - t, - "b (head d) (h ph) (w pw) -> b (ph pw) head d (h w)", - ph=self.ps, - pw=self.ps, - head=self.heads, - ), - qkv, - ) - - q = F.normalize(q, dim=-1) - k = F.normalize(k, dim=-1) - - attn = (q @ k.transpose(-2, -1)) * self.temperature - attn = attn.softmax(dim=-1) - out = attn @ v - - out = rearrange( - out, - "b (ph pw) head d (h w) -> b (head d) (h ph) (w pw)", - h=h // self.ps, - w=w // self.ps, - ph=self.ps, - pw=self.ps, - head=self.heads, - ) - - out = self.project_out(out) - - return out - - -class OSA_Block(nn.Module): - def __init__( - self, - channel_num=64, - bias=True, - ffn_bias=True, - window_size=8, - with_pe=False, - dropout=0.0, - ): - super(OSA_Block, self).__init__() - - w = window_size - - self.layer = nn.Sequential( - MBConv( - channel_num, - channel_num, - downsample=False, - expansion_rate=1, - shrinkage_rate=0.25, - ), - Rearrange( - "b d (x w1) (y w2) -> b x y w1 w2 d", w1=w, w2=w - ), # block-like attention - PreNormResidual( - channel_num, - Attention( - dim=channel_num, - dim_head=channel_num // 4, - dropout=dropout, - window_size=window_size, - with_pe=with_pe, - ), - ), - Rearrange("b x y w1 w2 d -> b d (x w1) (y w2)"), - Conv_PreNormResidual( - channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout) - ), - # channel-like attention - Conv_PreNormResidual( - channel_num, - Channel_Attention( - dim=channel_num, heads=4, dropout=dropout, window_size=window_size - ), - ), - Conv_PreNormResidual( - channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout) - ), - Rearrange( - "b d (w1 x) (w2 y) -> b x y w1 w2 d", w1=w, w2=w - ), # grid-like attention - PreNormResidual( - channel_num, - Attention( - dim=channel_num, - dim_head=channel_num // 4, - dropout=dropout, - window_size=window_size, - with_pe=with_pe, - ), - ), - Rearrange("b x y w1 w2 d -> b d (w1 x) (w2 y)"), - Conv_PreNormResidual( - channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout) - ), - # channel-like attention - Conv_PreNormResidual( - channel_num, - Channel_Attention_grid( - dim=channel_num, heads=4, dropout=dropout, window_size=window_size - ), - ), - Conv_PreNormResidual( - channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout) - ), - ) - - def forward(self, x): - out = self.layer(x) - return out diff --git a/comfy_extras/chainner_models/architecture/OmniSR/OSAG.py b/comfy_extras/chainner_models/architecture/OmniSR/OSAG.py deleted file mode 100644 index 477e81f9..00000000 --- a/comfy_extras/chainner_models/architecture/OmniSR/OSAG.py +++ /dev/null @@ -1,60 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: OSAG.py -# Created Date: Tuesday April 28th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Sunday, 23rd April 2023 3:08:49 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2020 Shanghai Jiao Tong University -############################################################# - - -import torch.nn as nn - -from .esa import ESA -from .OSA import OSA_Block - - -class OSAG(nn.Module): - def __init__( - self, - channel_num=64, - bias=True, - block_num=4, - ffn_bias=False, - window_size=0, - pe=False, - ): - super(OSAG, self).__init__() - - # print("window_size: %d" % (window_size)) - # print("with_pe", pe) - # print("ffn_bias: %d" % (ffn_bias)) - - # block_script_name = kwargs.get("block_script_name", "OSA") - # block_class_name = kwargs.get("block_class_name", "OSA_Block") - - # script_name = "." + block_script_name - # package = __import__(script_name, fromlist=True) - block_class = OSA_Block # getattr(package, block_class_name) - group_list = [] - for _ in range(block_num): - temp_res = block_class( - channel_num, - bias, - ffn_bias=ffn_bias, - window_size=window_size, - with_pe=pe, - ) - group_list.append(temp_res) - group_list.append(nn.Conv2d(channel_num, channel_num, 1, 1, 0, bias=bias)) - self.residual_layer = nn.Sequential(*group_list) - esa_channel = max(channel_num // 4, 16) - self.esa = ESA(esa_channel, channel_num) - - def forward(self, x): - out = self.residual_layer(x) - out = out + x - return self.esa(out) diff --git a/comfy_extras/chainner_models/architecture/OmniSR/OmniSR.py b/comfy_extras/chainner_models/architecture/OmniSR/OmniSR.py deleted file mode 100644 index 1e1c3f35..00000000 --- a/comfy_extras/chainner_models/architecture/OmniSR/OmniSR.py +++ /dev/null @@ -1,143 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: OmniSR.py -# Created Date: Tuesday April 28th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Sunday, 23rd April 2023 3:06:36 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2020 Shanghai Jiao Tong University -############################################################# - -import math - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from .OSAG import OSAG -from .pixelshuffle import pixelshuffle_block - - -class OmniSR(nn.Module): - def __init__( - self, - state_dict, - **kwargs, - ): - super(OmniSR, self).__init__() - self.state = state_dict - - bias = True # Fine to assume this for now - block_num = 1 # Fine to assume this for now - ffn_bias = True - pe = True - - num_feat = state_dict["input.weight"].shape[0] or 64 - num_in_ch = state_dict["input.weight"].shape[1] or 3 - num_out_ch = num_in_ch # we can just assume this for now. pixelshuffle smh - - pixelshuffle_shape = state_dict["up.0.weight"].shape[0] - up_scale = math.sqrt(pixelshuffle_shape / num_out_ch) - if up_scale - int(up_scale) > 0: - print( - "out_nc is probably different than in_nc, scale calculation might be wrong" - ) - up_scale = int(up_scale) - res_num = 0 - for key in state_dict.keys(): - if "residual_layer" in key: - temp_res_num = int(key.split(".")[1]) - if temp_res_num > res_num: - res_num = temp_res_num - res_num = res_num + 1 # zero-indexed - - residual_layer = [] - self.res_num = res_num - - if ( - "residual_layer.0.residual_layer.0.layer.2.fn.rel_pos_bias.weight" - in state_dict.keys() - ): - rel_pos_bias_weight = state_dict[ - "residual_layer.0.residual_layer.0.layer.2.fn.rel_pos_bias.weight" - ].shape[0] - self.window_size = int((math.sqrt(rel_pos_bias_weight) + 1) / 2) - else: - self.window_size = 8 - - self.up_scale = up_scale - - for _ in range(res_num): - temp_res = OSAG( - channel_num=num_feat, - bias=bias, - block_num=block_num, - ffn_bias=ffn_bias, - window_size=self.window_size, - pe=pe, - ) - residual_layer.append(temp_res) - self.residual_layer = nn.Sequential(*residual_layer) - self.input = nn.Conv2d( - in_channels=num_in_ch, - out_channels=num_feat, - kernel_size=3, - stride=1, - padding=1, - bias=bias, - ) - self.output = nn.Conv2d( - in_channels=num_feat, - out_channels=num_feat, - kernel_size=3, - stride=1, - padding=1, - bias=bias, - ) - self.up = pixelshuffle_block(num_feat, num_out_ch, up_scale, bias=bias) - - # self.tail = pixelshuffle_block(num_feat,num_out_ch,up_scale,bias=bias) - - # for m in self.modules(): - # if isinstance(m, nn.Conv2d): - # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels - # m.weight.data.normal_(0, sqrt(2. / n)) - - # chaiNNer specific stuff - self.model_arch = "OmniSR" - self.sub_type = "SR" - self.in_nc = num_in_ch - self.out_nc = num_out_ch - self.num_feat = num_feat - self.scale = up_scale - - self.supports_fp16 = True # TODO: Test this - self.supports_bfp16 = True - self.min_size_restriction = 16 - - self.load_state_dict(state_dict, strict=False) - - def check_image_size(self, x): - _, _, h, w = x.size() - # import pdb; pdb.set_trace() - mod_pad_h = (self.window_size - h % self.window_size) % self.window_size - mod_pad_w = (self.window_size - w % self.window_size) % self.window_size - # x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect') - x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "constant", 0) - return x - - def forward(self, x): - H, W = x.shape[2:] - x = self.check_image_size(x) - - residual = self.input(x) - out = self.residual_layer(residual) - - # origin - out = torch.add(self.output(out), residual) - out = self.up(out) - - out = out[:, :, : H * self.up_scale, : W * self.up_scale] - return out diff --git a/comfy_extras/chainner_models/architecture/OmniSR/esa.py b/comfy_extras/chainner_models/architecture/OmniSR/esa.py deleted file mode 100644 index f9ce7f7a..00000000 --- a/comfy_extras/chainner_models/architecture/OmniSR/esa.py +++ /dev/null @@ -1,294 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: esa.py -# Created Date: Tuesday April 28th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Thursday, 20th April 2023 9:28:06 am -# Modified By: Chen Xuanhong -# Copyright (c) 2020 Shanghai Jiao Tong University -############################################################# - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from .layernorm import LayerNorm2d - - -def moment(x, dim=(2, 3), k=2): - assert len(x.size()) == 4 - mean = torch.mean(x, dim=dim).unsqueeze(-1).unsqueeze(-1) - mk = (1 / (x.size(2) * x.size(3))) * torch.sum(torch.pow(x - mean, k), dim=dim) - return mk - - -class ESA(nn.Module): - """ - Modification of Enhanced Spatial Attention (ESA), which is proposed by - `Residual Feature Aggregation Network for Image Super-Resolution` - Note: `conv_max` and `conv3_` are NOT used here, so the corresponding codes - are deleted. - """ - - def __init__(self, esa_channels, n_feats, conv=nn.Conv2d): - super(ESA, self).__init__() - f = esa_channels - self.conv1 = conv(n_feats, f, kernel_size=1) - self.conv_f = conv(f, f, kernel_size=1) - self.conv2 = conv(f, f, kernel_size=3, stride=2, padding=0) - self.conv3 = conv(f, f, kernel_size=3, padding=1) - self.conv4 = conv(f, n_feats, kernel_size=1) - self.sigmoid = nn.Sigmoid() - self.relu = nn.ReLU(inplace=True) - - def forward(self, x): - c1_ = self.conv1(x) - c1 = self.conv2(c1_) - v_max = F.max_pool2d(c1, kernel_size=7, stride=3) - c3 = self.conv3(v_max) - c3 = F.interpolate( - c3, (x.size(2), x.size(3)), mode="bilinear", align_corners=False - ) - cf = self.conv_f(c1_) - c4 = self.conv4(c3 + cf) - m = self.sigmoid(c4) - return x * m - - -class LK_ESA(nn.Module): - def __init__( - self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True - ): - super(LK_ESA, self).__init__() - f = esa_channels - self.conv1 = conv(n_feats, f, kernel_size=1) - self.conv_f = conv(f, f, kernel_size=1) - - kernel_size = 17 - kernel_expand = kernel_expand - padding = kernel_size // 2 - - self.vec_conv = nn.Conv2d( - in_channels=f * kernel_expand, - out_channels=f * kernel_expand, - kernel_size=(1, kernel_size), - padding=(0, padding), - groups=2, - bias=bias, - ) - self.vec_conv3x1 = nn.Conv2d( - in_channels=f * kernel_expand, - out_channels=f * kernel_expand, - kernel_size=(1, 3), - padding=(0, 1), - groups=2, - bias=bias, - ) - - self.hor_conv = nn.Conv2d( - in_channels=f * kernel_expand, - out_channels=f * kernel_expand, - kernel_size=(kernel_size, 1), - padding=(padding, 0), - groups=2, - bias=bias, - ) - self.hor_conv1x3 = nn.Conv2d( - in_channels=f * kernel_expand, - out_channels=f * kernel_expand, - kernel_size=(3, 1), - padding=(1, 0), - groups=2, - bias=bias, - ) - - self.conv4 = conv(f, n_feats, kernel_size=1) - self.sigmoid = nn.Sigmoid() - self.relu = nn.ReLU(inplace=True) - - def forward(self, x): - c1_ = self.conv1(x) - - res = self.vec_conv(c1_) + self.vec_conv3x1(c1_) - res = self.hor_conv(res) + self.hor_conv1x3(res) - - cf = self.conv_f(c1_) - c4 = self.conv4(res + cf) - m = self.sigmoid(c4) - return x * m - - -class LK_ESA_LN(nn.Module): - def __init__( - self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True - ): - super(LK_ESA_LN, self).__init__() - f = esa_channels - self.conv1 = conv(n_feats, f, kernel_size=1) - self.conv_f = conv(f, f, kernel_size=1) - - kernel_size = 17 - kernel_expand = kernel_expand - padding = kernel_size // 2 - - self.norm = LayerNorm2d(n_feats) - - self.vec_conv = nn.Conv2d( - in_channels=f * kernel_expand, - out_channels=f * kernel_expand, - kernel_size=(1, kernel_size), - padding=(0, padding), - groups=2, - bias=bias, - ) - self.vec_conv3x1 = nn.Conv2d( - in_channels=f * kernel_expand, - out_channels=f * kernel_expand, - kernel_size=(1, 3), - padding=(0, 1), - groups=2, - bias=bias, - ) - - self.hor_conv = nn.Conv2d( - in_channels=f * kernel_expand, - out_channels=f * kernel_expand, - kernel_size=(kernel_size, 1), - padding=(padding, 0), - groups=2, - bias=bias, - ) - self.hor_conv1x3 = nn.Conv2d( - in_channels=f * kernel_expand, - out_channels=f * kernel_expand, - kernel_size=(3, 1), - padding=(1, 0), - groups=2, - bias=bias, - ) - - self.conv4 = conv(f, n_feats, kernel_size=1) - self.sigmoid = nn.Sigmoid() - self.relu = nn.ReLU(inplace=True) - - def forward(self, x): - c1_ = self.norm(x) - c1_ = self.conv1(c1_) - - res = self.vec_conv(c1_) + self.vec_conv3x1(c1_) - res = self.hor_conv(res) + self.hor_conv1x3(res) - - cf = self.conv_f(c1_) - c4 = self.conv4(res + cf) - m = self.sigmoid(c4) - return x * m - - -class AdaGuidedFilter(nn.Module): - def __init__( - self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True - ): - super(AdaGuidedFilter, self).__init__() - - self.gap = nn.AdaptiveAvgPool2d(1) - self.fc = nn.Conv2d( - in_channels=n_feats, - out_channels=1, - kernel_size=1, - padding=0, - stride=1, - groups=1, - bias=True, - ) - - self.r = 5 - - def box_filter(self, x, r): - channel = x.shape[1] - kernel_size = 2 * r + 1 - weight = 1.0 / (kernel_size**2) - box_kernel = weight * torch.ones( - (channel, 1, kernel_size, kernel_size), dtype=torch.float32, device=x.device - ) - output = F.conv2d(x, weight=box_kernel, stride=1, padding=r, groups=channel) - return output - - def forward(self, x): - _, _, H, W = x.shape - N = self.box_filter( - torch.ones((1, 1, H, W), dtype=x.dtype, device=x.device), self.r - ) - - # epsilon = self.fc(self.gap(x)) - # epsilon = torch.pow(epsilon, 2) - epsilon = 1e-2 - - mean_x = self.box_filter(x, self.r) / N - var_x = self.box_filter(x * x, self.r) / N - mean_x * mean_x - - A = var_x / (var_x + epsilon) - b = (1 - A) * mean_x - m = A * x + b - - # mean_A = self.box_filter(A, self.r) / N - # mean_b = self.box_filter(b, self.r) / N - # m = mean_A * x + mean_b - return x * m - - -class AdaConvGuidedFilter(nn.Module): - def __init__( - self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True - ): - super(AdaConvGuidedFilter, self).__init__() - f = esa_channels - - self.conv_f = conv(f, f, kernel_size=1) - - kernel_size = 17 - kernel_expand = kernel_expand - padding = kernel_size // 2 - - self.vec_conv = nn.Conv2d( - in_channels=f, - out_channels=f, - kernel_size=(1, kernel_size), - padding=(0, padding), - groups=f, - bias=bias, - ) - - self.hor_conv = nn.Conv2d( - in_channels=f, - out_channels=f, - kernel_size=(kernel_size, 1), - padding=(padding, 0), - groups=f, - bias=bias, - ) - - self.gap = nn.AdaptiveAvgPool2d(1) - self.fc = nn.Conv2d( - in_channels=f, - out_channels=f, - kernel_size=1, - padding=0, - stride=1, - groups=1, - bias=True, - ) - - def forward(self, x): - y = self.vec_conv(x) - y = self.hor_conv(y) - - sigma = torch.pow(y, 2) - epsilon = self.fc(self.gap(y)) - - weight = sigma / (sigma + epsilon) - - m = weight * x + (1 - weight) - - return x * m diff --git a/comfy_extras/chainner_models/architecture/OmniSR/layernorm.py b/comfy_extras/chainner_models/architecture/OmniSR/layernorm.py deleted file mode 100644 index 731a25f7..00000000 --- a/comfy_extras/chainner_models/architecture/OmniSR/layernorm.py +++ /dev/null @@ -1,70 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: layernorm.py -# Created Date: Tuesday April 28th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Thursday, 20th April 2023 9:28:20 am -# Modified By: Chen Xuanhong -# Copyright (c) 2020 Shanghai Jiao Tong University -############################################################# - -import torch -import torch.nn as nn - - -class LayerNormFunction(torch.autograd.Function): - @staticmethod - def forward(ctx, x, weight, bias, eps): - ctx.eps = eps - N, C, H, W = x.size() - mu = x.mean(1, keepdim=True) - var = (x - mu).pow(2).mean(1, keepdim=True) - y = (x - mu) / (var + eps).sqrt() - ctx.save_for_backward(y, var, weight) - y = weight.view(1, C, 1, 1) * y + bias.view(1, C, 1, 1) - return y - - @staticmethod - def backward(ctx, grad_output): - eps = ctx.eps - - N, C, H, W = grad_output.size() - y, var, weight = ctx.saved_variables - g = grad_output * weight.view(1, C, 1, 1) - mean_g = g.mean(dim=1, keepdim=True) - - mean_gy = (g * y).mean(dim=1, keepdim=True) - gx = 1.0 / torch.sqrt(var + eps) * (g - y * mean_gy - mean_g) - return ( - gx, - (grad_output * y).sum(dim=3).sum(dim=2).sum(dim=0), - grad_output.sum(dim=3).sum(dim=2).sum(dim=0), - None, - ) - - -class LayerNorm2d(nn.Module): - def __init__(self, channels, eps=1e-6): - super(LayerNorm2d, self).__init__() - self.register_parameter("weight", nn.Parameter(torch.ones(channels))) - self.register_parameter("bias", nn.Parameter(torch.zeros(channels))) - self.eps = eps - - def forward(self, x): - return LayerNormFunction.apply(x, self.weight, self.bias, self.eps) - - -class GRN(nn.Module): - """GRN (Global Response Normalization) layer""" - - def __init__(self, dim): - super().__init__() - self.gamma = nn.Parameter(torch.zeros(1, dim, 1, 1)) - self.beta = nn.Parameter(torch.zeros(1, dim, 1, 1)) - - def forward(self, x): - Gx = torch.norm(x, p=2, dim=(2, 3), keepdim=True) - Nx = Gx / (Gx.mean(dim=1, keepdim=True) + 1e-6) - return self.gamma * (x * Nx) + self.beta + x diff --git a/comfy_extras/chainner_models/architecture/OmniSR/pixelshuffle.py b/comfy_extras/chainner_models/architecture/OmniSR/pixelshuffle.py deleted file mode 100644 index 4260fb7c..00000000 --- a/comfy_extras/chainner_models/architecture/OmniSR/pixelshuffle.py +++ /dev/null @@ -1,31 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: pixelshuffle.py -# Created Date: Friday July 1st 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Friday, 1st July 2022 10:18:39 am -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - -import torch.nn as nn - - -def pixelshuffle_block( - in_channels, out_channels, upscale_factor=2, kernel_size=3, bias=False -): - """ - Upsample features according to `upscale_factor`. - """ - padding = kernel_size // 2 - conv = nn.Conv2d( - in_channels, - out_channels * (upscale_factor**2), - kernel_size, - padding=1, - bias=bias, - ) - pixel_shuffle = nn.PixelShuffle(upscale_factor) - return nn.Sequential(*[conv, pixel_shuffle]) diff --git a/comfy_extras/chainner_models/architecture/RRDB.py b/comfy_extras/chainner_models/architecture/RRDB.py deleted file mode 100644 index b50db7c2..00000000 --- a/comfy_extras/chainner_models/architecture/RRDB.py +++ /dev/null @@ -1,296 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -import functools -import math -import re -from collections import OrderedDict - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from . import block as B - - -# Borrowed from https://github.com/rlaphoenix/VSGAN/blob/master/vsgan/archs/ESRGAN.py -# Which enhanced stuff that was already here -class RRDBNet(nn.Module): - def __init__( - self, - state_dict, - norm=None, - act: str = "leakyrelu", - upsampler: str = "upconv", - mode: B.ConvMode = "CNA", - ) -> None: - """ - ESRGAN - Enhanced Super-Resolution Generative Adversarial Networks. - By Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, - and Chen Change Loy. - This is old-arch Residual in Residual Dense Block Network and is not - the newest revision that's available at github.com/xinntao/ESRGAN. - This is on purpose, the newest Network has severely limited the - potential use of the Network with no benefits. - This network supports model files from both new and old-arch. - Args: - norm: Normalization layer - act: Activation layer - upsampler: Upsample layer. upconv, pixel_shuffle - mode: Convolution mode - """ - super(RRDBNet, self).__init__() - self.model_arch = "ESRGAN" - self.sub_type = "SR" - - self.state = state_dict - self.norm = norm - self.act = act - self.upsampler = upsampler - self.mode = mode - - self.state_map = { - # currently supports old, new, and newer RRDBNet arch models - # ESRGAN, BSRGAN/RealSR, Real-ESRGAN - "model.0.weight": ("conv_first.weight",), - "model.0.bias": ("conv_first.bias",), - "model.1.sub./NB/.weight": ("trunk_conv.weight", "conv_body.weight"), - "model.1.sub./NB/.bias": ("trunk_conv.bias", "conv_body.bias"), - r"model.1.sub.\1.RDB\2.conv\3.0.\4": ( - r"RRDB_trunk\.(\d+)\.RDB(\d)\.conv(\d+)\.(weight|bias)", - r"body\.(\d+)\.rdb(\d)\.conv(\d+)\.(weight|bias)", - ), - } - if "params_ema" in self.state: - self.state = self.state["params_ema"] - # self.model_arch = "RealESRGAN" - self.num_blocks = self.get_num_blocks() - self.plus = any("conv1x1" in k for k in self.state.keys()) - if self.plus: - self.model_arch = "ESRGAN+" - - self.state = self.new_to_old_arch(self.state) - - self.key_arr = list(self.state.keys()) - - self.in_nc: int = self.state[self.key_arr[0]].shape[1] - self.out_nc: int = self.state[self.key_arr[-1]].shape[0] - - self.scale: int = self.get_scale() - self.num_filters: int = self.state[self.key_arr[0]].shape[0] - - c2x2 = False - if self.state["model.0.weight"].shape[-2] == 2: - c2x2 = True - self.scale = round(math.sqrt(self.scale / 4)) - self.model_arch = "ESRGAN-2c2" - - self.supports_fp16 = True - self.supports_bfp16 = True - self.min_size_restriction = None - - # Detect if pixelunshuffle was used (Real-ESRGAN) - if self.in_nc in (self.out_nc * 4, self.out_nc * 16) and self.out_nc in ( - self.in_nc / 4, - self.in_nc / 16, - ): - self.shuffle_factor = int(math.sqrt(self.in_nc / self.out_nc)) - else: - self.shuffle_factor = None - - upsample_block = { - "upconv": B.upconv_block, - "pixel_shuffle": B.pixelshuffle_block, - }.get(self.upsampler) - if upsample_block is None: - raise NotImplementedError(f"Upsample mode [{self.upsampler}] is not found") - - if self.scale == 3: - upsample_blocks = upsample_block( - in_nc=self.num_filters, - out_nc=self.num_filters, - upscale_factor=3, - act_type=self.act, - c2x2=c2x2, - ) - else: - upsample_blocks = [ - upsample_block( - in_nc=self.num_filters, - out_nc=self.num_filters, - act_type=self.act, - c2x2=c2x2, - ) - for _ in range(int(math.log(self.scale, 2))) - ] - - self.model = B.sequential( - # fea conv - B.conv_block( - in_nc=self.in_nc, - out_nc=self.num_filters, - kernel_size=3, - norm_type=None, - act_type=None, - c2x2=c2x2, - ), - B.ShortcutBlock( - B.sequential( - # rrdb blocks - *[ - B.RRDB( - nf=self.num_filters, - kernel_size=3, - gc=32, - stride=1, - bias=True, - pad_type="zero", - norm_type=self.norm, - act_type=self.act, - mode="CNA", - plus=self.plus, - c2x2=c2x2, - ) - for _ in range(self.num_blocks) - ], - # lr conv - B.conv_block( - in_nc=self.num_filters, - out_nc=self.num_filters, - kernel_size=3, - norm_type=self.norm, - act_type=None, - mode=self.mode, - c2x2=c2x2, - ), - ) - ), - *upsample_blocks, - # hr_conv0 - B.conv_block( - in_nc=self.num_filters, - out_nc=self.num_filters, - kernel_size=3, - norm_type=None, - act_type=self.act, - c2x2=c2x2, - ), - # hr_conv1 - B.conv_block( - in_nc=self.num_filters, - out_nc=self.out_nc, - kernel_size=3, - norm_type=None, - act_type=None, - c2x2=c2x2, - ), - ) - - # Adjust these properties for calculations outside of the model - if self.shuffle_factor: - self.in_nc //= self.shuffle_factor**2 - self.scale //= self.shuffle_factor - - self.load_state_dict(self.state, strict=False) - - def new_to_old_arch(self, state): - """Convert a new-arch model state dictionary to an old-arch dictionary.""" - if "params_ema" in state: - state = state["params_ema"] - - if "conv_first.weight" not in state: - # model is already old arch, this is a loose check, but should be sufficient - return state - - # add nb to state keys - for kind in ("weight", "bias"): - self.state_map[f"model.1.sub.{self.num_blocks}.{kind}"] = self.state_map[ - f"model.1.sub./NB/.{kind}" - ] - del self.state_map[f"model.1.sub./NB/.{kind}"] - - old_state = OrderedDict() - for old_key, new_keys in self.state_map.items(): - for new_key in new_keys: - if r"\1" in old_key: - for k, v in state.items(): - sub = re.sub(new_key, old_key, k) - if sub != k: - old_state[sub] = v - else: - if new_key in state: - old_state[old_key] = state[new_key] - - # upconv layers - max_upconv = 0 - for key in state.keys(): - match = re.match(r"(upconv|conv_up)(\d)\.(weight|bias)", key) - if match is not None: - _, key_num, key_type = match.groups() - old_state[f"model.{int(key_num) * 3}.{key_type}"] = state[key] - max_upconv = max(max_upconv, int(key_num) * 3) - - # final layers - for key in state.keys(): - if key in ("HRconv.weight", "conv_hr.weight"): - old_state[f"model.{max_upconv + 2}.weight"] = state[key] - elif key in ("HRconv.bias", "conv_hr.bias"): - old_state[f"model.{max_upconv + 2}.bias"] = state[key] - elif key in ("conv_last.weight",): - old_state[f"model.{max_upconv + 4}.weight"] = state[key] - elif key in ("conv_last.bias",): - old_state[f"model.{max_upconv + 4}.bias"] = state[key] - - # Sort by first numeric value of each layer - def compare(item1, item2): - parts1 = item1.split(".") - parts2 = item2.split(".") - int1 = int(parts1[1]) - int2 = int(parts2[1]) - return int1 - int2 - - sorted_keys = sorted(old_state.keys(), key=functools.cmp_to_key(compare)) - - # Rebuild the output dict in the right order - out_dict = OrderedDict((k, old_state[k]) for k in sorted_keys) - - return out_dict - - def get_scale(self, min_part: int = 6) -> int: - n = 0 - for part in list(self.state): - parts = part.split(".")[1:] - if len(parts) == 2: - part_num = int(parts[0]) - if part_num > min_part and parts[1] == "weight": - n += 1 - return 2**n - - def get_num_blocks(self) -> int: - nbs = [] - state_keys = self.state_map[r"model.1.sub.\1.RDB\2.conv\3.0.\4"] + ( - r"model\.\d+\.sub\.(\d+)\.RDB(\d+)\.conv(\d+)\.0\.(weight|bias)", - ) - for state_key in state_keys: - for k in self.state: - m = re.search(state_key, k) - if m: - nbs.append(int(m.group(1))) - if nbs: - break - return max(*nbs) + 1 - - def forward(self, x): - if self.shuffle_factor: - _, _, h, w = x.size() - mod_pad_h = ( - self.shuffle_factor - h % self.shuffle_factor - ) % self.shuffle_factor - mod_pad_w = ( - self.shuffle_factor - w % self.shuffle_factor - ) % self.shuffle_factor - x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") - x = torch.pixel_unshuffle(x, downscale_factor=self.shuffle_factor) - x = self.model(x) - return x[:, :, : h * self.scale, : w * self.scale] - return self.model(x) diff --git a/comfy_extras/chainner_models/architecture/SCUNet.py b/comfy_extras/chainner_models/architecture/SCUNet.py deleted file mode 100644 index b8354a87..00000000 --- a/comfy_extras/chainner_models/architecture/SCUNet.py +++ /dev/null @@ -1,455 +0,0 @@ -# pylint: skip-file -# ----------------------------------------------------------------------------------- -# SCUNet: Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis, https://arxiv.org/abs/2203.13278 -# Zhang, Kai and Li, Yawei and Liang, Jingyun and Cao, Jiezhang and Zhang, Yulun and Tang, Hao and Timofte, Radu and Van Gool, Luc -# ----------------------------------------------------------------------------------- - -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from einops import rearrange -from einops.layers.torch import Rearrange - -from .timm.drop import DropPath -from .timm.weight_init import trunc_normal_ - - -# Borrowed from https://github.com/cszn/SCUNet/blob/main/models/network_scunet.py -class WMSA(nn.Module): - """Self-attention module in Swin Transformer""" - - def __init__(self, input_dim, output_dim, head_dim, window_size, type): - super(WMSA, self).__init__() - self.input_dim = input_dim - self.output_dim = output_dim - self.head_dim = head_dim - self.scale = self.head_dim**-0.5 - self.n_heads = input_dim // head_dim - self.window_size = window_size - self.type = type - self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True) - - self.relative_position_params = nn.Parameter( - torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads) - ) - # TODO recover - # self.relative_position_params = nn.Parameter(torch.zeros(self.n_heads, 2 * window_size - 1, 2 * window_size -1)) - self.relative_position_params = nn.Parameter( - torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads) - ) - - self.linear = nn.Linear(self.input_dim, self.output_dim) - - trunc_normal_(self.relative_position_params, std=0.02) - self.relative_position_params = torch.nn.Parameter( - self.relative_position_params.view( - 2 * window_size - 1, 2 * window_size - 1, self.n_heads - ) - .transpose(1, 2) - .transpose(0, 1) - ) - - def generate_mask(self, h, w, p, shift): - """generating the mask of SW-MSA - Args: - shift: shift parameters in CyclicShift. - Returns: - attn_mask: should be (1 1 w p p), - """ - # supporting square. - attn_mask = torch.zeros( - h, - w, - p, - p, - p, - p, - dtype=torch.bool, - device=self.relative_position_params.device, - ) - if self.type == "W": - return attn_mask - - s = p - shift - attn_mask[-1, :, :s, :, s:, :] = True - attn_mask[-1, :, s:, :, :s, :] = True - attn_mask[:, -1, :, :s, :, s:] = True - attn_mask[:, -1, :, s:, :, :s] = True - attn_mask = rearrange( - attn_mask, "w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)" - ) - return attn_mask - - def forward(self, x): - """Forward pass of Window Multi-head Self-attention module. - Args: - x: input tensor with shape of [b h w c]; - attn_mask: attention mask, fill -inf where the value is True; - Returns: - output: tensor shape [b h w c] - """ - if self.type != "W": - x = torch.roll( - x, - shifts=(-(self.window_size // 2), -(self.window_size // 2)), - dims=(1, 2), - ) - - x = rearrange( - x, - "b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c", - p1=self.window_size, - p2=self.window_size, - ) - h_windows = x.size(1) - w_windows = x.size(2) - # square validation - # assert h_windows == w_windows - - x = rearrange( - x, - "b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c", - p1=self.window_size, - p2=self.window_size, - ) - qkv = self.embedding_layer(x) - q, k, v = rearrange( - qkv, "b nw np (threeh c) -> threeh b nw np c", c=self.head_dim - ).chunk(3, dim=0) - sim = torch.einsum("hbwpc,hbwqc->hbwpq", q, k) * self.scale - # Adding learnable relative embedding - sim = sim + rearrange(self.relative_embedding(), "h p q -> h 1 1 p q") - # Using Attn Mask to distinguish different subwindows. - if self.type != "W": - attn_mask = self.generate_mask( - h_windows, w_windows, self.window_size, shift=self.window_size // 2 - ) - sim = sim.masked_fill_(attn_mask, float("-inf")) - - probs = nn.functional.softmax(sim, dim=-1) - output = torch.einsum("hbwij,hbwjc->hbwic", probs, v) - output = rearrange(output, "h b w p c -> b w p (h c)") - output = self.linear(output) - output = rearrange( - output, - "b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c", - w1=h_windows, - p1=self.window_size, - ) - - if self.type != "W": - output = torch.roll( - output, - shifts=(self.window_size // 2, self.window_size // 2), - dims=(1, 2), - ) - - return output - - def relative_embedding(self): - cord = torch.tensor( - np.array( - [ - [i, j] - for i in range(self.window_size) - for j in range(self.window_size) - ] - ) - ) - relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1 - # negative is allowed - return self.relative_position_params[ - :, relation[:, :, 0].long(), relation[:, :, 1].long() - ] - - -class Block(nn.Module): - def __init__( - self, - input_dim, - output_dim, - head_dim, - window_size, - drop_path, - type="W", - input_resolution=None, - ): - """SwinTransformer Block""" - super(Block, self).__init__() - self.input_dim = input_dim - self.output_dim = output_dim - assert type in ["W", "SW"] - self.type = type - if input_resolution <= window_size: - self.type = "W" - - self.ln1 = nn.LayerNorm(input_dim) - self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type) - self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() - self.ln2 = nn.LayerNorm(input_dim) - self.mlp = nn.Sequential( - nn.Linear(input_dim, 4 * input_dim), - nn.GELU(), - nn.Linear(4 * input_dim, output_dim), - ) - - def forward(self, x): - x = x + self.drop_path(self.msa(self.ln1(x))) - x = x + self.drop_path(self.mlp(self.ln2(x))) - return x - - -class ConvTransBlock(nn.Module): - def __init__( - self, - conv_dim, - trans_dim, - head_dim, - window_size, - drop_path, - type="W", - input_resolution=None, - ): - """SwinTransformer and Conv Block""" - super(ConvTransBlock, self).__init__() - self.conv_dim = conv_dim - self.trans_dim = trans_dim - self.head_dim = head_dim - self.window_size = window_size - self.drop_path = drop_path - self.type = type - self.input_resolution = input_resolution - - assert self.type in ["W", "SW"] - if self.input_resolution <= self.window_size: - self.type = "W" - - self.trans_block = Block( - self.trans_dim, - self.trans_dim, - self.head_dim, - self.window_size, - self.drop_path, - self.type, - self.input_resolution, - ) - self.conv1_1 = nn.Conv2d( - self.conv_dim + self.trans_dim, - self.conv_dim + self.trans_dim, - 1, - 1, - 0, - bias=True, - ) - self.conv1_2 = nn.Conv2d( - self.conv_dim + self.trans_dim, - self.conv_dim + self.trans_dim, - 1, - 1, - 0, - bias=True, - ) - - self.conv_block = nn.Sequential( - nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False), - nn.ReLU(True), - nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False), - ) - - def forward(self, x): - conv_x, trans_x = torch.split( - self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1 - ) - conv_x = self.conv_block(conv_x) + conv_x - trans_x = Rearrange("b c h w -> b h w c")(trans_x) - trans_x = self.trans_block(trans_x) - trans_x = Rearrange("b h w c -> b c h w")(trans_x) - res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1)) - x = x + res - - return x - - -class SCUNet(nn.Module): - def __init__( - self, - state_dict, - in_nc=3, - config=[4, 4, 4, 4, 4, 4, 4], - dim=64, - drop_path_rate=0.0, - input_resolution=256, - ): - super(SCUNet, self).__init__() - self.model_arch = "SCUNet" - self.sub_type = "SR" - - self.num_filters: int = 0 - - self.state = state_dict - self.config = config - self.dim = dim - self.head_dim = 32 - self.window_size = 8 - - self.in_nc = in_nc - self.out_nc = self.in_nc - self.scale = 1 - self.supports_fp16 = True - - # drop path rate for each layer - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))] - - self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)] - - begin = 0 - self.m_down1 = [ - ConvTransBlock( - dim // 2, - dim // 2, - self.head_dim, - self.window_size, - dpr[i + begin], - "W" if not i % 2 else "SW", - input_resolution, - ) - for i in range(config[0]) - ] + [nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)] - - begin += config[0] - self.m_down2 = [ - ConvTransBlock( - dim, - dim, - self.head_dim, - self.window_size, - dpr[i + begin], - "W" if not i % 2 else "SW", - input_resolution // 2, - ) - for i in range(config[1]) - ] + [nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)] - - begin += config[1] - self.m_down3 = [ - ConvTransBlock( - 2 * dim, - 2 * dim, - self.head_dim, - self.window_size, - dpr[i + begin], - "W" if not i % 2 else "SW", - input_resolution // 4, - ) - for i in range(config[2]) - ] + [nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)] - - begin += config[2] - self.m_body = [ - ConvTransBlock( - 4 * dim, - 4 * dim, - self.head_dim, - self.window_size, - dpr[i + begin], - "W" if not i % 2 else "SW", - input_resolution // 8, - ) - for i in range(config[3]) - ] - - begin += config[3] - self.m_up3 = [ - nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), - ] + [ - ConvTransBlock( - 2 * dim, - 2 * dim, - self.head_dim, - self.window_size, - dpr[i + begin], - "W" if not i % 2 else "SW", - input_resolution // 4, - ) - for i in range(config[4]) - ] - - begin += config[4] - self.m_up2 = [ - nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), - ] + [ - ConvTransBlock( - dim, - dim, - self.head_dim, - self.window_size, - dpr[i + begin], - "W" if not i % 2 else "SW", - input_resolution // 2, - ) - for i in range(config[5]) - ] - - begin += config[5] - self.m_up1 = [ - nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), - ] + [ - ConvTransBlock( - dim // 2, - dim // 2, - self.head_dim, - self.window_size, - dpr[i + begin], - "W" if not i % 2 else "SW", - input_resolution, - ) - for i in range(config[6]) - ] - - self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)] - - self.m_head = nn.Sequential(*self.m_head) - self.m_down1 = nn.Sequential(*self.m_down1) - self.m_down2 = nn.Sequential(*self.m_down2) - self.m_down3 = nn.Sequential(*self.m_down3) - self.m_body = nn.Sequential(*self.m_body) - self.m_up3 = nn.Sequential(*self.m_up3) - self.m_up2 = nn.Sequential(*self.m_up2) - self.m_up1 = nn.Sequential(*self.m_up1) - self.m_tail = nn.Sequential(*self.m_tail) - # self.apply(self._init_weights) - self.load_state_dict(state_dict, strict=True) - - def check_image_size(self, x): - _, _, h, w = x.size() - mod_pad_h = (64 - h % 64) % 64 - mod_pad_w = (64 - w % 64) % 64 - x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") - return x - - def forward(self, x0): - h, w = x0.size()[-2:] - x0 = self.check_image_size(x0) - - x1 = self.m_head(x0) - x2 = self.m_down1(x1) - x3 = self.m_down2(x2) - x4 = self.m_down3(x3) - x = self.m_body(x4) - x = self.m_up3(x + x4) - x = self.m_up2(x + x3) - x = self.m_up1(x + x2) - x = self.m_tail(x + x1) - - x = x[:, :, :h, :w] - return x - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=0.02) - if m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) diff --git a/comfy_extras/chainner_models/architecture/SPSR.py b/comfy_extras/chainner_models/architecture/SPSR.py deleted file mode 100644 index c3cefff1..00000000 --- a/comfy_extras/chainner_models/architecture/SPSR.py +++ /dev/null @@ -1,383 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -import math - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from . import block as B - - -class Get_gradient_nopadding(nn.Module): - def __init__(self): - super(Get_gradient_nopadding, self).__init__() - kernel_v = [[0, -1, 0], [0, 0, 0], [0, 1, 0]] - kernel_h = [[0, 0, 0], [-1, 0, 1], [0, 0, 0]] - kernel_h = torch.FloatTensor(kernel_h).unsqueeze(0).unsqueeze(0) - kernel_v = torch.FloatTensor(kernel_v).unsqueeze(0).unsqueeze(0) - self.weight_h = nn.Parameter(data=kernel_h, requires_grad=False) # type: ignore - - self.weight_v = nn.Parameter(data=kernel_v, requires_grad=False) # type: ignore - - def forward(self, x): - x_list = [] - for i in range(x.shape[1]): - x_i = x[:, i] - x_i_v = F.conv2d(x_i.unsqueeze(1), self.weight_v, padding=1) - x_i_h = F.conv2d(x_i.unsqueeze(1), self.weight_h, padding=1) - x_i = torch.sqrt(torch.pow(x_i_v, 2) + torch.pow(x_i_h, 2) + 1e-6) - x_list.append(x_i) - - x = torch.cat(x_list, dim=1) - - return x - - -class SPSRNet(nn.Module): - def __init__( - self, - state_dict, - norm=None, - act: str = "leakyrelu", - upsampler: str = "upconv", - mode: B.ConvMode = "CNA", - ): - super(SPSRNet, self).__init__() - self.model_arch = "SPSR" - self.sub_type = "SR" - - self.state = state_dict - self.norm = norm - self.act = act - self.upsampler = upsampler - self.mode = mode - - self.num_blocks = self.get_num_blocks() - - self.in_nc: int = self.state["model.0.weight"].shape[1] - self.out_nc: int = self.state["f_HR_conv1.0.bias"].shape[0] - - self.scale = self.get_scale(4) - self.num_filters: int = self.state["model.0.weight"].shape[0] - - self.supports_fp16 = True - self.supports_bfp16 = True - self.min_size_restriction = None - - n_upscale = int(math.log(self.scale, 2)) - if self.scale == 3: - n_upscale = 1 - - fea_conv = B.conv_block( - self.in_nc, self.num_filters, kernel_size=3, norm_type=None, act_type=None - ) - rb_blocks = [ - B.RRDB( - self.num_filters, - kernel_size=3, - gc=32, - stride=1, - bias=True, - pad_type="zero", - norm_type=norm, - act_type=act, - mode="CNA", - ) - for _ in range(self.num_blocks) - ] - LR_conv = B.conv_block( - self.num_filters, - self.num_filters, - kernel_size=3, - norm_type=norm, - act_type=None, - mode=mode, - ) - - if upsampler == "upconv": - upsample_block = B.upconv_block - elif upsampler == "pixelshuffle": - upsample_block = B.pixelshuffle_block - else: - raise NotImplementedError(f"upsample mode [{upsampler}] is not found") - if self.scale == 3: - a_upsampler = upsample_block( - self.num_filters, self.num_filters, 3, act_type=act - ) - else: - a_upsampler = [ - upsample_block(self.num_filters, self.num_filters, act_type=act) - for _ in range(n_upscale) - ] - self.HR_conv0_new = B.conv_block( - self.num_filters, - self.num_filters, - kernel_size=3, - norm_type=None, - act_type=act, - ) - self.HR_conv1_new = B.conv_block( - self.num_filters, - self.num_filters, - kernel_size=3, - norm_type=None, - act_type=None, - ) - - self.model = B.sequential( - fea_conv, - B.ShortcutBlockSPSR(B.sequential(*rb_blocks, LR_conv)), - *a_upsampler, - self.HR_conv0_new, - ) - - self.get_g_nopadding = Get_gradient_nopadding() - - self.b_fea_conv = B.conv_block( - self.in_nc, self.num_filters, kernel_size=3, norm_type=None, act_type=None - ) - - self.b_concat_1 = B.conv_block( - 2 * self.num_filters, - self.num_filters, - kernel_size=3, - norm_type=None, - act_type=None, - ) - self.b_block_1 = B.RRDB( - self.num_filters * 2, - kernel_size=3, - gc=32, - stride=1, - bias=True, - pad_type="zero", - norm_type=norm, - act_type=act, - mode="CNA", - ) - - self.b_concat_2 = B.conv_block( - 2 * self.num_filters, - self.num_filters, - kernel_size=3, - norm_type=None, - act_type=None, - ) - self.b_block_2 = B.RRDB( - self.num_filters * 2, - kernel_size=3, - gc=32, - stride=1, - bias=True, - pad_type="zero", - norm_type=norm, - act_type=act, - mode="CNA", - ) - - self.b_concat_3 = B.conv_block( - 2 * self.num_filters, - self.num_filters, - kernel_size=3, - norm_type=None, - act_type=None, - ) - self.b_block_3 = B.RRDB( - self.num_filters * 2, - kernel_size=3, - gc=32, - stride=1, - bias=True, - pad_type="zero", - norm_type=norm, - act_type=act, - mode="CNA", - ) - - self.b_concat_4 = B.conv_block( - 2 * self.num_filters, - self.num_filters, - kernel_size=3, - norm_type=None, - act_type=None, - ) - self.b_block_4 = B.RRDB( - self.num_filters * 2, - kernel_size=3, - gc=32, - stride=1, - bias=True, - pad_type="zero", - norm_type=norm, - act_type=act, - mode="CNA", - ) - - self.b_LR_conv = B.conv_block( - self.num_filters, - self.num_filters, - kernel_size=3, - norm_type=norm, - act_type=None, - mode=mode, - ) - - if upsampler == "upconv": - upsample_block = B.upconv_block - elif upsampler == "pixelshuffle": - upsample_block = B.pixelshuffle_block - else: - raise NotImplementedError(f"upsample mode [{upsampler}] is not found") - if self.scale == 3: - b_upsampler = upsample_block( - self.num_filters, self.num_filters, 3, act_type=act - ) - else: - b_upsampler = [ - upsample_block(self.num_filters, self.num_filters, act_type=act) - for _ in range(n_upscale) - ] - - b_HR_conv0 = B.conv_block( - self.num_filters, - self.num_filters, - kernel_size=3, - norm_type=None, - act_type=act, - ) - b_HR_conv1 = B.conv_block( - self.num_filters, - self.num_filters, - kernel_size=3, - norm_type=None, - act_type=None, - ) - - self.b_module = B.sequential(*b_upsampler, b_HR_conv0, b_HR_conv1) - - self.conv_w = B.conv_block( - self.num_filters, self.out_nc, kernel_size=1, norm_type=None, act_type=None - ) - - self.f_concat = B.conv_block( - self.num_filters * 2, - self.num_filters, - kernel_size=3, - norm_type=None, - act_type=None, - ) - - self.f_block = B.RRDB( - self.num_filters * 2, - kernel_size=3, - gc=32, - stride=1, - bias=True, - pad_type="zero", - norm_type=norm, - act_type=act, - mode="CNA", - ) - - self.f_HR_conv0 = B.conv_block( - self.num_filters, - self.num_filters, - kernel_size=3, - norm_type=None, - act_type=act, - ) - self.f_HR_conv1 = B.conv_block( - self.num_filters, self.out_nc, kernel_size=3, norm_type=None, act_type=None - ) - - self.load_state_dict(self.state, strict=False) - - def get_scale(self, min_part: int = 4) -> int: - n = 0 - for part in list(self.state): - parts = part.split(".") - if len(parts) == 3: - part_num = int(parts[1]) - if part_num > min_part and parts[0] == "model" and parts[2] == "weight": - n += 1 - return 2**n - - def get_num_blocks(self) -> int: - nb = 0 - for part in list(self.state): - parts = part.split(".") - n_parts = len(parts) - if n_parts == 5 and parts[2] == "sub": - nb = int(parts[3]) - return nb - - def forward(self, x): - x_grad = self.get_g_nopadding(x) - x = self.model[0](x) - - x, block_list = self.model[1](x) - - x_ori = x - for i in range(5): - x = block_list[i](x) - x_fea1 = x - - for i in range(5): - x = block_list[i + 5](x) - x_fea2 = x - - for i in range(5): - x = block_list[i + 10](x) - x_fea3 = x - - for i in range(5): - x = block_list[i + 15](x) - x_fea4 = x - - x = block_list[20:](x) - # short cut - x = x_ori + x - x = self.model[2:](x) - x = self.HR_conv1_new(x) - - x_b_fea = self.b_fea_conv(x_grad) - x_cat_1 = torch.cat([x_b_fea, x_fea1], dim=1) - - x_cat_1 = self.b_block_1(x_cat_1) - x_cat_1 = self.b_concat_1(x_cat_1) - - x_cat_2 = torch.cat([x_cat_1, x_fea2], dim=1) - - x_cat_2 = self.b_block_2(x_cat_2) - x_cat_2 = self.b_concat_2(x_cat_2) - - x_cat_3 = torch.cat([x_cat_2, x_fea3], dim=1) - - x_cat_3 = self.b_block_3(x_cat_3) - x_cat_3 = self.b_concat_3(x_cat_3) - - x_cat_4 = torch.cat([x_cat_3, x_fea4], dim=1) - - x_cat_4 = self.b_block_4(x_cat_4) - x_cat_4 = self.b_concat_4(x_cat_4) - - x_cat_4 = self.b_LR_conv(x_cat_4) - - # short cut - x_cat_4 = x_cat_4 + x_b_fea - x_branch = self.b_module(x_cat_4) - - # x_out_branch = self.conv_w(x_branch) - ######## - x_branch_d = x_branch - x_f_cat = torch.cat([x_branch_d, x], dim=1) - x_f_cat = self.f_block(x_f_cat) - x_out = self.f_concat(x_f_cat) - x_out = self.f_HR_conv0(x_out) - x_out = self.f_HR_conv1(x_out) - - ######### - # return x_out_branch, x_out, x_grad - return x_out diff --git a/comfy_extras/chainner_models/architecture/SRVGG.py b/comfy_extras/chainner_models/architecture/SRVGG.py deleted file mode 100644 index 7a8ec37a..00000000 --- a/comfy_extras/chainner_models/architecture/SRVGG.py +++ /dev/null @@ -1,114 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -import math - -import torch.nn as nn -import torch.nn.functional as F - - -class SRVGGNetCompact(nn.Module): - """A compact VGG-style network structure for super-resolution. - It is a compact network structure, which performs upsampling in the last layer and no convolution is - conducted on the HR feature space. - Args: - num_in_ch (int): Channel number of inputs. Default: 3. - num_out_ch (int): Channel number of outputs. Default: 3. - num_feat (int): Channel number of intermediate features. Default: 64. - num_conv (int): Number of convolution layers in the body network. Default: 16. - upscale (int): Upsampling factor. Default: 4. - act_type (str): Activation type, options: 'relu', 'prelu', 'leakyrelu'. Default: prelu. - """ - - def __init__( - self, - state_dict, - act_type: str = "prelu", - ): - super(SRVGGNetCompact, self).__init__() - self.model_arch = "SRVGG (RealESRGAN)" - self.sub_type = "SR" - - self.act_type = act_type - - self.state = state_dict - - if "params" in self.state: - self.state = self.state["params"] - - self.key_arr = list(self.state.keys()) - - self.in_nc = self.get_in_nc() - self.num_feat = self.get_num_feats() - self.num_conv = self.get_num_conv() - self.out_nc = self.in_nc # :( - self.pixelshuffle_shape = None # Defined in get_scale() - self.scale = self.get_scale() - - self.supports_fp16 = True - self.supports_bfp16 = True - self.min_size_restriction = None - - self.body = nn.ModuleList() - # the first conv - self.body.append(nn.Conv2d(self.in_nc, self.num_feat, 3, 1, 1)) - # the first activation - if act_type == "relu": - activation = nn.ReLU(inplace=True) - elif act_type == "prelu": - activation = nn.PReLU(num_parameters=self.num_feat) - elif act_type == "leakyrelu": - activation = nn.LeakyReLU(negative_slope=0.1, inplace=True) - self.body.append(activation) # type: ignore - - # the body structure - for _ in range(self.num_conv): - self.body.append(nn.Conv2d(self.num_feat, self.num_feat, 3, 1, 1)) - # activation - if act_type == "relu": - activation = nn.ReLU(inplace=True) - elif act_type == "prelu": - activation = nn.PReLU(num_parameters=self.num_feat) - elif act_type == "leakyrelu": - activation = nn.LeakyReLU(negative_slope=0.1, inplace=True) - self.body.append(activation) # type: ignore - - # the last conv - self.body.append(nn.Conv2d(self.num_feat, self.pixelshuffle_shape, 3, 1, 1)) # type: ignore - # upsample - self.upsampler = nn.PixelShuffle(self.scale) - - self.load_state_dict(self.state, strict=False) - - def get_num_conv(self) -> int: - return (int(self.key_arr[-1].split(".")[1]) - 2) // 2 - - def get_num_feats(self) -> int: - return self.state[self.key_arr[0]].shape[0] - - def get_in_nc(self) -> int: - return self.state[self.key_arr[0]].shape[1] - - def get_scale(self) -> int: - self.pixelshuffle_shape = self.state[self.key_arr[-1]].shape[0] - # Assume out_nc is the same as in_nc - # I cant think of a better way to do that - self.out_nc = self.in_nc - scale = math.sqrt(self.pixelshuffle_shape / self.out_nc) - if scale - int(scale) > 0: - print( - "out_nc is probably different than in_nc, scale calculation might be wrong" - ) - scale = int(scale) - return scale - - def forward(self, x): - out = x - for i in range(0, len(self.body)): - out = self.body[i](out) - - out = self.upsampler(out) - # add the nearest upsampled image, so that the network learns the residual - base = F.interpolate(x, scale_factor=self.scale, mode="nearest") - out += base - return out diff --git a/comfy_extras/chainner_models/architecture/SwiftSRGAN.py b/comfy_extras/chainner_models/architecture/SwiftSRGAN.py deleted file mode 100644 index dbb7725b..00000000 --- a/comfy_extras/chainner_models/architecture/SwiftSRGAN.py +++ /dev/null @@ -1,161 +0,0 @@ -# From https://github.com/Koushik0901/Swift-SRGAN/blob/master/swift-srgan/models.py - -import torch -from torch import nn - - -class SeperableConv2d(nn.Module): - def __init__( - self, in_channels, out_channels, kernel_size, stride=1, padding=1, bias=True - ): - super(SeperableConv2d, self).__init__() - self.depthwise = nn.Conv2d( - in_channels, - in_channels, - kernel_size=kernel_size, - stride=stride, - groups=in_channels, - bias=bias, - padding=padding, - ) - self.pointwise = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=bias) - - def forward(self, x): - return self.pointwise(self.depthwise(x)) - - -class ConvBlock(nn.Module): - def __init__( - self, - in_channels, - out_channels, - use_act=True, - use_bn=True, - discriminator=False, - **kwargs, - ): - super(ConvBlock, self).__init__() - - self.use_act = use_act - self.cnn = SeperableConv2d(in_channels, out_channels, **kwargs, bias=not use_bn) - self.bn = nn.BatchNorm2d(out_channels) if use_bn else nn.Identity() - self.act = ( - nn.LeakyReLU(0.2, inplace=True) - if discriminator - else nn.PReLU(num_parameters=out_channels) - ) - - def forward(self, x): - return self.act(self.bn(self.cnn(x))) if self.use_act else self.bn(self.cnn(x)) - - -class UpsampleBlock(nn.Module): - def __init__(self, in_channels, scale_factor): - super(UpsampleBlock, self).__init__() - - self.conv = SeperableConv2d( - in_channels, - in_channels * scale_factor**2, - kernel_size=3, - stride=1, - padding=1, - ) - self.ps = nn.PixelShuffle( - scale_factor - ) # (in_channels * 4, H, W) -> (in_channels, H*2, W*2) - self.act = nn.PReLU(num_parameters=in_channels) - - def forward(self, x): - return self.act(self.ps(self.conv(x))) - - -class ResidualBlock(nn.Module): - def __init__(self, in_channels): - super(ResidualBlock, self).__init__() - - self.block1 = ConvBlock( - in_channels, in_channels, kernel_size=3, stride=1, padding=1 - ) - self.block2 = ConvBlock( - in_channels, in_channels, kernel_size=3, stride=1, padding=1, use_act=False - ) - - def forward(self, x): - out = self.block1(x) - out = self.block2(out) - return out + x - - -class Generator(nn.Module): - """Swift-SRGAN Generator - Args: - in_channels (int): number of input image channels. - num_channels (int): number of hidden channels. - num_blocks (int): number of residual blocks. - upscale_factor (int): factor to upscale the image [2x, 4x, 8x]. - Returns: - torch.Tensor: super resolution image - """ - - def __init__( - self, - state_dict, - ): - super(Generator, self).__init__() - self.model_arch = "Swift-SRGAN" - self.sub_type = "SR" - self.state = state_dict - if "model" in self.state: - self.state = self.state["model"] - - self.in_nc: int = self.state["initial.cnn.depthwise.weight"].shape[0] - self.out_nc: int = self.state["final_conv.pointwise.weight"].shape[0] - self.num_filters: int = self.state["initial.cnn.pointwise.weight"].shape[0] - self.num_blocks = len( - set([x.split(".")[1] for x in self.state.keys() if "residual" in x]) - ) - self.scale: int = 2 ** len( - set([x.split(".")[1] for x in self.state.keys() if "upsampler" in x]) - ) - - in_channels = self.in_nc - num_channels = self.num_filters - num_blocks = self.num_blocks - upscale_factor = self.scale - - self.supports_fp16 = True - self.supports_bfp16 = True - self.min_size_restriction = None - - self.initial = ConvBlock( - in_channels, num_channels, kernel_size=9, stride=1, padding=4, use_bn=False - ) - self.residual = nn.Sequential( - *[ResidualBlock(num_channels) for _ in range(num_blocks)] - ) - self.convblock = ConvBlock( - num_channels, - num_channels, - kernel_size=3, - stride=1, - padding=1, - use_act=False, - ) - self.upsampler = nn.Sequential( - *[ - UpsampleBlock(num_channels, scale_factor=2) - for _ in range(upscale_factor // 2) - ] - ) - self.final_conv = SeperableConv2d( - num_channels, in_channels, kernel_size=9, stride=1, padding=4 - ) - - self.load_state_dict(self.state, strict=False) - - def forward(self, x): - initial = self.initial(x) - x = self.residual(initial) - x = self.convblock(x) + initial - x = self.upsampler(x) - return (torch.tanh(self.final_conv(x)) + 1) / 2 diff --git a/comfy_extras/chainner_models/architecture/Swin2SR.py b/comfy_extras/chainner_models/architecture/Swin2SR.py deleted file mode 100644 index cb57ecfc..00000000 --- a/comfy_extras/chainner_models/architecture/Swin2SR.py +++ /dev/null @@ -1,1377 +0,0 @@ -# pylint: skip-file -# ----------------------------------------------------------------------------------- -# Swin2SR: Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration, https://arxiv.org/abs/2209.11345 -# Written by Conde and Choi et al. -# From: https://raw.githubusercontent.com/mv-lab/swin2sr/main/models/network_swin2sr.py -# ----------------------------------------------------------------------------------- - -import math -import re - -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.utils.checkpoint as checkpoint - -# Originally from the timm package -from .timm.drop import DropPath -from .timm.helpers import to_2tuple -from .timm.weight_init import trunc_normal_ - - -class Mlp(nn.Module): - def __init__( - self, - in_features, - hidden_features=None, - out_features=None, - act_layer=nn.GELU, - drop=0.0, - ): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - - -def window_partition(x, window_size): - """ - Args: - x: (B, H, W, C) - window_size (int): window size - Returns: - windows: (num_windows*B, window_size, window_size, C) - """ - B, H, W, C = x.shape - x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) - windows = ( - x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) - ) - return windows - - -def window_reverse(windows, window_size, H, W): - """ - Args: - windows: (num_windows*B, window_size, window_size, C) - window_size (int): Window size - H (int): Height of image - W (int): Width of image - Returns: - x: (B, H, W, C) - """ - B = int(windows.shape[0] / (H * W / window_size / window_size)) - x = windows.view( - B, H // window_size, W // window_size, window_size, window_size, -1 - ) - x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) - return x - - -class WindowAttention(nn.Module): - r"""Window based multi-head self attention (W-MSA) module with relative position bias. - It supports both of shifted and non-shifted window. - Args: - dim (int): Number of input channels. - window_size (tuple[int]): The height and width of the window. - num_heads (int): Number of attention heads. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 - proj_drop (float, optional): Dropout ratio of output. Default: 0.0 - pretrained_window_size (tuple[int]): The height and width of the window in pre-training. - """ - - def __init__( - self, - dim, - window_size, - num_heads, - qkv_bias=True, - attn_drop=0.0, - proj_drop=0.0, - pretrained_window_size=[0, 0], - ): - super().__init__() - self.dim = dim - self.window_size = window_size # Wh, Ww - self.pretrained_window_size = pretrained_window_size - self.num_heads = num_heads - - self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True) # type: ignore - - # mlp to generate continuous relative position bias - self.cpb_mlp = nn.Sequential( - nn.Linear(2, 512, bias=True), - nn.ReLU(inplace=True), - nn.Linear(512, num_heads, bias=False), - ) - - # get relative_coords_table - relative_coords_h = torch.arange( - -(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32 - ) - relative_coords_w = torch.arange( - -(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32 - ) - relative_coords_table = ( - torch.stack(torch.meshgrid([relative_coords_h, relative_coords_w])) - .permute(1, 2, 0) - .contiguous() - .unsqueeze(0) - ) # 1, 2*Wh-1, 2*Ww-1, 2 - if pretrained_window_size[0] > 0: - relative_coords_table[:, :, :, 0] /= pretrained_window_size[0] - 1 - relative_coords_table[:, :, :, 1] /= pretrained_window_size[1] - 1 - else: - relative_coords_table[:, :, :, 0] /= self.window_size[0] - 1 - relative_coords_table[:, :, :, 1] /= self.window_size[1] - 1 - relative_coords_table *= 8 # normalize to -8, 8 - relative_coords_table = ( - torch.sign(relative_coords_table) - * torch.log2(torch.abs(relative_coords_table) + 1.0) - / np.log2(8) - ) - - self.register_buffer("relative_coords_table", relative_coords_table) - - # get pair-wise relative position index for each token inside the window - coords_h = torch.arange(self.window_size[0]) - coords_w = torch.arange(self.window_size[1]) - coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww - coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww - relative_coords = ( - coords_flatten[:, :, None] - coords_flatten[:, None, :] - ) # 2, Wh*Ww, Wh*Ww - relative_coords = relative_coords.permute( - 1, 2, 0 - ).contiguous() # Wh*Ww, Wh*Ww, 2 - relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 - relative_coords[:, :, 1] += self.window_size[1] - 1 - relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 - relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww - self.register_buffer("relative_position_index", relative_position_index) - - self.qkv = nn.Linear(dim, dim * 3, bias=False) - if qkv_bias: - self.q_bias = nn.Parameter(torch.zeros(dim)) # type: ignore - self.v_bias = nn.Parameter(torch.zeros(dim)) # type: ignore - else: - self.q_bias = None - self.v_bias = None - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - self.proj_drop = nn.Dropout(proj_drop) - self.softmax = nn.Softmax(dim=-1) - - def forward(self, x, mask=None): - """ - Args: - x: input features with shape of (num_windows*B, N, C) - mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None - """ - B_, N, C = x.shape - qkv_bias = None - if self.q_bias is not None: - qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) # type: ignore - qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) - qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) - q, k, v = ( - qkv[0], - qkv[1], - qkv[2], - ) # make torchscript happy (cannot use tensor as tuple) - - # cosine attention - attn = F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1) - logit_scale = torch.clamp( - self.logit_scale, - max=torch.log(torch.tensor(1.0 / 0.01)).to(self.logit_scale.device), - ).exp() - attn = attn * logit_scale - - relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view( - -1, self.num_heads - ) - relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view( # type: ignore - self.window_size[0] * self.window_size[1], - self.window_size[0] * self.window_size[1], - -1, - ) # Wh*Ww,Wh*Ww,nH - relative_position_bias = relative_position_bias.permute( - 2, 0, 1 - ).contiguous() # nH, Wh*Ww, Wh*Ww - relative_position_bias = 16 * torch.sigmoid(relative_position_bias) - attn = attn + relative_position_bias.unsqueeze(0) - - if mask is not None: - nW = mask.shape[0] - attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze( - 1 - ).unsqueeze(0) - attn = attn.view(-1, self.num_heads, N, N) - attn = self.softmax(attn) - else: - attn = self.softmax(attn) - - attn = self.attn_drop(attn) - - x = (attn @ v).transpose(1, 2).reshape(B_, N, C) - x = self.proj(x) - x = self.proj_drop(x) - return x - - def extra_repr(self) -> str: - return ( - f"dim={self.dim}, window_size={self.window_size}, " - f"pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}" - ) - - def flops(self, N): - # calculate flops for 1 window with token length of N - flops = 0 - # qkv = self.qkv(x) - flops += N * self.dim * 3 * self.dim - # attn = (q @ k.transpose(-2, -1)) - flops += self.num_heads * N * (self.dim // self.num_heads) * N - # x = (attn @ v) - flops += self.num_heads * N * N * (self.dim // self.num_heads) - # x = self.proj(x) - flops += N * self.dim * self.dim - return flops - - -class SwinTransformerBlock(nn.Module): - r"""Swin Transformer Block. - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resulotion. - num_heads (int): Number of attention heads. - window_size (int): Window size. - shift_size (int): Shift size for SW-MSA. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float, optional): Stochastic depth rate. Default: 0.0 - act_layer (nn.Module, optional): Activation layer. Default: nn.GELU - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - pretrained_window_size (int): Window size in pre-training. - """ - - def __init__( - self, - dim, - input_resolution, - num_heads, - window_size=7, - shift_size=0, - mlp_ratio=4.0, - qkv_bias=True, - drop=0.0, - attn_drop=0.0, - drop_path=0.0, - act_layer=nn.GELU, - norm_layer=nn.LayerNorm, - pretrained_window_size=0, - ): - super().__init__() - self.dim = dim - self.input_resolution = input_resolution - self.num_heads = num_heads - self.window_size = window_size - self.shift_size = shift_size - self.mlp_ratio = mlp_ratio - if min(self.input_resolution) <= self.window_size: - # if window size is larger than input resolution, we don't partition windows - self.shift_size = 0 - self.window_size = min(self.input_resolution) - assert ( - 0 <= self.shift_size < self.window_size - ), "shift_size must in 0-window_size" - - self.norm1 = norm_layer(dim) - self.attn = WindowAttention( - dim, - window_size=to_2tuple(self.window_size), - num_heads=num_heads, - qkv_bias=qkv_bias, - attn_drop=attn_drop, - proj_drop=drop, - pretrained_window_size=to_2tuple(pretrained_window_size), - ) - - self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp( - in_features=dim, - hidden_features=mlp_hidden_dim, - act_layer=act_layer, - drop=drop, - ) - - if self.shift_size > 0: - attn_mask = self.calculate_mask(self.input_resolution) - else: - attn_mask = None - - self.register_buffer("attn_mask", attn_mask) - - def calculate_mask(self, x_size): - # calculate attention mask for SW-MSA - H, W = x_size - img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 - h_slices = ( - slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None), - ) - w_slices = ( - slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None), - ) - cnt = 0 - for h in h_slices: - for w in w_slices: - img_mask[:, h, w, :] = cnt - cnt += 1 - - mask_windows = window_partition( - img_mask, self.window_size - ) # nW, window_size, window_size, 1 - mask_windows = mask_windows.view(-1, self.window_size * self.window_size) - attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) - attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill( - attn_mask == 0, float(0.0) - ) - - return attn_mask - - def forward(self, x, x_size): - H, W = x_size - B, L, C = x.shape - # assert L == H * W, "input feature has wrong size" - - shortcut = x - x = x.view(B, H, W, C) - - # cyclic shift - if self.shift_size > 0: - shifted_x = torch.roll( - x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2) - ) - else: - shifted_x = x - - # partition windows - x_windows = window_partition( - shifted_x, self.window_size - ) # nW*B, window_size, window_size, C - x_windows = x_windows.view( - -1, self.window_size * self.window_size, C - ) # nW*B, window_size*window_size, C - - # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size - if self.input_resolution == x_size: - attn_windows = self.attn( - x_windows, mask=self.attn_mask - ) # nW*B, window_size*window_size, C - else: - attn_windows = self.attn( - x_windows, mask=self.calculate_mask(x_size).to(x.device) - ) - - # merge windows - attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) - shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C - - # reverse cyclic shift - if self.shift_size > 0: - x = torch.roll( - shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2) - ) - else: - x = shifted_x - x = x.view(B, H * W, C) - x = shortcut + self.drop_path(self.norm1(x)) - - # FFN - x = x + self.drop_path(self.norm2(self.mlp(x))) - - return x - - def extra_repr(self) -> str: - return ( - f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " - f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" - ) - - def flops(self): - flops = 0 - H, W = self.input_resolution - # norm1 - flops += self.dim * H * W - # W-MSA/SW-MSA - nW = H * W / self.window_size / self.window_size - flops += nW * self.attn.flops(self.window_size * self.window_size) - # mlp - flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio - # norm2 - flops += self.dim * H * W - return flops - - -class PatchMerging(nn.Module): - r"""Patch Merging Layer. - Args: - input_resolution (tuple[int]): Resolution of input feature. - dim (int): Number of input channels. - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - - def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): - super().__init__() - self.input_resolution = input_resolution - self.dim = dim - self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) - self.norm = norm_layer(2 * dim) - - def forward(self, x): - """ - x: B, H*W, C - """ - H, W = self.input_resolution - B, L, C = x.shape - assert L == H * W, "input feature has wrong size" - assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." - - x = x.view(B, H, W, C) - - x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C - x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C - x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C - x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C - x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C - x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C - - x = self.reduction(x) - x = self.norm(x) - - return x - - def extra_repr(self) -> str: - return f"input_resolution={self.input_resolution}, dim={self.dim}" - - def flops(self): - H, W = self.input_resolution - flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim - flops += H * W * self.dim // 2 - return flops - - -class BasicLayer(nn.Module): - """A basic Swin Transformer layer for one stage. - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resolution. - depth (int): Number of blocks. - num_heads (int): Number of attention heads. - window_size (int): Local window size. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. - pretrained_window_size (int): Local window size in pre-training. - """ - - def __init__( - self, - dim, - input_resolution, - depth, - num_heads, - window_size, - mlp_ratio=4.0, - qkv_bias=True, - drop=0.0, - attn_drop=0.0, - drop_path=0.0, - norm_layer=nn.LayerNorm, - downsample=None, - use_checkpoint=False, - pretrained_window_size=0, - ): - super().__init__() - self.dim = dim - self.input_resolution = input_resolution - self.depth = depth - self.use_checkpoint = use_checkpoint - - # build blocks - self.blocks = nn.ModuleList( - [ - SwinTransformerBlock( - dim=dim, - input_resolution=input_resolution, - num_heads=num_heads, - window_size=window_size, - shift_size=0 if (i % 2 == 0) else window_size // 2, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - drop=drop, - attn_drop=attn_drop, - drop_path=drop_path[i] - if isinstance(drop_path, list) - else drop_path, - norm_layer=norm_layer, - pretrained_window_size=pretrained_window_size, - ) - for i in range(depth) - ] - ) - - # patch merging layer - if downsample is not None: - self.downsample = downsample( - input_resolution, dim=dim, norm_layer=norm_layer - ) - else: - self.downsample = None - - def forward(self, x, x_size): - for blk in self.blocks: - if self.use_checkpoint: - x = checkpoint.checkpoint(blk, x, x_size) - else: - x = blk(x, x_size) - if self.downsample is not None: - x = self.downsample(x) - return x - - def extra_repr(self) -> str: - return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" - - def flops(self): - flops = 0 - for blk in self.blocks: - flops += blk.flops() # type: ignore - if self.downsample is not None: - flops += self.downsample.flops() - return flops - - def _init_respostnorm(self): - for blk in self.blocks: - nn.init.constant_(blk.norm1.bias, 0) # type: ignore - nn.init.constant_(blk.norm1.weight, 0) # type: ignore - nn.init.constant_(blk.norm2.bias, 0) # type: ignore - nn.init.constant_(blk.norm2.weight, 0) # type: ignore - - -class PatchEmbed(nn.Module): - r"""Image to Patch Embedding - Args: - img_size (int): Image size. Default: 224. - patch_size (int): Patch token size. Default: 4. - in_chans (int): Number of input image channels. Default: 3. - embed_dim (int): Number of linear projection output channels. Default: 96. - norm_layer (nn.Module, optional): Normalization layer. Default: None - """ - - def __init__( - self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None - ): - super().__init__() - img_size = to_2tuple(img_size) - patch_size = to_2tuple(patch_size) - patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] # type: ignore - self.img_size = img_size - self.patch_size = patch_size - self.patches_resolution = patches_resolution - self.num_patches = patches_resolution[0] * patches_resolution[1] - - self.in_chans = in_chans - self.embed_dim = embed_dim - - self.proj = nn.Conv2d( - in_chans, embed_dim, kernel_size=patch_size, stride=patch_size # type: ignore - ) - if norm_layer is not None: - self.norm = norm_layer(embed_dim) - else: - self.norm = None - - def forward(self, x): - B, C, H, W = x.shape - # FIXME look at relaxing size constraints - # assert H == self.img_size[0] and W == self.img_size[1], - # f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." - x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C - if self.norm is not None: - x = self.norm(x) - return x - - def flops(self): - Ho, Wo = self.patches_resolution - flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) # type: ignore - if self.norm is not None: - flops += Ho * Wo * self.embed_dim - return flops - - -class RSTB(nn.Module): - """Residual Swin Transformer Block (RSTB). - - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resolution. - depth (int): Number of blocks. - num_heads (int): Number of attention heads. - window_size (int): Local window size. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. - img_size: Input image size. - patch_size: Patch size. - resi_connection: The convolutional block before residual connection. - """ - - def __init__( - self, - dim, - input_resolution, - depth, - num_heads, - window_size, - mlp_ratio=4.0, - qkv_bias=True, - drop=0.0, - attn_drop=0.0, - drop_path=0.0, - norm_layer=nn.LayerNorm, - downsample=None, - use_checkpoint=False, - img_size=224, - patch_size=4, - resi_connection="1conv", - ): - super(RSTB, self).__init__() - - self.dim = dim - self.input_resolution = input_resolution - - self.residual_group = BasicLayer( - dim=dim, - input_resolution=input_resolution, - depth=depth, - num_heads=num_heads, - window_size=window_size, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - drop=drop, - attn_drop=attn_drop, - drop_path=drop_path, - norm_layer=norm_layer, - downsample=downsample, - use_checkpoint=use_checkpoint, - ) - - if resi_connection == "1conv": - self.conv = nn.Conv2d(dim, dim, 3, 1, 1) - elif resi_connection == "3conv": - # to save parameters and memory - self.conv = nn.Sequential( - nn.Conv2d(dim, dim // 4, 3, 1, 1), - nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), - nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(dim // 4, dim, 3, 1, 1), - ) - - self.patch_embed = PatchEmbed( - img_size=img_size, - patch_size=patch_size, - in_chans=dim, - embed_dim=dim, - norm_layer=None, - ) - - self.patch_unembed = PatchUnEmbed( - img_size=img_size, - patch_size=patch_size, - in_chans=dim, - embed_dim=dim, - norm_layer=None, - ) - - def forward(self, x, x_size): - return ( - self.patch_embed( - self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size)) - ) - + x - ) - - def flops(self): - flops = 0 - flops += self.residual_group.flops() - H, W = self.input_resolution - flops += H * W * self.dim * self.dim * 9 - flops += self.patch_embed.flops() - flops += self.patch_unembed.flops() - - return flops - - -class PatchUnEmbed(nn.Module): - r"""Image to Patch Unembedding - - Args: - img_size (int): Image size. Default: 224. - patch_size (int): Patch token size. Default: 4. - in_chans (int): Number of input image channels. Default: 3. - embed_dim (int): Number of linear projection output channels. Default: 96. - norm_layer (nn.Module, optional): Normalization layer. Default: None - """ - - def __init__( - self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None - ): - super().__init__() - img_size = to_2tuple(img_size) - patch_size = to_2tuple(patch_size) - patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] # type: ignore - self.img_size = img_size - self.patch_size = patch_size - self.patches_resolution = patches_resolution - self.num_patches = patches_resolution[0] * patches_resolution[1] - - self.in_chans = in_chans - self.embed_dim = embed_dim - - def forward(self, x, x_size): - B, HW, C = x.shape - x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C - return x - - def flops(self): - flops = 0 - return flops - - -class Upsample(nn.Sequential): - """Upsample module. - - Args: - scale (int): Scale factor. Supported scales: 2^n and 3. - num_feat (int): Channel number of intermediate features. - """ - - def __init__(self, scale, num_feat): - m = [] - if (scale & (scale - 1)) == 0: # scale = 2^n - for _ in range(int(math.log(scale, 2))): - m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) - m.append(nn.PixelShuffle(2)) - elif scale == 3: - m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) - m.append(nn.PixelShuffle(3)) - else: - raise ValueError( - f"scale {scale} is not supported. " "Supported scales: 2^n and 3." - ) - super(Upsample, self).__init__(*m) - - -class Upsample_hf(nn.Sequential): - """Upsample module. - - Args: - scale (int): Scale factor. Supported scales: 2^n and 3. - num_feat (int): Channel number of intermediate features. - """ - - def __init__(self, scale, num_feat): - m = [] - if (scale & (scale - 1)) == 0: # scale = 2^n - for _ in range(int(math.log(scale, 2))): - m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) - m.append(nn.PixelShuffle(2)) - elif scale == 3: - m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) - m.append(nn.PixelShuffle(3)) - else: - raise ValueError( - f"scale {scale} is not supported. " "Supported scales: 2^n and 3." - ) - super(Upsample_hf, self).__init__(*m) - - -class UpsampleOneStep(nn.Sequential): - """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) - Used in lightweight SR to save parameters. - - Args: - scale (int): Scale factor. Supported scales: 2^n and 3. - num_feat (int): Channel number of intermediate features. - - """ - - def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): - self.num_feat = num_feat - self.input_resolution = input_resolution - m = [] - m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1)) - m.append(nn.PixelShuffle(scale)) - super(UpsampleOneStep, self).__init__(*m) - - def flops(self): - H, W = self.input_resolution # type: ignore - flops = H * W * self.num_feat * 3 * 9 - return flops - - -class Swin2SR(nn.Module): - r"""Swin2SR - A PyTorch impl of : `Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration`. - - Args: - img_size (int | tuple(int)): Input image size. Default 64 - patch_size (int | tuple(int)): Patch size. Default: 1 - in_chans (int): Number of input image channels. Default: 3 - embed_dim (int): Patch embedding dimension. Default: 96 - depths (tuple(int)): Depth of each Swin Transformer layer. - num_heads (tuple(int)): Number of attention heads in different layers. - window_size (int): Window size. Default: 7 - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 - qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True - drop_rate (float): Dropout rate. Default: 0 - attn_drop_rate (float): Attention dropout rate. Default: 0 - drop_path_rate (float): Stochastic depth rate. Default: 0.1 - norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. - ape (bool): If True, add absolute position embedding to the patch embedding. Default: False - patch_norm (bool): If True, add normalization after patch embedding. Default: True - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False - upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction - img_range: Image range. 1. or 255. - upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None - resi_connection: The convolutional block before residual connection. '1conv'/'3conv' - """ - - def __init__( - self, - state_dict, - **kwargs, - ): - super(Swin2SR, self).__init__() - - # Defaults - img_size = 128 - patch_size = 1 - in_chans = 3 - embed_dim = 96 - depths = [6, 6, 6, 6] - num_heads = [6, 6, 6, 6] - window_size = 7 - mlp_ratio = 4.0 - qkv_bias = True - drop_rate = 0.0 - attn_drop_rate = 0.0 - drop_path_rate = 0.1 - norm_layer = nn.LayerNorm - ape = False - patch_norm = True - use_checkpoint = False - upscale = 2 - img_range = 1.0 - upsampler = "" - resi_connection = "1conv" - num_in_ch = in_chans - num_out_ch = in_chans - num_feat = 64 - - self.model_arch = "Swin2SR" - self.sub_type = "SR" - self.state = state_dict - if "params_ema" in self.state: - self.state = self.state["params_ema"] - elif "params" in self.state: - self.state = self.state["params"] - - state_keys = self.state.keys() - - if "conv_before_upsample.0.weight" in state_keys: - if "conv_aux.weight" in state_keys: - upsampler = "pixelshuffle_aux" - elif "conv_up1.weight" in state_keys: - upsampler = "nearest+conv" - else: - upsampler = "pixelshuffle" - supports_fp16 = False - elif "upsample.0.weight" in state_keys: - upsampler = "pixelshuffledirect" - else: - upsampler = "" - - num_feat = ( - self.state.get("conv_before_upsample.0.weight", None).shape[1] - if self.state.get("conv_before_upsample.weight", None) - else 64 - ) - - num_in_ch = self.state["conv_first.weight"].shape[1] - in_chans = num_in_ch - if "conv_last.weight" in state_keys: - num_out_ch = self.state["conv_last.weight"].shape[0] - else: - num_out_ch = num_in_ch - - upscale = 1 - if upsampler == "nearest+conv": - upsample_keys = [ - x for x in state_keys if "conv_up" in x and "bias" not in x - ] - - for upsample_key in upsample_keys: - upscale *= 2 - elif upsampler == "pixelshuffle" or upsampler == "pixelshuffle_aux": - upsample_keys = [ - x - for x in state_keys - if "upsample" in x and "conv" not in x and "bias" not in x - ] - for upsample_key in upsample_keys: - shape = self.state[upsample_key].shape[0] - upscale *= math.sqrt(shape // num_feat) - upscale = int(upscale) - elif upsampler == "pixelshuffledirect": - upscale = int( - math.sqrt(self.state["upsample.0.bias"].shape[0] // num_out_ch) - ) - - max_layer_num = 0 - max_block_num = 0 - for key in state_keys: - result = re.match( - r"layers.(\d*).residual_group.blocks.(\d*).norm1.weight", key - ) - if result: - layer_num, block_num = result.groups() - max_layer_num = max(max_layer_num, int(layer_num)) - max_block_num = max(max_block_num, int(block_num)) - - depths = [max_block_num + 1 for _ in range(max_layer_num + 1)] - - if ( - "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" - in state_keys - ): - num_heads_num = self.state[ - "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" - ].shape[-1] - num_heads = [num_heads_num for _ in range(max_layer_num + 1)] - else: - num_heads = depths - - embed_dim = self.state["conv_first.weight"].shape[0] - - mlp_ratio = float( - self.state["layers.0.residual_group.blocks.0.mlp.fc1.bias"].shape[0] - / embed_dim - ) - - # TODO: could actually count the layers, but this should do - if "layers.0.conv.4.weight" in state_keys: - resi_connection = "3conv" - else: - resi_connection = "1conv" - - window_size = int( - math.sqrt( - self.state[ - "layers.0.residual_group.blocks.0.attn.relative_position_index" - ].shape[0] - ) - ) - - if "layers.0.residual_group.blocks.1.attn_mask" in state_keys: - img_size = int( - math.sqrt( - self.state["layers.0.residual_group.blocks.1.attn_mask"].shape[0] - ) - * window_size - ) - - # The JPEG models are the only ones with window-size 7, and they also use this range - img_range = 255.0 if window_size == 7 else 1.0 - - self.in_nc = num_in_ch - self.out_nc = num_out_ch - self.num_feat = num_feat - self.embed_dim = embed_dim - self.num_heads = num_heads - self.depths = depths - self.window_size = window_size - self.mlp_ratio = mlp_ratio - self.scale = upscale - self.upsampler = upsampler - self.img_size = img_size - self.img_range = img_range - self.resi_connection = resi_connection - - self.supports_fp16 = False # Too much weirdness to support this at the moment - self.supports_bfp16 = True - self.min_size_restriction = 16 - - ## END AUTO DETECTION - - if in_chans == 3: - rgb_mean = (0.4488, 0.4371, 0.4040) - self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) - else: - self.mean = torch.zeros(1, 1, 1, 1) - self.upscale = upscale - self.upsampler = upsampler - self.window_size = window_size - - ##################################################################################################### - ################################### 1, shallow feature extraction ################################### - self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) - - ##################################################################################################### - ################################### 2, deep feature extraction ###################################### - self.num_layers = len(depths) - self.embed_dim = embed_dim - self.ape = ape - self.patch_norm = patch_norm - self.num_features = embed_dim - self.mlp_ratio = mlp_ratio - - # split image into non-overlapping patches - self.patch_embed = PatchEmbed( - img_size=img_size, - patch_size=patch_size, - in_chans=embed_dim, - embed_dim=embed_dim, - norm_layer=norm_layer if self.patch_norm else None, - ) - num_patches = self.patch_embed.num_patches - patches_resolution = self.patch_embed.patches_resolution - self.patches_resolution = patches_resolution - - # merge non-overlapping patches into image - self.patch_unembed = PatchUnEmbed( - img_size=img_size, - patch_size=patch_size, - in_chans=embed_dim, - embed_dim=embed_dim, - norm_layer=norm_layer if self.patch_norm else None, - ) - - # absolute position embedding - if self.ape: - self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) # type: ignore - trunc_normal_(self.absolute_pos_embed, std=0.02) - - self.pos_drop = nn.Dropout(p=drop_rate) - - # stochastic depth - dpr = [ - x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) - ] # stochastic depth decay rule - - # build Residual Swin Transformer blocks (RSTB) - self.layers = nn.ModuleList() - for i_layer in range(self.num_layers): - layer = RSTB( - dim=embed_dim, - input_resolution=(patches_resolution[0], patches_resolution[1]), - depth=depths[i_layer], - num_heads=num_heads[i_layer], - window_size=window_size, - mlp_ratio=self.mlp_ratio, - qkv_bias=qkv_bias, - drop=drop_rate, - attn_drop=attn_drop_rate, - drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])], # type: ignore # no impact on SR results - norm_layer=norm_layer, - downsample=None, - use_checkpoint=use_checkpoint, - img_size=img_size, - patch_size=patch_size, - resi_connection=resi_connection, - ) - self.layers.append(layer) - - if self.upsampler == "pixelshuffle_hf": - self.layers_hf = nn.ModuleList() - for i_layer in range(self.num_layers): - layer = RSTB( - dim=embed_dim, - input_resolution=(patches_resolution[0], patches_resolution[1]), - depth=depths[i_layer], - num_heads=num_heads[i_layer], - window_size=window_size, - mlp_ratio=self.mlp_ratio, - qkv_bias=qkv_bias, - drop=drop_rate, - attn_drop=attn_drop_rate, - drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])], # type: ignore # no impact on SR results # type: ignore - norm_layer=norm_layer, - downsample=None, - use_checkpoint=use_checkpoint, - img_size=img_size, - patch_size=patch_size, - resi_connection=resi_connection, - ) - self.layers_hf.append(layer) - - self.norm = norm_layer(self.num_features) - - # build the last conv layer in deep feature extraction - if resi_connection == "1conv": - self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) - elif resi_connection == "3conv": - # to save parameters and memory - self.conv_after_body = nn.Sequential( - nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), - nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), - nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1), - ) - - ##################################################################################################### - ################################ 3, high quality image reconstruction ################################ - if self.upsampler == "pixelshuffle": - # for classical SR - self.conv_before_upsample = nn.Sequential( - nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) - ) - self.upsample = Upsample(upscale, num_feat) - self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - elif self.upsampler == "pixelshuffle_aux": - self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) - self.conv_before_upsample = nn.Sequential( - nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) - ) - self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - self.conv_after_aux = nn.Sequential( - nn.Conv2d(3, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) - ) - self.upsample = Upsample(upscale, num_feat) - self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - - elif self.upsampler == "pixelshuffle_hf": - self.conv_before_upsample = nn.Sequential( - nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) - ) - self.upsample = Upsample(upscale, num_feat) - self.upsample_hf = Upsample_hf(upscale, num_feat) - self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - self.conv_first_hf = nn.Sequential( - nn.Conv2d(num_feat, embed_dim, 3, 1, 1), nn.LeakyReLU(inplace=True) - ) - self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) - self.conv_before_upsample_hf = nn.Sequential( - nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) - ) - self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - - elif self.upsampler == "pixelshuffledirect": - # for lightweight SR (to save parameters) - self.upsample = UpsampleOneStep( - upscale, - embed_dim, - num_out_ch, - (patches_resolution[0], patches_resolution[1]), - ) - elif self.upsampler == "nearest+conv": - # for real-world SR (less artifacts) - assert self.upscale == 4, "only support x4 now." - self.conv_before_upsample = nn.Sequential( - nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) - ) - self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) - else: - # for image denoising and JPEG compression artifact reduction - self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1) - - self.apply(self._init_weights) - - self.load_state_dict(state_dict) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=0.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - @torch.jit.ignore # type: ignore - def no_weight_decay(self): - return {"absolute_pos_embed"} - - @torch.jit.ignore # type: ignore - def no_weight_decay_keywords(self): - return {"relative_position_bias_table"} - - def check_image_size(self, x): - _, _, h, w = x.size() - mod_pad_h = (self.window_size - h % self.window_size) % self.window_size - mod_pad_w = (self.window_size - w % self.window_size) % self.window_size - x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") - return x - - def forward_features(self, x): - x_size = (x.shape[2], x.shape[3]) - x = self.patch_embed(x) - if self.ape: - x = x + self.absolute_pos_embed - x = self.pos_drop(x) - - for layer in self.layers: - x = layer(x, x_size) - - x = self.norm(x) # B L C - x = self.patch_unembed(x, x_size) - - return x - - def forward_features_hf(self, x): - x_size = (x.shape[2], x.shape[3]) - x = self.patch_embed(x) - if self.ape: - x = x + self.absolute_pos_embed - x = self.pos_drop(x) - - for layer in self.layers_hf: - x = layer(x, x_size) - - x = self.norm(x) # B L C - x = self.patch_unembed(x, x_size) - - return x - - def forward(self, x): - H, W = x.shape[2:] - x = self.check_image_size(x) - - self.mean = self.mean.type_as(x) - x = (x - self.mean) * self.img_range - - if self.upsampler == "pixelshuffle": - # for classical SR - x = self.conv_first(x) - x = self.conv_after_body(self.forward_features(x)) + x - x = self.conv_before_upsample(x) - x = self.conv_last(self.upsample(x)) - elif self.upsampler == "pixelshuffle_aux": - bicubic = F.interpolate( - x, - size=(H * self.upscale, W * self.upscale), - mode="bicubic", - align_corners=False, - ) - bicubic = self.conv_bicubic(bicubic) - x = self.conv_first(x) - x = self.conv_after_body(self.forward_features(x)) + x - x = self.conv_before_upsample(x) - aux = self.conv_aux(x) # b, 3, LR_H, LR_W - x = self.conv_after_aux(aux) - x = ( - self.upsample(x)[:, :, : H * self.upscale, : W * self.upscale] - + bicubic[:, :, : H * self.upscale, : W * self.upscale] - ) - x = self.conv_last(x) - aux = aux / self.img_range + self.mean - elif self.upsampler == "pixelshuffle_hf": - # for classical SR with HF - x = self.conv_first(x) - x = self.conv_after_body(self.forward_features(x)) + x - x_before = self.conv_before_upsample(x) - x_out = self.conv_last(self.upsample(x_before)) - - x_hf = self.conv_first_hf(x_before) - x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf - x_hf = self.conv_before_upsample_hf(x_hf) - x_hf = self.conv_last_hf(self.upsample_hf(x_hf)) - x = x_out + x_hf - x_hf = x_hf / self.img_range + self.mean - - elif self.upsampler == "pixelshuffledirect": - # for lightweight SR - x = self.conv_first(x) - x = self.conv_after_body(self.forward_features(x)) + x - x = self.upsample(x) - elif self.upsampler == "nearest+conv": - # for real-world SR - x = self.conv_first(x) - x = self.conv_after_body(self.forward_features(x)) + x - x = self.conv_before_upsample(x) - x = self.lrelu( - self.conv_up1( - torch.nn.functional.interpolate(x, scale_factor=2, mode="nearest") - ) - ) - x = self.lrelu( - self.conv_up2( - torch.nn.functional.interpolate(x, scale_factor=2, mode="nearest") - ) - ) - x = self.conv_last(self.lrelu(self.conv_hr(x))) - else: - # for image denoising and JPEG compression artifact reduction - x_first = self.conv_first(x) - res = self.conv_after_body(self.forward_features(x_first)) + x_first - x = x + self.conv_last(res) - - x = x / self.img_range + self.mean - if self.upsampler == "pixelshuffle_aux": - # NOTE: I removed an "aux" output here. not sure what that was for - return x[:, :, : H * self.upscale, : W * self.upscale] # type: ignore - - elif self.upsampler == "pixelshuffle_hf": - x_out = x_out / self.img_range + self.mean # type: ignore - return x_out[:, :, : H * self.upscale, : W * self.upscale], x[:, :, : H * self.upscale, : W * self.upscale], x_hf[:, :, : H * self.upscale, : W * self.upscale] # type: ignore - - else: - return x[:, :, : H * self.upscale, : W * self.upscale] - - def flops(self): - flops = 0 - H, W = self.patches_resolution - flops += H * W * 3 * self.embed_dim * 9 - flops += self.patch_embed.flops() - for i, layer in enumerate(self.layers): - flops += layer.flops() # type: ignore - flops += H * W * 3 * self.embed_dim * self.embed_dim - flops += self.upsample.flops() # type: ignore - return flops diff --git a/comfy_extras/chainner_models/architecture/SwinIR.py b/comfy_extras/chainner_models/architecture/SwinIR.py deleted file mode 100644 index 439dcbcb..00000000 --- a/comfy_extras/chainner_models/architecture/SwinIR.py +++ /dev/null @@ -1,1224 +0,0 @@ -# pylint: skip-file -# ----------------------------------------------------------------------------------- -# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257 -# Originally Written by Ze Liu, Modified by Jingyun Liang. -# ----------------------------------------------------------------------------------- - -import math -import re - -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.utils.checkpoint as checkpoint - -# Originally from the timm package -from .timm.drop import DropPath -from .timm.helpers import to_2tuple -from .timm.weight_init import trunc_normal_ - - -class Mlp(nn.Module): - def __init__( - self, - in_features, - hidden_features=None, - out_features=None, - act_layer=nn.GELU, - drop=0.0, - ): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - - -def window_partition(x, window_size): - """ - Args: - x: (B, H, W, C) - window_size (int): window size - - Returns: - windows: (num_windows*B, window_size, window_size, C) - """ - B, H, W, C = x.shape - x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) - windows = ( - x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) - ) - return windows - - -def window_reverse(windows, window_size, H, W): - """ - Args: - windows: (num_windows*B, window_size, window_size, C) - window_size (int): Window size - H (int): Height of image - W (int): Width of image - - Returns: - x: (B, H, W, C) - """ - B = int(windows.shape[0] / (H * W / window_size / window_size)) - x = windows.view( - B, H // window_size, W // window_size, window_size, window_size, -1 - ) - x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) - return x - - -class WindowAttention(nn.Module): - r"""Window based multi-head self attention (W-MSA) module with relative position bias. - It supports both of shifted and non-shifted window. - - Args: - dim (int): Number of input channels. - window_size (tuple[int]): The height and width of the window. - num_heads (int): Number of attention heads. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set - attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 - proj_drop (float, optional): Dropout ratio of output. Default: 0.0 - """ - - def __init__( - self, - dim, - window_size, - num_heads, - qkv_bias=True, - qk_scale=None, - attn_drop=0.0, - proj_drop=0.0, - ): - super().__init__() - self.dim = dim - self.window_size = window_size # Wh, Ww - self.num_heads = num_heads - head_dim = dim // num_heads - self.scale = qk_scale or head_dim**-0.5 - - # define a parameter table of relative position bias - self.relative_position_bias_table = nn.Parameter( # type: ignore - torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads) - ) # 2*Wh-1 * 2*Ww-1, nH - - # get pair-wise relative position index for each token inside the window - coords_h = torch.arange(self.window_size[0]) - coords_w = torch.arange(self.window_size[1]) - coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww - coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww - relative_coords = ( - coords_flatten[:, :, None] - coords_flatten[:, None, :] - ) # 2, Wh*Ww, Wh*Ww - relative_coords = relative_coords.permute( - 1, 2, 0 - ).contiguous() # Wh*Ww, Wh*Ww, 2 - relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 - relative_coords[:, :, 1] += self.window_size[1] - 1 - relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 - relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww - self.register_buffer("relative_position_index", relative_position_index) - - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - - self.proj_drop = nn.Dropout(proj_drop) - - trunc_normal_(self.relative_position_bias_table, std=0.02) - self.softmax = nn.Softmax(dim=-1) - - def forward(self, x, mask=None): - """ - Args: - x: input features with shape of (num_windows*B, N, C) - mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None - """ - B_, N, C = x.shape - qkv = ( - self.qkv(x) - .reshape(B_, N, 3, self.num_heads, C // self.num_heads) - .permute(2, 0, 3, 1, 4) - ) - q, k, v = ( - qkv[0], - qkv[1], - qkv[2], - ) # make torchscript happy (cannot use tensor as tuple) - - q = q * self.scale - attn = q @ k.transpose(-2, -1) - - relative_position_bias = self.relative_position_bias_table[ - self.relative_position_index.view(-1) # type: ignore - ].view( - self.window_size[0] * self.window_size[1], - self.window_size[0] * self.window_size[1], - -1, - ) # Wh*Ww,Wh*Ww,nH - relative_position_bias = relative_position_bias.permute( - 2, 0, 1 - ).contiguous() # nH, Wh*Ww, Wh*Ww - attn = attn + relative_position_bias.unsqueeze(0) - - if mask is not None: - nW = mask.shape[0] - attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze( - 1 - ).unsqueeze(0) - attn = attn.view(-1, self.num_heads, N, N) - attn = self.softmax(attn) - else: - attn = self.softmax(attn) - - attn = self.attn_drop(attn) - - x = (attn @ v).transpose(1, 2).reshape(B_, N, C) - x = self.proj(x) - x = self.proj_drop(x) - return x - - def extra_repr(self) -> str: - return f"dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}" - - def flops(self, N): - # calculate flops for 1 window with token length of N - flops = 0 - # qkv = self.qkv(x) - flops += N * self.dim * 3 * self.dim - # attn = (q @ k.transpose(-2, -1)) - flops += self.num_heads * N * (self.dim // self.num_heads) * N - # x = (attn @ v) - flops += self.num_heads * N * N * (self.dim // self.num_heads) - # x = self.proj(x) - flops += N * self.dim * self.dim - return flops - - -class SwinTransformerBlock(nn.Module): - r"""Swin Transformer Block. - - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resulotion. - num_heads (int): Number of attention heads. - window_size (int): Window size. - shift_size (int): Shift size for SW-MSA. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float, optional): Stochastic depth rate. Default: 0.0 - act_layer (nn.Module, optional): Activation layer. Default: nn.GELU - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - - def __init__( - self, - dim, - input_resolution, - num_heads, - window_size=7, - shift_size=0, - mlp_ratio=4.0, - qkv_bias=True, - qk_scale=None, - drop=0.0, - attn_drop=0.0, - drop_path=0.0, - act_layer=nn.GELU, - norm_layer=nn.LayerNorm, - ): - super().__init__() - self.dim = dim - self.input_resolution = input_resolution - self.num_heads = num_heads - self.window_size = window_size - self.shift_size = shift_size - self.mlp_ratio = mlp_ratio - if min(self.input_resolution) <= self.window_size: - # if window size is larger than input resolution, we don't partition windows - self.shift_size = 0 - self.window_size = min(self.input_resolution) - assert ( - 0 <= self.shift_size < self.window_size - ), "shift_size must in 0-window_size" - - self.norm1 = norm_layer(dim) - self.attn = WindowAttention( - dim, - window_size=to_2tuple(self.window_size), - num_heads=num_heads, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - attn_drop=attn_drop, - proj_drop=drop, - ) - - self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp( - in_features=dim, - hidden_features=mlp_hidden_dim, - act_layer=act_layer, - drop=drop, - ) - - if self.shift_size > 0: - attn_mask = self.calculate_mask(self.input_resolution) - else: - attn_mask = None - - self.register_buffer("attn_mask", attn_mask) - - def calculate_mask(self, x_size): - # calculate attention mask for SW-MSA - H, W = x_size - img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 - h_slices = ( - slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None), - ) - w_slices = ( - slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None), - ) - cnt = 0 - for h in h_slices: - for w in w_slices: - img_mask[:, h, w, :] = cnt - cnt += 1 - - mask_windows = window_partition( - img_mask, self.window_size - ) # nW, window_size, window_size, 1 - mask_windows = mask_windows.view(-1, self.window_size * self.window_size) - attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) - attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill( - attn_mask == 0, float(0.0) - ) - - return attn_mask - - def forward(self, x, x_size): - H, W = x_size - B, L, C = x.shape - # assert L == H * W, "input feature has wrong size" - - shortcut = x - x = self.norm1(x) - x = x.view(B, H, W, C) - - # cyclic shift - if self.shift_size > 0: - shifted_x = torch.roll( - x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2) - ) - else: - shifted_x = x - - # partition windows - x_windows = window_partition( - shifted_x, self.window_size - ) # nW*B, window_size, window_size, C - x_windows = x_windows.view( - -1, self.window_size * self.window_size, C - ) # nW*B, window_size*window_size, C - - # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size - if self.input_resolution == x_size: - attn_windows = self.attn( - x_windows, mask=self.attn_mask - ) # nW*B, window_size*window_size, C - else: - attn_windows = self.attn( - x_windows, mask=self.calculate_mask(x_size).to(x.device) - ) - - # merge windows - attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) - shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C - - # reverse cyclic shift - if self.shift_size > 0: - x = torch.roll( - shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2) - ) - else: - x = shifted_x - x = x.view(B, H * W, C) - - # FFN - x = shortcut + self.drop_path(x) - x = x + self.drop_path(self.mlp(self.norm2(x))) - - return x - - def extra_repr(self) -> str: - return ( - f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " - f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" - ) - - def flops(self): - flops = 0 - H, W = self.input_resolution - # norm1 - flops += self.dim * H * W - # W-MSA/SW-MSA - nW = H * W / self.window_size / self.window_size - flops += nW * self.attn.flops(self.window_size * self.window_size) - # mlp - flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio - # norm2 - flops += self.dim * H * W - return flops - - -class PatchMerging(nn.Module): - r"""Patch Merging Layer. - - Args: - input_resolution (tuple[int]): Resolution of input feature. - dim (int): Number of input channels. - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - - def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): - super().__init__() - self.input_resolution = input_resolution - self.dim = dim - self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) - self.norm = norm_layer(4 * dim) - - def forward(self, x): - """ - x: B, H*W, C - """ - H, W = self.input_resolution - B, L, C = x.shape - assert L == H * W, "input feature has wrong size" - assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." - - x = x.view(B, H, W, C) - - x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C - x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C - x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C - x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C - x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C - x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C - - x = self.norm(x) - x = self.reduction(x) - - return x - - def extra_repr(self) -> str: - return f"input_resolution={self.input_resolution}, dim={self.dim}" - - def flops(self): - H, W = self.input_resolution - flops = H * W * self.dim - flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim - return flops - - -class BasicLayer(nn.Module): - """A basic Swin Transformer layer for one stage. - - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resolution. - depth (int): Number of blocks. - num_heads (int): Number of attention heads. - window_size (int): Local window size. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. - """ - - def __init__( - self, - dim, - input_resolution, - depth, - num_heads, - window_size, - mlp_ratio=4.0, - qkv_bias=True, - qk_scale=None, - drop=0.0, - attn_drop=0.0, - drop_path=0.0, - norm_layer=nn.LayerNorm, - downsample=None, - use_checkpoint=False, - ): - super().__init__() - self.dim = dim - self.input_resolution = input_resolution - self.depth = depth - self.use_checkpoint = use_checkpoint - - # build blocks - self.blocks = nn.ModuleList( - [ - SwinTransformerBlock( - dim=dim, - input_resolution=input_resolution, - num_heads=num_heads, - window_size=window_size, - shift_size=0 if (i % 2 == 0) else window_size // 2, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop, - attn_drop=attn_drop, - drop_path=drop_path[i] - if isinstance(drop_path, list) - else drop_path, - norm_layer=norm_layer, - ) - for i in range(depth) - ] - ) - - # patch merging layer - if downsample is not None: - self.downsample = downsample( - input_resolution, dim=dim, norm_layer=norm_layer - ) - else: - self.downsample = None - - def forward(self, x, x_size): - for blk in self.blocks: - if self.use_checkpoint: - x = checkpoint.checkpoint(blk, x, x_size) - else: - x = blk(x, x_size) - if self.downsample is not None: - x = self.downsample(x) - return x - - def extra_repr(self) -> str: - return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" - - def flops(self): - flops = 0 - for blk in self.blocks: - flops += blk.flops() # type: ignore - if self.downsample is not None: - flops += self.downsample.flops() - return flops - - -class RSTB(nn.Module): - """Residual Swin Transformer Block (RSTB). - - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resolution. - depth (int): Number of blocks. - num_heads (int): Number of attention heads. - window_size (int): Local window size. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. - img_size: Input image size. - patch_size: Patch size. - resi_connection: The convolutional block before residual connection. - """ - - def __init__( - self, - dim, - input_resolution, - depth, - num_heads, - window_size, - mlp_ratio=4.0, - qkv_bias=True, - qk_scale=None, - drop=0.0, - attn_drop=0.0, - drop_path=0.0, - norm_layer=nn.LayerNorm, - downsample=None, - use_checkpoint=False, - img_size=224, - patch_size=4, - resi_connection="1conv", - ): - super(RSTB, self).__init__() - - self.dim = dim - self.input_resolution = input_resolution - - self.residual_group = BasicLayer( - dim=dim, - input_resolution=input_resolution, - depth=depth, - num_heads=num_heads, - window_size=window_size, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop, - attn_drop=attn_drop, - drop_path=drop_path, - norm_layer=norm_layer, - downsample=downsample, - use_checkpoint=use_checkpoint, - ) - - if resi_connection == "1conv": - self.conv = nn.Conv2d(dim, dim, 3, 1, 1) - elif resi_connection == "3conv": - # to save parameters and memory - self.conv = nn.Sequential( - nn.Conv2d(dim, dim // 4, 3, 1, 1), - nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), - nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(dim // 4, dim, 3, 1, 1), - ) - - self.patch_embed = PatchEmbed( - img_size=img_size, - patch_size=patch_size, - in_chans=0, - embed_dim=dim, - norm_layer=None, - ) - - self.patch_unembed = PatchUnEmbed( - img_size=img_size, - patch_size=patch_size, - in_chans=0, - embed_dim=dim, - norm_layer=None, - ) - - def forward(self, x, x_size): - return ( - self.patch_embed( - self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size)) - ) - + x - ) - - def flops(self): - flops = 0 - flops += self.residual_group.flops() - H, W = self.input_resolution - flops += H * W * self.dim * self.dim * 9 - flops += self.patch_embed.flops() - flops += self.patch_unembed.flops() - - return flops - - -class PatchEmbed(nn.Module): - r"""Image to Patch Embedding - - Args: - img_size (int): Image size. Default: 224. - patch_size (int): Patch token size. Default: 4. - in_chans (int): Number of input image channels. Default: 3. - embed_dim (int): Number of linear projection output channels. Default: 96. - norm_layer (nn.Module, optional): Normalization layer. Default: None - """ - - def __init__( - self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None - ): - super().__init__() - img_size = to_2tuple(img_size) - patch_size = to_2tuple(patch_size) - patches_resolution = [ - img_size[0] // patch_size[0], # type: ignore - img_size[1] // patch_size[1], # type: ignore - ] - self.img_size = img_size - self.patch_size = patch_size - self.patches_resolution = patches_resolution - self.num_patches = patches_resolution[0] * patches_resolution[1] - - self.in_chans = in_chans - self.embed_dim = embed_dim - - if norm_layer is not None: - self.norm = norm_layer(embed_dim) - else: - self.norm = None - - def forward(self, x): - x = x.flatten(2).transpose(1, 2) # B Ph*Pw C - if self.norm is not None: - x = self.norm(x) - return x - - def flops(self): - flops = 0 - H, W = self.img_size - if self.norm is not None: - flops += H * W * self.embed_dim # type: ignore - return flops - - -class PatchUnEmbed(nn.Module): - r"""Image to Patch Unembedding - - Args: - img_size (int): Image size. Default: 224. - patch_size (int): Patch token size. Default: 4. - in_chans (int): Number of input image channels. Default: 3. - embed_dim (int): Number of linear projection output channels. Default: 96. - norm_layer (nn.Module, optional): Normalization layer. Default: None - """ - - def __init__( - self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None - ): - super().__init__() - img_size = to_2tuple(img_size) - patch_size = to_2tuple(patch_size) - patches_resolution = [ - img_size[0] // patch_size[0], # type: ignore - img_size[1] // patch_size[1], # type: ignore - ] - self.img_size = img_size - self.patch_size = patch_size - self.patches_resolution = patches_resolution - self.num_patches = patches_resolution[0] * patches_resolution[1] - - self.in_chans = in_chans - self.embed_dim = embed_dim - - def forward(self, x, x_size): - B, HW, C = x.shape - x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C - return x - - def flops(self): - flops = 0 - return flops - - -class Upsample(nn.Sequential): - """Upsample module. - - Args: - scale (int): Scale factor. Supported scales: 2^n and 3. - num_feat (int): Channel number of intermediate features. - """ - - def __init__(self, scale, num_feat): - m = [] - if (scale & (scale - 1)) == 0: # scale = 2^n - for _ in range(int(math.log(scale, 2))): - m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) - m.append(nn.PixelShuffle(2)) - elif scale == 3: - m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) - m.append(nn.PixelShuffle(3)) - else: - raise ValueError( - f"scale {scale} is not supported. " "Supported scales: 2^n and 3." - ) - super(Upsample, self).__init__(*m) - - -class UpsampleOneStep(nn.Sequential): - """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) - Used in lightweight SR to save parameters. - - Args: - scale (int): Scale factor. Supported scales: 2^n and 3. - num_feat (int): Channel number of intermediate features. - - """ - - def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): - self.num_feat = num_feat - self.input_resolution = input_resolution - m = [] - m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1)) - m.append(nn.PixelShuffle(scale)) - super(UpsampleOneStep, self).__init__(*m) - - def flops(self): - H, W = self.input_resolution # type: ignore - flops = H * W * self.num_feat * 3 * 9 - return flops - - -class SwinIR(nn.Module): - r"""SwinIR - A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer. - - Args: - img_size (int | tuple(int)): Input image size. Default 64 - patch_size (int | tuple(int)): Patch size. Default: 1 - in_chans (int): Number of input image channels. Default: 3 - embed_dim (int): Patch embedding dimension. Default: 96 - depths (tuple(int)): Depth of each Swin Transformer layer. - num_heads (tuple(int)): Number of attention heads in different layers. - window_size (int): Window size. Default: 7 - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 - qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None - drop_rate (float): Dropout rate. Default: 0 - attn_drop_rate (float): Attention dropout rate. Default: 0 - drop_path_rate (float): Stochastic depth rate. Default: 0.1 - norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. - ape (bool): If True, add absolute position embedding to the patch embedding. Default: False - patch_norm (bool): If True, add normalization after patch embedding. Default: True - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False - upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction - img_range: Image range. 1. or 255. - upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None - resi_connection: The convolutional block before residual connection. '1conv'/'3conv' - """ - - def __init__( - self, - state_dict, - **kwargs, - ): - super(SwinIR, self).__init__() - - # Defaults - img_size = 64 - patch_size = 1 - in_chans = 3 - embed_dim = 96 - depths = [6, 6, 6, 6] - num_heads = [6, 6, 6, 6] - window_size = 7 - mlp_ratio = 4.0 - qkv_bias = True - qk_scale = None - drop_rate = 0.0 - attn_drop_rate = 0.0 - drop_path_rate = 0.1 - norm_layer = nn.LayerNorm - ape = False - patch_norm = True - use_checkpoint = False - upscale = 2 - img_range = 1.0 - upsampler = "" - resi_connection = "1conv" - num_feat = 64 - num_in_ch = in_chans - num_out_ch = in_chans - supports_fp16 = True - self.start_unshuffle = 1 - - self.model_arch = "SwinIR" - self.sub_type = "SR" - self.state = state_dict - if "params_ema" in self.state: - self.state = self.state["params_ema"] - elif "params" in self.state: - self.state = self.state["params"] - - state_keys = self.state.keys() - - if "conv_before_upsample.0.weight" in state_keys: - if "conv_up1.weight" in state_keys: - upsampler = "nearest+conv" - else: - upsampler = "pixelshuffle" - supports_fp16 = False - elif "upsample.0.weight" in state_keys: - upsampler = "pixelshuffledirect" - else: - upsampler = "" - - num_feat = ( - self.state.get("conv_before_upsample.0.weight", None).shape[1] - if self.state.get("conv_before_upsample.weight", None) - else 64 - ) - - if "conv_first.1.weight" in self.state: - self.state["conv_first.weight"] = self.state.pop("conv_first.1.weight") - self.state["conv_first.bias"] = self.state.pop("conv_first.1.bias") - self.start_unshuffle = round(math.sqrt(self.state["conv_first.weight"].shape[1] // 3)) - - num_in_ch = self.state["conv_first.weight"].shape[1] - in_chans = num_in_ch - if "conv_last.weight" in state_keys: - num_out_ch = self.state["conv_last.weight"].shape[0] - else: - num_out_ch = num_in_ch - - upscale = 1 - if upsampler == "nearest+conv": - upsample_keys = [ - x for x in state_keys if "conv_up" in x and "bias" not in x - ] - - for upsample_key in upsample_keys: - upscale *= 2 - elif upsampler == "pixelshuffle": - upsample_keys = [ - x - for x in state_keys - if "upsample" in x and "conv" not in x and "bias" not in x - ] - for upsample_key in upsample_keys: - shape = self.state[upsample_key].shape[0] - upscale *= math.sqrt(shape // num_feat) - upscale = int(upscale) - elif upsampler == "pixelshuffledirect": - upscale = int( - math.sqrt(self.state["upsample.0.bias"].shape[0] // num_out_ch) - ) - - max_layer_num = 0 - max_block_num = 0 - for key in state_keys: - result = re.match( - r"layers.(\d*).residual_group.blocks.(\d*).norm1.weight", key - ) - if result: - layer_num, block_num = result.groups() - max_layer_num = max(max_layer_num, int(layer_num)) - max_block_num = max(max_block_num, int(block_num)) - - depths = [max_block_num + 1 for _ in range(max_layer_num + 1)] - - if ( - "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" - in state_keys - ): - num_heads_num = self.state[ - "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" - ].shape[-1] - num_heads = [num_heads_num for _ in range(max_layer_num + 1)] - else: - num_heads = depths - - embed_dim = self.state["conv_first.weight"].shape[0] - - mlp_ratio = float( - self.state["layers.0.residual_group.blocks.0.mlp.fc1.bias"].shape[0] - / embed_dim - ) - - # TODO: could actually count the layers, but this should do - if "layers.0.conv.4.weight" in state_keys: - resi_connection = "3conv" - else: - resi_connection = "1conv" - - window_size = int( - math.sqrt( - self.state[ - "layers.0.residual_group.blocks.0.attn.relative_position_index" - ].shape[0] - ) - ) - - if "layers.0.residual_group.blocks.1.attn_mask" in state_keys: - img_size = int( - math.sqrt( - self.state["layers.0.residual_group.blocks.1.attn_mask"].shape[0] - ) - * window_size - ) - - # The JPEG models are the only ones with window-size 7, and they also use this range - img_range = 255.0 if window_size == 7 else 1.0 - - self.in_nc = num_in_ch - self.out_nc = num_out_ch - self.num_feat = num_feat - self.embed_dim = embed_dim - self.num_heads = num_heads - self.depths = depths - self.window_size = window_size - self.mlp_ratio = mlp_ratio - self.scale = upscale / self.start_unshuffle - self.upsampler = upsampler - self.img_size = img_size - self.img_range = img_range - self.resi_connection = resi_connection - - self.supports_fp16 = False # Too much weirdness to support this at the moment - self.supports_bfp16 = True - self.min_size_restriction = 16 - - self.img_range = img_range - if in_chans == 3: - rgb_mean = (0.4488, 0.4371, 0.4040) - self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) - else: - self.mean = torch.zeros(1, 1, 1, 1) - self.upscale = upscale - self.upsampler = upsampler - self.window_size = window_size - - ##################################################################################################### - ################################### 1, shallow feature extraction ################################### - self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) - - ##################################################################################################### - ################################### 2, deep feature extraction ###################################### - self.num_layers = len(depths) - self.embed_dim = embed_dim - self.ape = ape - self.patch_norm = patch_norm - self.num_features = embed_dim - self.mlp_ratio = mlp_ratio - - # split image into non-overlapping patches - self.patch_embed = PatchEmbed( - img_size=img_size, - patch_size=patch_size, - in_chans=embed_dim, - embed_dim=embed_dim, - norm_layer=norm_layer if self.patch_norm else None, - ) - num_patches = self.patch_embed.num_patches - patches_resolution = self.patch_embed.patches_resolution - self.patches_resolution = patches_resolution - - # merge non-overlapping patches into image - self.patch_unembed = PatchUnEmbed( - img_size=img_size, - patch_size=patch_size, - in_chans=embed_dim, - embed_dim=embed_dim, - norm_layer=norm_layer if self.patch_norm else None, - ) - - # absolute position embedding - if self.ape: - self.absolute_pos_embed = nn.Parameter( # type: ignore - torch.zeros(1, num_patches, embed_dim) - ) - trunc_normal_(self.absolute_pos_embed, std=0.02) - - self.pos_drop = nn.Dropout(p=drop_rate) - - # stochastic depth - dpr = [ - x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) - ] # stochastic depth decay rule - - # build Residual Swin Transformer blocks (RSTB) - self.layers = nn.ModuleList() - for i_layer in range(self.num_layers): - layer = RSTB( - dim=embed_dim, - input_resolution=(patches_resolution[0], patches_resolution[1]), - depth=depths[i_layer], - num_heads=num_heads[i_layer], - window_size=window_size, - mlp_ratio=self.mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop_rate, - attn_drop=attn_drop_rate, - drop_path=dpr[ - sum(depths[:i_layer]) : sum(depths[: i_layer + 1]) # type: ignore - ], # no impact on SR results - norm_layer=norm_layer, - downsample=None, - use_checkpoint=use_checkpoint, - img_size=img_size, - patch_size=patch_size, - resi_connection=resi_connection, - ) - self.layers.append(layer) - self.norm = norm_layer(self.num_features) - - # build the last conv layer in deep feature extraction - if resi_connection == "1conv": - self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) - elif resi_connection == "3conv": - # to save parameters and memory - self.conv_after_body = nn.Sequential( - nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), - nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), - nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1), - ) - - ##################################################################################################### - ################################ 3, high quality image reconstruction ################################ - if self.upsampler == "pixelshuffle": - # for classical SR - self.conv_before_upsample = nn.Sequential( - nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) - ) - self.upsample = Upsample(upscale, num_feat) - self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - elif self.upsampler == "pixelshuffledirect": - # for lightweight SR (to save parameters) - self.upsample = UpsampleOneStep( - upscale, - embed_dim, - num_out_ch, - (patches_resolution[0], patches_resolution[1]), - ) - elif self.upsampler == "nearest+conv": - # for real-world SR (less artifacts) - self.conv_before_upsample = nn.Sequential( - nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) - ) - self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - if self.upscale == 4: - self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - elif self.upscale == 8: - self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_up3 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) - else: - # for image denoising and JPEG compression artifact reduction - self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1) - - self.apply(self._init_weights) - self.load_state_dict(self.state, strict=False) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=0.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - @torch.jit.ignore # type: ignore - def no_weight_decay(self): - return {"absolute_pos_embed"} - - @torch.jit.ignore # type: ignore - def no_weight_decay_keywords(self): - return {"relative_position_bias_table"} - - def check_image_size(self, x): - _, _, h, w = x.size() - mod_pad_h = (self.window_size - h % self.window_size) % self.window_size - mod_pad_w = (self.window_size - w % self.window_size) % self.window_size - x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") - return x - - def forward_features(self, x): - x_size = (x.shape[2], x.shape[3]) - x = self.patch_embed(x) - if self.ape: - x = x + self.absolute_pos_embed - x = self.pos_drop(x) - - for layer in self.layers: - x = layer(x, x_size) - - x = self.norm(x) # B L C - x = self.patch_unembed(x, x_size) - - return x - - def forward(self, x): - H, W = x.shape[2:] - x = self.check_image_size(x) - - self.mean = self.mean.type_as(x) - x = (x - self.mean) * self.img_range - - if self.start_unshuffle > 1: - x = torch.nn.functional.pixel_unshuffle(x, self.start_unshuffle) - - if self.upsampler == "pixelshuffle": - # for classical SR - x = self.conv_first(x) - x = self.conv_after_body(self.forward_features(x)) + x - x = self.conv_before_upsample(x) - x = self.conv_last(self.upsample(x)) - elif self.upsampler == "pixelshuffledirect": - # for lightweight SR - x = self.conv_first(x) - x = self.conv_after_body(self.forward_features(x)) + x - x = self.upsample(x) - elif self.upsampler == "nearest+conv": - # for real-world SR - x = self.conv_first(x) - x = self.conv_after_body(self.forward_features(x)) + x - x = self.conv_before_upsample(x) - x = self.lrelu( - self.conv_up1( - torch.nn.functional.interpolate(x, scale_factor=2, mode="nearest") # type: ignore - ) - ) - if self.upscale == 4: - x = self.lrelu( - self.conv_up2( - torch.nn.functional.interpolate( # type: ignore - x, scale_factor=2, mode="nearest" - ) - ) - ) - elif self.upscale == 8: - x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) - x = self.lrelu(self.conv_up3(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) - x = self.conv_last(self.lrelu(self.conv_hr(x))) - else: - # for image denoising and JPEG compression artifact reduction - x_first = self.conv_first(x) - res = self.conv_after_body(self.forward_features(x_first)) + x_first - x = x + self.conv_last(res) - - x = x / self.img_range + self.mean - - return x[:, :, : H * self.upscale, : W * self.upscale] - - def flops(self): - flops = 0 - H, W = self.patches_resolution - flops += H * W * 3 * self.embed_dim * 9 - flops += self.patch_embed.flops() - for i, layer in enumerate(self.layers): - flops += layer.flops() # type: ignore - flops += H * W * 3 * self.embed_dim * self.embed_dim - flops += self.upsample.flops() # type: ignore - return flops diff --git a/comfy_extras/chainner_models/architecture/__init__.py b/comfy_extras/chainner_models/architecture/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/comfy_extras/chainner_models/architecture/block.py b/comfy_extras/chainner_models/architecture/block.py deleted file mode 100644 index d7bc5d22..00000000 --- a/comfy_extras/chainner_models/architecture/block.py +++ /dev/null @@ -1,546 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -from __future__ import annotations - -from collections import OrderedDict -try: - from typing import Literal -except ImportError: - from typing_extensions import Literal - -import torch -import torch.nn as nn - -#################### -# Basic blocks -#################### - - -def act(act_type: str, inplace=True, neg_slope=0.2, n_prelu=1): - # helper selecting activation - # neg_slope: for leakyrelu and init of prelu - # n_prelu: for p_relu num_parameters - act_type = act_type.lower() - if act_type == "relu": - layer = nn.ReLU(inplace) - elif act_type == "leakyrelu": - layer = nn.LeakyReLU(neg_slope, inplace) - elif act_type == "prelu": - layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope) - else: - raise NotImplementedError( - "activation layer [{:s}] is not found".format(act_type) - ) - return layer - - -def norm(norm_type: str, nc: int): - # helper selecting normalization layer - norm_type = norm_type.lower() - if norm_type == "batch": - layer = nn.BatchNorm2d(nc, affine=True) - elif norm_type == "instance": - layer = nn.InstanceNorm2d(nc, affine=False) - else: - raise NotImplementedError( - "normalization layer [{:s}] is not found".format(norm_type) - ) - return layer - - -def pad(pad_type: str, padding): - # helper selecting padding layer - # if padding is 'zero', do by conv layers - pad_type = pad_type.lower() - if padding == 0: - return None - if pad_type == "reflect": - layer = nn.ReflectionPad2d(padding) - elif pad_type == "replicate": - layer = nn.ReplicationPad2d(padding) - else: - raise NotImplementedError( - "padding layer [{:s}] is not implemented".format(pad_type) - ) - return layer - - -def get_valid_padding(kernel_size, dilation): - kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1) - padding = (kernel_size - 1) // 2 - return padding - - -class ConcatBlock(nn.Module): - # Concat the output of a submodule to its input - def __init__(self, submodule): - super(ConcatBlock, self).__init__() - self.sub = submodule - - def forward(self, x): - output = torch.cat((x, self.sub(x)), dim=1) - return output - - def __repr__(self): - tmpstr = "Identity .. \n|" - modstr = self.sub.__repr__().replace("\n", "\n|") - tmpstr = tmpstr + modstr - return tmpstr - - -class ShortcutBlock(nn.Module): - # Elementwise sum the output of a submodule to its input - def __init__(self, submodule): - super(ShortcutBlock, self).__init__() - self.sub = submodule - - def forward(self, x): - output = x + self.sub(x) - return output - - def __repr__(self): - tmpstr = "Identity + \n|" - modstr = self.sub.__repr__().replace("\n", "\n|") - tmpstr = tmpstr + modstr - return tmpstr - - -class ShortcutBlockSPSR(nn.Module): - # Elementwise sum the output of a submodule to its input - def __init__(self, submodule): - super(ShortcutBlockSPSR, self).__init__() - self.sub = submodule - - def forward(self, x): - return x, self.sub - - def __repr__(self): - tmpstr = "Identity + \n|" - modstr = self.sub.__repr__().replace("\n", "\n|") - tmpstr = tmpstr + modstr - return tmpstr - - -def sequential(*args): - # Flatten Sequential. It unwraps nn.Sequential. - if len(args) == 1: - if isinstance(args[0], OrderedDict): - raise NotImplementedError("sequential does not support OrderedDict input.") - return args[0] # No sequential is needed. - modules = [] - for module in args: - if isinstance(module, nn.Sequential): - for submodule in module.children(): - modules.append(submodule) - elif isinstance(module, nn.Module): - modules.append(module) - return nn.Sequential(*modules) - - -ConvMode = Literal["CNA", "NAC", "CNAC"] - - -# 2x2x2 Conv Block -def conv_block_2c2( - in_nc, - out_nc, - act_type="relu", -): - return sequential( - nn.Conv2d(in_nc, out_nc, kernel_size=2, padding=1), - nn.Conv2d(out_nc, out_nc, kernel_size=2, padding=0), - act(act_type) if act_type else None, - ) - - -def conv_block( - in_nc: int, - out_nc: int, - kernel_size, - stride=1, - dilation=1, - groups=1, - bias=True, - pad_type="zero", - norm_type: str | None = None, - act_type: str | None = "relu", - mode: ConvMode = "CNA", - c2x2=False, -): - """ - Conv layer with padding, normalization, activation - mode: CNA --> Conv -> Norm -> Act - NAC --> Norm -> Act --> Conv (Identity Mappings in Deep Residual Networks, ECCV16) - """ - - if c2x2: - return conv_block_2c2(in_nc, out_nc, act_type=act_type) - - assert mode in ("CNA", "NAC", "CNAC"), "Wrong conv mode [{:s}]".format(mode) - padding = get_valid_padding(kernel_size, dilation) - p = pad(pad_type, padding) if pad_type and pad_type != "zero" else None - padding = padding if pad_type == "zero" else 0 - - c = nn.Conv2d( - in_nc, - out_nc, - kernel_size=kernel_size, - stride=stride, - padding=padding, - dilation=dilation, - bias=bias, - groups=groups, - ) - a = act(act_type) if act_type else None - if mode in ("CNA", "CNAC"): - n = norm(norm_type, out_nc) if norm_type else None - return sequential(p, c, n, a) - elif mode == "NAC": - if norm_type is None and act_type is not None: - a = act(act_type, inplace=False) - # Important! - # input----ReLU(inplace)----Conv--+----output - # |________________________| - # inplace ReLU will modify the input, therefore wrong output - n = norm(norm_type, in_nc) if norm_type else None - return sequential(n, a, p, c) - else: - assert False, f"Invalid conv mode {mode}" - - -#################### -# Useful blocks -#################### - - -class ResNetBlock(nn.Module): - """ - ResNet Block, 3-3 style - with extra residual scaling used in EDSR - (Enhanced Deep Residual Networks for Single Image Super-Resolution, CVPRW 17) - """ - - def __init__( - self, - in_nc, - mid_nc, - out_nc, - kernel_size=3, - stride=1, - dilation=1, - groups=1, - bias=True, - pad_type="zero", - norm_type=None, - act_type="relu", - mode: ConvMode = "CNA", - res_scale=1, - ): - super(ResNetBlock, self).__init__() - conv0 = conv_block( - in_nc, - mid_nc, - kernel_size, - stride, - dilation, - groups, - bias, - pad_type, - norm_type, - act_type, - mode, - ) - if mode == "CNA": - act_type = None - if mode == "CNAC": # Residual path: |-CNAC-| - act_type = None - norm_type = None - conv1 = conv_block( - mid_nc, - out_nc, - kernel_size, - stride, - dilation, - groups, - bias, - pad_type, - norm_type, - act_type, - mode, - ) - # if in_nc != out_nc: - # self.project = conv_block(in_nc, out_nc, 1, stride, dilation, 1, bias, pad_type, \ - # None, None) - # print('Need a projecter in ResNetBlock.') - # else: - # self.project = lambda x:x - self.res = sequential(conv0, conv1) - self.res_scale = res_scale - - def forward(self, x): - res = self.res(x).mul(self.res_scale) - return x + res - - -class RRDB(nn.Module): - """ - Residual in Residual Dense Block - (ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks) - """ - - def __init__( - self, - nf, - kernel_size=3, - gc=32, - stride=1, - bias: bool = True, - pad_type="zero", - norm_type=None, - act_type="leakyrelu", - mode: ConvMode = "CNA", - _convtype="Conv2D", - _spectral_norm=False, - plus=False, - c2x2=False, - ): - super(RRDB, self).__init__() - self.RDB1 = ResidualDenseBlock_5C( - nf, - kernel_size, - gc, - stride, - bias, - pad_type, - norm_type, - act_type, - mode, - plus=plus, - c2x2=c2x2, - ) - self.RDB2 = ResidualDenseBlock_5C( - nf, - kernel_size, - gc, - stride, - bias, - pad_type, - norm_type, - act_type, - mode, - plus=plus, - c2x2=c2x2, - ) - self.RDB3 = ResidualDenseBlock_5C( - nf, - kernel_size, - gc, - stride, - bias, - pad_type, - norm_type, - act_type, - mode, - plus=plus, - c2x2=c2x2, - ) - - def forward(self, x): - out = self.RDB1(x) - out = self.RDB2(out) - out = self.RDB3(out) - return out * 0.2 + x - - -class ResidualDenseBlock_5C(nn.Module): - """ - Residual Dense Block - style: 5 convs - The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18) - Modified options that can be used: - - "Partial Convolution based Padding" arXiv:1811.11718 - - "Spectral normalization" arXiv:1802.05957 - - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C. - {Rakotonirina} and A. {Rasoanaivo} - - Args: - nf (int): Channel number of intermediate features (num_feat). - gc (int): Channels for each growth (num_grow_ch: growth channel, - i.e. intermediate channels). - convtype (str): the type of convolution to use. Default: 'Conv2D' - gaussian_noise (bool): enable the ESRGAN+ gaussian noise (no new - trainable parameters) - plus (bool): enable the additional residual paths from ESRGAN+ - (adds trainable parameters) - """ - - def __init__( - self, - nf=64, - kernel_size=3, - gc=32, - stride=1, - bias: bool = True, - pad_type="zero", - norm_type=None, - act_type="leakyrelu", - mode: ConvMode = "CNA", - plus=False, - c2x2=False, - ): - super(ResidualDenseBlock_5C, self).__init__() - - ## + - self.conv1x1 = conv1x1(nf, gc) if plus else None - ## + - - self.conv1 = conv_block( - nf, - gc, - kernel_size, - stride, - bias=bias, - pad_type=pad_type, - norm_type=norm_type, - act_type=act_type, - mode=mode, - c2x2=c2x2, - ) - self.conv2 = conv_block( - nf + gc, - gc, - kernel_size, - stride, - bias=bias, - pad_type=pad_type, - norm_type=norm_type, - act_type=act_type, - mode=mode, - c2x2=c2x2, - ) - self.conv3 = conv_block( - nf + 2 * gc, - gc, - kernel_size, - stride, - bias=bias, - pad_type=pad_type, - norm_type=norm_type, - act_type=act_type, - mode=mode, - c2x2=c2x2, - ) - self.conv4 = conv_block( - nf + 3 * gc, - gc, - kernel_size, - stride, - bias=bias, - pad_type=pad_type, - norm_type=norm_type, - act_type=act_type, - mode=mode, - c2x2=c2x2, - ) - if mode == "CNA": - last_act = None - else: - last_act = act_type - self.conv5 = conv_block( - nf + 4 * gc, - nf, - 3, - stride, - bias=bias, - pad_type=pad_type, - norm_type=norm_type, - act_type=last_act, - mode=mode, - c2x2=c2x2, - ) - - def forward(self, x): - x1 = self.conv1(x) - x2 = self.conv2(torch.cat((x, x1), 1)) - if self.conv1x1: - # pylint: disable=not-callable - x2 = x2 + self.conv1x1(x) # + - x3 = self.conv3(torch.cat((x, x1, x2), 1)) - x4 = self.conv4(torch.cat((x, x1, x2, x3), 1)) - if self.conv1x1: - x4 = x4 + x2 # + - x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) - return x5 * 0.2 + x - - -def conv1x1(in_planes, out_planes, stride=1): - return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) - - -#################### -# Upsampler -#################### - - -def pixelshuffle_block( - in_nc: int, - out_nc: int, - upscale_factor=2, - kernel_size=3, - stride=1, - bias=True, - pad_type="zero", - norm_type: str | None = None, - act_type="relu", -): - """ - Pixel shuffle layer - (Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional - Neural Network, CVPR17) - """ - conv = conv_block( - in_nc, - out_nc * (upscale_factor**2), - kernel_size, - stride, - bias=bias, - pad_type=pad_type, - norm_type=None, - act_type=None, - ) - pixel_shuffle = nn.PixelShuffle(upscale_factor) - - n = norm(norm_type, out_nc) if norm_type else None - a = act(act_type) if act_type else None - return sequential(conv, pixel_shuffle, n, a) - - -def upconv_block( - in_nc: int, - out_nc: int, - upscale_factor=2, - kernel_size=3, - stride=1, - bias=True, - pad_type="zero", - norm_type: str | None = None, - act_type="relu", - mode="nearest", - c2x2=False, -): - # Up conv - # described in https://distill.pub/2016/deconv-checkerboard/ - upsample = nn.Upsample(scale_factor=upscale_factor, mode=mode) - conv = conv_block( - in_nc, - out_nc, - kernel_size, - stride, - bias=bias, - pad_type=pad_type, - norm_type=norm_type, - act_type=act_type, - c2x2=c2x2, - ) - return sequential(upsample, conv) diff --git a/comfy_extras/chainner_models/architecture/face/LICENSE-GFPGAN b/comfy_extras/chainner_models/architecture/face/LICENSE-GFPGAN deleted file mode 100644 index 5ac273fd..00000000 --- a/comfy_extras/chainner_models/architecture/face/LICENSE-GFPGAN +++ /dev/null @@ -1,351 +0,0 @@ -Tencent is pleased to support the open source community by making GFPGAN available. - -Copyright (C) 2021 THL A29 Limited, a Tencent company. 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Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in - the documentation and/or other materials provided with the - distribution. - -3. Neither the name of the copyright holder nor the names of its - contributors may be used to endorse or promote products derived - from this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -In the event that redistribution and/or use for commercial purpose in -source or binary forms, with or without modification is required, -please contact the contributor(s) of the work. diff --git a/comfy_extras/chainner_models/architecture/face/arcface_arch.py b/comfy_extras/chainner_models/architecture/face/arcface_arch.py deleted file mode 100644 index b548af05..00000000 --- a/comfy_extras/chainner_models/architecture/face/arcface_arch.py +++ /dev/null @@ -1,265 +0,0 @@ -import torch.nn as nn - - -def conv3x3(inplanes, outplanes, stride=1): - """A simple wrapper for 3x3 convolution with padding. - - Args: - inplanes (int): Channel number of inputs. - outplanes (int): Channel number of outputs. - stride (int): Stride in convolution. Default: 1. - """ - return nn.Conv2d( - inplanes, outplanes, kernel_size=3, stride=stride, padding=1, bias=False - ) - - -class BasicBlock(nn.Module): - """Basic residual block used in the ResNetArcFace architecture. - - Args: - inplanes (int): Channel number of inputs. - planes (int): Channel number of outputs. - stride (int): Stride in convolution. Default: 1. - downsample (nn.Module): The downsample module. Default: None. - """ - - expansion = 1 # output channel expansion ratio - - def __init__(self, inplanes, planes, stride=1, downsample=None): - super(BasicBlock, self).__init__() - self.conv1 = conv3x3(inplanes, planes, stride) - self.bn1 = nn.BatchNorm2d(planes) - self.relu = nn.ReLU(inplace=True) - self.conv2 = conv3x3(planes, planes) - self.bn2 = nn.BatchNorm2d(planes) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - residual = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.bn2(out) - - if self.downsample is not None: - residual = self.downsample(x) - - out += residual - out = self.relu(out) - - return out - - -class IRBlock(nn.Module): - """Improved residual block (IR Block) used in the ResNetArcFace architecture. - - Args: - inplanes (int): Channel number of inputs. - planes (int): Channel number of outputs. - stride (int): Stride in convolution. Default: 1. - downsample (nn.Module): The downsample module. Default: None. - use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True. - """ - - expansion = 1 # output channel expansion ratio - - def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True): - super(IRBlock, self).__init__() - self.bn0 = nn.BatchNorm2d(inplanes) - self.conv1 = conv3x3(inplanes, inplanes) - self.bn1 = nn.BatchNorm2d(inplanes) - self.prelu = nn.PReLU() - self.conv2 = conv3x3(inplanes, planes, stride) - self.bn2 = nn.BatchNorm2d(planes) - self.downsample = downsample - self.stride = stride - self.use_se = use_se - if self.use_se: - self.se = SEBlock(planes) - - def forward(self, x): - residual = x - out = self.bn0(x) - out = self.conv1(out) - out = self.bn1(out) - out = self.prelu(out) - - out = self.conv2(out) - out = self.bn2(out) - if self.use_se: - out = self.se(out) - - if self.downsample is not None: - residual = self.downsample(x) - - out += residual - out = self.prelu(out) - - return out - - -class Bottleneck(nn.Module): - """Bottleneck block used in the ResNetArcFace architecture. - - Args: - inplanes (int): Channel number of inputs. - planes (int): Channel number of outputs. - stride (int): Stride in convolution. Default: 1. - downsample (nn.Module): The downsample module. Default: None. - """ - - expansion = 4 # output channel expansion ratio - - def __init__(self, inplanes, planes, stride=1, downsample=None): - super(Bottleneck, self).__init__() - self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) - self.bn1 = nn.BatchNorm2d(planes) - self.conv2 = nn.Conv2d( - planes, planes, kernel_size=3, stride=stride, padding=1, bias=False - ) - self.bn2 = nn.BatchNorm2d(planes) - self.conv3 = nn.Conv2d( - planes, planes * self.expansion, kernel_size=1, bias=False - ) - self.bn3 = nn.BatchNorm2d(planes * self.expansion) - self.relu = nn.ReLU(inplace=True) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - residual = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.bn2(out) - out = self.relu(out) - - out = self.conv3(out) - out = self.bn3(out) - - if self.downsample is not None: - residual = self.downsample(x) - - out += residual - out = self.relu(out) - - return out - - -class SEBlock(nn.Module): - """The squeeze-and-excitation block (SEBlock) used in the IRBlock. - - Args: - channel (int): Channel number of inputs. - reduction (int): Channel reduction ration. Default: 16. - """ - - def __init__(self, channel, reduction=16): - super(SEBlock, self).__init__() - self.avg_pool = nn.AdaptiveAvgPool2d( - 1 - ) # pool to 1x1 without spatial information - self.fc = nn.Sequential( - nn.Linear(channel, channel // reduction), - nn.PReLU(), - nn.Linear(channel // reduction, channel), - nn.Sigmoid(), - ) - - def forward(self, x): - b, c, _, _ = x.size() - y = self.avg_pool(x).view(b, c) - y = self.fc(y).view(b, c, 1, 1) - return x * y - - -class ResNetArcFace(nn.Module): - """ArcFace with ResNet architectures. - - Ref: ArcFace: Additive Angular Margin Loss for Deep Face Recognition. - - Args: - block (str): Block used in the ArcFace architecture. - layers (tuple(int)): Block numbers in each layer. - use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True. - """ - - def __init__(self, block, layers, use_se=True): - if block == "IRBlock": - block = IRBlock - self.inplanes = 64 - self.use_se = use_se - super(ResNetArcFace, self).__init__() - - self.conv1 = nn.Conv2d(1, 64, kernel_size=3, padding=1, bias=False) - self.bn1 = nn.BatchNorm2d(64) - self.prelu = nn.PReLU() - self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2) - self.layer1 = self._make_layer(block, 64, layers[0]) - self.layer2 = self._make_layer(block, 128, layers[1], stride=2) - self.layer3 = self._make_layer(block, 256, layers[2], stride=2) - self.layer4 = self._make_layer(block, 512, layers[3], stride=2) - self.bn4 = nn.BatchNorm2d(512) - self.dropout = nn.Dropout() - self.fc5 = nn.Linear(512 * 8 * 8, 512) - self.bn5 = nn.BatchNorm1d(512) - - # initialization - for m in self.modules(): - if isinstance(m, nn.Conv2d): - nn.init.xavier_normal_(m.weight) - elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d): - nn.init.constant_(m.weight, 1) - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.Linear): - nn.init.xavier_normal_(m.weight) - nn.init.constant_(m.bias, 0) - - def _make_layer(self, block, planes, num_blocks, stride=1): - downsample = None - if stride != 1 or self.inplanes != planes * block.expansion: - downsample = nn.Sequential( - nn.Conv2d( - self.inplanes, - planes * block.expansion, - kernel_size=1, - stride=stride, - bias=False, - ), - nn.BatchNorm2d(planes * block.expansion), - ) - layers = [] - layers.append( - block(self.inplanes, planes, stride, downsample, use_se=self.use_se) - ) - self.inplanes = planes - for _ in range(1, num_blocks): - layers.append(block(self.inplanes, planes, use_se=self.use_se)) - - return nn.Sequential(*layers) - - def forward(self, x): - x = self.conv1(x) - x = self.bn1(x) - x = self.prelu(x) - x = self.maxpool(x) - - x = self.layer1(x) - x = self.layer2(x) - x = self.layer3(x) - x = self.layer4(x) - x = self.bn4(x) - x = self.dropout(x) - x = x.view(x.size(0), -1) - x = self.fc5(x) - x = self.bn5(x) - - return x diff --git a/comfy_extras/chainner_models/architecture/face/codeformer.py b/comfy_extras/chainner_models/architecture/face/codeformer.py deleted file mode 100644 index 06614007..00000000 --- a/comfy_extras/chainner_models/architecture/face/codeformer.py +++ /dev/null @@ -1,790 +0,0 @@ -""" -Modified from https://github.com/sczhou/CodeFormer -VQGAN code, adapted from the original created by the Unleashing Transformers authors: -https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py -This verison of the arch specifically was gathered from an old version of GFPGAN. If this is a problem, please contact me. -""" -import math -from typing import Optional - -import torch -import torch.nn as nn -import torch.nn.functional as F -import logging as logger -from torch import Tensor - - -class VectorQuantizer(nn.Module): - def __init__(self, codebook_size, emb_dim, beta): - super(VectorQuantizer, self).__init__() - self.codebook_size = codebook_size # number of embeddings - self.emb_dim = emb_dim # dimension of embedding - self.beta = beta # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2 - self.embedding = nn.Embedding(self.codebook_size, self.emb_dim) - self.embedding.weight.data.uniform_( - -1.0 / self.codebook_size, 1.0 / self.codebook_size - ) - - def forward(self, z): - # reshape z -> (batch, height, width, channel) and flatten - z = z.permute(0, 2, 3, 1).contiguous() - z_flattened = z.view(-1, self.emb_dim) - - # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z - d = ( - (z_flattened**2).sum(dim=1, keepdim=True) - + (self.embedding.weight**2).sum(1) - - 2 * torch.matmul(z_flattened, self.embedding.weight.t()) - ) - - mean_distance = torch.mean(d) - # find closest encodings - # min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1) - min_encoding_scores, min_encoding_indices = torch.topk( - d, 1, dim=1, largest=False - ) - # [0-1], higher score, higher confidence - min_encoding_scores = torch.exp(-min_encoding_scores / 10) - - min_encodings = torch.zeros( - min_encoding_indices.shape[0], self.codebook_size - ).to(z) - min_encodings.scatter_(1, min_encoding_indices, 1) - - # get quantized latent vectors - z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) - # compute loss for embedding - loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean( - (z_q - z.detach()) ** 2 - ) - # preserve gradients - z_q = z + (z_q - z).detach() - - # perplexity - e_mean = torch.mean(min_encodings, dim=0) - perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) - # reshape back to match original input shape - z_q = z_q.permute(0, 3, 1, 2).contiguous() - - return ( - z_q, - loss, - { - "perplexity": perplexity, - "min_encodings": min_encodings, - "min_encoding_indices": min_encoding_indices, - "min_encoding_scores": min_encoding_scores, - "mean_distance": mean_distance, - }, - ) - - def get_codebook_feat(self, indices, shape): - # input indices: batch*token_num -> (batch*token_num)*1 - # shape: batch, height, width, channel - indices = indices.view(-1, 1) - min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices) - min_encodings.scatter_(1, indices, 1) - # get quantized latent vectors - z_q = torch.matmul(min_encodings.float(), self.embedding.weight) - - if shape is not None: # reshape back to match original input shape - z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous() - - return z_q - - -class GumbelQuantizer(nn.Module): - def __init__( - self, - codebook_size, - emb_dim, - num_hiddens, - straight_through=False, - kl_weight=5e-4, - temp_init=1.0, - ): - super().__init__() - self.codebook_size = codebook_size # number of embeddings - self.emb_dim = emb_dim # dimension of embedding - self.straight_through = straight_through - self.temperature = temp_init - self.kl_weight = kl_weight - self.proj = nn.Conv2d( - num_hiddens, codebook_size, 1 - ) # projects last encoder layer to quantized logits - self.embed = nn.Embedding(codebook_size, emb_dim) - - def forward(self, z): - hard = self.straight_through if self.training else True - - logits = self.proj(z) - - soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard) - - z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight) - - # + kl divergence to the prior loss - qy = F.softmax(logits, dim=1) - diff = ( - self.kl_weight - * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean() - ) - min_encoding_indices = soft_one_hot.argmax(dim=1) - - return z_q, diff, {"min_encoding_indices": min_encoding_indices} - - -class Downsample(nn.Module): - def __init__(self, in_channels): - super().__init__() - self.conv = torch.nn.Conv2d( - in_channels, in_channels, kernel_size=3, stride=2, padding=0 - ) - - def forward(self, x): - pad = (0, 1, 0, 1) - x = torch.nn.functional.pad(x, pad, mode="constant", value=0) - x = self.conv(x) - return x - - -class Upsample(nn.Module): - def __init__(self, in_channels): - super().__init__() - self.conv = nn.Conv2d( - in_channels, in_channels, kernel_size=3, stride=1, padding=1 - ) - - def forward(self, x): - x = F.interpolate(x, scale_factor=2.0, mode="nearest") - x = self.conv(x) - - return x - - -class AttnBlock(nn.Module): - def __init__(self, in_channels): - super().__init__() - self.in_channels = in_channels - - self.norm = normalize(in_channels) - self.q = torch.nn.Conv2d( - in_channels, in_channels, kernel_size=1, stride=1, padding=0 - ) - self.k = torch.nn.Conv2d( - in_channels, in_channels, kernel_size=1, stride=1, padding=0 - ) - self.v = torch.nn.Conv2d( - in_channels, in_channels, kernel_size=1, stride=1, padding=0 - ) - self.proj_out = torch.nn.Conv2d( - in_channels, in_channels, kernel_size=1, stride=1, padding=0 - ) - - def forward(self, x): - h_ = x - h_ = self.norm(h_) - q = self.q(h_) - k = self.k(h_) - v = self.v(h_) - - # compute attention - b, c, h, w = q.shape - q = q.reshape(b, c, h * w) - q = q.permute(0, 2, 1) - k = k.reshape(b, c, h * w) - w_ = torch.bmm(q, k) - w_ = w_ * (int(c) ** (-0.5)) - w_ = F.softmax(w_, dim=2) - - # attend to values - v = v.reshape(b, c, h * w) - w_ = w_.permute(0, 2, 1) - h_ = torch.bmm(v, w_) - h_ = h_.reshape(b, c, h, w) - - h_ = self.proj_out(h_) - - return x + h_ - - -class Encoder(nn.Module): - def __init__( - self, - in_channels, - nf, - out_channels, - ch_mult, - num_res_blocks, - resolution, - attn_resolutions, - ): - super().__init__() - self.nf = nf - self.num_resolutions = len(ch_mult) - self.num_res_blocks = num_res_blocks - self.resolution = resolution - self.attn_resolutions = attn_resolutions - - curr_res = self.resolution - in_ch_mult = (1,) + tuple(ch_mult) - - blocks = [] - # initial convultion - blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1)) - - # residual and downsampling blocks, with attention on smaller res (16x16) - for i in range(self.num_resolutions): - block_in_ch = nf * in_ch_mult[i] - block_out_ch = nf * ch_mult[i] - for _ in range(self.num_res_blocks): - blocks.append(ResBlock(block_in_ch, block_out_ch)) - block_in_ch = block_out_ch - if curr_res in attn_resolutions: - blocks.append(AttnBlock(block_in_ch)) - - if i != self.num_resolutions - 1: - blocks.append(Downsample(block_in_ch)) - curr_res = curr_res // 2 - - # non-local attention block - blocks.append(ResBlock(block_in_ch, block_in_ch)) # type: ignore - blocks.append(AttnBlock(block_in_ch)) # type: ignore - blocks.append(ResBlock(block_in_ch, block_in_ch)) # type: ignore - - # normalise and convert to latent size - blocks.append(normalize(block_in_ch)) # type: ignore - blocks.append( - nn.Conv2d(block_in_ch, out_channels, kernel_size=3, stride=1, padding=1) # type: ignore - ) - self.blocks = nn.ModuleList(blocks) - - def forward(self, x): - for block in self.blocks: - x = block(x) - - return x - - -class Generator(nn.Module): - def __init__(self, nf, ch_mult, res_blocks, img_size, attn_resolutions, emb_dim): - super().__init__() - self.nf = nf - self.ch_mult = ch_mult - self.num_resolutions = len(self.ch_mult) - self.num_res_blocks = res_blocks - self.resolution = img_size - self.attn_resolutions = attn_resolutions - self.in_channels = emb_dim - self.out_channels = 3 - block_in_ch = self.nf * self.ch_mult[-1] - curr_res = self.resolution // 2 ** (self.num_resolutions - 1) - - blocks = [] - # initial conv - blocks.append( - nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1) - ) - - # non-local attention block - blocks.append(ResBlock(block_in_ch, block_in_ch)) - blocks.append(AttnBlock(block_in_ch)) - blocks.append(ResBlock(block_in_ch, block_in_ch)) - - for i in reversed(range(self.num_resolutions)): - block_out_ch = self.nf * self.ch_mult[i] - - for _ in range(self.num_res_blocks): - blocks.append(ResBlock(block_in_ch, block_out_ch)) - block_in_ch = block_out_ch - - if curr_res in self.attn_resolutions: - blocks.append(AttnBlock(block_in_ch)) - - if i != 0: - blocks.append(Upsample(block_in_ch)) - curr_res = curr_res * 2 - - blocks.append(normalize(block_in_ch)) - blocks.append( - nn.Conv2d( - block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1 - ) - ) - - self.blocks = nn.ModuleList(blocks) - - def forward(self, x): - for block in self.blocks: - x = block(x) - - return x - - -class VQAutoEncoder(nn.Module): - def __init__( - self, - img_size, - nf, - ch_mult, - quantizer="nearest", - res_blocks=2, - attn_resolutions=[16], - codebook_size=1024, - emb_dim=256, - beta=0.25, - gumbel_straight_through=False, - gumbel_kl_weight=1e-8, - model_path=None, - ): - super().__init__() - self.in_channels = 3 - self.nf = nf - self.n_blocks = res_blocks - self.codebook_size = codebook_size - self.embed_dim = emb_dim - self.ch_mult = ch_mult - self.resolution = img_size - self.attn_resolutions = attn_resolutions - self.quantizer_type = quantizer - self.encoder = Encoder( - self.in_channels, - self.nf, - self.embed_dim, - self.ch_mult, - self.n_blocks, - self.resolution, - self.attn_resolutions, - ) - if self.quantizer_type == "nearest": - self.beta = beta # 0.25 - self.quantize = VectorQuantizer( - self.codebook_size, self.embed_dim, self.beta - ) - elif self.quantizer_type == "gumbel": - self.gumbel_num_hiddens = emb_dim - self.straight_through = gumbel_straight_through - self.kl_weight = gumbel_kl_weight - self.quantize = GumbelQuantizer( - self.codebook_size, - self.embed_dim, - self.gumbel_num_hiddens, - self.straight_through, - self.kl_weight, - ) - self.generator = Generator( - nf, ch_mult, res_blocks, img_size, attn_resolutions, emb_dim - ) - - if model_path is not None: - chkpt = torch.load(model_path, map_location="cpu") - if "params_ema" in chkpt: - self.load_state_dict( - torch.load(model_path, map_location="cpu")["params_ema"] - ) - logger.info(f"vqgan is loaded from: {model_path} [params_ema]") - elif "params" in chkpt: - self.load_state_dict( - torch.load(model_path, map_location="cpu")["params"] - ) - logger.info(f"vqgan is loaded from: {model_path} [params]") - else: - raise ValueError("Wrong params!") - - def forward(self, x): - x = self.encoder(x) - quant, codebook_loss, quant_stats = self.quantize(x) - x = self.generator(quant) - return x, codebook_loss, quant_stats - - -def calc_mean_std(feat, eps=1e-5): - """Calculate mean and std for adaptive_instance_normalization. - Args: - feat (Tensor): 4D tensor. - eps (float): A small value added to the variance to avoid - divide-by-zero. Default: 1e-5. - """ - size = feat.size() - assert len(size) == 4, "The input feature should be 4D tensor." - b, c = size[:2] - feat_var = feat.view(b, c, -1).var(dim=2) + eps - feat_std = feat_var.sqrt().view(b, c, 1, 1) - feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1) - return feat_mean, feat_std - - -def adaptive_instance_normalization(content_feat, style_feat): - """Adaptive instance normalization. - Adjust the reference features to have the similar color and illuminations - as those in the degradate features. - Args: - content_feat (Tensor): The reference feature. - style_feat (Tensor): The degradate features. - """ - size = content_feat.size() - style_mean, style_std = calc_mean_std(style_feat) - content_mean, content_std = calc_mean_std(content_feat) - normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand( - size - ) - return normalized_feat * style_std.expand(size) + style_mean.expand(size) - - -class PositionEmbeddingSine(nn.Module): - """ - This is a more standard version of the position embedding, very similar to the one - used by the Attention is all you need paper, generalized to work on images. - """ - - def __init__( - self, num_pos_feats=64, temperature=10000, normalize=False, scale=None - ): - super().__init__() - self.num_pos_feats = num_pos_feats - self.temperature = temperature - self.normalize = normalize - if scale is not None and normalize is False: - raise ValueError("normalize should be True if scale is passed") - if scale is None: - scale = 2 * math.pi - self.scale = scale - - def forward(self, x, mask=None): - if mask is None: - mask = torch.zeros( - (x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool - ) - not_mask = ~mask # pylint: disable=invalid-unary-operand-type - y_embed = not_mask.cumsum(1, dtype=torch.float32) - x_embed = not_mask.cumsum(2, dtype=torch.float32) - if self.normalize: - eps = 1e-6 - y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale - x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale - - dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) - dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) - - pos_x = x_embed[:, :, :, None] / dim_t - pos_y = y_embed[:, :, :, None] / dim_t - pos_x = torch.stack( - (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 - ).flatten(3) - pos_y = torch.stack( - (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 - ).flatten(3) - pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) - return pos - - -def _get_activation_fn(activation): - """Return an activation function given a string""" - if activation == "relu": - return F.relu - if activation == "gelu": - return F.gelu - if activation == "glu": - return F.glu - raise RuntimeError(f"activation should be relu/gelu, not {activation}.") - - -class TransformerSALayer(nn.Module): - def __init__( - self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation="gelu" - ): - super().__init__() - self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout) - # Implementation of Feedforward model - MLP - self.linear1 = nn.Linear(embed_dim, dim_mlp) - self.dropout = nn.Dropout(dropout) - self.linear2 = nn.Linear(dim_mlp, embed_dim) - - self.norm1 = nn.LayerNorm(embed_dim) - self.norm2 = nn.LayerNorm(embed_dim) - self.dropout1 = nn.Dropout(dropout) - self.dropout2 = nn.Dropout(dropout) - - self.activation = _get_activation_fn(activation) - - def with_pos_embed(self, tensor, pos: Optional[Tensor]): - return tensor if pos is None else tensor + pos - - def forward( - self, - tgt, - tgt_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None, - ): - # self attention - tgt2 = self.norm1(tgt) - q = k = self.with_pos_embed(tgt2, query_pos) - tgt2 = self.self_attn( - q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask - )[0] - tgt = tgt + self.dropout1(tgt2) - - # ffn - tgt2 = self.norm2(tgt) - tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) - tgt = tgt + self.dropout2(tgt2) - return tgt - - -def normalize(in_channels): - return torch.nn.GroupNorm( - num_groups=32, num_channels=in_channels, eps=1e-6, affine=True - ) - - -@torch.jit.script # type: ignore -def swish(x): - return x * torch.sigmoid(x) - - -class ResBlock(nn.Module): - def __init__(self, in_channels, out_channels=None): - super(ResBlock, self).__init__() - self.in_channels = in_channels - self.out_channels = in_channels if out_channels is None else out_channels - self.norm1 = normalize(in_channels) - self.conv1 = nn.Conv2d( - in_channels, out_channels, kernel_size=3, stride=1, padding=1 # type: ignore - ) - self.norm2 = normalize(out_channels) - self.conv2 = nn.Conv2d( - out_channels, out_channels, kernel_size=3, stride=1, padding=1 # type: ignore - ) - if self.in_channels != self.out_channels: - self.conv_out = nn.Conv2d( - in_channels, out_channels, kernel_size=1, stride=1, padding=0 # type: ignore - ) - - def forward(self, x_in): - x = x_in - x = self.norm1(x) - x = swish(x) - x = self.conv1(x) - x = self.norm2(x) - x = swish(x) - x = self.conv2(x) - if self.in_channels != self.out_channels: - x_in = self.conv_out(x_in) - - return x + x_in - - -class Fuse_sft_block(nn.Module): - def __init__(self, in_ch, out_ch): - super().__init__() - self.encode_enc = ResBlock(2 * in_ch, out_ch) - - self.scale = nn.Sequential( - nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1), - nn.LeakyReLU(0.2, True), - nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1), - ) - - self.shift = nn.Sequential( - nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1), - nn.LeakyReLU(0.2, True), - nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1), - ) - - def forward(self, enc_feat, dec_feat, w=1): - enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1)) - scale = self.scale(enc_feat) - shift = self.shift(enc_feat) - residual = w * (dec_feat * scale + shift) - out = dec_feat + residual - return out - - -class CodeFormer(VQAutoEncoder): - def __init__(self, state_dict): - dim_embd = 512 - n_head = 8 - n_layers = 9 - codebook_size = 1024 - latent_size = 256 - connect_list = ["32", "64", "128", "256"] - fix_modules = ["quantize", "generator"] - - # This is just a guess as I only have one model to look at - position_emb = state_dict["position_emb"] - dim_embd = position_emb.shape[1] - latent_size = position_emb.shape[0] - - try: - n_layers = len( - set([x.split(".")[1] for x in state_dict.keys() if "ft_layers" in x]) - ) - except: - pass - - codebook_size = state_dict["quantize.embedding.weight"].shape[0] - - # This is also just another guess - n_head_exp = ( - state_dict["ft_layers.0.self_attn.in_proj_weight"].shape[0] // dim_embd - ) - n_head = 2**n_head_exp - - in_nc = state_dict["encoder.blocks.0.weight"].shape[1] - - self.model_arch = "CodeFormer" - self.sub_type = "Face SR" - self.scale = 8 - self.in_nc = in_nc - self.out_nc = in_nc - - self.state = state_dict - - self.supports_fp16 = False - self.supports_bf16 = True - self.min_size_restriction = 16 - - super(CodeFormer, self).__init__( - 512, 64, [1, 2, 2, 4, 4, 8], "nearest", 2, [16], codebook_size - ) - - if fix_modules is not None: - for module in fix_modules: - for param in getattr(self, module).parameters(): - param.requires_grad = False - - self.connect_list = connect_list - self.n_layers = n_layers - self.dim_embd = dim_embd - self.dim_mlp = dim_embd * 2 - - self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd)) # type: ignore - self.feat_emb = nn.Linear(256, self.dim_embd) - - # transformer - self.ft_layers = nn.Sequential( - *[ - TransformerSALayer( - embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0 - ) - for _ in range(self.n_layers) - ] - ) - - # logits_predict head - self.idx_pred_layer = nn.Sequential( - nn.LayerNorm(dim_embd), nn.Linear(dim_embd, codebook_size, bias=False) - ) - - self.channels = { - "16": 512, - "32": 256, - "64": 256, - "128": 128, - "256": 128, - "512": 64, - } - - # after second residual block for > 16, before attn layer for ==16 - self.fuse_encoder_block = { - "512": 2, - "256": 5, - "128": 8, - "64": 11, - "32": 14, - "16": 18, - } - # after first residual block for > 16, before attn layer for ==16 - self.fuse_generator_block = { - "16": 6, - "32": 9, - "64": 12, - "128": 15, - "256": 18, - "512": 21, - } - - # fuse_convs_dict - self.fuse_convs_dict = nn.ModuleDict() - for f_size in self.connect_list: - in_ch = self.channels[f_size] - self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch) - - self.load_state_dict(state_dict) - - def _init_weights(self, module): - if isinstance(module, (nn.Linear, nn.Embedding)): - module.weight.data.normal_(mean=0.0, std=0.02) - if isinstance(module, nn.Linear) and module.bias is not None: - module.bias.data.zero_() - elif isinstance(module, nn.LayerNorm): - module.bias.data.zero_() - module.weight.data.fill_(1.0) - - def forward(self, x, weight=0.5, **kwargs): - detach_16 = True - code_only = False - adain = True - # ################### Encoder ##################### - enc_feat_dict = {} - out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list] - for i, block in enumerate(self.encoder.blocks): - x = block(x) - if i in out_list: - enc_feat_dict[str(x.shape[-1])] = x.clone() - - lq_feat = x - # ################# Transformer ################### - # quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat) - pos_emb = self.position_emb.unsqueeze(1).repeat(1, x.shape[0], 1) - # BCHW -> BC(HW) -> (HW)BC - feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2, 0, 1)) - query_emb = feat_emb - # Transformer encoder - for layer in self.ft_layers: - query_emb = layer(query_emb, query_pos=pos_emb) - - # output logits - logits = self.idx_pred_layer(query_emb) # (hw)bn - logits = logits.permute(1, 0, 2) # (hw)bn -> b(hw)n - - if code_only: # for training stage II - # logits doesn't need softmax before cross_entropy loss - return logits, lq_feat - - # ################# Quantization ################### - # if self.training: - # quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight]) - # # b(hw)c -> bc(hw) -> bchw - # quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape) - # ------------ - soft_one_hot = F.softmax(logits, dim=2) - _, top_idx = torch.topk(soft_one_hot, 1, dim=2) - quant_feat = self.quantize.get_codebook_feat( - top_idx, shape=[x.shape[0], 16, 16, 256] # type: ignore - ) - # preserve gradients - # quant_feat = lq_feat + (quant_feat - lq_feat).detach() - - if detach_16: - quant_feat = quant_feat.detach() # for training stage III - if adain: - quant_feat = adaptive_instance_normalization(quant_feat, lq_feat) - - # ################## Generator #################### - x = quant_feat - fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list] - - for i, block in enumerate(self.generator.blocks): - x = block(x) - if i in fuse_list: # fuse after i-th block - f_size = str(x.shape[-1]) - if weight > 0: - x = self.fuse_convs_dict[f_size]( - enc_feat_dict[f_size].detach(), x, weight - ) - out = x - # logits doesn't need softmax before cross_entropy loss - # return out, logits, lq_feat - return out, logits diff --git a/comfy_extras/chainner_models/architecture/face/fused_act.py b/comfy_extras/chainner_models/architecture/face/fused_act.py deleted file mode 100644 index 7ed52654..00000000 --- a/comfy_extras/chainner_models/architecture/face/fused_act.py +++ /dev/null @@ -1,81 +0,0 @@ -# pylint: skip-file -# type: ignore -# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_act.py # noqa:E501 - -import torch -from torch import nn -from torch.autograd import Function - -fused_act_ext = None - - -class FusedLeakyReLUFunctionBackward(Function): - @staticmethod - def forward(ctx, grad_output, out, negative_slope, scale): - ctx.save_for_backward(out) - ctx.negative_slope = negative_slope - ctx.scale = scale - - empty = grad_output.new_empty(0) - - grad_input = fused_act_ext.fused_bias_act( - grad_output, empty, out, 3, 1, negative_slope, scale - ) - - dim = [0] - - if grad_input.ndim > 2: - dim += list(range(2, grad_input.ndim)) - - grad_bias = grad_input.sum(dim).detach() - - return grad_input, grad_bias - - @staticmethod - def backward(ctx, gradgrad_input, gradgrad_bias): - (out,) = ctx.saved_tensors - gradgrad_out = fused_act_ext.fused_bias_act( - gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale - ) - - return gradgrad_out, None, None, None - - -class FusedLeakyReLUFunction(Function): - @staticmethod - def forward(ctx, input, bias, negative_slope, scale): - empty = input.new_empty(0) - out = fused_act_ext.fused_bias_act( - input, bias, empty, 3, 0, negative_slope, scale - ) - ctx.save_for_backward(out) - ctx.negative_slope = negative_slope - ctx.scale = scale - - return out - - @staticmethod - def backward(ctx, grad_output): - (out,) = ctx.saved_tensors - - grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply( - grad_output, out, ctx.negative_slope, ctx.scale - ) - - return grad_input, grad_bias, None, None - - -class FusedLeakyReLU(nn.Module): - def __init__(self, channel, negative_slope=0.2, scale=2**0.5): - super().__init__() - - self.bias = nn.Parameter(torch.zeros(channel)) - self.negative_slope = negative_slope - self.scale = scale - - def forward(self, input): - return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale) - - -def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2**0.5): - return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale) diff --git a/comfy_extras/chainner_models/architecture/face/gfpgan_bilinear_arch.py b/comfy_extras/chainner_models/architecture/face/gfpgan_bilinear_arch.py deleted file mode 100644 index b6e820e0..00000000 --- a/comfy_extras/chainner_models/architecture/face/gfpgan_bilinear_arch.py +++ /dev/null @@ -1,389 +0,0 @@ -# pylint: skip-file -# type: ignore -import math -import random - -import torch -from torch import nn - -from .gfpganv1_arch import ResUpBlock -from .stylegan2_bilinear_arch import ( - ConvLayer, - EqualConv2d, - EqualLinear, - ResBlock, - ScaledLeakyReLU, - StyleGAN2GeneratorBilinear, -) - - -class StyleGAN2GeneratorBilinearSFT(StyleGAN2GeneratorBilinear): - """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform). - It is the bilinear version. It does not use the complicated UpFirDnSmooth function that is not friendly for - deployment. It can be easily converted to the clean version: StyleGAN2GeneratorCSFT. - Args: - out_size (int): The spatial size of outputs. - num_style_feat (int): Channel number of style features. Default: 512. - num_mlp (int): Layer number of MLP style layers. Default: 8. - channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. - lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. - narrow (float): The narrow ratio for channels. Default: 1. - sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. - """ - - def __init__( - self, - out_size, - num_style_feat=512, - num_mlp=8, - channel_multiplier=2, - lr_mlp=0.01, - narrow=1, - sft_half=False, - ): - super(StyleGAN2GeneratorBilinearSFT, self).__init__( - out_size, - num_style_feat=num_style_feat, - num_mlp=num_mlp, - channel_multiplier=channel_multiplier, - lr_mlp=lr_mlp, - narrow=narrow, - ) - self.sft_half = sft_half - - def forward( - self, - styles, - conditions, - input_is_latent=False, - noise=None, - randomize_noise=True, - truncation=1, - truncation_latent=None, - inject_index=None, - return_latents=False, - ): - """Forward function for StyleGAN2GeneratorBilinearSFT. - Args: - styles (list[Tensor]): Sample codes of styles. - conditions (list[Tensor]): SFT conditions to generators. - input_is_latent (bool): Whether input is latent style. Default: False. - noise (Tensor | None): Input noise or None. Default: None. - randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. - truncation (float): The truncation ratio. Default: 1. - truncation_latent (Tensor | None): The truncation latent tensor. Default: None. - inject_index (int | None): The injection index for mixing noise. Default: None. - return_latents (bool): Whether to return style latents. Default: False. - """ - # style codes -> latents with Style MLP layer - if not input_is_latent: - styles = [self.style_mlp(s) for s in styles] - # noises - if noise is None: - if randomize_noise: - noise = [None] * self.num_layers # for each style conv layer - else: # use the stored noise - noise = [ - getattr(self.noises, f"noise{i}") for i in range(self.num_layers) - ] - # style truncation - if truncation < 1: - style_truncation = [] - for style in styles: - style_truncation.append( - truncation_latent + truncation * (style - truncation_latent) - ) - styles = style_truncation - # get style latents with injection - if len(styles) == 1: - inject_index = self.num_latent - - if styles[0].ndim < 3: - # repeat latent code for all the layers - latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - else: # used for encoder with different latent code for each layer - latent = styles[0] - elif len(styles) == 2: # mixing noises - if inject_index is None: - inject_index = random.randint(1, self.num_latent - 1) - latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - latent2 = ( - styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) - ) - latent = torch.cat([latent1, latent2], 1) - - # main generation - out = self.constant_input(latent.shape[0]) - out = self.style_conv1(out, latent[:, 0], noise=noise[0]) - skip = self.to_rgb1(out, latent[:, 1]) - - i = 1 - for conv1, conv2, noise1, noise2, to_rgb in zip( - self.style_convs[::2], - self.style_convs[1::2], - noise[1::2], - noise[2::2], - self.to_rgbs, - ): - out = conv1(out, latent[:, i], noise=noise1) - - # the conditions may have fewer levels - if i < len(conditions): - # SFT part to combine the conditions - if self.sft_half: # only apply SFT to half of the channels - out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1) - out_sft = out_sft * conditions[i - 1] + conditions[i] - out = torch.cat([out_same, out_sft], dim=1) - else: # apply SFT to all the channels - out = out * conditions[i - 1] + conditions[i] - - out = conv2(out, latent[:, i + 1], noise=noise2) - skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space - i += 2 - - image = skip - - if return_latents: - return image, latent - else: - return image, None - - -class GFPGANBilinear(nn.Module): - """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT. - It is the bilinear version and it does not use the complicated UpFirDnSmooth function that is not friendly for - deployment. It can be easily converted to the clean version: GFPGANv1Clean. - Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. - Args: - out_size (int): The spatial size of outputs. - num_style_feat (int): Channel number of style features. Default: 512. - channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. - decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None. - fix_decoder (bool): Whether to fix the decoder. Default: True. - num_mlp (int): Layer number of MLP style layers. Default: 8. - lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. - input_is_latent (bool): Whether input is latent style. Default: False. - different_w (bool): Whether to use different latent w for different layers. Default: False. - narrow (float): The narrow ratio for channels. Default: 1. - sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. - """ - - def __init__( - self, - out_size, - num_style_feat=512, - channel_multiplier=1, - decoder_load_path=None, - fix_decoder=True, - # for stylegan decoder - num_mlp=8, - lr_mlp=0.01, - input_is_latent=False, - different_w=False, - narrow=1, - sft_half=False, - ): - super(GFPGANBilinear, self).__init__() - self.input_is_latent = input_is_latent - self.different_w = different_w - self.num_style_feat = num_style_feat - self.min_size_restriction = 512 - - unet_narrow = narrow * 0.5 # by default, use a half of input channels - channels = { - "4": int(512 * unet_narrow), - "8": int(512 * unet_narrow), - "16": int(512 * unet_narrow), - "32": int(512 * unet_narrow), - "64": int(256 * channel_multiplier * unet_narrow), - "128": int(128 * channel_multiplier * unet_narrow), - "256": int(64 * channel_multiplier * unet_narrow), - "512": int(32 * channel_multiplier * unet_narrow), - "1024": int(16 * channel_multiplier * unet_narrow), - } - - self.log_size = int(math.log(out_size, 2)) - first_out_size = 2 ** (int(math.log(out_size, 2))) - - self.conv_body_first = ConvLayer( - 3, channels[f"{first_out_size}"], 1, bias=True, activate=True - ) - - # downsample - in_channels = channels[f"{first_out_size}"] - self.conv_body_down = nn.ModuleList() - for i in range(self.log_size, 2, -1): - out_channels = channels[f"{2**(i - 1)}"] - self.conv_body_down.append(ResBlock(in_channels, out_channels)) - in_channels = out_channels - - self.final_conv = ConvLayer( - in_channels, channels["4"], 3, bias=True, activate=True - ) - - # upsample - in_channels = channels["4"] - self.conv_body_up = nn.ModuleList() - for i in range(3, self.log_size + 1): - out_channels = channels[f"{2**i}"] - self.conv_body_up.append(ResUpBlock(in_channels, out_channels)) - in_channels = out_channels - - # to RGB - self.toRGB = nn.ModuleList() - for i in range(3, self.log_size + 1): - self.toRGB.append( - EqualConv2d( - channels[f"{2**i}"], - 3, - 1, - stride=1, - padding=0, - bias=True, - bias_init_val=0, - ) - ) - - if different_w: - linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat - else: - linear_out_channel = num_style_feat - - self.final_linear = EqualLinear( - channels["4"] * 4 * 4, - linear_out_channel, - bias=True, - bias_init_val=0, - lr_mul=1, - activation=None, - ) - - # the decoder: stylegan2 generator with SFT modulations - self.stylegan_decoder = StyleGAN2GeneratorBilinearSFT( - out_size=out_size, - num_style_feat=num_style_feat, - num_mlp=num_mlp, - channel_multiplier=channel_multiplier, - lr_mlp=lr_mlp, - narrow=narrow, - sft_half=sft_half, - ) - - # load pre-trained stylegan2 model if necessary - if decoder_load_path: - self.stylegan_decoder.load_state_dict( - torch.load( - decoder_load_path, map_location=lambda storage, loc: storage - )["params_ema"] - ) - # fix decoder without updating params - if fix_decoder: - for _, param in self.stylegan_decoder.named_parameters(): - param.requires_grad = False - - # for SFT modulations (scale and shift) - self.condition_scale = nn.ModuleList() - self.condition_shift = nn.ModuleList() - for i in range(3, self.log_size + 1): - out_channels = channels[f"{2**i}"] - if sft_half: - sft_out_channels = out_channels - else: - sft_out_channels = out_channels * 2 - self.condition_scale.append( - nn.Sequential( - EqualConv2d( - out_channels, - out_channels, - 3, - stride=1, - padding=1, - bias=True, - bias_init_val=0, - ), - ScaledLeakyReLU(0.2), - EqualConv2d( - out_channels, - sft_out_channels, - 3, - stride=1, - padding=1, - bias=True, - bias_init_val=1, - ), - ) - ) - self.condition_shift.append( - nn.Sequential( - EqualConv2d( - out_channels, - out_channels, - 3, - stride=1, - padding=1, - bias=True, - bias_init_val=0, - ), - ScaledLeakyReLU(0.2), - EqualConv2d( - out_channels, - sft_out_channels, - 3, - stride=1, - padding=1, - bias=True, - bias_init_val=0, - ), - ) - ) - - def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True): - """Forward function for GFPGANBilinear. - Args: - x (Tensor): Input images. - return_latents (bool): Whether to return style latents. Default: False. - return_rgb (bool): Whether return intermediate rgb images. Default: True. - randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. - """ - conditions = [] - unet_skips = [] - out_rgbs = [] - - # encoder - feat = self.conv_body_first(x) - for i in range(self.log_size - 2): - feat = self.conv_body_down[i](feat) - unet_skips.insert(0, feat) - - feat = self.final_conv(feat) - - # style code - style_code = self.final_linear(feat.view(feat.size(0), -1)) - if self.different_w: - style_code = style_code.view(style_code.size(0), -1, self.num_style_feat) - - # decode - for i in range(self.log_size - 2): - # add unet skip - feat = feat + unet_skips[i] - # ResUpLayer - feat = self.conv_body_up[i](feat) - # generate scale and shift for SFT layers - scale = self.condition_scale[i](feat) - conditions.append(scale.clone()) - shift = self.condition_shift[i](feat) - conditions.append(shift.clone()) - # generate rgb images - if return_rgb: - out_rgbs.append(self.toRGB[i](feat)) - - # decoder - image, _ = self.stylegan_decoder( - [style_code], - conditions, - return_latents=return_latents, - input_is_latent=self.input_is_latent, - randomize_noise=randomize_noise, - ) - - return image, out_rgbs diff --git a/comfy_extras/chainner_models/architecture/face/gfpganv1_arch.py b/comfy_extras/chainner_models/architecture/face/gfpganv1_arch.py deleted file mode 100644 index 72d72fc8..00000000 --- a/comfy_extras/chainner_models/architecture/face/gfpganv1_arch.py +++ /dev/null @@ -1,566 +0,0 @@ -# pylint: skip-file -# type: ignore -import math -import random - -import torch -from torch import nn -from torch.nn import functional as F - -from .fused_act import FusedLeakyReLU -from .stylegan2_arch import ( - ConvLayer, - EqualConv2d, - EqualLinear, - ResBlock, - ScaledLeakyReLU, - StyleGAN2Generator, -) - - -class StyleGAN2GeneratorSFT(StyleGAN2Generator): - """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform). - Args: - out_size (int): The spatial size of outputs. - num_style_feat (int): Channel number of style features. Default: 512. - num_mlp (int): Layer number of MLP style layers. Default: 8. - channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. - resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be - applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1). - lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. - narrow (float): The narrow ratio for channels. Default: 1. - sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. - """ - - def __init__( - self, - out_size, - num_style_feat=512, - num_mlp=8, - channel_multiplier=2, - resample_kernel=(1, 3, 3, 1), - lr_mlp=0.01, - narrow=1, - sft_half=False, - ): - super(StyleGAN2GeneratorSFT, self).__init__( - out_size, - num_style_feat=num_style_feat, - num_mlp=num_mlp, - channel_multiplier=channel_multiplier, - resample_kernel=resample_kernel, - lr_mlp=lr_mlp, - narrow=narrow, - ) - self.sft_half = sft_half - - def forward( - self, - styles, - conditions, - input_is_latent=False, - noise=None, - randomize_noise=True, - truncation=1, - truncation_latent=None, - inject_index=None, - return_latents=False, - ): - """Forward function for StyleGAN2GeneratorSFT. - Args: - styles (list[Tensor]): Sample codes of styles. - conditions (list[Tensor]): SFT conditions to generators. - input_is_latent (bool): Whether input is latent style. Default: False. - noise (Tensor | None): Input noise or None. Default: None. - randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. - truncation (float): The truncation ratio. Default: 1. - truncation_latent (Tensor | None): The truncation latent tensor. Default: None. - inject_index (int | None): The injection index for mixing noise. Default: None. - return_latents (bool): Whether to return style latents. Default: False. - """ - # style codes -> latents with Style MLP layer - if not input_is_latent: - styles = [self.style_mlp(s) for s in styles] - # noises - if noise is None: - if randomize_noise: - noise = [None] * self.num_layers # for each style conv layer - else: # use the stored noise - noise = [ - getattr(self.noises, f"noise{i}") for i in range(self.num_layers) - ] - # style truncation - if truncation < 1: - style_truncation = [] - for style in styles: - style_truncation.append( - truncation_latent + truncation * (style - truncation_latent) - ) - styles = style_truncation - # get style latents with injection - if len(styles) == 1: - inject_index = self.num_latent - - if styles[0].ndim < 3: - # repeat latent code for all the layers - latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - else: # used for encoder with different latent code for each layer - latent = styles[0] - elif len(styles) == 2: # mixing noises - if inject_index is None: - inject_index = random.randint(1, self.num_latent - 1) - latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - latent2 = ( - styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) - ) - latent = torch.cat([latent1, latent2], 1) - - # main generation - out = self.constant_input(latent.shape[0]) - out = self.style_conv1(out, latent[:, 0], noise=noise[0]) - skip = self.to_rgb1(out, latent[:, 1]) - - i = 1 - for conv1, conv2, noise1, noise2, to_rgb in zip( - self.style_convs[::2], - self.style_convs[1::2], - noise[1::2], - noise[2::2], - self.to_rgbs, - ): - out = conv1(out, latent[:, i], noise=noise1) - - # the conditions may have fewer levels - if i < len(conditions): - # SFT part to combine the conditions - if self.sft_half: # only apply SFT to half of the channels - out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1) - out_sft = out_sft * conditions[i - 1] + conditions[i] - out = torch.cat([out_same, out_sft], dim=1) - else: # apply SFT to all the channels - out = out * conditions[i - 1] + conditions[i] - - out = conv2(out, latent[:, i + 1], noise=noise2) - skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space - i += 2 - - image = skip - - if return_latents: - return image, latent - else: - return image, None - - -class ConvUpLayer(nn.Module): - """Convolutional upsampling layer. It uses bilinear upsampler + Conv. - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - stride (int): Stride of the convolution. Default: 1 - padding (int): Zero-padding added to both sides of the input. Default: 0. - bias (bool): If ``True``, adds a learnable bias to the output. Default: ``True``. - bias_init_val (float): Bias initialized value. Default: 0. - activate (bool): Whether use activateion. Default: True. - """ - - def __init__( - self, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - bias=True, - bias_init_val=0, - activate=True, - ): - super(ConvUpLayer, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.stride = stride - self.padding = padding - # self.scale is used to scale the convolution weights, which is related to the common initializations. - self.scale = 1 / math.sqrt(in_channels * kernel_size**2) - - self.weight = nn.Parameter( - torch.randn(out_channels, in_channels, kernel_size, kernel_size) - ) - - if bias and not activate: - self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) - else: - self.register_parameter("bias", None) - - # activation - if activate: - if bias: - self.activation = FusedLeakyReLU(out_channels) - else: - self.activation = ScaledLeakyReLU(0.2) - else: - self.activation = None - - def forward(self, x): - # bilinear upsample - out = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=False) - # conv - out = F.conv2d( - out, - self.weight * self.scale, - bias=self.bias, - stride=self.stride, - padding=self.padding, - ) - # activation - if self.activation is not None: - out = self.activation(out) - return out - - -class ResUpBlock(nn.Module): - """Residual block with upsampling. - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - """ - - def __init__(self, in_channels, out_channels): - super(ResUpBlock, self).__init__() - - self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) - self.conv2 = ConvUpLayer( - in_channels, out_channels, 3, stride=1, padding=1, bias=True, activate=True - ) - self.skip = ConvUpLayer( - in_channels, out_channels, 1, bias=False, activate=False - ) - - def forward(self, x): - out = self.conv1(x) - out = self.conv2(out) - skip = self.skip(x) - out = (out + skip) / math.sqrt(2) - return out - - -class GFPGANv1(nn.Module): - """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT. - Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. - Args: - out_size (int): The spatial size of outputs. - num_style_feat (int): Channel number of style features. Default: 512. - channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. - resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be - applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1). - decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None. - fix_decoder (bool): Whether to fix the decoder. Default: True. - num_mlp (int): Layer number of MLP style layers. Default: 8. - lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. - input_is_latent (bool): Whether input is latent style. Default: False. - different_w (bool): Whether to use different latent w for different layers. Default: False. - narrow (float): The narrow ratio for channels. Default: 1. - sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. - """ - - def __init__( - self, - out_size, - num_style_feat=512, - channel_multiplier=1, - resample_kernel=(1, 3, 3, 1), - decoder_load_path=None, - fix_decoder=True, - # for stylegan decoder - num_mlp=8, - lr_mlp=0.01, - input_is_latent=False, - different_w=False, - narrow=1, - sft_half=False, - ): - super(GFPGANv1, self).__init__() - self.input_is_latent = input_is_latent - self.different_w = different_w - self.num_style_feat = num_style_feat - - unet_narrow = narrow * 0.5 # by default, use a half of input channels - channels = { - "4": int(512 * unet_narrow), - "8": int(512 * unet_narrow), - "16": int(512 * unet_narrow), - "32": int(512 * unet_narrow), - "64": int(256 * channel_multiplier * unet_narrow), - "128": int(128 * channel_multiplier * unet_narrow), - "256": int(64 * channel_multiplier * unet_narrow), - "512": int(32 * channel_multiplier * unet_narrow), - "1024": int(16 * channel_multiplier * unet_narrow), - } - - self.log_size = int(math.log(out_size, 2)) - first_out_size = 2 ** (int(math.log(out_size, 2))) - - self.conv_body_first = ConvLayer( - 3, channels[f"{first_out_size}"], 1, bias=True, activate=True - ) - - # downsample - in_channels = channels[f"{first_out_size}"] - self.conv_body_down = nn.ModuleList() - for i in range(self.log_size, 2, -1): - out_channels = channels[f"{2**(i - 1)}"] - self.conv_body_down.append( - ResBlock(in_channels, out_channels, resample_kernel) - ) - in_channels = out_channels - - self.final_conv = ConvLayer( - in_channels, channels["4"], 3, bias=True, activate=True - ) - - # upsample - in_channels = channels["4"] - self.conv_body_up = nn.ModuleList() - for i in range(3, self.log_size + 1): - out_channels = channels[f"{2**i}"] - self.conv_body_up.append(ResUpBlock(in_channels, out_channels)) - in_channels = out_channels - - # to RGB - self.toRGB = nn.ModuleList() - for i in range(3, self.log_size + 1): - self.toRGB.append( - EqualConv2d( - channels[f"{2**i}"], - 3, - 1, - stride=1, - padding=0, - bias=True, - bias_init_val=0, - ) - ) - - if different_w: - linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat - else: - linear_out_channel = num_style_feat - - self.final_linear = EqualLinear( - channels["4"] * 4 * 4, - linear_out_channel, - bias=True, - bias_init_val=0, - lr_mul=1, - activation=None, - ) - - # the decoder: stylegan2 generator with SFT modulations - self.stylegan_decoder = StyleGAN2GeneratorSFT( - out_size=out_size, - num_style_feat=num_style_feat, - num_mlp=num_mlp, - channel_multiplier=channel_multiplier, - resample_kernel=resample_kernel, - lr_mlp=lr_mlp, - narrow=narrow, - sft_half=sft_half, - ) - - # load pre-trained stylegan2 model if necessary - if decoder_load_path: - self.stylegan_decoder.load_state_dict( - torch.load( - decoder_load_path, map_location=lambda storage, loc: storage - )["params_ema"] - ) - # fix decoder without updating params - if fix_decoder: - for _, param in self.stylegan_decoder.named_parameters(): - param.requires_grad = False - - # for SFT modulations (scale and shift) - self.condition_scale = nn.ModuleList() - self.condition_shift = nn.ModuleList() - for i in range(3, self.log_size + 1): - out_channels = channels[f"{2**i}"] - if sft_half: - sft_out_channels = out_channels - else: - sft_out_channels = out_channels * 2 - self.condition_scale.append( - nn.Sequential( - EqualConv2d( - out_channels, - out_channels, - 3, - stride=1, - padding=1, - bias=True, - bias_init_val=0, - ), - ScaledLeakyReLU(0.2), - EqualConv2d( - out_channels, - sft_out_channels, - 3, - stride=1, - padding=1, - bias=True, - bias_init_val=1, - ), - ) - ) - self.condition_shift.append( - nn.Sequential( - EqualConv2d( - out_channels, - out_channels, - 3, - stride=1, - padding=1, - bias=True, - bias_init_val=0, - ), - ScaledLeakyReLU(0.2), - EqualConv2d( - out_channels, - sft_out_channels, - 3, - stride=1, - padding=1, - bias=True, - bias_init_val=0, - ), - ) - ) - - def forward( - self, x, return_latents=False, return_rgb=True, randomize_noise=True, **kwargs - ): - """Forward function for GFPGANv1. - Args: - x (Tensor): Input images. - return_latents (bool): Whether to return style latents. Default: False. - return_rgb (bool): Whether return intermediate rgb images. Default: True. - randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. - """ - conditions = [] - unet_skips = [] - out_rgbs = [] - - # encoder - feat = self.conv_body_first(x) - for i in range(self.log_size - 2): - feat = self.conv_body_down[i](feat) - unet_skips.insert(0, feat) - - feat = self.final_conv(feat) - - # style code - style_code = self.final_linear(feat.view(feat.size(0), -1)) - if self.different_w: - style_code = style_code.view(style_code.size(0), -1, self.num_style_feat) - - # decode - for i in range(self.log_size - 2): - # add unet skip - feat = feat + unet_skips[i] - # ResUpLayer - feat = self.conv_body_up[i](feat) - # generate scale and shift for SFT layers - scale = self.condition_scale[i](feat) - conditions.append(scale.clone()) - shift = self.condition_shift[i](feat) - conditions.append(shift.clone()) - # generate rgb images - if return_rgb: - out_rgbs.append(self.toRGB[i](feat)) - - # decoder - image, _ = self.stylegan_decoder( - [style_code], - conditions, - return_latents=return_latents, - input_is_latent=self.input_is_latent, - randomize_noise=randomize_noise, - ) - - return image, out_rgbs - - -class FacialComponentDiscriminator(nn.Module): - """Facial component (eyes, mouth, noise) discriminator used in GFPGAN.""" - - def __init__(self): - super(FacialComponentDiscriminator, self).__init__() - # It now uses a VGG-style architectrue with fixed model size - self.conv1 = ConvLayer( - 3, - 64, - 3, - downsample=False, - resample_kernel=(1, 3, 3, 1), - bias=True, - activate=True, - ) - self.conv2 = ConvLayer( - 64, - 128, - 3, - downsample=True, - resample_kernel=(1, 3, 3, 1), - bias=True, - activate=True, - ) - self.conv3 = ConvLayer( - 128, - 128, - 3, - downsample=False, - resample_kernel=(1, 3, 3, 1), - bias=True, - activate=True, - ) - self.conv4 = ConvLayer( - 128, - 256, - 3, - downsample=True, - resample_kernel=(1, 3, 3, 1), - bias=True, - activate=True, - ) - self.conv5 = ConvLayer( - 256, - 256, - 3, - downsample=False, - resample_kernel=(1, 3, 3, 1), - bias=True, - activate=True, - ) - self.final_conv = ConvLayer(256, 1, 3, bias=True, activate=False) - - def forward(self, x, return_feats=False, **kwargs): - """Forward function for FacialComponentDiscriminator. - Args: - x (Tensor): Input images. - return_feats (bool): Whether to return intermediate features. Default: False. - """ - feat = self.conv1(x) - feat = self.conv3(self.conv2(feat)) - rlt_feats = [] - if return_feats: - rlt_feats.append(feat.clone()) - feat = self.conv5(self.conv4(feat)) - if return_feats: - rlt_feats.append(feat.clone()) - out = self.final_conv(feat) - - if return_feats: - return out, rlt_feats - else: - return out, None diff --git a/comfy_extras/chainner_models/architecture/face/gfpganv1_clean_arch.py b/comfy_extras/chainner_models/architecture/face/gfpganv1_clean_arch.py deleted file mode 100644 index 16470d63..00000000 --- a/comfy_extras/chainner_models/architecture/face/gfpganv1_clean_arch.py +++ /dev/null @@ -1,370 +0,0 @@ -# pylint: skip-file -# type: ignore -import math -import random - -import torch -from torch import nn -from torch.nn import functional as F - -from .stylegan2_clean_arch import StyleGAN2GeneratorClean - - -class StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean): - """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform). - It is the clean version without custom compiled CUDA extensions used in StyleGAN2. - Args: - out_size (int): The spatial size of outputs. - num_style_feat (int): Channel number of style features. Default: 512. - num_mlp (int): Layer number of MLP style layers. Default: 8. - channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. - narrow (float): The narrow ratio for channels. Default: 1. - sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. - """ - - def __init__( - self, - out_size, - num_style_feat=512, - num_mlp=8, - channel_multiplier=2, - narrow=1, - sft_half=False, - ): - super(StyleGAN2GeneratorCSFT, self).__init__( - out_size, - num_style_feat=num_style_feat, - num_mlp=num_mlp, - channel_multiplier=channel_multiplier, - narrow=narrow, - ) - self.sft_half = sft_half - - def forward( - self, - styles, - conditions, - input_is_latent=False, - noise=None, - randomize_noise=True, - truncation=1, - truncation_latent=None, - inject_index=None, - return_latents=False, - ): - """Forward function for StyleGAN2GeneratorCSFT. - Args: - styles (list[Tensor]): Sample codes of styles. - conditions (list[Tensor]): SFT conditions to generators. - input_is_latent (bool): Whether input is latent style. Default: False. - noise (Tensor | None): Input noise or None. Default: None. - randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. - truncation (float): The truncation ratio. Default: 1. - truncation_latent (Tensor | None): The truncation latent tensor. Default: None. - inject_index (int | None): The injection index for mixing noise. Default: None. - return_latents (bool): Whether to return style latents. Default: False. - """ - # style codes -> latents with Style MLP layer - if not input_is_latent: - styles = [self.style_mlp(s) for s in styles] - # noises - if noise is None: - if randomize_noise: - noise = [None] * self.num_layers # for each style conv layer - else: # use the stored noise - noise = [ - getattr(self.noises, f"noise{i}") for i in range(self.num_layers) - ] - # style truncation - if truncation < 1: - style_truncation = [] - for style in styles: - style_truncation.append( - truncation_latent + truncation * (style - truncation_latent) - ) - styles = style_truncation - # get style latents with injection - if len(styles) == 1: - inject_index = self.num_latent - - if styles[0].ndim < 3: - # repeat latent code for all the layers - latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - else: # used for encoder with different latent code for each layer - latent = styles[0] - elif len(styles) == 2: # mixing noises - if inject_index is None: - inject_index = random.randint(1, self.num_latent - 1) - latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - latent2 = ( - styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) - ) - latent = torch.cat([latent1, latent2], 1) - - # main generation - out = self.constant_input(latent.shape[0]) - out = self.style_conv1(out, latent[:, 0], noise=noise[0]) - skip = self.to_rgb1(out, latent[:, 1]) - - i = 1 - for conv1, conv2, noise1, noise2, to_rgb in zip( - self.style_convs[::2], - self.style_convs[1::2], - noise[1::2], - noise[2::2], - self.to_rgbs, - ): - out = conv1(out, latent[:, i], noise=noise1) - - # the conditions may have fewer levels - if i < len(conditions): - # SFT part to combine the conditions - if self.sft_half: # only apply SFT to half of the channels - out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1) - out_sft = out_sft * conditions[i - 1] + conditions[i] - out = torch.cat([out_same, out_sft], dim=1) - else: # apply SFT to all the channels - out = out * conditions[i - 1] + conditions[i] - - out = conv2(out, latent[:, i + 1], noise=noise2) - skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space - i += 2 - - image = skip - - if return_latents: - return image, latent - else: - return image, None - - -class ResBlock(nn.Module): - """Residual block with bilinear upsampling/downsampling. - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - mode (str): Upsampling/downsampling mode. Options: down | up. Default: down. - """ - - def __init__(self, in_channels, out_channels, mode="down"): - super(ResBlock, self).__init__() - - self.conv1 = nn.Conv2d(in_channels, in_channels, 3, 1, 1) - self.conv2 = nn.Conv2d(in_channels, out_channels, 3, 1, 1) - self.skip = nn.Conv2d(in_channels, out_channels, 1, bias=False) - if mode == "down": - self.scale_factor = 0.5 - elif mode == "up": - self.scale_factor = 2 - - def forward(self, x): - out = F.leaky_relu_(self.conv1(x), negative_slope=0.2) - # upsample/downsample - out = F.interpolate( - out, scale_factor=self.scale_factor, mode="bilinear", align_corners=False - ) - out = F.leaky_relu_(self.conv2(out), negative_slope=0.2) - # skip - x = F.interpolate( - x, scale_factor=self.scale_factor, mode="bilinear", align_corners=False - ) - skip = self.skip(x) - out = out + skip - return out - - -class GFPGANv1Clean(nn.Module): - """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT. - It is the clean version without custom compiled CUDA extensions used in StyleGAN2. - Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. - Args: - out_size (int): The spatial size of outputs. - num_style_feat (int): Channel number of style features. Default: 512. - channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. - decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None. - fix_decoder (bool): Whether to fix the decoder. Default: True. - num_mlp (int): Layer number of MLP style layers. Default: 8. - input_is_latent (bool): Whether input is latent style. Default: False. - different_w (bool): Whether to use different latent w for different layers. Default: False. - narrow (float): The narrow ratio for channels. Default: 1. - sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. - """ - - def __init__( - self, - state_dict, - ): - super(GFPGANv1Clean, self).__init__() - - out_size = 512 - num_style_feat = 512 - channel_multiplier = 2 - decoder_load_path = None - fix_decoder = False - num_mlp = 8 - input_is_latent = True - different_w = True - narrow = 1 - sft_half = True - - self.model_arch = "GFPGAN" - self.sub_type = "Face SR" - self.scale = 8 - self.in_nc = 3 - self.out_nc = 3 - self.state = state_dict - - self.supports_fp16 = False - self.supports_bf16 = True - self.min_size_restriction = 512 - - self.input_is_latent = input_is_latent - self.different_w = different_w - self.num_style_feat = num_style_feat - - unet_narrow = narrow * 0.5 # by default, use a half of input channels - channels = { - "4": int(512 * unet_narrow), - "8": int(512 * unet_narrow), - "16": int(512 * unet_narrow), - "32": int(512 * unet_narrow), - "64": int(256 * channel_multiplier * unet_narrow), - "128": int(128 * channel_multiplier * unet_narrow), - "256": int(64 * channel_multiplier * unet_narrow), - "512": int(32 * channel_multiplier * unet_narrow), - "1024": int(16 * channel_multiplier * unet_narrow), - } - - self.log_size = int(math.log(out_size, 2)) - first_out_size = 2 ** (int(math.log(out_size, 2))) - - self.conv_body_first = nn.Conv2d(3, channels[f"{first_out_size}"], 1) - - # downsample - in_channels = channels[f"{first_out_size}"] - self.conv_body_down = nn.ModuleList() - for i in range(self.log_size, 2, -1): - out_channels = channels[f"{2**(i - 1)}"] - self.conv_body_down.append(ResBlock(in_channels, out_channels, mode="down")) - in_channels = out_channels - - self.final_conv = nn.Conv2d(in_channels, channels["4"], 3, 1, 1) - - # upsample - in_channels = channels["4"] - self.conv_body_up = nn.ModuleList() - for i in range(3, self.log_size + 1): - out_channels = channels[f"{2**i}"] - self.conv_body_up.append(ResBlock(in_channels, out_channels, mode="up")) - in_channels = out_channels - - # to RGB - self.toRGB = nn.ModuleList() - for i in range(3, self.log_size + 1): - self.toRGB.append(nn.Conv2d(channels[f"{2**i}"], 3, 1)) - - if different_w: - linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat - else: - linear_out_channel = num_style_feat - - self.final_linear = nn.Linear(channels["4"] * 4 * 4, linear_out_channel) - - # the decoder: stylegan2 generator with SFT modulations - self.stylegan_decoder = StyleGAN2GeneratorCSFT( - out_size=out_size, - num_style_feat=num_style_feat, - num_mlp=num_mlp, - channel_multiplier=channel_multiplier, - narrow=narrow, - sft_half=sft_half, - ) - - # load pre-trained stylegan2 model if necessary - if decoder_load_path: - self.stylegan_decoder.load_state_dict( - torch.load( - decoder_load_path, map_location=lambda storage, loc: storage - )["params_ema"] - ) - # fix decoder without updating params - if fix_decoder: - for _, param in self.stylegan_decoder.named_parameters(): - param.requires_grad = False - - # for SFT modulations (scale and shift) - self.condition_scale = nn.ModuleList() - self.condition_shift = nn.ModuleList() - for i in range(3, self.log_size + 1): - out_channels = channels[f"{2**i}"] - if sft_half: - sft_out_channels = out_channels - else: - sft_out_channels = out_channels * 2 - self.condition_scale.append( - nn.Sequential( - nn.Conv2d(out_channels, out_channels, 3, 1, 1), - nn.LeakyReLU(0.2, True), - nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1), - ) - ) - self.condition_shift.append( - nn.Sequential( - nn.Conv2d(out_channels, out_channels, 3, 1, 1), - nn.LeakyReLU(0.2, True), - nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1), - ) - ) - self.load_state_dict(state_dict) - - def forward( - self, x, return_latents=False, return_rgb=True, randomize_noise=True, **kwargs - ): - """Forward function for GFPGANv1Clean. - Args: - x (Tensor): Input images. - return_latents (bool): Whether to return style latents. Default: False. - return_rgb (bool): Whether return intermediate rgb images. Default: True. - randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. - """ - conditions = [] - unet_skips = [] - out_rgbs = [] - - # encoder - feat = F.leaky_relu_(self.conv_body_first(x), negative_slope=0.2) - for i in range(self.log_size - 2): - feat = self.conv_body_down[i](feat) - unet_skips.insert(0, feat) - feat = F.leaky_relu_(self.final_conv(feat), negative_slope=0.2) - - # style code - style_code = self.final_linear(feat.view(feat.size(0), -1)) - if self.different_w: - style_code = style_code.view(style_code.size(0), -1, self.num_style_feat) - - # decode - for i in range(self.log_size - 2): - # add unet skip - feat = feat + unet_skips[i] - # ResUpLayer - feat = self.conv_body_up[i](feat) - # generate scale and shift for SFT layers - scale = self.condition_scale[i](feat) - conditions.append(scale.clone()) - shift = self.condition_shift[i](feat) - conditions.append(shift.clone()) - # generate rgb images - if return_rgb: - out_rgbs.append(self.toRGB[i](feat)) - - # decoder - image, _ = self.stylegan_decoder( - [style_code], - conditions, - return_latents=return_latents, - input_is_latent=self.input_is_latent, - randomize_noise=randomize_noise, - ) - - return image, out_rgbs diff --git a/comfy_extras/chainner_models/architecture/face/restoreformer_arch.py b/comfy_extras/chainner_models/architecture/face/restoreformer_arch.py deleted file mode 100644 index 44922602..00000000 --- a/comfy_extras/chainner_models/architecture/face/restoreformer_arch.py +++ /dev/null @@ -1,776 +0,0 @@ -# pylint: skip-file -# type: ignore -"""Modified from https://github.com/wzhouxiff/RestoreFormer -""" -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class VectorQuantizer(nn.Module): - """ - see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py - ____________________________________________ - Discretization bottleneck part of the VQ-VAE. - Inputs: - - n_e : number of embeddings - - e_dim : dimension of embedding - - beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2 - _____________________________________________ - """ - - def __init__(self, n_e, e_dim, beta): - super(VectorQuantizer, self).__init__() - self.n_e = n_e - self.e_dim = e_dim - self.beta = beta - - self.embedding = nn.Embedding(self.n_e, self.e_dim) - self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) - - def forward(self, z): - """ - Inputs the output of the encoder network z and maps it to a discrete - one-hot vector that is the index of the closest embedding vector e_j - z (continuous) -> z_q (discrete) - z.shape = (batch, channel, height, width) - quantization pipeline: - 1. get encoder input (B,C,H,W) - 2. flatten input to (B*H*W,C) - """ - # reshape z -> (batch, height, width, channel) and flatten - z = z.permute(0, 2, 3, 1).contiguous() - z_flattened = z.view(-1, self.e_dim) - # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z - - d = ( - torch.sum(z_flattened**2, dim=1, keepdim=True) - + torch.sum(self.embedding.weight**2, dim=1) - - 2 * torch.matmul(z_flattened, self.embedding.weight.t()) - ) - - # could possible replace this here - # #\start... - # find closest encodings - - min_value, min_encoding_indices = torch.min(d, dim=1) - - min_encoding_indices = min_encoding_indices.unsqueeze(1) - - min_encodings = torch.zeros(min_encoding_indices.shape[0], self.n_e).to(z) - min_encodings.scatter_(1, min_encoding_indices, 1) - - # dtype min encodings: torch.float32 - # min_encodings shape: torch.Size([2048, 512]) - # min_encoding_indices.shape: torch.Size([2048, 1]) - - # get quantized latent vectors - z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) - # .........\end - - # with: - # .........\start - # min_encoding_indices = torch.argmin(d, dim=1) - # z_q = self.embedding(min_encoding_indices) - # ......\end......... (TODO) - - # compute loss for embedding - loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean( - (z_q - z.detach()) ** 2 - ) - - # preserve gradients - z_q = z + (z_q - z).detach() - - # perplexity - - e_mean = torch.mean(min_encodings, dim=0) - perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) - - # reshape back to match original input shape - z_q = z_q.permute(0, 3, 1, 2).contiguous() - - return z_q, loss, (perplexity, min_encodings, min_encoding_indices, d) - - def get_codebook_entry(self, indices, shape): - # shape specifying (batch, height, width, channel) - # TODO: check for more easy handling with nn.Embedding - min_encodings = torch.zeros(indices.shape[0], self.n_e).to(indices) - min_encodings.scatter_(1, indices[:, None], 1) - - # get quantized latent vectors - z_q = torch.matmul(min_encodings.float(), self.embedding.weight) - - if shape is not None: - z_q = z_q.view(shape) - - # reshape back to match original input shape - z_q = z_q.permute(0, 3, 1, 2).contiguous() - - return z_q - - -# pytorch_diffusion + derived encoder decoder -def nonlinearity(x): - # swish - return x * torch.sigmoid(x) - - -def Normalize(in_channels): - return torch.nn.GroupNorm( - num_groups=32, num_channels=in_channels, eps=1e-6, affine=True - ) - - -class Upsample(nn.Module): - def __init__(self, in_channels, with_conv): - super().__init__() - self.with_conv = with_conv - if self.with_conv: - self.conv = torch.nn.Conv2d( - in_channels, in_channels, kernel_size=3, stride=1, padding=1 - ) - - def forward(self, x): - x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") - if self.with_conv: - x = self.conv(x) - return x - - -class Downsample(nn.Module): - def __init__(self, in_channels, with_conv): - super().__init__() - self.with_conv = with_conv - if self.with_conv: - # no asymmetric padding in torch conv, must do it ourselves - self.conv = torch.nn.Conv2d( - in_channels, in_channels, kernel_size=3, stride=2, padding=0 - ) - - def forward(self, x): - if self.with_conv: - pad = (0, 1, 0, 1) - x = torch.nn.functional.pad(x, pad, mode="constant", value=0) - x = self.conv(x) - else: - x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) - return x - - -class ResnetBlock(nn.Module): - def __init__( - self, - *, - in_channels, - out_channels=None, - conv_shortcut=False, - dropout, - temb_channels=512 - ): - super().__init__() - self.in_channels = in_channels - out_channels = in_channels if out_channels is None else out_channels - self.out_channels = out_channels - self.use_conv_shortcut = conv_shortcut - - self.norm1 = Normalize(in_channels) - self.conv1 = torch.nn.Conv2d( - in_channels, out_channels, kernel_size=3, stride=1, padding=1 - ) - if temb_channels > 0: - self.temb_proj = torch.nn.Linear(temb_channels, out_channels) - self.norm2 = Normalize(out_channels) - self.dropout = torch.nn.Dropout(dropout) - self.conv2 = torch.nn.Conv2d( - out_channels, out_channels, kernel_size=3, stride=1, padding=1 - ) - if self.in_channels != self.out_channels: - if self.use_conv_shortcut: - self.conv_shortcut = torch.nn.Conv2d( - in_channels, out_channels, kernel_size=3, stride=1, padding=1 - ) - else: - self.nin_shortcut = torch.nn.Conv2d( - in_channels, out_channels, kernel_size=1, stride=1, padding=0 - ) - - def forward(self, x, temb): - h = x - h = self.norm1(h) - h = nonlinearity(h) - h = self.conv1(h) - - if temb is not None: - h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None] - - h = self.norm2(h) - h = nonlinearity(h) - h = self.dropout(h) - h = self.conv2(h) - - if self.in_channels != self.out_channels: - if self.use_conv_shortcut: - x = self.conv_shortcut(x) - else: - x = self.nin_shortcut(x) - - return x + h - - -class MultiHeadAttnBlock(nn.Module): - def __init__(self, in_channels, head_size=1): - super().__init__() - self.in_channels = in_channels - self.head_size = head_size - self.att_size = in_channels // head_size - assert ( - in_channels % head_size == 0 - ), "The size of head should be divided by the number of channels." - - self.norm1 = Normalize(in_channels) - self.norm2 = Normalize(in_channels) - - self.q = torch.nn.Conv2d( - in_channels, in_channels, kernel_size=1, stride=1, padding=0 - ) - self.k = torch.nn.Conv2d( - in_channels, in_channels, kernel_size=1, stride=1, padding=0 - ) - self.v = torch.nn.Conv2d( - in_channels, in_channels, kernel_size=1, stride=1, padding=0 - ) - self.proj_out = torch.nn.Conv2d( - in_channels, in_channels, kernel_size=1, stride=1, padding=0 - ) - self.num = 0 - - def forward(self, x, y=None): - h_ = x - h_ = self.norm1(h_) - if y is None: - y = h_ - else: - y = self.norm2(y) - - q = self.q(y) - k = self.k(h_) - v = self.v(h_) - - # compute attention - b, c, h, w = q.shape - q = q.reshape(b, self.head_size, self.att_size, h * w) - q = q.permute(0, 3, 1, 2) # b, hw, head, att - - k = k.reshape(b, self.head_size, self.att_size, h * w) - k = k.permute(0, 3, 1, 2) - - v = v.reshape(b, self.head_size, self.att_size, h * w) - v = v.permute(0, 3, 1, 2) - - q = q.transpose(1, 2) - v = v.transpose(1, 2) - k = k.transpose(1, 2).transpose(2, 3) - - scale = int(self.att_size) ** (-0.5) - q.mul_(scale) - w_ = torch.matmul(q, k) - w_ = F.softmax(w_, dim=3) - - w_ = w_.matmul(v) - - w_ = w_.transpose(1, 2).contiguous() # [b, h*w, head, att] - w_ = w_.view(b, h, w, -1) - w_ = w_.permute(0, 3, 1, 2) - - w_ = self.proj_out(w_) - - return x + w_ - - -class MultiHeadEncoder(nn.Module): - def __init__( - self, - ch, - out_ch, - ch_mult=(1, 2, 4, 8), - num_res_blocks=2, - attn_resolutions=(16,), - dropout=0.0, - resamp_with_conv=True, - in_channels=3, - resolution=512, - z_channels=256, - double_z=True, - enable_mid=True, - head_size=1, - **ignore_kwargs - ): - super().__init__() - self.ch = ch - self.temb_ch = 0 - self.num_resolutions = len(ch_mult) - self.num_res_blocks = num_res_blocks - self.resolution = resolution - self.in_channels = in_channels - self.enable_mid = enable_mid - - # downsampling - self.conv_in = torch.nn.Conv2d( - in_channels, self.ch, kernel_size=3, stride=1, padding=1 - ) - - curr_res = resolution - in_ch_mult = (1,) + tuple(ch_mult) - self.down = nn.ModuleList() - for i_level in range(self.num_resolutions): - block = nn.ModuleList() - attn = nn.ModuleList() - block_in = ch * in_ch_mult[i_level] - block_out = ch * ch_mult[i_level] - for i_block in range(self.num_res_blocks): - block.append( - ResnetBlock( - in_channels=block_in, - out_channels=block_out, - temb_channels=self.temb_ch, - dropout=dropout, - ) - ) - block_in = block_out - if curr_res in attn_resolutions: - attn.append(MultiHeadAttnBlock(block_in, head_size)) - down = nn.Module() - down.block = block - down.attn = attn - if i_level != self.num_resolutions - 1: - down.downsample = Downsample(block_in, resamp_with_conv) - curr_res = curr_res // 2 - self.down.append(down) - - # middle - if self.enable_mid: - self.mid = nn.Module() - self.mid.block_1 = ResnetBlock( - in_channels=block_in, - out_channels=block_in, - temb_channels=self.temb_ch, - dropout=dropout, - ) - self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size) - self.mid.block_2 = ResnetBlock( - in_channels=block_in, - out_channels=block_in, - temb_channels=self.temb_ch, - dropout=dropout, - ) - - # end - self.norm_out = Normalize(block_in) - self.conv_out = torch.nn.Conv2d( - block_in, - 2 * z_channels if double_z else z_channels, - kernel_size=3, - stride=1, - padding=1, - ) - - def forward(self, x): - hs = {} - # timestep embedding - temb = None - - # downsampling - h = self.conv_in(x) - hs["in"] = h - for i_level in range(self.num_resolutions): - for i_block in range(self.num_res_blocks): - h = self.down[i_level].block[i_block](h, temb) - if len(self.down[i_level].attn) > 0: - h = self.down[i_level].attn[i_block](h) - - if i_level != self.num_resolutions - 1: - # hs.append(h) - hs["block_" + str(i_level)] = h - h = self.down[i_level].downsample(h) - - # middle - # h = hs[-1] - if self.enable_mid: - h = self.mid.block_1(h, temb) - hs["block_" + str(i_level) + "_atten"] = h - h = self.mid.attn_1(h) - h = self.mid.block_2(h, temb) - hs["mid_atten"] = h - - # end - h = self.norm_out(h) - h = nonlinearity(h) - h = self.conv_out(h) - # hs.append(h) - hs["out"] = h - - return hs - - -class MultiHeadDecoder(nn.Module): - def __init__( - self, - ch, - out_ch, - ch_mult=(1, 2, 4, 8), - num_res_blocks=2, - attn_resolutions=(16,), - dropout=0.0, - resamp_with_conv=True, - in_channels=3, - resolution=512, - z_channels=256, - give_pre_end=False, - enable_mid=True, - head_size=1, - **ignorekwargs - ): - super().__init__() - self.ch = ch - self.temb_ch = 0 - self.num_resolutions = len(ch_mult) - self.num_res_blocks = num_res_blocks - self.resolution = resolution - self.in_channels = in_channels - self.give_pre_end = give_pre_end - self.enable_mid = enable_mid - - # compute in_ch_mult, block_in and curr_res at lowest res - block_in = ch * ch_mult[self.num_resolutions - 1] - curr_res = resolution // 2 ** (self.num_resolutions - 1) - self.z_shape = (1, z_channels, curr_res, curr_res) - print( - "Working with z of shape {} = {} dimensions.".format( - self.z_shape, np.prod(self.z_shape) - ) - ) - - # z to block_in - self.conv_in = torch.nn.Conv2d( - z_channels, block_in, kernel_size=3, stride=1, padding=1 - ) - - # middle - if self.enable_mid: - self.mid = nn.Module() - self.mid.block_1 = ResnetBlock( - in_channels=block_in, - out_channels=block_in, - temb_channels=self.temb_ch, - dropout=dropout, - ) - self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size) - self.mid.block_2 = ResnetBlock( - in_channels=block_in, - out_channels=block_in, - temb_channels=self.temb_ch, - dropout=dropout, - ) - - # upsampling - self.up = nn.ModuleList() - for i_level in reversed(range(self.num_resolutions)): - block = nn.ModuleList() - attn = nn.ModuleList() - block_out = ch * ch_mult[i_level] - for i_block in range(self.num_res_blocks + 1): - block.append( - ResnetBlock( - in_channels=block_in, - out_channels=block_out, - temb_channels=self.temb_ch, - dropout=dropout, - ) - ) - block_in = block_out - if curr_res in attn_resolutions: - attn.append(MultiHeadAttnBlock(block_in, head_size)) - up = nn.Module() - up.block = block - up.attn = attn - if i_level != 0: - up.upsample = Upsample(block_in, resamp_with_conv) - curr_res = curr_res * 2 - self.up.insert(0, up) # prepend to get consistent order - - # end - self.norm_out = Normalize(block_in) - self.conv_out = torch.nn.Conv2d( - block_in, out_ch, kernel_size=3, stride=1, padding=1 - ) - - def forward(self, z): - # assert z.shape[1:] == self.z_shape[1:] - self.last_z_shape = z.shape - - # timestep embedding - temb = None - - # z to block_in - h = self.conv_in(z) - - # middle - if self.enable_mid: - h = self.mid.block_1(h, temb) - h = self.mid.attn_1(h) - h = self.mid.block_2(h, temb) - - # upsampling - for i_level in reversed(range(self.num_resolutions)): - for i_block in range(self.num_res_blocks + 1): - h = self.up[i_level].block[i_block](h, temb) - if len(self.up[i_level].attn) > 0: - h = self.up[i_level].attn[i_block](h) - if i_level != 0: - h = self.up[i_level].upsample(h) - - # end - if self.give_pre_end: - return h - - h = self.norm_out(h) - h = nonlinearity(h) - h = self.conv_out(h) - return h - - -class MultiHeadDecoderTransformer(nn.Module): - def __init__( - self, - ch, - out_ch, - ch_mult=(1, 2, 4, 8), - num_res_blocks=2, - attn_resolutions=(16,), - dropout=0.0, - resamp_with_conv=True, - in_channels=3, - resolution=512, - z_channels=256, - give_pre_end=False, - enable_mid=True, - head_size=1, - **ignorekwargs - ): - super().__init__() - self.ch = ch - self.temb_ch = 0 - self.num_resolutions = len(ch_mult) - self.num_res_blocks = num_res_blocks - self.resolution = resolution - self.in_channels = in_channels - self.give_pre_end = give_pre_end - self.enable_mid = enable_mid - - # compute in_ch_mult, block_in and curr_res at lowest res - block_in = ch * ch_mult[self.num_resolutions - 1] - curr_res = resolution // 2 ** (self.num_resolutions - 1) - self.z_shape = (1, z_channels, curr_res, curr_res) - print( - "Working with z of shape {} = {} dimensions.".format( - self.z_shape, np.prod(self.z_shape) - ) - ) - - # z to block_in - self.conv_in = torch.nn.Conv2d( - z_channels, block_in, kernel_size=3, stride=1, padding=1 - ) - - # middle - if self.enable_mid: - self.mid = nn.Module() - self.mid.block_1 = ResnetBlock( - in_channels=block_in, - out_channels=block_in, - temb_channels=self.temb_ch, - dropout=dropout, - ) - self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size) - self.mid.block_2 = ResnetBlock( - in_channels=block_in, - out_channels=block_in, - temb_channels=self.temb_ch, - dropout=dropout, - ) - - # upsampling - self.up = nn.ModuleList() - for i_level in reversed(range(self.num_resolutions)): - block = nn.ModuleList() - attn = nn.ModuleList() - block_out = ch * ch_mult[i_level] - for i_block in range(self.num_res_blocks + 1): - block.append( - ResnetBlock( - in_channels=block_in, - out_channels=block_out, - temb_channels=self.temb_ch, - dropout=dropout, - ) - ) - block_in = block_out - if curr_res in attn_resolutions: - attn.append(MultiHeadAttnBlock(block_in, head_size)) - up = nn.Module() - up.block = block - up.attn = attn - if i_level != 0: - up.upsample = Upsample(block_in, resamp_with_conv) - curr_res = curr_res * 2 - self.up.insert(0, up) # prepend to get consistent order - - # end - self.norm_out = Normalize(block_in) - self.conv_out = torch.nn.Conv2d( - block_in, out_ch, kernel_size=3, stride=1, padding=1 - ) - - def forward(self, z, hs): - # assert z.shape[1:] == self.z_shape[1:] - # self.last_z_shape = z.shape - - # timestep embedding - temb = None - - # z to block_in - h = self.conv_in(z) - - # middle - if self.enable_mid: - h = self.mid.block_1(h, temb) - h = self.mid.attn_1(h, hs["mid_atten"]) - h = self.mid.block_2(h, temb) - - # upsampling - for i_level in reversed(range(self.num_resolutions)): - for i_block in range(self.num_res_blocks + 1): - h = self.up[i_level].block[i_block](h, temb) - if len(self.up[i_level].attn) > 0: - h = self.up[i_level].attn[i_block]( - h, hs["block_" + str(i_level) + "_atten"] - ) - # hfeature = h.clone() - if i_level != 0: - h = self.up[i_level].upsample(h) - - # end - if self.give_pre_end: - return h - - h = self.norm_out(h) - h = nonlinearity(h) - h = self.conv_out(h) - return h - - -class RestoreFormer(nn.Module): - def __init__( - self, - state_dict, - ): - super(RestoreFormer, self).__init__() - - n_embed = 1024 - embed_dim = 256 - ch = 64 - out_ch = 3 - ch_mult = (1, 2, 2, 4, 4, 8) - num_res_blocks = 2 - attn_resolutions = (16,) - dropout = 0.0 - in_channels = 3 - resolution = 512 - z_channels = 256 - double_z = False - enable_mid = True - fix_decoder = False - fix_codebook = True - fix_encoder = False - head_size = 8 - - self.model_arch = "RestoreFormer" - self.sub_type = "Face SR" - self.scale = 8 - self.in_nc = 3 - self.out_nc = out_ch - self.state = state_dict - - self.supports_fp16 = False - self.supports_bf16 = True - self.min_size_restriction = 16 - - self.encoder = MultiHeadEncoder( - ch=ch, - out_ch=out_ch, - ch_mult=ch_mult, - num_res_blocks=num_res_blocks, - attn_resolutions=attn_resolutions, - dropout=dropout, - in_channels=in_channels, - resolution=resolution, - z_channels=z_channels, - double_z=double_z, - enable_mid=enable_mid, - head_size=head_size, - ) - self.decoder = MultiHeadDecoderTransformer( - ch=ch, - out_ch=out_ch, - ch_mult=ch_mult, - num_res_blocks=num_res_blocks, - attn_resolutions=attn_resolutions, - dropout=dropout, - in_channels=in_channels, - resolution=resolution, - z_channels=z_channels, - enable_mid=enable_mid, - head_size=head_size, - ) - - self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25) - - self.quant_conv = torch.nn.Conv2d(z_channels, embed_dim, 1) - self.post_quant_conv = torch.nn.Conv2d(embed_dim, z_channels, 1) - - if fix_decoder: - for _, param in self.decoder.named_parameters(): - param.requires_grad = False - for _, param in self.post_quant_conv.named_parameters(): - param.requires_grad = False - for _, param in self.quantize.named_parameters(): - param.requires_grad = False - elif fix_codebook: - for _, param in self.quantize.named_parameters(): - param.requires_grad = False - - if fix_encoder: - for _, param in self.encoder.named_parameters(): - param.requires_grad = False - - self.load_state_dict(state_dict) - - def encode(self, x): - hs = self.encoder(x) - h = self.quant_conv(hs["out"]) - quant, emb_loss, info = self.quantize(h) - return quant, emb_loss, info, hs - - def decode(self, quant, hs): - quant = self.post_quant_conv(quant) - dec = self.decoder(quant, hs) - - return dec - - def forward(self, input, **kwargs): - quant, diff, info, hs = self.encode(input) - dec = self.decode(quant, hs) - - return dec, None diff --git a/comfy_extras/chainner_models/architecture/face/stylegan2_arch.py b/comfy_extras/chainner_models/architecture/face/stylegan2_arch.py deleted file mode 100644 index 1eb0e9f1..00000000 --- a/comfy_extras/chainner_models/architecture/face/stylegan2_arch.py +++ /dev/null @@ -1,865 +0,0 @@ -# pylint: skip-file -# type: ignore -import math -import random - -import torch -from torch import nn -from torch.nn import functional as F - -from .fused_act import FusedLeakyReLU, fused_leaky_relu -from .upfirdn2d import upfirdn2d - - -class NormStyleCode(nn.Module): - def forward(self, x): - """Normalize the style codes. - - Args: - x (Tensor): Style codes with shape (b, c). - - Returns: - Tensor: Normalized tensor. - """ - return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) - - -def make_resample_kernel(k): - """Make resampling kernel for UpFirDn. - - Args: - k (list[int]): A list indicating the 1D resample kernel magnitude. - - Returns: - Tensor: 2D resampled kernel. - """ - k = torch.tensor(k, dtype=torch.float32) - if k.ndim == 1: - k = k[None, :] * k[:, None] # to 2D kernel, outer product - # normalize - k /= k.sum() - return k - - -class UpFirDnUpsample(nn.Module): - """Upsample, FIR filter, and downsample (upsampole version). - - References: - 1. https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.upfirdn.html # noqa: E501 - 2. http://www.ece.northwestern.edu/local-apps/matlabhelp/toolbox/signal/upfirdn.html # noqa: E501 - - Args: - resample_kernel (list[int]): A list indicating the 1D resample kernel - magnitude. - factor (int): Upsampling scale factor. Default: 2. - """ - - def __init__(self, resample_kernel, factor=2): - super(UpFirDnUpsample, self).__init__() - self.kernel = make_resample_kernel(resample_kernel) * (factor**2) - self.factor = factor - - pad = self.kernel.shape[0] - factor - self.pad = ((pad + 1) // 2 + factor - 1, pad // 2) - - def forward(self, x): - out = upfirdn2d(x, self.kernel.type_as(x), up=self.factor, down=1, pad=self.pad) - return out - - def __repr__(self): - return f"{self.__class__.__name__}(factor={self.factor})" - - -class UpFirDnDownsample(nn.Module): - """Upsample, FIR filter, and downsample (downsampole version). - - Args: - resample_kernel (list[int]): A list indicating the 1D resample kernel - magnitude. - factor (int): Downsampling scale factor. Default: 2. - """ - - def __init__(self, resample_kernel, factor=2): - super(UpFirDnDownsample, self).__init__() - self.kernel = make_resample_kernel(resample_kernel) - self.factor = factor - - pad = self.kernel.shape[0] - factor - self.pad = ((pad + 1) // 2, pad // 2) - - def forward(self, x): - out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=self.factor, pad=self.pad) - return out - - def __repr__(self): - return f"{self.__class__.__name__}(factor={self.factor})" - - -class UpFirDnSmooth(nn.Module): - """Upsample, FIR filter, and downsample (smooth version). - - Args: - resample_kernel (list[int]): A list indicating the 1D resample kernel - magnitude. - upsample_factor (int): Upsampling scale factor. Default: 1. - downsample_factor (int): Downsampling scale factor. Default: 1. - kernel_size (int): Kernel size: Default: 1. - """ - - def __init__( - self, resample_kernel, upsample_factor=1, downsample_factor=1, kernel_size=1 - ): - super(UpFirDnSmooth, self).__init__() - self.upsample_factor = upsample_factor - self.downsample_factor = downsample_factor - self.kernel = make_resample_kernel(resample_kernel) - if upsample_factor > 1: - self.kernel = self.kernel * (upsample_factor**2) - - if upsample_factor > 1: - pad = (self.kernel.shape[0] - upsample_factor) - (kernel_size - 1) - self.pad = ((pad + 1) // 2 + upsample_factor - 1, pad // 2 + 1) - elif downsample_factor > 1: - pad = (self.kernel.shape[0] - downsample_factor) + (kernel_size - 1) - self.pad = ((pad + 1) // 2, pad // 2) - else: - raise NotImplementedError - - def forward(self, x): - out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=1, pad=self.pad) - return out - - def __repr__(self): - return ( - f"{self.__class__.__name__}(upsample_factor={self.upsample_factor}" - f", downsample_factor={self.downsample_factor})" - ) - - -class EqualLinear(nn.Module): - """Equalized Linear as StyleGAN2. - - Args: - in_channels (int): Size of each sample. - out_channels (int): Size of each output sample. - bias (bool): If set to ``False``, the layer will not learn an additive - bias. Default: ``True``. - bias_init_val (float): Bias initialized value. Default: 0. - lr_mul (float): Learning rate multiplier. Default: 1. - activation (None | str): The activation after ``linear`` operation. - Supported: 'fused_lrelu', None. Default: None. - """ - - def __init__( - self, - in_channels, - out_channels, - bias=True, - bias_init_val=0, - lr_mul=1, - activation=None, - ): - super(EqualLinear, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.lr_mul = lr_mul - self.activation = activation - if self.activation not in ["fused_lrelu", None]: - raise ValueError( - f"Wrong activation value in EqualLinear: {activation}" - "Supported ones are: ['fused_lrelu', None]." - ) - self.scale = (1 / math.sqrt(in_channels)) * lr_mul - - self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul)) - if bias: - self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) - else: - self.register_parameter("bias", None) - - def forward(self, x): - if self.bias is None: - bias = None - else: - bias = self.bias * self.lr_mul - if self.activation == "fused_lrelu": - out = F.linear(x, self.weight * self.scale) - out = fused_leaky_relu(out, bias) - else: - out = F.linear(x, self.weight * self.scale, bias=bias) - return out - - def __repr__(self): - return ( - f"{self.__class__.__name__}(in_channels={self.in_channels}, " - f"out_channels={self.out_channels}, bias={self.bias is not None})" - ) - - -class ModulatedConv2d(nn.Module): - """Modulated Conv2d used in StyleGAN2. - - There is no bias in ModulatedConv2d. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - num_style_feat (int): Channel number of style features. - demodulate (bool): Whether to demodulate in the conv layer. - Default: True. - sample_mode (str | None): Indicating 'upsample', 'downsample' or None. - Default: None. - resample_kernel (list[int]): A list indicating the 1D resample kernel - magnitude. Default: (1, 3, 3, 1). - eps (float): A value added to the denominator for numerical stability. - Default: 1e-8. - """ - - def __init__( - self, - in_channels, - out_channels, - kernel_size, - num_style_feat, - demodulate=True, - sample_mode=None, - resample_kernel=(1, 3, 3, 1), - eps=1e-8, - ): - super(ModulatedConv2d, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.demodulate = demodulate - self.sample_mode = sample_mode - self.eps = eps - - if self.sample_mode == "upsample": - self.smooth = UpFirDnSmooth( - resample_kernel, - upsample_factor=2, - downsample_factor=1, - kernel_size=kernel_size, - ) - elif self.sample_mode == "downsample": - self.smooth = UpFirDnSmooth( - resample_kernel, - upsample_factor=1, - downsample_factor=2, - kernel_size=kernel_size, - ) - elif self.sample_mode is None: - pass - else: - raise ValueError( - f"Wrong sample mode {self.sample_mode}, " - "supported ones are ['upsample', 'downsample', None]." - ) - - self.scale = 1 / math.sqrt(in_channels * kernel_size**2) - # modulation inside each modulated conv - self.modulation = EqualLinear( - num_style_feat, - in_channels, - bias=True, - bias_init_val=1, - lr_mul=1, - activation=None, - ) - - self.weight = nn.Parameter( - torch.randn(1, out_channels, in_channels, kernel_size, kernel_size) - ) - self.padding = kernel_size // 2 - - def forward(self, x, style): - """Forward function. - - Args: - x (Tensor): Tensor with shape (b, c, h, w). - style (Tensor): Tensor with shape (b, num_style_feat). - - Returns: - Tensor: Modulated tensor after convolution. - """ - b, c, h, w = x.shape # c = c_in - # weight modulation - style = self.modulation(style).view(b, 1, c, 1, 1) - # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1) - weight = self.scale * self.weight * style # (b, c_out, c_in, k, k) - - if self.demodulate: - demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) - weight = weight * demod.view(b, self.out_channels, 1, 1, 1) - - weight = weight.view( - b * self.out_channels, c, self.kernel_size, self.kernel_size - ) - - if self.sample_mode == "upsample": - x = x.view(1, b * c, h, w) - weight = weight.view( - b, self.out_channels, c, self.kernel_size, self.kernel_size - ) - weight = weight.transpose(1, 2).reshape( - b * c, self.out_channels, self.kernel_size, self.kernel_size - ) - out = F.conv_transpose2d(x, weight, padding=0, stride=2, groups=b) - out = out.view(b, self.out_channels, *out.shape[2:4]) - out = self.smooth(out) - elif self.sample_mode == "downsample": - x = self.smooth(x) - x = x.view(1, b * c, *x.shape[2:4]) - out = F.conv2d(x, weight, padding=0, stride=2, groups=b) - out = out.view(b, self.out_channels, *out.shape[2:4]) - else: - x = x.view(1, b * c, h, w) - # weight: (b*c_out, c_in, k, k), groups=b - out = F.conv2d(x, weight, padding=self.padding, groups=b) - out = out.view(b, self.out_channels, *out.shape[2:4]) - - return out - - def __repr__(self): - return ( - f"{self.__class__.__name__}(in_channels={self.in_channels}, " - f"out_channels={self.out_channels}, " - f"kernel_size={self.kernel_size}, " - f"demodulate={self.demodulate}, sample_mode={self.sample_mode})" - ) - - -class StyleConv(nn.Module): - """Style conv. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - num_style_feat (int): Channel number of style features. - demodulate (bool): Whether demodulate in the conv layer. Default: True. - sample_mode (str | None): Indicating 'upsample', 'downsample' or None. - Default: None. - resample_kernel (list[int]): A list indicating the 1D resample kernel - magnitude. Default: (1, 3, 3, 1). - """ - - def __init__( - self, - in_channels, - out_channels, - kernel_size, - num_style_feat, - demodulate=True, - sample_mode=None, - resample_kernel=(1, 3, 3, 1), - ): - super(StyleConv, self).__init__() - self.modulated_conv = ModulatedConv2d( - in_channels, - out_channels, - kernel_size, - num_style_feat, - demodulate=demodulate, - sample_mode=sample_mode, - resample_kernel=resample_kernel, - ) - self.weight = nn.Parameter(torch.zeros(1)) # for noise injection - self.activate = FusedLeakyReLU(out_channels) - - def forward(self, x, style, noise=None): - # modulate - out = self.modulated_conv(x, style) - # noise injection - if noise is None: - b, _, h, w = out.shape - noise = out.new_empty(b, 1, h, w).normal_() - out = out + self.weight * noise - # activation (with bias) - out = self.activate(out) - return out - - -class ToRGB(nn.Module): - """To RGB from features. - - Args: - in_channels (int): Channel number of input. - num_style_feat (int): Channel number of style features. - upsample (bool): Whether to upsample. Default: True. - resample_kernel (list[int]): A list indicating the 1D resample kernel - magnitude. Default: (1, 3, 3, 1). - """ - - def __init__( - self, in_channels, num_style_feat, upsample=True, resample_kernel=(1, 3, 3, 1) - ): - super(ToRGB, self).__init__() - if upsample: - self.upsample = UpFirDnUpsample(resample_kernel, factor=2) - else: - self.upsample = None - self.modulated_conv = ModulatedConv2d( - in_channels, - 3, - kernel_size=1, - num_style_feat=num_style_feat, - demodulate=False, - sample_mode=None, - ) - self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) - - def forward(self, x, style, skip=None): - """Forward function. - - Args: - x (Tensor): Feature tensor with shape (b, c, h, w). - style (Tensor): Tensor with shape (b, num_style_feat). - skip (Tensor): Base/skip tensor. Default: None. - - Returns: - Tensor: RGB images. - """ - out = self.modulated_conv(x, style) - out = out + self.bias - if skip is not None: - if self.upsample: - skip = self.upsample(skip) - out = out + skip - return out - - -class ConstantInput(nn.Module): - """Constant input. - - Args: - num_channel (int): Channel number of constant input. - size (int): Spatial size of constant input. - """ - - def __init__(self, num_channel, size): - super(ConstantInput, self).__init__() - self.weight = nn.Parameter(torch.randn(1, num_channel, size, size)) - - def forward(self, batch): - out = self.weight.repeat(batch, 1, 1, 1) - return out - - -class StyleGAN2Generator(nn.Module): - """StyleGAN2 Generator. - - Args: - out_size (int): The spatial size of outputs. - num_style_feat (int): Channel number of style features. Default: 512. - num_mlp (int): Layer number of MLP style layers. Default: 8. - channel_multiplier (int): Channel multiplier for large networks of - StyleGAN2. Default: 2. - resample_kernel (list[int]): A list indicating the 1D resample kernel - magnitude. A cross production will be applied to extent 1D resample - kernel to 2D resample kernel. Default: (1, 3, 3, 1). - lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. - narrow (float): Narrow ratio for channels. Default: 1.0. - """ - - def __init__( - self, - out_size, - num_style_feat=512, - num_mlp=8, - channel_multiplier=2, - resample_kernel=(1, 3, 3, 1), - lr_mlp=0.01, - narrow=1, - ): - super(StyleGAN2Generator, self).__init__() - # Style MLP layers - self.num_style_feat = num_style_feat - style_mlp_layers = [NormStyleCode()] - for i in range(num_mlp): - style_mlp_layers.append( - EqualLinear( - num_style_feat, - num_style_feat, - bias=True, - bias_init_val=0, - lr_mul=lr_mlp, - activation="fused_lrelu", - ) - ) - self.style_mlp = nn.Sequential(*style_mlp_layers) - - channels = { - "4": int(512 * narrow), - "8": int(512 * narrow), - "16": int(512 * narrow), - "32": int(512 * narrow), - "64": int(256 * channel_multiplier * narrow), - "128": int(128 * channel_multiplier * narrow), - "256": int(64 * channel_multiplier * narrow), - "512": int(32 * channel_multiplier * narrow), - "1024": int(16 * channel_multiplier * narrow), - } - self.channels = channels - - self.constant_input = ConstantInput(channels["4"], size=4) - self.style_conv1 = StyleConv( - channels["4"], - channels["4"], - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode=None, - resample_kernel=resample_kernel, - ) - self.to_rgb1 = ToRGB( - channels["4"], - num_style_feat, - upsample=False, - resample_kernel=resample_kernel, - ) - - self.log_size = int(math.log(out_size, 2)) - self.num_layers = (self.log_size - 2) * 2 + 1 - self.num_latent = self.log_size * 2 - 2 - - self.style_convs = nn.ModuleList() - self.to_rgbs = nn.ModuleList() - self.noises = nn.Module() - - in_channels = channels["4"] - # noise - for layer_idx in range(self.num_layers): - resolution = 2 ** ((layer_idx + 5) // 2) - shape = [1, 1, resolution, resolution] - self.noises.register_buffer(f"noise{layer_idx}", torch.randn(*shape)) - # style convs and to_rgbs - for i in range(3, self.log_size + 1): - out_channels = channels[f"{2**i}"] - self.style_convs.append( - StyleConv( - in_channels, - out_channels, - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode="upsample", - resample_kernel=resample_kernel, - ) - ) - self.style_convs.append( - StyleConv( - out_channels, - out_channels, - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode=None, - resample_kernel=resample_kernel, - ) - ) - self.to_rgbs.append( - ToRGB( - out_channels, - num_style_feat, - upsample=True, - resample_kernel=resample_kernel, - ) - ) - in_channels = out_channels - - def make_noise(self): - """Make noise for noise injection.""" - device = self.constant_input.weight.device - noises = [torch.randn(1, 1, 4, 4, device=device)] - - for i in range(3, self.log_size + 1): - for _ in range(2): - noises.append(torch.randn(1, 1, 2**i, 2**i, device=device)) - - return noises - - def get_latent(self, x): - return self.style_mlp(x) - - def mean_latent(self, num_latent): - latent_in = torch.randn( - num_latent, self.num_style_feat, device=self.constant_input.weight.device - ) - latent = self.style_mlp(latent_in).mean(0, keepdim=True) - return latent - - def forward( - self, - styles, - input_is_latent=False, - noise=None, - randomize_noise=True, - truncation=1, - truncation_latent=None, - inject_index=None, - return_latents=False, - ): - """Forward function for StyleGAN2Generator. - - Args: - styles (list[Tensor]): Sample codes of styles. - input_is_latent (bool): Whether input is latent style. - Default: False. - noise (Tensor | None): Input noise or None. Default: None. - randomize_noise (bool): Randomize noise, used when 'noise' is - False. Default: True. - truncation (float): TODO. Default: 1. - truncation_latent (Tensor | None): TODO. Default: None. - inject_index (int | None): The injection index for mixing noise. - Default: None. - return_latents (bool): Whether to return style latents. - Default: False. - """ - # style codes -> latents with Style MLP layer - if not input_is_latent: - styles = [self.style_mlp(s) for s in styles] - # noises - if noise is None: - if randomize_noise: - noise = [None] * self.num_layers # for each style conv layer - else: # use the stored noise - noise = [ - getattr(self.noises, f"noise{i}") for i in range(self.num_layers) - ] - # style truncation - if truncation < 1: - style_truncation = [] - for style in styles: - style_truncation.append( - truncation_latent + truncation * (style - truncation_latent) - ) - styles = style_truncation - # get style latent with injection - if len(styles) == 1: - inject_index = self.num_latent - - if styles[0].ndim < 3: - # repeat latent code for all the layers - latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - else: # used for encoder with different latent code for each layer - latent = styles[0] - elif len(styles) == 2: # mixing noises - if inject_index is None: - inject_index = random.randint(1, self.num_latent - 1) - latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - latent2 = ( - styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) - ) - latent = torch.cat([latent1, latent2], 1) - - # main generation - out = self.constant_input(latent.shape[0]) - out = self.style_conv1(out, latent[:, 0], noise=noise[0]) - skip = self.to_rgb1(out, latent[:, 1]) - - i = 1 - for conv1, conv2, noise1, noise2, to_rgb in zip( - self.style_convs[::2], - self.style_convs[1::2], - noise[1::2], - noise[2::2], - self.to_rgbs, - ): - out = conv1(out, latent[:, i], noise=noise1) - out = conv2(out, latent[:, i + 1], noise=noise2) - skip = to_rgb(out, latent[:, i + 2], skip) - i += 2 - - image = skip - - if return_latents: - return image, latent - else: - return image, None - - -class ScaledLeakyReLU(nn.Module): - """Scaled LeakyReLU. - - Args: - negative_slope (float): Negative slope. Default: 0.2. - """ - - def __init__(self, negative_slope=0.2): - super(ScaledLeakyReLU, self).__init__() - self.negative_slope = negative_slope - - def forward(self, x): - out = F.leaky_relu(x, negative_slope=self.negative_slope) - return out * math.sqrt(2) - - -class EqualConv2d(nn.Module): - """Equalized Linear as StyleGAN2. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - stride (int): Stride of the convolution. Default: 1 - padding (int): Zero-padding added to both sides of the input. - Default: 0. - bias (bool): If ``True``, adds a learnable bias to the output. - Default: ``True``. - bias_init_val (float): Bias initialized value. Default: 0. - """ - - def __init__( - self, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - bias=True, - bias_init_val=0, - ): - super(EqualConv2d, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.stride = stride - self.padding = padding - self.scale = 1 / math.sqrt(in_channels * kernel_size**2) - - self.weight = nn.Parameter( - torch.randn(out_channels, in_channels, kernel_size, kernel_size) - ) - if bias: - self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) - else: - self.register_parameter("bias", None) - - def forward(self, x): - out = F.conv2d( - x, - self.weight * self.scale, - bias=self.bias, - stride=self.stride, - padding=self.padding, - ) - - return out - - def __repr__(self): - return ( - f"{self.__class__.__name__}(in_channels={self.in_channels}, " - f"out_channels={self.out_channels}, " - f"kernel_size={self.kernel_size}," - f" stride={self.stride}, padding={self.padding}, " - f"bias={self.bias is not None})" - ) - - -class ConvLayer(nn.Sequential): - """Conv Layer used in StyleGAN2 Discriminator. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Kernel size. - downsample (bool): Whether downsample by a factor of 2. - Default: False. - resample_kernel (list[int]): A list indicating the 1D resample - kernel magnitude. A cross production will be applied to - extent 1D resample kernel to 2D resample kernel. - Default: (1, 3, 3, 1). - bias (bool): Whether with bias. Default: True. - activate (bool): Whether use activateion. Default: True. - """ - - def __init__( - self, - in_channels, - out_channels, - kernel_size, - downsample=False, - resample_kernel=(1, 3, 3, 1), - bias=True, - activate=True, - ): - layers = [] - # downsample - if downsample: - layers.append( - UpFirDnSmooth( - resample_kernel, - upsample_factor=1, - downsample_factor=2, - kernel_size=kernel_size, - ) - ) - stride = 2 - self.padding = 0 - else: - stride = 1 - self.padding = kernel_size // 2 - # conv - layers.append( - EqualConv2d( - in_channels, - out_channels, - kernel_size, - stride=stride, - padding=self.padding, - bias=bias and not activate, - ) - ) - # activation - if activate: - if bias: - layers.append(FusedLeakyReLU(out_channels)) - else: - layers.append(ScaledLeakyReLU(0.2)) - - super(ConvLayer, self).__init__(*layers) - - -class ResBlock(nn.Module): - """Residual block used in StyleGAN2 Discriminator. - - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - resample_kernel (list[int]): A list indicating the 1D resample - kernel magnitude. A cross production will be applied to - extent 1D resample kernel to 2D resample kernel. - Default: (1, 3, 3, 1). - """ - - def __init__(self, in_channels, out_channels, resample_kernel=(1, 3, 3, 1)): - super(ResBlock, self).__init__() - - self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) - self.conv2 = ConvLayer( - in_channels, - out_channels, - 3, - downsample=True, - resample_kernel=resample_kernel, - bias=True, - activate=True, - ) - self.skip = ConvLayer( - in_channels, - out_channels, - 1, - downsample=True, - resample_kernel=resample_kernel, - bias=False, - activate=False, - ) - - def forward(self, x): - out = self.conv1(x) - out = self.conv2(out) - skip = self.skip(x) - out = (out + skip) / math.sqrt(2) - return out diff --git a/comfy_extras/chainner_models/architecture/face/stylegan2_bilinear_arch.py b/comfy_extras/chainner_models/architecture/face/stylegan2_bilinear_arch.py deleted file mode 100644 index 601f8cc4..00000000 --- a/comfy_extras/chainner_models/architecture/face/stylegan2_bilinear_arch.py +++ /dev/null @@ -1,709 +0,0 @@ -# pylint: skip-file -# type: ignore -import math -import random - -import torch -from torch import nn -from torch.nn import functional as F - -from .fused_act import FusedLeakyReLU, fused_leaky_relu - - -class NormStyleCode(nn.Module): - def forward(self, x): - """Normalize the style codes. - Args: - x (Tensor): Style codes with shape (b, c). - Returns: - Tensor: Normalized tensor. - """ - return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) - - -class EqualLinear(nn.Module): - """Equalized Linear as StyleGAN2. - Args: - in_channels (int): Size of each sample. - out_channels (int): Size of each output sample. - bias (bool): If set to ``False``, the layer will not learn an additive - bias. Default: ``True``. - bias_init_val (float): Bias initialized value. Default: 0. - lr_mul (float): Learning rate multiplier. Default: 1. - activation (None | str): The activation after ``linear`` operation. - Supported: 'fused_lrelu', None. Default: None. - """ - - def __init__( - self, - in_channels, - out_channels, - bias=True, - bias_init_val=0, - lr_mul=1, - activation=None, - ): - super(EqualLinear, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.lr_mul = lr_mul - self.activation = activation - if self.activation not in ["fused_lrelu", None]: - raise ValueError( - f"Wrong activation value in EqualLinear: {activation}" - "Supported ones are: ['fused_lrelu', None]." - ) - self.scale = (1 / math.sqrt(in_channels)) * lr_mul - - self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul)) - if bias: - self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) - else: - self.register_parameter("bias", None) - - def forward(self, x): - if self.bias is None: - bias = None - else: - bias = self.bias * self.lr_mul - if self.activation == "fused_lrelu": - out = F.linear(x, self.weight * self.scale) - out = fused_leaky_relu(out, bias) - else: - out = F.linear(x, self.weight * self.scale, bias=bias) - return out - - def __repr__(self): - return ( - f"{self.__class__.__name__}(in_channels={self.in_channels}, " - f"out_channels={self.out_channels}, bias={self.bias is not None})" - ) - - -class ModulatedConv2d(nn.Module): - """Modulated Conv2d used in StyleGAN2. - There is no bias in ModulatedConv2d. - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - num_style_feat (int): Channel number of style features. - demodulate (bool): Whether to demodulate in the conv layer. - Default: True. - sample_mode (str | None): Indicating 'upsample', 'downsample' or None. - Default: None. - eps (float): A value added to the denominator for numerical stability. - Default: 1e-8. - """ - - def __init__( - self, - in_channels, - out_channels, - kernel_size, - num_style_feat, - demodulate=True, - sample_mode=None, - eps=1e-8, - interpolation_mode="bilinear", - ): - super(ModulatedConv2d, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.demodulate = demodulate - self.sample_mode = sample_mode - self.eps = eps - self.interpolation_mode = interpolation_mode - if self.interpolation_mode == "nearest": - self.align_corners = None - else: - self.align_corners = False - - self.scale = 1 / math.sqrt(in_channels * kernel_size**2) - # modulation inside each modulated conv - self.modulation = EqualLinear( - num_style_feat, - in_channels, - bias=True, - bias_init_val=1, - lr_mul=1, - activation=None, - ) - - self.weight = nn.Parameter( - torch.randn(1, out_channels, in_channels, kernel_size, kernel_size) - ) - self.padding = kernel_size // 2 - - def forward(self, x, style): - """Forward function. - Args: - x (Tensor): Tensor with shape (b, c, h, w). - style (Tensor): Tensor with shape (b, num_style_feat). - Returns: - Tensor: Modulated tensor after convolution. - """ - b, c, h, w = x.shape # c = c_in - # weight modulation - style = self.modulation(style).view(b, 1, c, 1, 1) - # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1) - weight = self.scale * self.weight * style # (b, c_out, c_in, k, k) - - if self.demodulate: - demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) - weight = weight * demod.view(b, self.out_channels, 1, 1, 1) - - weight = weight.view( - b * self.out_channels, c, self.kernel_size, self.kernel_size - ) - - if self.sample_mode == "upsample": - x = F.interpolate( - x, - scale_factor=2, - mode=self.interpolation_mode, - align_corners=self.align_corners, - ) - elif self.sample_mode == "downsample": - x = F.interpolate( - x, - scale_factor=0.5, - mode=self.interpolation_mode, - align_corners=self.align_corners, - ) - - b, c, h, w = x.shape - x = x.view(1, b * c, h, w) - # weight: (b*c_out, c_in, k, k), groups=b - out = F.conv2d(x, weight, padding=self.padding, groups=b) - out = out.view(b, self.out_channels, *out.shape[2:4]) - - return out - - def __repr__(self): - return ( - f"{self.__class__.__name__}(in_channels={self.in_channels}, " - f"out_channels={self.out_channels}, " - f"kernel_size={self.kernel_size}, " - f"demodulate={self.demodulate}, sample_mode={self.sample_mode})" - ) - - -class StyleConv(nn.Module): - """Style conv. - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - num_style_feat (int): Channel number of style features. - demodulate (bool): Whether demodulate in the conv layer. Default: True. - sample_mode (str | None): Indicating 'upsample', 'downsample' or None. - Default: None. - """ - - def __init__( - self, - in_channels, - out_channels, - kernel_size, - num_style_feat, - demodulate=True, - sample_mode=None, - interpolation_mode="bilinear", - ): - super(StyleConv, self).__init__() - self.modulated_conv = ModulatedConv2d( - in_channels, - out_channels, - kernel_size, - num_style_feat, - demodulate=demodulate, - sample_mode=sample_mode, - interpolation_mode=interpolation_mode, - ) - self.weight = nn.Parameter(torch.zeros(1)) # for noise injection - self.activate = FusedLeakyReLU(out_channels) - - def forward(self, x, style, noise=None): - # modulate - out = self.modulated_conv(x, style) - # noise injection - if noise is None: - b, _, h, w = out.shape - noise = out.new_empty(b, 1, h, w).normal_() - out = out + self.weight * noise - # activation (with bias) - out = self.activate(out) - return out - - -class ToRGB(nn.Module): - """To RGB from features. - Args: - in_channels (int): Channel number of input. - num_style_feat (int): Channel number of style features. - upsample (bool): Whether to upsample. Default: True. - """ - - def __init__( - self, in_channels, num_style_feat, upsample=True, interpolation_mode="bilinear" - ): - super(ToRGB, self).__init__() - self.upsample = upsample - self.interpolation_mode = interpolation_mode - if self.interpolation_mode == "nearest": - self.align_corners = None - else: - self.align_corners = False - self.modulated_conv = ModulatedConv2d( - in_channels, - 3, - kernel_size=1, - num_style_feat=num_style_feat, - demodulate=False, - sample_mode=None, - interpolation_mode=interpolation_mode, - ) - self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) - - def forward(self, x, style, skip=None): - """Forward function. - Args: - x (Tensor): Feature tensor with shape (b, c, h, w). - style (Tensor): Tensor with shape (b, num_style_feat). - skip (Tensor): Base/skip tensor. Default: None. - Returns: - Tensor: RGB images. - """ - out = self.modulated_conv(x, style) - out = out + self.bias - if skip is not None: - if self.upsample: - skip = F.interpolate( - skip, - scale_factor=2, - mode=self.interpolation_mode, - align_corners=self.align_corners, - ) - out = out + skip - return out - - -class ConstantInput(nn.Module): - """Constant input. - Args: - num_channel (int): Channel number of constant input. - size (int): Spatial size of constant input. - """ - - def __init__(self, num_channel, size): - super(ConstantInput, self).__init__() - self.weight = nn.Parameter(torch.randn(1, num_channel, size, size)) - - def forward(self, batch): - out = self.weight.repeat(batch, 1, 1, 1) - return out - - -class StyleGAN2GeneratorBilinear(nn.Module): - """StyleGAN2 Generator. - Args: - out_size (int): The spatial size of outputs. - num_style_feat (int): Channel number of style features. Default: 512. - num_mlp (int): Layer number of MLP style layers. Default: 8. - channel_multiplier (int): Channel multiplier for large networks of - StyleGAN2. Default: 2. - lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. - narrow (float): Narrow ratio for channels. Default: 1.0. - """ - - def __init__( - self, - out_size, - num_style_feat=512, - num_mlp=8, - channel_multiplier=2, - lr_mlp=0.01, - narrow=1, - interpolation_mode="bilinear", - ): - super(StyleGAN2GeneratorBilinear, self).__init__() - # Style MLP layers - self.num_style_feat = num_style_feat - style_mlp_layers = [NormStyleCode()] - for i in range(num_mlp): - style_mlp_layers.append( - EqualLinear( - num_style_feat, - num_style_feat, - bias=True, - bias_init_val=0, - lr_mul=lr_mlp, - activation="fused_lrelu", - ) - ) - self.style_mlp = nn.Sequential(*style_mlp_layers) - - channels = { - "4": int(512 * narrow), - "8": int(512 * narrow), - "16": int(512 * narrow), - "32": int(512 * narrow), - "64": int(256 * channel_multiplier * narrow), - "128": int(128 * channel_multiplier * narrow), - "256": int(64 * channel_multiplier * narrow), - "512": int(32 * channel_multiplier * narrow), - "1024": int(16 * channel_multiplier * narrow), - } - self.channels = channels - - self.constant_input = ConstantInput(channels["4"], size=4) - self.style_conv1 = StyleConv( - channels["4"], - channels["4"], - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode=None, - interpolation_mode=interpolation_mode, - ) - self.to_rgb1 = ToRGB( - channels["4"], - num_style_feat, - upsample=False, - interpolation_mode=interpolation_mode, - ) - - self.log_size = int(math.log(out_size, 2)) - self.num_layers = (self.log_size - 2) * 2 + 1 - self.num_latent = self.log_size * 2 - 2 - - self.style_convs = nn.ModuleList() - self.to_rgbs = nn.ModuleList() - self.noises = nn.Module() - - in_channels = channels["4"] - # noise - for layer_idx in range(self.num_layers): - resolution = 2 ** ((layer_idx + 5) // 2) - shape = [1, 1, resolution, resolution] - self.noises.register_buffer(f"noise{layer_idx}", torch.randn(*shape)) - # style convs and to_rgbs - for i in range(3, self.log_size + 1): - out_channels = channels[f"{2**i}"] - self.style_convs.append( - StyleConv( - in_channels, - out_channels, - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode="upsample", - interpolation_mode=interpolation_mode, - ) - ) - self.style_convs.append( - StyleConv( - out_channels, - out_channels, - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode=None, - interpolation_mode=interpolation_mode, - ) - ) - self.to_rgbs.append( - ToRGB( - out_channels, - num_style_feat, - upsample=True, - interpolation_mode=interpolation_mode, - ) - ) - in_channels = out_channels - - def make_noise(self): - """Make noise for noise injection.""" - device = self.constant_input.weight.device - noises = [torch.randn(1, 1, 4, 4, device=device)] - - for i in range(3, self.log_size + 1): - for _ in range(2): - noises.append(torch.randn(1, 1, 2**i, 2**i, device=device)) - - return noises - - def get_latent(self, x): - return self.style_mlp(x) - - def mean_latent(self, num_latent): - latent_in = torch.randn( - num_latent, self.num_style_feat, device=self.constant_input.weight.device - ) - latent = self.style_mlp(latent_in).mean(0, keepdim=True) - return latent - - def forward( - self, - styles, - input_is_latent=False, - noise=None, - randomize_noise=True, - truncation=1, - truncation_latent=None, - inject_index=None, - return_latents=False, - ): - """Forward function for StyleGAN2Generator. - Args: - styles (list[Tensor]): Sample codes of styles. - input_is_latent (bool): Whether input is latent style. - Default: False. - noise (Tensor | None): Input noise or None. Default: None. - randomize_noise (bool): Randomize noise, used when 'noise' is - False. Default: True. - truncation (float): TODO. Default: 1. - truncation_latent (Tensor | None): TODO. Default: None. - inject_index (int | None): The injection index for mixing noise. - Default: None. - return_latents (bool): Whether to return style latents. - Default: False. - """ - # style codes -> latents with Style MLP layer - if not input_is_latent: - styles = [self.style_mlp(s) for s in styles] - # noises - if noise is None: - if randomize_noise: - noise = [None] * self.num_layers # for each style conv layer - else: # use the stored noise - noise = [ - getattr(self.noises, f"noise{i}") for i in range(self.num_layers) - ] - # style truncation - if truncation < 1: - style_truncation = [] - for style in styles: - style_truncation.append( - truncation_latent + truncation * (style - truncation_latent) - ) - styles = style_truncation - # get style latent with injection - if len(styles) == 1: - inject_index = self.num_latent - - if styles[0].ndim < 3: - # repeat latent code for all the layers - latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - else: # used for encoder with different latent code for each layer - latent = styles[0] - elif len(styles) == 2: # mixing noises - if inject_index is None: - inject_index = random.randint(1, self.num_latent - 1) - latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - latent2 = ( - styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) - ) - latent = torch.cat([latent1, latent2], 1) - - # main generation - out = self.constant_input(latent.shape[0]) - out = self.style_conv1(out, latent[:, 0], noise=noise[0]) - skip = self.to_rgb1(out, latent[:, 1]) - - i = 1 - for conv1, conv2, noise1, noise2, to_rgb in zip( - self.style_convs[::2], - self.style_convs[1::2], - noise[1::2], - noise[2::2], - self.to_rgbs, - ): - out = conv1(out, latent[:, i], noise=noise1) - out = conv2(out, latent[:, i + 1], noise=noise2) - skip = to_rgb(out, latent[:, i + 2], skip) - i += 2 - - image = skip - - if return_latents: - return image, latent - else: - return image, None - - -class ScaledLeakyReLU(nn.Module): - """Scaled LeakyReLU. - Args: - negative_slope (float): Negative slope. Default: 0.2. - """ - - def __init__(self, negative_slope=0.2): - super(ScaledLeakyReLU, self).__init__() - self.negative_slope = negative_slope - - def forward(self, x): - out = F.leaky_relu(x, negative_slope=self.negative_slope) - return out * math.sqrt(2) - - -class EqualConv2d(nn.Module): - """Equalized Linear as StyleGAN2. - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - stride (int): Stride of the convolution. Default: 1 - padding (int): Zero-padding added to both sides of the input. - Default: 0. - bias (bool): If ``True``, adds a learnable bias to the output. - Default: ``True``. - bias_init_val (float): Bias initialized value. Default: 0. - """ - - def __init__( - self, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - bias=True, - bias_init_val=0, - ): - super(EqualConv2d, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.stride = stride - self.padding = padding - self.scale = 1 / math.sqrt(in_channels * kernel_size**2) - - self.weight = nn.Parameter( - torch.randn(out_channels, in_channels, kernel_size, kernel_size) - ) - if bias: - self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) - else: - self.register_parameter("bias", None) - - def forward(self, x): - out = F.conv2d( - x, - self.weight * self.scale, - bias=self.bias, - stride=self.stride, - padding=self.padding, - ) - - return out - - def __repr__(self): - return ( - f"{self.__class__.__name__}(in_channels={self.in_channels}, " - f"out_channels={self.out_channels}, " - f"kernel_size={self.kernel_size}," - f" stride={self.stride}, padding={self.padding}, " - f"bias={self.bias is not None})" - ) - - -class ConvLayer(nn.Sequential): - """Conv Layer used in StyleGAN2 Discriminator. - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Kernel size. - downsample (bool): Whether downsample by a factor of 2. - Default: False. - bias (bool): Whether with bias. Default: True. - activate (bool): Whether use activateion. Default: True. - """ - - def __init__( - self, - in_channels, - out_channels, - kernel_size, - downsample=False, - bias=True, - activate=True, - interpolation_mode="bilinear", - ): - layers = [] - self.interpolation_mode = interpolation_mode - # downsample - if downsample: - if self.interpolation_mode == "nearest": - self.align_corners = None - else: - self.align_corners = False - - layers.append( - torch.nn.Upsample( - scale_factor=0.5, - mode=interpolation_mode, - align_corners=self.align_corners, - ) - ) - stride = 1 - self.padding = kernel_size // 2 - # conv - layers.append( - EqualConv2d( - in_channels, - out_channels, - kernel_size, - stride=stride, - padding=self.padding, - bias=bias and not activate, - ) - ) - # activation - if activate: - if bias: - layers.append(FusedLeakyReLU(out_channels)) - else: - layers.append(ScaledLeakyReLU(0.2)) - - super(ConvLayer, self).__init__(*layers) - - -class ResBlock(nn.Module): - """Residual block used in StyleGAN2 Discriminator. - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - """ - - def __init__(self, in_channels, out_channels, interpolation_mode="bilinear"): - super(ResBlock, self).__init__() - - self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) - self.conv2 = ConvLayer( - in_channels, - out_channels, - 3, - downsample=True, - interpolation_mode=interpolation_mode, - bias=True, - activate=True, - ) - self.skip = ConvLayer( - in_channels, - out_channels, - 1, - downsample=True, - interpolation_mode=interpolation_mode, - bias=False, - activate=False, - ) - - def forward(self, x): - out = self.conv1(x) - out = self.conv2(out) - skip = self.skip(x) - out = (out + skip) / math.sqrt(2) - return out diff --git a/comfy_extras/chainner_models/architecture/face/stylegan2_clean_arch.py b/comfy_extras/chainner_models/architecture/face/stylegan2_clean_arch.py deleted file mode 100644 index c48de9af..00000000 --- a/comfy_extras/chainner_models/architecture/face/stylegan2_clean_arch.py +++ /dev/null @@ -1,453 +0,0 @@ -# pylint: skip-file -# type: ignore -import math - -import torch -from torch import nn -from torch.nn import functional as F -from torch.nn import init -from torch.nn.modules.batchnorm import _BatchNorm - - -@torch.no_grad() -def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs): - """Initialize network weights. - Args: - module_list (list[nn.Module] | nn.Module): Modules to be initialized. - scale (float): Scale initialized weights, especially for residual - blocks. Default: 1. - bias_fill (float): The value to fill bias. Default: 0 - kwargs (dict): Other arguments for initialization function. - """ - if not isinstance(module_list, list): - module_list = [module_list] - for module in module_list: - for m in module.modules(): - if isinstance(m, nn.Conv2d): - init.kaiming_normal_(m.weight, **kwargs) - m.weight.data *= scale - if m.bias is not None: - m.bias.data.fill_(bias_fill) - elif isinstance(m, nn.Linear): - init.kaiming_normal_(m.weight, **kwargs) - m.weight.data *= scale - if m.bias is not None: - m.bias.data.fill_(bias_fill) - elif isinstance(m, _BatchNorm): - init.constant_(m.weight, 1) - if m.bias is not None: - m.bias.data.fill_(bias_fill) - - -class NormStyleCode(nn.Module): - def forward(self, x): - """Normalize the style codes. - Args: - x (Tensor): Style codes with shape (b, c). - Returns: - Tensor: Normalized tensor. - """ - return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) - - -class ModulatedConv2d(nn.Module): - """Modulated Conv2d used in StyleGAN2. - There is no bias in ModulatedConv2d. - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - num_style_feat (int): Channel number of style features. - demodulate (bool): Whether to demodulate in the conv layer. Default: True. - sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None. - eps (float): A value added to the denominator for numerical stability. Default: 1e-8. - """ - - def __init__( - self, - in_channels, - out_channels, - kernel_size, - num_style_feat, - demodulate=True, - sample_mode=None, - eps=1e-8, - ): - super(ModulatedConv2d, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.demodulate = demodulate - self.sample_mode = sample_mode - self.eps = eps - - # modulation inside each modulated conv - self.modulation = nn.Linear(num_style_feat, in_channels, bias=True) - # initialization - default_init_weights( - self.modulation, - scale=1, - bias_fill=1, - a=0, - mode="fan_in", - nonlinearity="linear", - ) - - self.weight = nn.Parameter( - torch.randn(1, out_channels, in_channels, kernel_size, kernel_size) - / math.sqrt(in_channels * kernel_size**2) - ) - self.padding = kernel_size // 2 - - def forward(self, x, style): - """Forward function. - Args: - x (Tensor): Tensor with shape (b, c, h, w). - style (Tensor): Tensor with shape (b, num_style_feat). - Returns: - Tensor: Modulated tensor after convolution. - """ - b, c, h, w = x.shape # c = c_in - # weight modulation - style = self.modulation(style).view(b, 1, c, 1, 1) - # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1) - weight = self.weight * style # (b, c_out, c_in, k, k) - - if self.demodulate: - demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) - weight = weight * demod.view(b, self.out_channels, 1, 1, 1) - - weight = weight.view( - b * self.out_channels, c, self.kernel_size, self.kernel_size - ) - - # upsample or downsample if necessary - if self.sample_mode == "upsample": - x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=False) - elif self.sample_mode == "downsample": - x = F.interpolate(x, scale_factor=0.5, mode="bilinear", align_corners=False) - - b, c, h, w = x.shape - x = x.view(1, b * c, h, w) - # weight: (b*c_out, c_in, k, k), groups=b - out = F.conv2d(x, weight, padding=self.padding, groups=b) - out = out.view(b, self.out_channels, *out.shape[2:4]) - - return out - - def __repr__(self): - return ( - f"{self.__class__.__name__}(in_channels={self.in_channels}, out_channels={self.out_channels}, " - f"kernel_size={self.kernel_size}, demodulate={self.demodulate}, sample_mode={self.sample_mode})" - ) - - -class StyleConv(nn.Module): - """Style conv used in StyleGAN2. - Args: - in_channels (int): Channel number of the input. - out_channels (int): Channel number of the output. - kernel_size (int): Size of the convolving kernel. - num_style_feat (int): Channel number of style features. - demodulate (bool): Whether demodulate in the conv layer. Default: True. - sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None. - """ - - def __init__( - self, - in_channels, - out_channels, - kernel_size, - num_style_feat, - demodulate=True, - sample_mode=None, - ): - super(StyleConv, self).__init__() - self.modulated_conv = ModulatedConv2d( - in_channels, - out_channels, - kernel_size, - num_style_feat, - demodulate=demodulate, - sample_mode=sample_mode, - ) - self.weight = nn.Parameter(torch.zeros(1)) # for noise injection - self.bias = nn.Parameter(torch.zeros(1, out_channels, 1, 1)) - self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True) - - def forward(self, x, style, noise=None): - # modulate - out = self.modulated_conv(x, style) * 2**0.5 # for conversion - # noise injection - if noise is None: - b, _, h, w = out.shape - noise = out.new_empty(b, 1, h, w).normal_() - out = out + self.weight * noise - # add bias - out = out + self.bias - # activation - out = self.activate(out) - return out - - -class ToRGB(nn.Module): - """To RGB (image space) from features. - Args: - in_channels (int): Channel number of input. - num_style_feat (int): Channel number of style features. - upsample (bool): Whether to upsample. Default: True. - """ - - def __init__(self, in_channels, num_style_feat, upsample=True): - super(ToRGB, self).__init__() - self.upsample = upsample - self.modulated_conv = ModulatedConv2d( - in_channels, - 3, - kernel_size=1, - num_style_feat=num_style_feat, - demodulate=False, - sample_mode=None, - ) - self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) - - def forward(self, x, style, skip=None): - """Forward function. - Args: - x (Tensor): Feature tensor with shape (b, c, h, w). - style (Tensor): Tensor with shape (b, num_style_feat). - skip (Tensor): Base/skip tensor. Default: None. - Returns: - Tensor: RGB images. - """ - out = self.modulated_conv(x, style) - out = out + self.bias - if skip is not None: - if self.upsample: - skip = F.interpolate( - skip, scale_factor=2, mode="bilinear", align_corners=False - ) - out = out + skip - return out - - -class ConstantInput(nn.Module): - """Constant input. - Args: - num_channel (int): Channel number of constant input. - size (int): Spatial size of constant input. - """ - - def __init__(self, num_channel, size): - super(ConstantInput, self).__init__() - self.weight = nn.Parameter(torch.randn(1, num_channel, size, size)) - - def forward(self, batch): - out = self.weight.repeat(batch, 1, 1, 1) - return out - - -class StyleGAN2GeneratorClean(nn.Module): - """Clean version of StyleGAN2 Generator. - Args: - out_size (int): The spatial size of outputs. - num_style_feat (int): Channel number of style features. Default: 512. - num_mlp (int): Layer number of MLP style layers. Default: 8. - channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. - narrow (float): Narrow ratio for channels. Default: 1.0. - """ - - def __init__( - self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1 - ): - super(StyleGAN2GeneratorClean, self).__init__() - # Style MLP layers - self.num_style_feat = num_style_feat - style_mlp_layers = [NormStyleCode()] - for i in range(num_mlp): - style_mlp_layers.extend( - [ - nn.Linear(num_style_feat, num_style_feat, bias=True), - nn.LeakyReLU(negative_slope=0.2, inplace=True), - ] - ) - self.style_mlp = nn.Sequential(*style_mlp_layers) - # initialization - default_init_weights( - self.style_mlp, - scale=1, - bias_fill=0, - a=0.2, - mode="fan_in", - nonlinearity="leaky_relu", - ) - - # channel list - channels = { - "4": int(512 * narrow), - "8": int(512 * narrow), - "16": int(512 * narrow), - "32": int(512 * narrow), - "64": int(256 * channel_multiplier * narrow), - "128": int(128 * channel_multiplier * narrow), - "256": int(64 * channel_multiplier * narrow), - "512": int(32 * channel_multiplier * narrow), - "1024": int(16 * channel_multiplier * narrow), - } - self.channels = channels - - self.constant_input = ConstantInput(channels["4"], size=4) - self.style_conv1 = StyleConv( - channels["4"], - channels["4"], - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode=None, - ) - self.to_rgb1 = ToRGB(channels["4"], num_style_feat, upsample=False) - - self.log_size = int(math.log(out_size, 2)) - self.num_layers = (self.log_size - 2) * 2 + 1 - self.num_latent = self.log_size * 2 - 2 - - self.style_convs = nn.ModuleList() - self.to_rgbs = nn.ModuleList() - self.noises = nn.Module() - - in_channels = channels["4"] - # noise - for layer_idx in range(self.num_layers): - resolution = 2 ** ((layer_idx + 5) // 2) - shape = [1, 1, resolution, resolution] - self.noises.register_buffer(f"noise{layer_idx}", torch.randn(*shape)) - # style convs and to_rgbs - for i in range(3, self.log_size + 1): - out_channels = channels[f"{2**i}"] - self.style_convs.append( - StyleConv( - in_channels, - out_channels, - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode="upsample", - ) - ) - self.style_convs.append( - StyleConv( - out_channels, - out_channels, - kernel_size=3, - num_style_feat=num_style_feat, - demodulate=True, - sample_mode=None, - ) - ) - self.to_rgbs.append(ToRGB(out_channels, num_style_feat, upsample=True)) - in_channels = out_channels - - def make_noise(self): - """Make noise for noise injection.""" - device = self.constant_input.weight.device - noises = [torch.randn(1, 1, 4, 4, device=device)] - - for i in range(3, self.log_size + 1): - for _ in range(2): - noises.append(torch.randn(1, 1, 2**i, 2**i, device=device)) - - return noises - - def get_latent(self, x): - return self.style_mlp(x) - - def mean_latent(self, num_latent): - latent_in = torch.randn( - num_latent, self.num_style_feat, device=self.constant_input.weight.device - ) - latent = self.style_mlp(latent_in).mean(0, keepdim=True) - return latent - - def forward( - self, - styles, - input_is_latent=False, - noise=None, - randomize_noise=True, - truncation=1, - truncation_latent=None, - inject_index=None, - return_latents=False, - ): - """Forward function for StyleGAN2GeneratorClean. - Args: - styles (list[Tensor]): Sample codes of styles. - input_is_latent (bool): Whether input is latent style. Default: False. - noise (Tensor | None): Input noise or None. Default: None. - randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. - truncation (float): The truncation ratio. Default: 1. - truncation_latent (Tensor | None): The truncation latent tensor. Default: None. - inject_index (int | None): The injection index for mixing noise. Default: None. - return_latents (bool): Whether to return style latents. Default: False. - """ - # style codes -> latents with Style MLP layer - if not input_is_latent: - styles = [self.style_mlp(s) for s in styles] - # noises - if noise is None: - if randomize_noise: - noise = [None] * self.num_layers # for each style conv layer - else: # use the stored noise - noise = [ - getattr(self.noises, f"noise{i}") for i in range(self.num_layers) - ] - # style truncation - if truncation < 1: - style_truncation = [] - for style in styles: - style_truncation.append( - truncation_latent + truncation * (style - truncation_latent) - ) - styles = style_truncation - # get style latents with injection - if len(styles) == 1: - inject_index = self.num_latent - - if styles[0].ndim < 3: - # repeat latent code for all the layers - latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - else: # used for encoder with different latent code for each layer - latent = styles[0] - elif len(styles) == 2: # mixing noises - if inject_index is None: - inject_index = random.randint(1, self.num_latent - 1) - latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - latent2 = ( - styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) - ) - latent = torch.cat([latent1, latent2], 1) - - # main generation - out = self.constant_input(latent.shape[0]) - out = self.style_conv1(out, latent[:, 0], noise=noise[0]) - skip = self.to_rgb1(out, latent[:, 1]) - - i = 1 - for conv1, conv2, noise1, noise2, to_rgb in zip( - self.style_convs[::2], - self.style_convs[1::2], - noise[1::2], - noise[2::2], - self.to_rgbs, - ): - out = conv1(out, latent[:, i], noise=noise1) - out = conv2(out, latent[:, i + 1], noise=noise2) - skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space - i += 2 - - image = skip - - if return_latents: - return image, latent - else: - return image, None diff --git a/comfy_extras/chainner_models/architecture/face/upfirdn2d.py b/comfy_extras/chainner_models/architecture/face/upfirdn2d.py deleted file mode 100644 index 4ea45415..00000000 --- a/comfy_extras/chainner_models/architecture/face/upfirdn2d.py +++ /dev/null @@ -1,194 +0,0 @@ -# pylint: skip-file -# type: ignore -# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.py # noqa:E501 - -import os - -import torch -from torch.autograd import Function -from torch.nn import functional as F - -upfirdn2d_ext = None - - -class UpFirDn2dBackward(Function): - @staticmethod - def forward( - ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size - ): - up_x, up_y = up - down_x, down_y = down - g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad - - grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1) - - grad_input = upfirdn2d_ext.upfirdn2d( - grad_output, - grad_kernel, - down_x, - down_y, - up_x, - up_y, - g_pad_x0, - g_pad_x1, - g_pad_y0, - g_pad_y1, - ) - grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3]) - - ctx.save_for_backward(kernel) - - pad_x0, pad_x1, pad_y0, pad_y1 = pad - - ctx.up_x = up_x - ctx.up_y = up_y - ctx.down_x = down_x - ctx.down_y = down_y - ctx.pad_x0 = pad_x0 - ctx.pad_x1 = pad_x1 - ctx.pad_y0 = pad_y0 - ctx.pad_y1 = pad_y1 - ctx.in_size = in_size - ctx.out_size = out_size - - return grad_input - - @staticmethod - def backward(ctx, gradgrad_input): - (kernel,) = ctx.saved_tensors - - gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1) - - gradgrad_out = upfirdn2d_ext.upfirdn2d( - gradgrad_input, - kernel, - ctx.up_x, - ctx.up_y, - ctx.down_x, - ctx.down_y, - ctx.pad_x0, - ctx.pad_x1, - ctx.pad_y0, - ctx.pad_y1, - ) - # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], - # ctx.out_size[1], ctx.in_size[3]) - gradgrad_out = gradgrad_out.view( - ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1] - ) - - return gradgrad_out, None, None, None, None, None, None, None, None - - -class UpFirDn2d(Function): - @staticmethod - def forward(ctx, input, kernel, up, down, pad): - up_x, up_y = up - down_x, down_y = down - pad_x0, pad_x1, pad_y0, pad_y1 = pad - - kernel_h, kernel_w = kernel.shape - _, channel, in_h, in_w = input.shape - ctx.in_size = input.shape - - input = input.reshape(-1, in_h, in_w, 1) - - ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1])) - - out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 - out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 - ctx.out_size = (out_h, out_w) - - ctx.up = (up_x, up_y) - ctx.down = (down_x, down_y) - ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1) - - g_pad_x0 = kernel_w - pad_x0 - 1 - g_pad_y0 = kernel_h - pad_y0 - 1 - g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1 - g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1 - - ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1) - - out = upfirdn2d_ext.upfirdn2d( - input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 - ) - # out = out.view(major, out_h, out_w, minor) - out = out.view(-1, channel, out_h, out_w) - - return out - - @staticmethod - def backward(ctx, grad_output): - kernel, grad_kernel = ctx.saved_tensors - - grad_input = UpFirDn2dBackward.apply( - grad_output, - kernel, - grad_kernel, - ctx.up, - ctx.down, - ctx.pad, - ctx.g_pad, - ctx.in_size, - ctx.out_size, - ) - - return grad_input, None, None, None, None - - -def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): - if input.device.type == "cpu": - out = upfirdn2d_native( - input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1] - ) - else: - out = UpFirDn2d.apply( - input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1]) - ) - - return out - - -def upfirdn2d_native( - input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 -): - _, channel, in_h, in_w = input.shape - input = input.reshape(-1, in_h, in_w, 1) - - _, in_h, in_w, minor = input.shape - kernel_h, kernel_w = kernel.shape - - out = input.view(-1, in_h, 1, in_w, 1, minor) - out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1]) - out = out.view(-1, in_h * up_y, in_w * up_x, minor) - - out = F.pad( - out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)] - ) - out = out[ - :, - max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0), - max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0), - :, - ] - - out = out.permute(0, 3, 1, 2) - out = out.reshape( - [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1] - ) - w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) - out = F.conv2d(out, w) - out = out.reshape( - -1, - minor, - in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, - in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, - ) - out = out.permute(0, 2, 3, 1) - out = out[:, ::down_y, ::down_x, :] - - out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 - out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 - - return out.view(-1, channel, out_h, out_w) diff --git a/comfy_extras/chainner_models/architecture/timm/LICENSE b/comfy_extras/chainner_models/architecture/timm/LICENSE deleted file mode 100644 index b4e9438b..00000000 --- a/comfy_extras/chainner_models/architecture/timm/LICENSE +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - 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However, in accepting such obligations, You may act only - on Your own behalf and on Your sole responsibility, not on behalf - of any other Contributor, and only if You agree to indemnify, - defend, and hold each Contributor harmless for any liability - incurred by, or claims asserted against, such Contributor by reason - of your accepting any such warranty or additional liability. - - END OF TERMS AND CONDITIONS - - APPENDIX: How to apply the Apache License to your work. - - To apply the Apache License to your work, attach the following - boilerplate notice, with the fields enclosed by brackets "{}" - replaced with your own identifying information. (Don't include - the brackets!) The text should be enclosed in the appropriate - comment syntax for the file format. We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright 2019 Ross Wightman - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. \ No newline at end of file diff --git a/comfy_extras/chainner_models/architecture/timm/drop.py b/comfy_extras/chainner_models/architecture/timm/drop.py deleted file mode 100644 index 14f0da91..00000000 --- a/comfy_extras/chainner_models/architecture/timm/drop.py +++ /dev/null @@ -1,223 +0,0 @@ -""" DropBlock, DropPath - -PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers. - -Papers: -DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890) - -Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382) - -Code: -DropBlock impl inspired by two Tensorflow impl that I liked: - - https://github.com/tensorflow/tpu/blob/master/models/official/resnet/resnet_model.py#L74 - - https://github.com/clovaai/assembled-cnn/blob/master/nets/blocks.py - -Hacked together by / Copyright 2020 Ross Wightman -""" -import torch -import torch.nn as nn -import torch.nn.functional as F - - -def drop_block_2d( - x, - drop_prob: float = 0.1, - block_size: int = 7, - gamma_scale: float = 1.0, - with_noise: bool = False, - inplace: bool = False, - batchwise: bool = False, -): - """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf - - DropBlock with an experimental gaussian noise option. This layer has been tested on a few training - runs with success, but needs further validation and possibly optimization for lower runtime impact. - """ - _, C, H, W = x.shape - total_size = W * H - clipped_block_size = min(block_size, min(W, H)) - # seed_drop_rate, the gamma parameter - gamma = ( - gamma_scale - * drop_prob - * total_size - / clipped_block_size**2 - / ((W - block_size + 1) * (H - block_size + 1)) - ) - - # Forces the block to be inside the feature map. - w_i, h_i = torch.meshgrid( - torch.arange(W).to(x.device), torch.arange(H).to(x.device) - ) - valid_block = ( - (w_i >= clipped_block_size // 2) & (w_i < W - (clipped_block_size - 1) // 2) - ) & ((h_i >= clipped_block_size // 2) & (h_i < H - (clipped_block_size - 1) // 2)) - valid_block = torch.reshape(valid_block, (1, 1, H, W)).to(dtype=x.dtype) - - if batchwise: - # one mask for whole batch, quite a bit faster - uniform_noise = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device) - else: - uniform_noise = torch.rand_like(x) - block_mask = ((2 - gamma - valid_block + uniform_noise) >= 1).to(dtype=x.dtype) - block_mask = -F.max_pool2d( - -block_mask, - kernel_size=clipped_block_size, # block_size, - stride=1, - padding=clipped_block_size // 2, - ) - - if with_noise: - normal_noise = ( - torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) - if batchwise - else torch.randn_like(x) - ) - if inplace: - x.mul_(block_mask).add_(normal_noise * (1 - block_mask)) - else: - x = x * block_mask + normal_noise * (1 - block_mask) - else: - normalize_scale = ( - block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7) - ).to(x.dtype) - if inplace: - x.mul_(block_mask * normalize_scale) - else: - x = x * block_mask * normalize_scale - return x - - -def drop_block_fast_2d( - x: torch.Tensor, - drop_prob: float = 0.1, - block_size: int = 7, - gamma_scale: float = 1.0, - with_noise: bool = False, - inplace: bool = False, -): - """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf - - DropBlock with an experimental gaussian noise option. Simplied from above without concern for valid - block mask at edges. - """ - _, _, H, W = x.shape - total_size = W * H - clipped_block_size = min(block_size, min(W, H)) - gamma = ( - gamma_scale - * drop_prob - * total_size - / clipped_block_size**2 - / ((W - block_size + 1) * (H - block_size + 1)) - ) - - block_mask = torch.empty_like(x).bernoulli_(gamma) - block_mask = F.max_pool2d( - block_mask.to(x.dtype), - kernel_size=clipped_block_size, - stride=1, - padding=clipped_block_size // 2, - ) - - if with_noise: - normal_noise = torch.empty_like(x).normal_() - if inplace: - x.mul_(1.0 - block_mask).add_(normal_noise * block_mask) - else: - x = x * (1.0 - block_mask) + normal_noise * block_mask - else: - block_mask = 1 - block_mask - normalize_scale = ( - block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-6) - ).to(dtype=x.dtype) - if inplace: - x.mul_(block_mask * normalize_scale) - else: - x = x * block_mask * normalize_scale - return x - - -class DropBlock2d(nn.Module): - """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf""" - - def __init__( - self, - drop_prob: float = 0.1, - block_size: int = 7, - gamma_scale: float = 1.0, - with_noise: bool = False, - inplace: bool = False, - batchwise: bool = False, - fast: bool = True, - ): - super(DropBlock2d, self).__init__() - self.drop_prob = drop_prob - self.gamma_scale = gamma_scale - self.block_size = block_size - self.with_noise = with_noise - self.inplace = inplace - self.batchwise = batchwise - self.fast = fast # FIXME finish comparisons of fast vs not - - def forward(self, x): - if not self.training or not self.drop_prob: - return x - if self.fast: - return drop_block_fast_2d( - x, - self.drop_prob, - self.block_size, - self.gamma_scale, - self.with_noise, - self.inplace, - ) - else: - return drop_block_2d( - x, - self.drop_prob, - self.block_size, - self.gamma_scale, - self.with_noise, - self.inplace, - self.batchwise, - ) - - -def drop_path( - x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True -): - """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). - - This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, - the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... - See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for - changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use - 'survival rate' as the argument. - - """ - if drop_prob == 0.0 or not training: - return x - keep_prob = 1 - drop_prob - shape = (x.shape[0],) + (1,) * ( - x.ndim - 1 - ) # work with diff dim tensors, not just 2D ConvNets - random_tensor = x.new_empty(shape).bernoulli_(keep_prob) - if keep_prob > 0.0 and scale_by_keep: - random_tensor.div_(keep_prob) - return x * random_tensor - - -class DropPath(nn.Module): - """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" - - def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True): - super(DropPath, self).__init__() - self.drop_prob = drop_prob - self.scale_by_keep = scale_by_keep - - def forward(self, x): - return drop_path(x, self.drop_prob, self.training, self.scale_by_keep) - - def extra_repr(self): - return f"drop_prob={round(self.drop_prob,3):0.3f}" diff --git a/comfy_extras/chainner_models/architecture/timm/helpers.py b/comfy_extras/chainner_models/architecture/timm/helpers.py deleted file mode 100644 index cdafee07..00000000 --- a/comfy_extras/chainner_models/architecture/timm/helpers.py +++ /dev/null @@ -1,31 +0,0 @@ -""" Layer/Module Helpers -Hacked together by / Copyright 2020 Ross Wightman -""" -import collections.abc -from itertools import repeat - - -# From PyTorch internals -def _ntuple(n): - def parse(x): - if isinstance(x, collections.abc.Iterable) and not isinstance(x, str): - return x - return tuple(repeat(x, n)) - - return parse - - -to_1tuple = _ntuple(1) -to_2tuple = _ntuple(2) -to_3tuple = _ntuple(3) -to_4tuple = _ntuple(4) -to_ntuple = _ntuple - - -def make_divisible(v, divisor=8, min_value=None, round_limit=0.9): - min_value = min_value or divisor - new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) - # Make sure that round down does not go down by more than 10%. - if new_v < round_limit * v: - new_v += divisor - return new_v diff --git a/comfy_extras/chainner_models/architecture/timm/weight_init.py b/comfy_extras/chainner_models/architecture/timm/weight_init.py deleted file mode 100644 index b0169774..00000000 --- a/comfy_extras/chainner_models/architecture/timm/weight_init.py +++ /dev/null @@ -1,128 +0,0 @@ -import math -import warnings - -import torch -from torch.nn.init import _calculate_fan_in_and_fan_out - - -def _no_grad_trunc_normal_(tensor, mean, std, a, b): - # Cut & paste from PyTorch official master until it's in a few official releases - RW - # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf - def norm_cdf(x): - # Computes standard normal cumulative distribution function - return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0 - - if (mean < a - 2 * std) or (mean > b + 2 * std): - warnings.warn( - "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " - "The distribution of values may be incorrect.", - stacklevel=2, - ) - - with torch.no_grad(): - # Values are generated by using a truncated uniform distribution and - # then using the inverse CDF for the normal distribution. - # Get upper and lower cdf values - l = norm_cdf((a - mean) / std) - u = norm_cdf((b - mean) / std) - - # Uniformly fill tensor with values from [l, u], then translate to - # [2l-1, 2u-1]. - tensor.uniform_(2 * l - 1, 2 * u - 1) - - # Use inverse cdf transform for normal distribution to get truncated - # standard normal - tensor.erfinv_() - - # Transform to proper mean, std - tensor.mul_(std * math.sqrt(2.0)) - tensor.add_(mean) - - # Clamp to ensure it's in the proper range - tensor.clamp_(min=a, max=b) - return tensor - - -def trunc_normal_( - tensor: torch.Tensor, mean=0.0, std=1.0, a=-2.0, b=2.0 -) -> torch.Tensor: - r"""Fills the input Tensor with values drawn from a truncated - normal distribution. The values are effectively drawn from the - normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` - with values outside :math:`[a, b]` redrawn until they are within - the bounds. The method used for generating the random values works - best when :math:`a \leq \text{mean} \leq b`. - - NOTE: this impl is similar to the PyTorch trunc_normal_, the bounds [a, b] are - applied while sampling the normal with mean/std applied, therefore a, b args - should be adjusted to match the range of mean, std args. - - Args: - tensor: an n-dimensional `torch.Tensor` - mean: the mean of the normal distribution - std: the standard deviation of the normal distribution - a: the minimum cutoff value - b: the maximum cutoff value - Examples: - >>> w = torch.empty(3, 5) - >>> nn.init.trunc_normal_(w) - """ - return _no_grad_trunc_normal_(tensor, mean, std, a, b) - - -def trunc_normal_tf_( - tensor: torch.Tensor, mean=0.0, std=1.0, a=-2.0, b=2.0 -) -> torch.Tensor: - r"""Fills the input Tensor with values drawn from a truncated - normal distribution. The values are effectively drawn from the - normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` - with values outside :math:`[a, b]` redrawn until they are within - the bounds. The method used for generating the random values works - best when :math:`a \leq \text{mean} \leq b`. - - NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the - bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0 - and the result is subsquently scaled and shifted by the mean and std args. - - Args: - tensor: an n-dimensional `torch.Tensor` - mean: the mean of the normal distribution - std: the standard deviation of the normal distribution - a: the minimum cutoff value - b: the maximum cutoff value - Examples: - >>> w = torch.empty(3, 5) - >>> nn.init.trunc_normal_(w) - """ - _no_grad_trunc_normal_(tensor, 0, 1.0, a, b) - with torch.no_grad(): - tensor.mul_(std).add_(mean) - return tensor - - -def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"): - fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) - if mode == "fan_in": - denom = fan_in - elif mode == "fan_out": - denom = fan_out - elif mode == "fan_avg": - denom = (fan_in + fan_out) / 2 - - variance = scale / denom # type: ignore - - if distribution == "truncated_normal": - # constant is stddev of standard normal truncated to (-2, 2) - trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978) - elif distribution == "normal": - tensor.normal_(std=math.sqrt(variance)) - elif distribution == "uniform": - bound = math.sqrt(3 * variance) - # pylint: disable=invalid-unary-operand-type - tensor.uniform_(-bound, bound) - else: - raise ValueError(f"invalid distribution {distribution}") - - -def lecun_normal_(tensor): - variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal") diff --git a/comfy_extras/chainner_models/model_loading.py b/comfy_extras/chainner_models/model_loading.py deleted file mode 100644 index e000871c..00000000 --- a/comfy_extras/chainner_models/model_loading.py +++ /dev/null @@ -1,99 +0,0 @@ -import logging as logger - -from .architecture.DAT import DAT -from .architecture.face.codeformer import CodeFormer -from .architecture.face.gfpganv1_clean_arch import GFPGANv1Clean -from .architecture.face.restoreformer_arch import RestoreFormer -from .architecture.HAT import HAT -from .architecture.LaMa import LaMa -from .architecture.OmniSR.OmniSR import OmniSR -from .architecture.RRDB import RRDBNet as ESRGAN -from .architecture.SCUNet import SCUNet -from .architecture.SPSR import SPSRNet as SPSR -from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2 -from .architecture.SwiftSRGAN import Generator as SwiftSRGAN -from .architecture.Swin2SR import Swin2SR -from .architecture.SwinIR import SwinIR -from .types import PyTorchModel - - -class UnsupportedModel(Exception): - pass - - -def load_state_dict(state_dict) -> PyTorchModel: - logger.debug(f"Loading state dict into pytorch model arch") - - state_dict_keys = list(state_dict.keys()) - - if "params_ema" in state_dict_keys: - state_dict = state_dict["params_ema"] - elif "params-ema" in state_dict_keys: - state_dict = state_dict["params-ema"] - elif "params" in state_dict_keys: - state_dict = state_dict["params"] - - state_dict_keys = list(state_dict.keys()) - # SRVGGNet Real-ESRGAN (v2) - if "body.0.weight" in state_dict_keys and "body.1.weight" in state_dict_keys: - model = RealESRGANv2(state_dict) - # SPSR (ESRGAN with lots of extra layers) - elif "f_HR_conv1.0.weight" in state_dict: - model = SPSR(state_dict) - # Swift-SRGAN - elif ( - "model" in state_dict_keys - and "initial.cnn.depthwise.weight" in state_dict["model"].keys() - ): - model = SwiftSRGAN(state_dict) - # SwinIR, Swin2SR, HAT - elif "layers.0.residual_group.blocks.0.norm1.weight" in state_dict_keys: - if ( - "layers.0.residual_group.blocks.0.conv_block.cab.0.weight" - in state_dict_keys - ): - model = HAT(state_dict) - elif "patch_embed.proj.weight" in state_dict_keys: - model = Swin2SR(state_dict) - else: - model = SwinIR(state_dict) - # GFPGAN - elif ( - "toRGB.0.weight" in state_dict_keys - and "stylegan_decoder.style_mlp.1.weight" in state_dict_keys - ): - model = GFPGANv1Clean(state_dict) - # RestoreFormer - elif ( - "encoder.conv_in.weight" in state_dict_keys - and "encoder.down.0.block.0.norm1.weight" in state_dict_keys - ): - model = RestoreFormer(state_dict) - elif ( - "encoder.blocks.0.weight" in state_dict_keys - and "quantize.embedding.weight" in state_dict_keys - ): - model = CodeFormer(state_dict) - # LaMa - elif ( - "model.model.1.bn_l.running_mean" in state_dict_keys - or "generator.model.1.bn_l.running_mean" in state_dict_keys - ): - model = LaMa(state_dict) - # Omni-SR - elif "residual_layer.0.residual_layer.0.layer.0.fn.0.weight" in state_dict_keys: - model = OmniSR(state_dict) - # SCUNet - elif "m_head.0.weight" in state_dict_keys and "m_tail.0.weight" in state_dict_keys: - model = SCUNet(state_dict) - # DAT - elif "layers.0.blocks.2.attn.attn_mask_0" in state_dict_keys: - model = DAT(state_dict) - # Regular ESRGAN, "new-arch" ESRGAN, Real-ESRGAN v1 - else: - try: - model = ESRGAN(state_dict) - except: - # pylint: disable=raise-missing-from - raise UnsupportedModel - return model diff --git a/comfy_extras/chainner_models/types.py b/comfy_extras/chainner_models/types.py deleted file mode 100644 index 193333b9..00000000 --- a/comfy_extras/chainner_models/types.py +++ /dev/null @@ -1,69 +0,0 @@ -from typing import Union - -from .architecture.DAT import DAT -from .architecture.face.codeformer import CodeFormer -from .architecture.face.gfpganv1_clean_arch import GFPGANv1Clean -from .architecture.face.restoreformer_arch import RestoreFormer -from .architecture.HAT import HAT -from .architecture.LaMa import LaMa -from .architecture.OmniSR.OmniSR import OmniSR -from .architecture.RRDB import RRDBNet as ESRGAN -from .architecture.SCUNet import SCUNet -from .architecture.SPSR import SPSRNet as SPSR -from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2 -from .architecture.SwiftSRGAN import Generator as SwiftSRGAN -from .architecture.Swin2SR import Swin2SR -from .architecture.SwinIR import SwinIR - -PyTorchSRModels = ( - RealESRGANv2, - SPSR, - SwiftSRGAN, - ESRGAN, - SwinIR, - Swin2SR, - HAT, - OmniSR, - SCUNet, - DAT, -) -PyTorchSRModel = Union[ - RealESRGANv2, - SPSR, - SwiftSRGAN, - ESRGAN, - SwinIR, - Swin2SR, - HAT, - OmniSR, - SCUNet, - DAT, -] - - -def is_pytorch_sr_model(model: object): - return isinstance(model, PyTorchSRModels) - - -PyTorchFaceModels = (GFPGANv1Clean, RestoreFormer, CodeFormer) -PyTorchFaceModel = Union[GFPGANv1Clean, RestoreFormer, CodeFormer] - - -def is_pytorch_face_model(model: object): - return isinstance(model, PyTorchFaceModels) - - -PyTorchInpaintModels = (LaMa,) -PyTorchInpaintModel = Union[LaMa] - - -def is_pytorch_inpaint_model(model: object): - return isinstance(model, PyTorchInpaintModels) - - -PyTorchModels = (*PyTorchSRModels, *PyTorchFaceModels, *PyTorchInpaintModels) -PyTorchModel = Union[PyTorchSRModel, PyTorchFaceModel, PyTorchInpaintModel] - - -def is_pytorch_model(model: object): - return isinstance(model, PyTorchModels) diff --git a/comfy_extras/nodes_upscale_model.py b/comfy_extras/nodes_upscale_model.py index 52c95df2..f0bbba76 100644 --- a/comfy_extras/nodes_upscale_model.py +++ b/comfy_extras/nodes_upscale_model.py @@ -1,5 +1,5 @@ import os -from comfy_extras.chainner_models import model_loading +from spandrel import ModelLoader, ImageModelDescriptor from comfy import model_management import torch import comfy.utils @@ -20,7 +20,11 @@ class UpscaleModelLoader: sd = comfy.utils.load_torch_file(model_path, safe_load=True) if "module.layers.0.residual_group.blocks.0.norm1.weight" in sd: sd = comfy.utils.state_dict_prefix_replace(sd, {"module.":""}) - out = model_loading.load_state_dict(sd).eval() + out = ModelLoader().load_from_state_dict(sd).eval() + + if not isinstance(out, ImageModelDescriptor): + raise Exception("Upscale model must be a single-image model.") + return (out, ) @@ -61,7 +65,7 @@ class ImageUpscaleWithModel: if tile < 128: raise e - upscale_model.cpu() + upscale_model.to("cpu") s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0) return (s,) diff --git a/requirements.txt b/requirements.txt index e7d8c0e9..a1a29218 100644 --- a/requirements.txt +++ b/requirements.txt @@ -11,3 +11,4 @@ scipy tqdm psutil kornia>=0.7.1 +spandrel==0.3.1 \ No newline at end of file From 9a151b7defb6bacb5ce67209e728b14922ad7454 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 26 May 2024 13:44:47 -0400 Subject: [PATCH 242/378] Fix issue and unpin spandrel package. --- comfy_extras/nodes_upscale_model.py | 2 +- requirements.txt | 4 +++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/comfy_extras/nodes_upscale_model.py b/comfy_extras/nodes_upscale_model.py index f0bbba76..03f29446 100644 --- a/comfy_extras/nodes_upscale_model.py +++ b/comfy_extras/nodes_upscale_model.py @@ -42,7 +42,7 @@ class ImageUpscaleWithModel: def upscale(self, upscale_model, image): device = model_management.get_torch_device() - memory_required = model_management.module_size(upscale_model) + memory_required = model_management.module_size(upscale_model.model) memory_required += (512 * 512 * 3) * image.element_size() * max(upscale_model.scale, 1.0) * 384.0 #The 384.0 is an estimate of how much some of these models take, TODO: make it more accurate memory_required += image.nelement() * image.element_size() model_management.free_memory(memory_required, device) diff --git a/requirements.txt b/requirements.txt index a1a29218..906b96ed 100644 --- a/requirements.txt +++ b/requirements.txt @@ -10,5 +10,7 @@ Pillow scipy tqdm psutil + +#non essential dependencies: kornia>=0.7.1 -spandrel==0.3.1 \ No newline at end of file +spandrel From 16a493a19042227baadd939fc095305716ae58db Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 26 May 2024 15:37:24 -0400 Subject: [PATCH 243/378] Keep compatibility with some custom nodes. --- comfy_extras/chainner_models/model_loading.py | 5 +++++ 1 file changed, 5 insertions(+) create mode 100644 comfy_extras/chainner_models/model_loading.py diff --git a/comfy_extras/chainner_models/model_loading.py b/comfy_extras/chainner_models/model_loading.py new file mode 100644 index 00000000..d48bc238 --- /dev/null +++ b/comfy_extras/chainner_models/model_loading.py @@ -0,0 +1,5 @@ +from spandrel import ModelLoader + +def load_state_dict(state_dict): + print("WARNING: comfy_extras.chainner_models is deprecated and has been replaced by the spandrel library.") + return ModelLoader().load_from_state_dict(state_dict).eval() From f6a203951f061759a3d6a9847b865dd9c7d77a38 Mon Sep 17 00:00:00 2001 From: "Regis Gaughan, III" Date: Mon, 27 May 2024 14:05:51 -0400 Subject: [PATCH 244/378] Extend core snapToGrid to LiteGraph Groups. (#3393) Extends the core Comfy.SnapToGrid behavior for nodes to apply to LiteGraph's LGraphGroup with the same behavior. Also, pulls out redundant rounding code into util function. --- web/extensions/core/snapToGrid.js | 96 ++++++++++++++++++++++++++++--- 1 file changed, 89 insertions(+), 7 deletions(-) diff --git a/web/extensions/core/snapToGrid.js b/web/extensions/core/snapToGrid.js index dc534d6e..aac01774 100644 --- a/web/extensions/core/snapToGrid.js +++ b/web/extensions/core/snapToGrid.js @@ -2,6 +2,13 @@ import { app } from "../../scripts/app.js"; // Shift + drag/resize to snap to grid +/** Rounds a Vector2 in-place to the current CANVAS_GRID_SIZE. */ +function roundVectorToGrid(vec) { + vec[0] = LiteGraph.CANVAS_GRID_SIZE * Math.round(vec[0] / LiteGraph.CANVAS_GRID_SIZE); + vec[1] = LiteGraph.CANVAS_GRID_SIZE * Math.round(vec[1] / LiteGraph.CANVAS_GRID_SIZE); + return vec; +} + app.registerExtension({ name: "Comfy.SnapToGrid", init() { @@ -43,10 +50,7 @@ app.registerExtension({ const onResize = node.onResize; node.onResize = function () { if (app.shiftDown) { - const w = LiteGraph.CANVAS_GRID_SIZE * Math.round(node.size[0] / LiteGraph.CANVAS_GRID_SIZE); - const h = LiteGraph.CANVAS_GRID_SIZE * Math.round(node.size[1] / LiteGraph.CANVAS_GRID_SIZE); - node.size[0] = w; - node.size[1] = h; + roundVectorToGrid(node.size); } return onResize?.apply(this, arguments); }; @@ -57,9 +61,7 @@ app.registerExtension({ const origDrawNode = LGraphCanvas.prototype.drawNode; LGraphCanvas.prototype.drawNode = function (node, ctx) { if (app.shiftDown && this.node_dragged && node.id in this.selected_nodes) { - const x = LiteGraph.CANVAS_GRID_SIZE * Math.round(node.pos[0] / LiteGraph.CANVAS_GRID_SIZE); - const y = LiteGraph.CANVAS_GRID_SIZE * Math.round(node.pos[1] / LiteGraph.CANVAS_GRID_SIZE); - + const [x, y] = roundVectorToGrid([...node.pos]); const shiftX = x - node.pos[0]; let shiftY = y - node.pos[1]; @@ -85,5 +87,85 @@ app.registerExtension({ return origDrawNode.apply(this, arguments); }; + + + + /** + * The currently moving, selected group only. Set after the `selected_group` has actually started + * moving. + */ + let selectedAndMovingGroup = null; + + /** + * Handles moving a group; tracking when a group has been moved (to show the ghost in `drawGroups` + * below) as well as handle the last move call from LiteGraph's `processMouseUp`. + */ + const groupMove = LGraphGroup.prototype.move; + LGraphGroup.prototype.move = function(deltax, deltay, ignore_nodes) { + const v = groupMove.apply(this, arguments); + // When we've started moving, set `selectedAndMovingGroup` as LiteGraph sets `selected_group` + // too eagerly and we don't want to behave like we're moving until we get a delta. + if (!selectedAndMovingGroup && app.canvas.selected_group === this && (deltax || deltay)) { + selectedAndMovingGroup = this; + } + + // LiteGraph will call group.move both on mouse-move as well as mouse-up though we only want + // to snap on a mouse-up which we can determine by checking if `app.canvas.last_mouse_dragging` + // has been set to `false`. Essentially, this check here is the equivilant to calling an + // `LGraphGroup.prototype.onNodeMoved` if it had existed. + if (app.canvas.last_mouse_dragging === false && app.shiftDown) { + // After moving a group (while app.shiftDown), snap all the child nodes and, finally, + // align the group itself. + this.recomputeInsideNodes(); + for (const node of this._nodes) { + node.alignToGrid(); + } + LGraphNode.prototype.alignToGrid.apply(this); + } + return v; + }; + + /** + * Handles drawing a group when, snapping the size when one is actively being resized tracking and/or + * drawing a ghost box when one is actively being moved. This mimics the node snapping behavior for + * both. + */ + const drawGroups = LGraphCanvas.prototype.drawGroups; + LGraphCanvas.prototype.drawGroups = function (canvas, ctx) { + if (this.selected_group && app.shiftDown) { + if (this.selected_group_resizing) { + roundVectorToGrid(this.selected_group.size); + } else if (selectedAndMovingGroup) { + const [x, y] = roundVectorToGrid([...selectedAndMovingGroup.pos]); + const f = ctx.fillStyle; + const s = ctx.strokeStyle; + ctx.fillStyle = "rgba(100, 100, 100, 0.33)"; + ctx.strokeStyle = "rgba(100, 100, 100, 0.66)"; + ctx.rect(x, y, ...selectedAndMovingGroup.size); + ctx.fill(); + ctx.stroke(); + ctx.fillStyle = f; + ctx.strokeStyle = s; + } + } else if (!this.selected_group) { + selectedAndMovingGroup = null; + } + return drawGroups.apply(this, arguments); + }; + + + /** Handles adding a group in a snapping-enabled state. */ + const onGroupAdd = LGraphCanvas.onGroupAdd; + LGraphCanvas.onGroupAdd = function() { + const v = onGroupAdd.apply(app.canvas, arguments); + if (app.shiftDown) { + const lastGroup = app.graph._groups[app.graph._groups.length - 1]; + if (lastGroup) { + roundVectorToGrid(lastGroup.pos); + roundVectorToGrid(lastGroup.size); + } + } + return v; + }; }, }); From 34030fed925ca6e11863a0c2f85d84303378e312 Mon Sep 17 00:00:00 2001 From: luke zhang Date: Tue, 28 May 2024 02:26:07 +0800 Subject: [PATCH 245/378] improve dom widget performance (#3584) --- web/scripts/domWidget.js | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/web/scripts/domWidget.js b/web/scripts/domWidget.js index d5eeebdb..b7f437ad 100644 --- a/web/scripts/domWidget.js +++ b/web/scripts/domWidget.js @@ -11,9 +11,10 @@ function intersect(a, b) { else return null; } -function getClipPath(node, element, elRect) { +function getClipPath(node, element) { const selectedNode = Object.values(app.canvas.selected_nodes)[0]; if (selectedNode && selectedNode !== node) { + const elRect = element.getBoundingClientRect(); const MARGIN = 7; const scale = app.canvas.ds.scale; @@ -269,7 +270,7 @@ LGraphNode.prototype.addDOMWidget = function (name, type, element, options) { }); if (enableDomClipping) { - element.style.clipPath = getClipPath(node, element, elRect); + element.style.clipPath = getClipPath(node, element); element.style.willChange = "clip-path"; } From 0920e0e5febc852cfc5496ac2ba6b44d12b5b35c Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 27 May 2024 19:03:56 -0400 Subject: [PATCH 246/378] Remove some unused imports. --- comfy/ldm/cascade/stage_b.py | 1 - comfy/ldm/cascade/stage_c.py | 1 - comfy/ldm/models/autoencoder.py | 2 -- comfy/ldm/modules/attention.py | 2 +- comfy/model_management.py | 1 - comfy/sd.py | 1 - comfy/sd2_clip.py | 1 - comfy_extras/nodes_canny.py | 7 +------ comfy_extras/nodes_sag.py | 1 - comfy_extras/nodes_sdupscale.py | 1 - 10 files changed, 2 insertions(+), 16 deletions(-) diff --git a/comfy/ldm/cascade/stage_b.py b/comfy/ldm/cascade/stage_b.py index 6d2c2223..7c3d8fea 100644 --- a/comfy/ldm/cascade/stage_b.py +++ b/comfy/ldm/cascade/stage_b.py @@ -17,7 +17,6 @@ """ import math -import numpy as np import torch from torch import nn from .common import AttnBlock, LayerNorm2d_op, ResBlock, FeedForwardBlock, TimestepBlock diff --git a/comfy/ldm/cascade/stage_c.py b/comfy/ldm/cascade/stage_c.py index 67c1e52b..c85da1f0 100644 --- a/comfy/ldm/cascade/stage_c.py +++ b/comfy/ldm/cascade/stage_c.py @@ -18,7 +18,6 @@ import torch from torch import nn -import numpy as np import math from .common import AttnBlock, LayerNorm2d_op, ResBlock, FeedForwardBlock, TimestepBlock # from .controlnet import ControlNetDeliverer diff --git a/comfy/ldm/models/autoencoder.py b/comfy/ldm/models/autoencoder.py index b91ec324..f5f4de28 100644 --- a/comfy/ldm/models/autoencoder.py +++ b/comfy/ldm/models/autoencoder.py @@ -1,6 +1,4 @@ import torch -# import pytorch_lightning as pl -import torch.nn.functional as F from contextlib import contextmanager from typing import Any, Dict, List, Optional, Tuple, Union diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py index 93c94458..da9f7aab 100644 --- a/comfy/ldm/modules/attention.py +++ b/comfy/ldm/modules/attention.py @@ -3,7 +3,7 @@ import torch import torch.nn.functional as F from torch import nn, einsum from einops import rearrange, repeat -from typing import Optional, Any +from typing import Optional import logging from .diffusionmodules.util import AlphaBlender, timestep_embedding diff --git a/comfy/model_management.py b/comfy/model_management.py index ef36a2c4..5c1afd3d 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -2,7 +2,6 @@ import psutil import logging from enum import Enum from comfy.cli_args import args -import comfy.utils import torch import sys import platform diff --git a/comfy/sd.py b/comfy/sd.py index 8044c184..343d2a02 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -14,7 +14,6 @@ import comfy.utils from . import clip_vision from . import gligen from . import diffusers_convert -from . import model_base from . import model_detection from . import sd1_clip diff --git a/comfy/sd2_clip.py b/comfy/sd2_clip.py index 9c878d54..d14b4454 100644 --- a/comfy/sd2_clip.py +++ b/comfy/sd2_clip.py @@ -1,5 +1,4 @@ from comfy import sd1_clip -import torch import os class SD2ClipHModel(sd1_clip.SDClipModel): diff --git a/comfy_extras/nodes_canny.py b/comfy_extras/nodes_canny.py index fab2ab7a..d85e6b85 100644 --- a/comfy_extras/nodes_canny.py +++ b/comfy_extras/nodes_canny.py @@ -1,10 +1,5 @@ -import math - -import torch -import torch.nn.functional as F -import comfy.model_management - from kornia.filters import canny +import comfy.model_management class Canny: diff --git a/comfy_extras/nodes_sag.py b/comfy_extras/nodes_sag.py index 8d786db5..010e9974 100644 --- a/comfy_extras/nodes_sag.py +++ b/comfy_extras/nodes_sag.py @@ -4,7 +4,6 @@ import torch.nn.functional as F import math from einops import rearrange, repeat -import os from comfy.ldm.modules.attention import optimized_attention import comfy.samplers diff --git a/comfy_extras/nodes_sdupscale.py b/comfy_extras/nodes_sdupscale.py index 28c1cb0f..bba67e8d 100644 --- a/comfy_extras/nodes_sdupscale.py +++ b/comfy_extras/nodes_sdupscale.py @@ -1,5 +1,4 @@ import torch -import nodes import comfy.utils class SD_4XUpscale_Conditioning: From b26da2245fbc65e003889539fc352bf14370cded Mon Sep 17 00:00:00 2001 From: JettHu <35261585+JettHu@users.noreply.github.com> Date: Tue, 28 May 2024 07:30:35 +0800 Subject: [PATCH 247/378] Fix UnetParams annotation typo (#3589) --- comfy/types.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/types.py b/comfy/types.py index a8a3d29f..70cf4b15 100644 --- a/comfy/types.py +++ b/comfy/types.py @@ -25,7 +25,7 @@ class UnetParams(TypedDict): timestep: torch.Tensor c: UnetApplyConds # List of [0, 1], [0], [1], ... - # 0 means unconditional, 1 means conditional + # 0 means conditional, 1 means conditional unconditional cond_or_uncond: List[int] From 91542d4f8b99dfb65a3f5c56cc24fea91d93858a Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 28 May 2024 01:37:40 -0400 Subject: [PATCH 248/378] Import spandrel_extra_arches if present. I will not add this dependency to the default ones because models in the spandrel_extra_arches package are non commercial and therefore not compatible with free software licenses like the one ComfyUI uses. If you don't mind this you can install it manually yourself. --- comfy_extras/nodes_upscale_model.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/comfy_extras/nodes_upscale_model.py b/comfy_extras/nodes_upscale_model.py index 03f29446..bca79ef2 100644 --- a/comfy_extras/nodes_upscale_model.py +++ b/comfy_extras/nodes_upscale_model.py @@ -1,10 +1,19 @@ import os +import logging from spandrel import ModelLoader, ImageModelDescriptor from comfy import model_management import torch import comfy.utils import folder_paths +try: + from spandrel_extra_arches import EXTRA_REGISTRY + from spandrel import MAIN_REGISTRY + MAIN_REGISTRY.add(*EXTRA_REGISTRY) + logging.info("Successfully imported spandrel_extra_arches: support for non commercial upscale models.") +except: + pass + class UpscaleModelLoader: @classmethod def INPUT_TYPES(s): From 71ec5b144ee2c46ee12f1035782d6cb4bc84cca9 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 29 May 2024 00:19:30 -0400 Subject: [PATCH 249/378] Update commands to install nightly pytorch in readme. --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index cf32014b..de0c062a 100644 --- a/README.md +++ b/README.md @@ -106,7 +106,7 @@ AMD users can install rocm and pytorch with pip if you don't have it already ins This is the command to install the nightly with ROCm 6.0 which might have some performance improvements: -```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.0``` +```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.1``` ### NVIDIA @@ -116,7 +116,7 @@ Nvidia users should install stable pytorch using this command: This is the command to install pytorch nightly instead which might have performance improvements: -```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu121``` +```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu124``` #### Troubleshooting From bf3e334d468876bbf69d022e7a6770b0c52dd615 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 30 May 2024 11:07:38 -0400 Subject: [PATCH 250/378] Disable non_blocking when --deterministic or directml. --- comfy/model_management.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/comfy/model_management.py b/comfy/model_management.py index 5c1afd3d..b353e50b 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -629,6 +629,10 @@ def supports_dtype(device, dtype): #TODO def device_supports_non_blocking(device): if is_device_mps(device): return False #pytorch bug? mps doesn't support non blocking + if args.deterministic: #TODO: figure out why deterministic breaks non blocking from gpu to cpu (previews) + return False + if directml_enabled: + return False return True def device_should_use_non_blocking(device): From 04b308229ee59b5aebc0c78ea416e0b3ac22c146 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 31 May 2024 11:18:37 -0400 Subject: [PATCH 251/378] Small refactor of preview code. --- latent_preview.py | 23 ++++++++++------------- 1 file changed, 10 insertions(+), 13 deletions(-) diff --git a/latent_preview.py b/latent_preview.py index b258fcf2..54aa233f 100644 --- a/latent_preview.py +++ b/latent_preview.py @@ -11,6 +11,13 @@ import logging MAX_PREVIEW_RESOLUTION = 512 +def preview_to_image(latent_image): + latents_ubyte = (((latent_image + 1.0) / 2.0).clamp(0, 1) # change scale from -1..1 to 0..1 + .mul(0xFF) # to 0..255 + ).to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(latent_image.device)) + + return Image.fromarray(latents_ubyte.numpy()) + class LatentPreviewer: def decode_latent_to_preview(self, x0): pass @@ -24,12 +31,8 @@ class TAESDPreviewerImpl(LatentPreviewer): self.taesd = taesd def decode_latent_to_preview(self, x0): - x_sample = self.taesd.decode(x0[:1])[0].detach() - x_sample = 255. * torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0) - x_sample = np.moveaxis(x_sample.to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(x_sample.device)).numpy(), 0, 2) - - preview_image = Image.fromarray(x_sample) - return preview_image + x_sample = self.taesd.decode(x0[:1])[0].movedim(0, 2) + return preview_to_image(x_sample) class Latent2RGBPreviewer(LatentPreviewer): @@ -39,13 +42,7 @@ class Latent2RGBPreviewer(LatentPreviewer): def decode_latent_to_preview(self, x0): self.latent_rgb_factors = self.latent_rgb_factors.to(dtype=x0.dtype, device=x0.device) latent_image = x0[0].permute(1, 2, 0) @ self.latent_rgb_factors - - latents_ubyte = (((latent_image + 1) / 2) - .clamp(0, 1) # change scale from -1..1 to 0..1 - .mul(0xFF) # to 0..255 - ).to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(latent_image.device)) - - return Image.fromarray(latents_ubyte.numpy()) + return preview_to_image(latent_image) def get_previewer(device, latent_format): From e2c585f3be4f8f59211b26ea28d175ea63629a78 Mon Sep 17 00:00:00 2001 From: Peter Crabtree Date: Sat, 1 Jun 2024 12:36:08 -0400 Subject: [PATCH 252/378] Fix to allow use of PerpNegGuider with cfg_function_post hooks (like PAG) (#3618) --- .gitignore | 3 ++- comfy_extras/nodes_perpneg.py | 30 ++++++++++++++++++++++++++++-- 2 files changed, 30 insertions(+), 3 deletions(-) diff --git a/.gitignore b/.gitignore index 9f038924..afad8148 100644 --- a/.gitignore +++ b/.gitignore @@ -15,4 +15,5 @@ venv/ !/web/extensions/logging.js.example !/web/extensions/core/ /tests-ui/data/object_info.json -/user/ \ No newline at end of file +/user/ +comfyui*.log diff --git a/comfy_extras/nodes_perpneg.py b/comfy_extras/nodes_perpneg.py index 306cf9cd..546276aa 100644 --- a/comfy_extras/nodes_perpneg.py +++ b/comfy_extras/nodes_perpneg.py @@ -61,12 +61,38 @@ class Guider_PerpNeg(comfy.samplers.CFGGuider): self.neg_scale = neg_scale def predict_noise(self, x, timestep, model_options={}, seed=None): + # in CFGGuider.predict_noise, we call sampling_function(), which uses cfg_function() to compute pos & neg + # but we'd rather do a single batch of sampling pos, neg, and empty, so we call calc_cond_batch([pos,neg,empty]) directly + positive_cond = self.conds.get("positive", None) negative_cond = self.conds.get("negative", None) empty_cond = self.conds.get("empty_negative_prompt", None) - out = comfy.samplers.calc_cond_batch(self.inner_model, [negative_cond, positive_cond, empty_cond], x, timestep, model_options) - return perp_neg(x, out[1], out[0], out[2], self.neg_scale, self.cfg) + (noise_pred_pos, noise_pred_neg, noise_pred_empty) = \ + comfy.samplers.calc_cond_batch(self.inner_model, [positive_cond, negative_cond, empty_cond], x, timestep, model_options) + cfg_result = perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_empty, self.neg_scale, self.cfg) + + # normally this would be done in cfg_function, but we skipped + # that for efficiency: we can compute the noise predictions in + # a single call to calc_cond_batch() (rather than two) + # so we replicate the hook here + for fn in model_options.get("sampler_post_cfg_function", []): + args = { + "denoised": cfg_result, + "cond": positive_cond, + "uncond": negative_cond, + "model": self.inner_model, + "uncond_denoised": noise_pred_neg, + "cond_denoised": noise_pred_pos, + "sigma": timestep, + "model_options": model_options, + "input": x, + # not in the original call in samplers.py:cfg_function, but made available for future hooks + "empty_cond": empty_cond, + "empty_cond_denoised": noise_pred_empty,} + cfg_result = fn(args) + + return cfg_result class PerpNegGuider: @classmethod From b249862080d4c046bd7f2680898c2f348c792a12 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 1 Jun 2024 12:47:31 -0400 Subject: [PATCH 253/378] Add an annoying print to a function I want to remove. --- .gitignore | 3 +-- comfy/model_management.py | 1 + 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.gitignore b/.gitignore index afad8148..9f038924 100644 --- a/.gitignore +++ b/.gitignore @@ -15,5 +15,4 @@ venv/ !/web/extensions/logging.js.example !/web/extensions/core/ /tests-ui/data/object_info.json -/user/ -comfyui*.log +/user/ \ No newline at end of file diff --git a/comfy/model_management.py b/comfy/model_management.py index b353e50b..3b9fad36 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -879,6 +879,7 @@ def unload_all_models(): def resolve_lowvram_weight(weight, model, key): #TODO: remove + print("WARNING: The comfy.model_management.resolve_lowvram_weight function will be removed soon, please stop using it.") return weight #TODO: might be cleaner to put this somewhere else From 809cc85a8e092ae416ca2652a4b73671b8d3c72b Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 2 Jun 2024 19:21:53 -0400 Subject: [PATCH 254/378] Remove useless code. --- comfy/model_patcher.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index 2e746d8a..84592f93 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -76,9 +76,7 @@ class ModelPatcher: def model_size(self): if self.size > 0: return self.size - model_sd = self.model.state_dict() self.size = comfy.model_management.module_size(self.model) - self.model_keys = set(model_sd.keys()) return self.size def clone(self): @@ -90,7 +88,6 @@ class ModelPatcher: n.object_patches = self.object_patches.copy() n.model_options = copy.deepcopy(self.model_options) - n.model_keys = self.model_keys n.backup = self.backup n.object_patches_backup = self.object_patches_backup return n @@ -210,8 +207,9 @@ class ModelPatcher: def add_patches(self, patches, strength_patch=1.0, strength_model=1.0): p = set() + model_sd = self.model.state_dict() for k in patches: - if k in self.model_keys: + if k in model_sd: p.add(k) current_patches = self.patches.get(k, []) current_patches.append((strength_patch, patches[k], strength_model)) From cb8d0ebccc93d3df6e00da1a57718a86d3dde300 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 3 Jun 2024 19:48:27 -0400 Subject: [PATCH 255/378] Don't load the view coordinates when loading a workflow from the history. I think this makes things slightly less annoying for some users. --- web/scripts/app.js | 4 ++-- web/scripts/ui.js | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/web/scripts/app.js b/web/scripts/app.js index 4dc011b9..f96d197a 100644 --- a/web/scripts/app.js +++ b/web/scripts/app.js @@ -1800,7 +1800,7 @@ export class ComfyApp { * @param {*} graphData A serialized graph object * @param { boolean } clean If the graph state, e.g. images, should be cleared */ - async loadGraphData(graphData, clean = true) { + async loadGraphData(graphData, clean = true, restore_view = true) { if (clean !== false) { this.clean(); } @@ -1836,7 +1836,7 @@ export class ComfyApp { try { this.graph.configure(graphData); - if (this.enableWorkflowViewRestore.value && graphData.extra?.ds) { + if (restore_view && this.enableWorkflowViewRestore.value && graphData.extra?.ds) { this.canvas.ds.offset = graphData.extra.ds.offset; this.canvas.ds.scale = graphData.extra.ds.scale; } diff --git a/web/scripts/ui.js b/web/scripts/ui.js index 36fed323..72e43d35 100644 --- a/web/scripts/ui.js +++ b/web/scripts/ui.js @@ -228,7 +228,7 @@ class ComfyList { $el("button", { textContent: "Load", onclick: async () => { - await app.loadGraphData(item.prompt[3].extra_pnginfo.workflow); + await app.loadGraphData(item.prompt[3].extra_pnginfo.workflow, true, false); if (item.outputs) { app.nodeOutputs = item.outputs; } From 20447e9ec92b7e7e3544a6fd2932c31c90333991 Mon Sep 17 00:00:00 2001 From: Denys Smirnov Date: Tue, 4 Jun 2024 23:37:11 +0300 Subject: [PATCH 256/378] Fix alpha in PorterDuffImageComposite. (#3411) There were two bugs in PorterDuffImageComposite. The first one is the fact that it uses the mask input directly as alpha, missing the conversion (`1-a`). The fix is similar to c16f5744. The second one is that all color composition formulas assume alpha premultiplied values, while the input is not premultiplied. This change fixes both of these issue. --- comfy_extras/nodes_compositing.py | 19 ++++++++++++++++--- 1 file changed, 16 insertions(+), 3 deletions(-) diff --git a/comfy_extras/nodes_compositing.py b/comfy_extras/nodes_compositing.py index 181b36ed..48fe5e3d 100644 --- a/comfy_extras/nodes_compositing.py +++ b/comfy_extras/nodes_compositing.py @@ -28,6 +28,14 @@ class PorterDuffMode(Enum): def porter_duff_composite(src_image: torch.Tensor, src_alpha: torch.Tensor, dst_image: torch.Tensor, dst_alpha: torch.Tensor, mode: PorterDuffMode): + # convert mask to alpha + src_alpha = 1 - src_alpha + dst_alpha = 1 - dst_alpha + # premultiply alpha + src_image = src_image * src_alpha + dst_image = dst_image * dst_alpha + + # composite ops below assume alpha-premultiplied images if mode == PorterDuffMode.ADD: out_alpha = torch.clamp(src_alpha + dst_alpha, 0, 1) out_image = torch.clamp(src_image + dst_image, 0, 1) @@ -35,7 +43,7 @@ def porter_duff_composite(src_image: torch.Tensor, src_alpha: torch.Tensor, dst_ out_alpha = torch.zeros_like(dst_alpha) out_image = torch.zeros_like(dst_image) elif mode == PorterDuffMode.DARKEN: - out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha + out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.min(src_image, dst_image) elif mode == PorterDuffMode.DST: out_alpha = dst_alpha @@ -84,8 +92,13 @@ def porter_duff_composite(src_image: torch.Tensor, src_alpha: torch.Tensor, dst_ out_alpha = (1 - dst_alpha) * src_alpha + (1 - src_alpha) * dst_alpha out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image else: - out_alpha = None - out_image = None + return None, None + + # back to non-premultiplied alpha + out_image = torch.where(out_alpha > 1e-5, out_image / out_alpha, torch.zeros_like(out_image)) + out_image = torch.clamp(out_image, 0, 1) + # convert alpha to mask + out_alpha = 1 - out_alpha return out_image, out_alpha From b1fd26fe9e55163f780bf9e5f56bf9bf5f035c93 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 4 Jun 2024 17:44:14 -0400 Subject: [PATCH 257/378] pytorch xpu should be flash or mem efficient attention? --- comfy/model_management.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/comfy/model_management.py b/comfy/model_management.py index 3b9fad36..a5142d30 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -693,6 +693,8 @@ def pytorch_attention_flash_attention(): #TODO: more reliable way of checking for flash attention? if is_nvidia(): #pytorch flash attention only works on Nvidia return True + if is_intel_xpu(): + return True return False def force_upcast_attention_dtype(): From 104fcea0c8672b138a9bdd1ae00603c9240867c1 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 5 Jun 2024 19:14:56 -0400 Subject: [PATCH 258/378] Add function to get the list of currently loaded models. --- comfy/model_management.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/comfy/model_management.py b/comfy/model_management.py index a5142d30..57aa8bca 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -276,6 +276,7 @@ class LoadedModel: self.device = model.load_device self.weights_loaded = False self.real_model = None + self.currently_used = True def model_memory(self): return self.model.model_size() @@ -365,6 +366,7 @@ def free_memory(memory_required, device, keep_loaded=[]): if shift_model.device == device: if shift_model not in keep_loaded: can_unload.append((sys.getrefcount(shift_model.model), shift_model.model_memory(), i)) + shift_model.currently_used = False for x in sorted(can_unload): i = x[-1] @@ -410,6 +412,7 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False): current_loaded_models.pop(loaded_model_index).model_unload(unpatch_weights=True) loaded = None else: + loaded.currently_used = True models_already_loaded.append(loaded) if loaded is None: @@ -466,6 +469,16 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False): def load_model_gpu(model): return load_models_gpu([model]) +def loaded_models(only_currently_used=False): + output = [] + for m in current_loaded_models: + if only_currently_used: + if not m.currently_used: + continue + + output.append(m.model) + return output + def cleanup_models(keep_clone_weights_loaded=False): to_delete = [] for i in range(len(current_loaded_models)): From 0dccb4617de61b81763321f01ae527dbe3b01202 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 6 Jun 2024 14:49:45 -0400 Subject: [PATCH 259/378] Remove some unnecessary arguments. --- nodes.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/nodes.py b/nodes.py index 34821ca3..f454ff8c 100644 --- a/nodes.py +++ b/nodes.py @@ -496,7 +496,7 @@ class CheckpointLoader: CATEGORY = "advanced/loaders" - def load_checkpoint(self, config_name, ckpt_name, output_vae=True, output_clip=True): + def load_checkpoint(self, config_name, ckpt_name): config_path = folder_paths.get_full_path("configs", config_name) ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) return comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) @@ -511,7 +511,7 @@ class CheckpointLoaderSimple: CATEGORY = "loaders" - def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): + def load_checkpoint(self, ckpt_name): ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) return out[:3] From 56333d48508f95bdef23870cad3239ba0ebdb8a9 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 7 Jun 2024 03:05:23 -0400 Subject: [PATCH 260/378] Use the end token for the text encoder attention mask. --- comfy/sd1_clip.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py index ff6db0d2..e7ebf046 100644 --- a/comfy/sd1_clip.py +++ b/comfy/sd1_clip.py @@ -168,11 +168,11 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): attention_mask = None if self.enable_attention_masks: attention_mask = torch.zeros_like(tokens) - max_token = self.transformer.get_input_embeddings().weight.shape[0] - 1 + end_token = self.special_tokens.get("end", -1) for x in range(attention_mask.shape[0]): for y in range(attention_mask.shape[1]): attention_mask[x, y] = 1 - if tokens[x, y] == max_token: + if tokens[x, y] == end_token: break outputs = self.transformer(tokens, attention_mask, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state) From 6cd8ffc465ed363b078249b081ea3f975e77cf15 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 8 Jun 2024 02:16:55 -0400 Subject: [PATCH 261/378] Reshape the empty latent image to the right amount of channels if needed. --- comfy/latent_formats.py | 2 ++ comfy/sample.py | 6 ++++++ comfy/utils.py | 10 +++++----- comfy_extras/nodes_custom_sampler.py | 2 ++ nodes.py | 2 ++ 5 files changed, 17 insertions(+), 5 deletions(-) diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py index 4ca466d9..69192bc6 100644 --- a/comfy/latent_formats.py +++ b/comfy/latent_formats.py @@ -2,6 +2,7 @@ import torch class LatentFormat: scale_factor = 1.0 + latent_channels = 4 latent_rgb_factors = None taesd_decoder_name = None @@ -72,6 +73,7 @@ class SD_X4(LatentFormat): ] class SC_Prior(LatentFormat): + latent_channels = 16 def __init__(self): self.scale_factor = 1.0 self.latent_rgb_factors = [ diff --git a/comfy/sample.py b/comfy/sample.py index e51bd67d..98dcaca7 100644 --- a/comfy/sample.py +++ b/comfy/sample.py @@ -24,6 +24,12 @@ def prepare_noise(latent_image, seed, noise_inds=None): noises = torch.cat(noises, axis=0) return noises +def fix_empty_latent_channels(model, latent_image): + latent_channels = model.get_model_object("latent_format").latent_channels #Resize the empty latent image so it has the right number of channels + if latent_channels != latent_image.shape[1] and torch.count_nonzero(latent_image) == 0: + latent_image = comfy.utils.repeat_to_batch_size(latent_image, latent_channels, dim=1) + return latent_image + def prepare_sampling(model, noise_shape, positive, negative, noise_mask): logging.warning("Warning: comfy.sample.prepare_sampling isn't used anymore and can be removed") return model, positive, negative, noise_mask, [] diff --git a/comfy/utils.py b/comfy/utils.py index ab47b8f2..884404cc 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -249,11 +249,11 @@ def unet_to_diffusers(unet_config): return diffusers_unet_map -def repeat_to_batch_size(tensor, batch_size): - if tensor.shape[0] > batch_size: - return tensor[:batch_size] - elif tensor.shape[0] < batch_size: - return tensor.repeat([math.ceil(batch_size / tensor.shape[0])] + [1] * (len(tensor.shape) - 1))[:batch_size] +def repeat_to_batch_size(tensor, batch_size, dim=0): + if tensor.shape[dim] > batch_size: + return tensor.narrow(dim, 0, batch_size) + elif tensor.shape[dim] < batch_size: + return tensor.repeat(dim * [1] + [math.ceil(batch_size / tensor.shape[dim])] + [1] * (len(tensor.shape) - 1 - dim)).narrow(dim, 0, batch_size) return tensor def resize_to_batch_size(tensor, batch_size): diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index 47f08bf6..45ef8cf4 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -380,6 +380,7 @@ class SamplerCustom: def sample(self, model, add_noise, noise_seed, cfg, positive, negative, sampler, sigmas, latent_image): latent = latent_image latent_image = latent["samples"] + latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image) if not add_noise: noise = Noise_EmptyNoise().generate_noise(latent) else: @@ -538,6 +539,7 @@ class SamplerCustomAdvanced: def sample(self, noise, guider, sampler, sigmas, latent_image): latent = latent_image latent_image = latent["samples"] + latent_image = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image) noise_mask = None if "noise_mask" in latent: diff --git a/nodes.py b/nodes.py index f454ff8c..b744b53f 100644 --- a/nodes.py +++ b/nodes.py @@ -1299,6 +1299,8 @@ class SetLatentNoiseMask: def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False): latent_image = latent["samples"] + latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image) + if disable_noise: noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") else: From 742d5720d1b128c78266bfd7156fb578d664a95a Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 9 Jun 2024 16:41:04 -0400 Subject: [PATCH 262/378] Support zeroing out text embeddings with the attention mask. --- comfy/sd1_clip.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py index e7ebf046..2729f14d 100644 --- a/comfy/sd1_clip.py +++ b/comfy/sd1_clip.py @@ -68,7 +68,8 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): ] def __init__(self, version="openai/clip-vit-large-patch14", device="cpu", max_length=77, freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=comfy.clip_model.CLIPTextModel, - special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, return_projected_pooled=True): # clip-vit-base-patch32 + special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, zero_out_masked=False, + return_projected_pooled=True): # clip-vit-base-patch32 super().__init__() assert layer in self.LAYERS @@ -90,6 +91,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055)) self.enable_attention_masks = enable_attention_masks + self.zero_out_masked = zero_out_masked self.layer_norm_hidden_state = layer_norm_hidden_state self.return_projected_pooled = return_projected_pooled @@ -179,9 +181,12 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): self.transformer.set_input_embeddings(backup_embeds) if self.layer == "last": - z = outputs[0] + z = outputs[0].float() else: - z = outputs[1] + z = outputs[1].float() + + if self.zero_out_masked and attention_mask is not None: + z *= attention_mask.unsqueeze(-1).float() pooled_output = None if len(outputs) >= 3: @@ -190,7 +195,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): elif outputs[2] is not None: pooled_output = outputs[2].float() - return z.float(), pooled_output + return z, pooled_output def encode(self, tokens): return self(tokens) From a5e6a632f9f16e5b3c72c428820bce67b05446bf Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 10 Jun 2024 01:05:53 -0400 Subject: [PATCH 263/378] Support sampling non 2D latents. --- comfy/samplers.py | 93 ++++++++++++++++++++++++++++++++--------------- 1 file changed, 63 insertions(+), 30 deletions(-) diff --git a/comfy/samplers.py b/comfy/samplers.py index 29962a91..656e0a28 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -8,7 +8,8 @@ import logging import comfy.sampler_helpers def get_area_and_mult(conds, x_in, timestep_in): - area = (x_in.shape[2], x_in.shape[3], 0, 0) + dims = tuple(x_in.shape[2:]) + area = None strength = 1.0 if 'timestep_start' in conds: @@ -20,11 +21,16 @@ def get_area_and_mult(conds, x_in, timestep_in): if timestep_in[0] < timestep_end: return None if 'area' in conds: - area = conds['area'] + area = list(conds['area']) if 'strength' in conds: strength = conds['strength'] - input_x = x_in[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] + input_x = x_in + if area is not None: + for i in range(len(dims)): + area[i] = min(input_x.shape[i + 2] - area[len(dims) + i], area[i]) + input_x = input_x.narrow(i + 2, area[len(dims) + i], area[i]) + if 'mask' in conds: # Scale the mask to the size of the input # The mask should have been resized as we began the sampling process @@ -32,28 +38,30 @@ def get_area_and_mult(conds, x_in, timestep_in): if "mask_strength" in conds: mask_strength = conds["mask_strength"] mask = conds['mask'] - assert(mask.shape[1] == x_in.shape[2]) - assert(mask.shape[2] == x_in.shape[3]) - mask = mask[:input_x.shape[0],area[2]:area[0] + area[2],area[3]:area[1] + area[3]] * mask_strength + assert(mask.shape[1:] == x_in.shape[2:]) + + mask = mask[:input_x.shape[0]] + if area is not None: + for i in range(len(dims)): + mask = mask.narrow(i + 1, area[len(dims) + i], area[i]) + + mask = mask * mask_strength mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1) else: mask = torch.ones_like(input_x) mult = mask * strength - if 'mask' not in conds: + if 'mask' not in conds and area is not None: rr = 8 - if area[2] != 0: - for t in range(rr): - mult[:,:,t:1+t,:] *= ((1.0/rr) * (t + 1)) - if (area[0] + area[2]) < x_in.shape[2]: - for t in range(rr): - mult[:,:,area[0] - 1 - t:area[0] - t,:] *= ((1.0/rr) * (t + 1)) - if area[3] != 0: - for t in range(rr): - mult[:,:,:,t:1+t] *= ((1.0/rr) * (t + 1)) - if (area[1] + area[3]) < x_in.shape[3]: - for t in range(rr): - mult[:,:,:,area[1] - 1 - t:area[1] - t] *= ((1.0/rr) * (t + 1)) + for i in range(len(dims)): + if area[len(dims) + i] != 0: + for t in range(rr): + m = mult.narrow(i + 2, t, 1) + m *= ((1.0/rr) * (t + 1)) + if (area[i] + area[len(dims) + i]) < x_in.shape[i + 2]: + for t in range(rr): + m = mult.narrow(i + 2, area[i] - 1 - t, 1) + m *= ((1.0/rr) * (t + 1)) conditioning = {} model_conds = conds["model_conds"] @@ -219,8 +227,19 @@ def calc_cond_batch(model, conds, x_in, timestep, model_options): for o in range(batch_chunks): cond_index = cond_or_uncond[o] - out_conds[cond_index][:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o] - out_counts[cond_index][:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o] + a = area[o] + if a is None: + out_conds[cond_index] += output[o] * mult[o] + out_counts[cond_index] += mult[o] + else: + out_c = out_conds[cond_index] + out_cts = out_counts[cond_index] + dims = len(a) // 2 + for i in range(dims): + out_c = out_c.narrow(i + 2, a[i + dims], a[i]) + out_cts = out_cts.narrow(i + 2, a[i + dims], a[i]) + out_c += output[o] * mult[o] + out_cts += mult[o] for i in range(len(out_conds)): out_conds[i] /= out_counts[i] @@ -335,7 +354,7 @@ def get_mask_aabb(masks): return bounding_boxes, is_empty -def resolve_areas_and_cond_masks(conditions, h, w, device): +def resolve_areas_and_cond_masks_multidim(conditions, dims, device): # We need to decide on an area outside the sampling loop in order to properly generate opposite areas of equal sizes. # While we're doing this, we can also resolve the mask device and scaling for performance reasons for i in range(len(conditions)): @@ -344,7 +363,14 @@ def resolve_areas_and_cond_masks(conditions, h, w, device): area = c['area'] if area[0] == "percentage": modified = c.copy() - area = (max(1, round(area[1] * h)), max(1, round(area[2] * w)), round(area[3] * h), round(area[4] * w)) + a = area[1:] + a_len = len(a) // 2 + area = () + for d in range(len(dims)): + area += (max(1, round(a[d] * dims[d])),) + for d in range(len(dims)): + area += (round(a[d + a_len] * dims[d]),) + modified['area'] = area c = modified conditions[i] = c @@ -353,12 +379,12 @@ def resolve_areas_and_cond_masks(conditions, h, w, device): mask = c['mask'] mask = mask.to(device=device) modified = c.copy() - if len(mask.shape) == 2: + if len(mask.shape) == len(dims): mask = mask.unsqueeze(0) - if mask.shape[1] != h or mask.shape[2] != w: - mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=(h, w), mode='bilinear', align_corners=False).squeeze(1) + if mask.shape[1:] != dims: + mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=dims, mode='bilinear', align_corners=False).squeeze(1) - if modified.get("set_area_to_bounds", False): + if modified.get("set_area_to_bounds", False): #TODO: handle dim != 2 bounds = torch.max(torch.abs(mask),dim=0).values.unsqueeze(0) boxes, is_empty = get_mask_aabb(bounds) if is_empty[0]: @@ -375,7 +401,11 @@ def resolve_areas_and_cond_masks(conditions, h, w, device): modified['mask'] = mask conditions[i] = modified -def create_cond_with_same_area_if_none(conds, c): +def resolve_areas_and_cond_masks(conditions, h, w, device): + logging.warning("WARNING: The comfy.samplers.resolve_areas_and_cond_masks function is deprecated please use the resolve_areas_and_cond_masks_multidim one instead.") + return resolve_areas_and_cond_masks_multidim(conditions, [h, w], device) + +def create_cond_with_same_area_if_none(conds, c): #TODO: handle dim != 2 if 'area' not in c: return @@ -479,7 +509,10 @@ def encode_model_conds(model_function, conds, noise, device, prompt_type, **kwar params = x.copy() params["device"] = device params["noise"] = noise - params["width"] = params.get("width", noise.shape[3] * 8) + default_width = None + if len(noise.shape) >= 4: #TODO: 8 multiple should be set by the model + default_width = noise.shape[3] * 8 + params["width"] = params.get("width", default_width) params["height"] = params.get("height", noise.shape[2] * 8) params["prompt_type"] = params.get("prompt_type", prompt_type) for k in kwargs: @@ -567,7 +600,7 @@ def ksampler(sampler_name, extra_options={}, inpaint_options={}): def process_conds(model, noise, conds, device, latent_image=None, denoise_mask=None, seed=None): for k in conds: conds[k] = conds[k][:] - resolve_areas_and_cond_masks(conds[k], noise.shape[2], noise.shape[3], device) + resolve_areas_and_cond_masks_multidim(conds[k], noise.shape[2:], device) for k in conds: calculate_start_end_timesteps(model, conds[k]) From 8c4a9befa7261b6fc78407ace90a57d21bfe631e Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 10 Jun 2024 13:26:25 -0400 Subject: [PATCH 264/378] SD3 Support. --- comfy/latent_formats.py | 33 +- comfy/ldm/modules/diffusionmodules/mmdit.py | 1023 + comfy/model_base.py | 26 + comfy/model_detection.py | 42 +- comfy/model_sampling.py | 61 + comfy/sd.py | 6 +- comfy/sd3_clip.py | 91 + comfy/supported_models.py | 25 +- comfy/t5.py | 231 + comfy/t5_config_base.json | 21 + comfy/t5_config_xxl.json | 21 + comfy/t5_tokenizer/special_tokens_map.json | 125 + comfy/t5_tokenizer/tokenizer.json | 129428 +++++++++++++++++ comfy/t5_tokenizer/tokenizer_config.json | 939 + comfy_extras/nodes_model_advanced.py | 27 + comfy_extras/nodes_sd3.py | 87 + nodes.py | 1 + 17 files changed, 132182 insertions(+), 5 deletions(-) create mode 100644 comfy/ldm/modules/diffusionmodules/mmdit.py create mode 100644 comfy/sd3_clip.py create mode 100644 comfy/t5.py create mode 100644 comfy/t5_config_base.json create mode 100644 comfy/t5_config_xxl.json create mode 100644 comfy/t5_tokenizer/special_tokens_map.json create mode 100644 comfy/t5_tokenizer/tokenizer.json create mode 100644 comfy/t5_tokenizer/tokenizer_config.json create mode 100644 comfy_extras/nodes_sd3.py diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py index 69192bc6..6a9a9620 100644 --- a/comfy/latent_formats.py +++ b/comfy/latent_formats.py @@ -25,8 +25,9 @@ class SD15(LatentFormat): self.taesd_decoder_name = "taesd_decoder" class SDXL(LatentFormat): + scale_factor = 0.13025 + def __init__(self): - self.scale_factor = 0.13025 self.latent_rgb_factors = [ # R G B [ 0.3920, 0.4054, 0.4549], @@ -104,3 +105,33 @@ class SC_B(LatentFormat): [-0.3087, -0.1535, 0.0366], [ 0.0290, -0.1574, -0.4078] ] + +class SD3(LatentFormat): + latent_channels = 16 + def __init__(self): + self.scale_factor = 1.5305 + self.shift_factor = 0.0609 + self.latent_rgb_factors = [ + [-0.0645, 0.0177, 0.1052], + [ 0.0028, 0.0312, 0.0650], + [ 0.1848, 0.0762, 0.0360], + [ 0.0944, 0.0360, 0.0889], + [ 0.0897, 0.0506, -0.0364], + [-0.0020, 0.1203, 0.0284], + [ 0.0855, 0.0118, 0.0283], + [-0.0539, 0.0658, 0.1047], + [-0.0057, 0.0116, 0.0700], + [-0.0412, 0.0281, -0.0039], + [ 0.1106, 0.1171, 0.1220], + [-0.0248, 0.0682, -0.0481], + [ 0.0815, 0.0846, 0.1207], + [-0.0120, -0.0055, -0.0867], + [-0.0749, -0.0634, -0.0456], + [-0.1418, -0.1457, -0.1259] + ] + + def process_in(self, latent): + return (latent - self.shift_factor) * self.scale_factor + + def process_out(self, latent): + return (latent / self.scale_factor) + self.shift_factor diff --git a/comfy/ldm/modules/diffusionmodules/mmdit.py b/comfy/ldm/modules/diffusionmodules/mmdit.py new file mode 100644 index 00000000..5e7afc8d --- /dev/null +++ b/comfy/ldm/modules/diffusionmodules/mmdit.py @@ -0,0 +1,1023 @@ +import logging +import math +from typing import Dict, Optional + +import numpy as np +import torch +import torch.nn as nn +from .. import attention +from einops import rearrange, repeat + +def default(x, y): + if x is not None: + return x + return y + +class Mlp(nn.Module): + """ MLP as used in Vision Transformer, MLP-Mixer and related networks + """ + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + norm_layer=None, + bias=True, + drop=0., + use_conv=False, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + drop_probs = drop + linear_layer = partial(operations.Conv2d, kernel_size=1) if use_conv else operations.Linear + + self.fc1 = linear_layer(in_features, hidden_features, bias=bias, dtype=dtype, device=device) + self.act = act_layer() + self.drop1 = nn.Dropout(drop_probs) + self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity() + self.fc2 = linear_layer(hidden_features, out_features, bias=bias, dtype=dtype, device=device) + self.drop2 = nn.Dropout(drop_probs) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop1(x) + x = self.norm(x) + x = self.fc2(x) + x = self.drop2(x) + return x + +class PatchEmbed(nn.Module): + """ 2D Image to Patch Embedding + """ + dynamic_img_pad: torch.jit.Final[bool] + + def __init__( + self, + img_size: Optional[int] = 224, + patch_size: int = 16, + in_chans: int = 3, + embed_dim: int = 768, + norm_layer = None, + flatten: bool = True, + bias: bool = True, + strict_img_size: bool = True, + dynamic_img_pad: bool = True, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + self.patch_size = (patch_size, patch_size) + if img_size is not None: + self.img_size = (img_size, img_size) + self.grid_size = tuple([s // p for s, p in zip(self.img_size, self.patch_size)]) + self.num_patches = self.grid_size[0] * self.grid_size[1] + else: + self.img_size = None + self.grid_size = None + self.num_patches = None + + # flatten spatial dim and transpose to channels last, kept for bwd compat + self.flatten = flatten + self.strict_img_size = strict_img_size + self.dynamic_img_pad = dynamic_img_pad + + self.proj = operations.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype, device=device) + self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() + + def forward(self, x): + B, C, H, W = x.shape + # if self.img_size is not None: + # if self.strict_img_size: + # _assert(H == self.img_size[0], f"Input height ({H}) doesn't match model ({self.img_size[0]}).") + # _assert(W == self.img_size[1], f"Input width ({W}) doesn't match model ({self.img_size[1]}).") + # elif not self.dynamic_img_pad: + # _assert( + # H % self.patch_size[0] == 0, + # f"Input height ({H}) should be divisible by patch size ({self.patch_size[0]})." + # ) + # _assert( + # W % self.patch_size[1] == 0, + # f"Input width ({W}) should be divisible by patch size ({self.patch_size[1]})." + # ) + if self.dynamic_img_pad: + pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0] + pad_w = (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1] + x = torch.nn.functional.pad(x, (0, pad_w, 0, pad_h), mode='reflect') + x = self.proj(x) + if self.flatten: + x = x.flatten(2).transpose(1, 2) # NCHW -> NLC + x = self.norm(x) + return x + +def modulate(x, shift, scale): + if shift is None: + shift = torch.zeros_like(scale) + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +################################################################################# +# Sine/Cosine Positional Embedding Functions # +################################################################################# + + +def get_2d_sincos_pos_embed( + embed_dim, + grid_size, + cls_token=False, + extra_tokens=0, + scaling_factor=None, + offset=None, +): + """ + grid_size: int of the grid height and width + return: + pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) + """ + grid_h = np.arange(grid_size, dtype=np.float32) + grid_w = np.arange(grid_size, dtype=np.float32) + grid = np.meshgrid(grid_w, grid_h) # here w goes first + grid = np.stack(grid, axis=0) + if scaling_factor is not None: + grid = grid / scaling_factor + if offset is not None: + grid = grid - offset + + grid = grid.reshape([2, 1, grid_size, grid_size]) + pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) + if cls_token and extra_tokens > 0: + pos_embed = np.concatenate( + [np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0 + ) + return pos_embed + + +def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): + assert embed_dim % 2 == 0 + + # use half of dimensions to encode grid_h + emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) + emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) + + emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) + return emb + + +def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): + """ + embed_dim: output dimension for each position + pos: a list of positions to be encoded: size (M,) + out: (M, D) + """ + assert embed_dim % 2 == 0 + omega = np.arange(embed_dim // 2, dtype=np.float64) + omega /= embed_dim / 2.0 + omega = 1.0 / 10000**omega # (D/2,) + + pos = pos.reshape(-1) # (M,) + out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product + + emb_sin = np.sin(out) # (M, D/2) + emb_cos = np.cos(out) # (M, D/2) + + emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) + return emb + +def get_1d_sincos_pos_embed_from_grid_torch(embed_dim, pos, device=None, dtype=torch.float32): + omega = torch.arange(embed_dim // 2, device=device, dtype=dtype) + omega /= embed_dim / 2.0 + omega = 1.0 / 10000**omega # (D/2,) + pos = pos.reshape(-1) # (M,) + out = torch.einsum("m,d->md", pos, omega) # (M, D/2), outer product + emb_sin = torch.sin(out) # (M, D/2) + emb_cos = torch.cos(out) # (M, D/2) + emb = torch.cat([emb_sin, emb_cos], dim=1) # (M, D) + return emb + +def get_2d_sincos_pos_embed_torch(embed_dim, w, h, val_center=7.5, val_magnitude=7.5, device=None, dtype=torch.float32): + small = min(h, w) + val_h = (h / small) * val_magnitude + val_w = (w / small) * val_magnitude + grid_h, grid_w = torch.meshgrid(torch.linspace(-val_h + val_center, val_h + val_center, h, device=device, dtype=dtype), torch.linspace(-val_w + val_center, val_w + val_center, w, device=device, dtype=dtype), indexing='ij') + emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype) + emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype) + emb = torch.cat([emb_w, emb_h], dim=1) # (H*W, D) + return emb + + +################################################################################# +# Embedding Layers for Timesteps and Class Labels # +################################################################################# + + +class TimestepEmbedder(nn.Module): + """ + Embeds scalar timesteps into vector representations. + """ + + def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None): + super().__init__() + self.mlp = nn.Sequential( + operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device), + ) + self.frequency_embedding_size = frequency_embedding_size + + @staticmethod + def timestep_embedding(t, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) + * torch.arange(start=0, end=half, dtype=torch.float32) + / half + ).to(device=t.device) + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat( + [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 + ) + if torch.is_floating_point(t): + embedding = embedding.to(dtype=t.dtype) + return embedding + + def forward(self, t, dtype, **kwargs): + t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype) + t_emb = self.mlp(t_freq) + return t_emb + + +class VectorEmbedder(nn.Module): + """ + Embeds a flat vector of dimension input_dim + """ + + def __init__(self, input_dim: int, hidden_size: int, dtype=None, device=None, operations=None): + super().__init__() + self.mlp = nn.Sequential( + operations.Linear(input_dim, hidden_size, bias=True, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + emb = self.mlp(x) + return emb + + +################################################################################# +# Core DiT Model # +################################################################################# + + +def split_qkv(qkv, head_dim): + qkv = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, -1, head_dim).movedim(2, 0) + return qkv[0], qkv[1], qkv[2] + +def optimized_attention(qkv, num_heads): + return attention.optimized_attention(qkv[0], qkv[1], qkv[2], num_heads) + +class SelfAttention(nn.Module): + ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug") + + def __init__( + self, + dim: int, + num_heads: int = 8, + qkv_bias: bool = False, + qk_scale: Optional[float] = None, + proj_drop: float = 0.0, + attn_mode: str = "xformers", + pre_only: bool = False, + qk_norm: Optional[str] = None, + rmsnorm: bool = False, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + self.num_heads = num_heads + self.head_dim = dim // num_heads + + self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device) + if not pre_only: + self.proj = operations.Linear(dim, dim, dtype=dtype, device=device) + self.proj_drop = nn.Dropout(proj_drop) + assert attn_mode in self.ATTENTION_MODES + self.attn_mode = attn_mode + self.pre_only = pre_only + + if qk_norm == "rms": + self.ln_q = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device) + self.ln_k = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device) + elif qk_norm == "ln": + self.ln_q = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device) + self.ln_k = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device) + elif qk_norm is None: + self.ln_q = nn.Identity() + self.ln_k = nn.Identity() + else: + raise ValueError(qk_norm) + + def pre_attention(self, x: torch.Tensor) -> torch.Tensor: + B, L, C = x.shape + qkv = self.qkv(x) + q, k, v = split_qkv(qkv, self.head_dim) + q = self.ln_q(q).reshape(q.shape[0], q.shape[1], -1) + k = self.ln_k(k).reshape(q.shape[0], q.shape[1], -1) + return (q, k, v) + + def post_attention(self, x: torch.Tensor) -> torch.Tensor: + assert not self.pre_only + x = self.proj(x) + x = self.proj_drop(x) + return x + + def forward(self, x: torch.Tensor) -> torch.Tensor: + qkv = self.pre_attention(x) + x = optimized_attention( + qkv, num_heads=self.num_heads + ) + x = self.post_attention(x) + return x + + +class RMSNorm(torch.nn.Module): + def __init__( + self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6, device=None, dtype=None + ): + """ + Initialize the RMSNorm normalization layer. + Args: + dim (int): The dimension of the input tensor. + eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6. + Attributes: + eps (float): A small value added to the denominator for numerical stability. + weight (nn.Parameter): Learnable scaling parameter. + """ + super().__init__() + self.eps = eps + self.learnable_scale = elementwise_affine + if self.learnable_scale: + self.weight = nn.Parameter(torch.empty(dim, device=device, dtype=dtype)) + else: + self.register_parameter("weight", None) + + def _norm(self, x): + """ + Apply the RMSNorm normalization to the input tensor. + Args: + x (torch.Tensor): The input tensor. + Returns: + torch.Tensor: The normalized tensor. + """ + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x): + """ + Forward pass through the RMSNorm layer. + Args: + x (torch.Tensor): The input tensor. + Returns: + torch.Tensor: The output tensor after applying RMSNorm. + """ + x = self._norm(x) + if self.learnable_scale: + return x * self.weight.to(device=x.device, dtype=x.dtype) + else: + return x + + +class SwiGLUFeedForward(nn.Module): + def __init__( + self, + dim: int, + hidden_dim: int, + multiple_of: int, + ffn_dim_multiplier: Optional[float] = None, + ): + """ + Initialize the FeedForward module. + + Args: + dim (int): Input dimension. + hidden_dim (int): Hidden dimension of the feedforward layer. + multiple_of (int): Value to ensure hidden dimension is a multiple of this value. + ffn_dim_multiplier (float, optional): Custom multiplier for hidden dimension. Defaults to None. + + Attributes: + w1 (ColumnParallelLinear): Linear transformation for the first layer. + w2 (RowParallelLinear): Linear transformation for the second layer. + w3 (ColumnParallelLinear): Linear transformation for the third layer. + + """ + super().__init__() + hidden_dim = int(2 * hidden_dim / 3) + # custom dim factor multiplier + if ffn_dim_multiplier is not None: + hidden_dim = int(ffn_dim_multiplier * hidden_dim) + hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) + + self.w1 = nn.Linear(dim, hidden_dim, bias=False) + self.w2 = nn.Linear(hidden_dim, dim, bias=False) + self.w3 = nn.Linear(dim, hidden_dim, bias=False) + + def forward(self, x): + return self.w2(nn.functional.silu(self.w1(x)) * self.w3(x)) + + +class DismantledBlock(nn.Module): + """ + A DiT block with gated adaptive layer norm (adaLN) conditioning. + """ + + ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug") + + def __init__( + self, + hidden_size: int, + num_heads: int, + mlp_ratio: float = 4.0, + attn_mode: str = "xformers", + qkv_bias: bool = False, + pre_only: bool = False, + rmsnorm: bool = False, + scale_mod_only: bool = False, + swiglu: bool = False, + qk_norm: Optional[str] = None, + dtype=None, + device=None, + operations=None, + **block_kwargs, + ): + super().__init__() + assert attn_mode in self.ATTENTION_MODES + if not rmsnorm: + self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + else: + self.norm1 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.attn = SelfAttention( + dim=hidden_size, + num_heads=num_heads, + qkv_bias=qkv_bias, + attn_mode=attn_mode, + pre_only=pre_only, + qk_norm=qk_norm, + rmsnorm=rmsnorm, + dtype=dtype, + device=device, + operations=operations + ) + if not pre_only: + if not rmsnorm: + self.norm2 = operations.LayerNorm( + hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device + ) + else: + self.norm2 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6) + mlp_hidden_dim = int(hidden_size * mlp_ratio) + if not pre_only: + if not swiglu: + self.mlp = Mlp( + in_features=hidden_size, + hidden_features=mlp_hidden_dim, + act_layer=lambda: nn.GELU(approximate="tanh"), + drop=0, + dtype=dtype, + device=device, + operations=operations + ) + else: + self.mlp = SwiGLUFeedForward( + dim=hidden_size, + hidden_dim=mlp_hidden_dim, + multiple_of=256, + ) + self.scale_mod_only = scale_mod_only + if not scale_mod_only: + n_mods = 6 if not pre_only else 2 + else: + n_mods = 4 if not pre_only else 1 + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), operations.Linear(hidden_size, n_mods * hidden_size, bias=True, dtype=dtype, device=device) + ) + self.pre_only = pre_only + + def pre_attention(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: + if not self.pre_only: + if not self.scale_mod_only: + ( + shift_msa, + scale_msa, + gate_msa, + shift_mlp, + scale_mlp, + gate_mlp, + ) = self.adaLN_modulation(c).chunk(6, dim=1) + else: + shift_msa = None + shift_mlp = None + ( + scale_msa, + gate_msa, + scale_mlp, + gate_mlp, + ) = self.adaLN_modulation( + c + ).chunk(4, dim=1) + qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa)) + return qkv, ( + x, + gate_msa, + shift_mlp, + scale_mlp, + gate_mlp, + ) + else: + if not self.scale_mod_only: + ( + shift_msa, + scale_msa, + ) = self.adaLN_modulation( + c + ).chunk(2, dim=1) + else: + shift_msa = None + scale_msa = self.adaLN_modulation(c) + qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa)) + return qkv, None + + def post_attention(self, attn, x, gate_msa, shift_mlp, scale_mlp, gate_mlp): + assert not self.pre_only + x = x + gate_msa.unsqueeze(1) * self.attn.post_attention(attn) + x = x + gate_mlp.unsqueeze(1) * self.mlp( + modulate(self.norm2(x), shift_mlp, scale_mlp) + ) + return x + + def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: + assert not self.pre_only + qkv, intermediates = self.pre_attention(x, c) + attn = optimized_attention( + qkv, + num_heads=self.attn.num_heads, + ) + return self.post_attention(attn, *intermediates) + + +def block_mixing(*args, use_checkpoint=True, **kwargs): + if use_checkpoint: + return torch.utils.checkpoint.checkpoint( + _block_mixing, *args, use_reentrant=False, **kwargs + ) + else: + return _block_mixing(*args, **kwargs) + + +def _block_mixing(context, x, context_block, x_block, c): + context_qkv, context_intermediates = context_block.pre_attention(context, c) + + x_qkv, x_intermediates = x_block.pre_attention(x, c) + + o = [] + for t in range(3): + o.append(torch.cat((context_qkv[t], x_qkv[t]), dim=1)) + qkv = tuple(o) + + attn = optimized_attention( + qkv, + num_heads=x_block.attn.num_heads, + ) + context_attn, x_attn = ( + attn[:, : context_qkv[0].shape[1]], + attn[:, context_qkv[0].shape[1] :], + ) + + if not context_block.pre_only: + context = context_block.post_attention(context_attn, *context_intermediates) + + else: + context = None + x = x_block.post_attention(x_attn, *x_intermediates) + return context, x + + +class JointBlock(nn.Module): + """just a small wrapper to serve as a fsdp unit""" + + def __init__( + self, + *args, + **kwargs, + ): + super().__init__() + pre_only = kwargs.pop("pre_only") + qk_norm = kwargs.pop("qk_norm", None) + self.context_block = DismantledBlock(*args, pre_only=pre_only, qk_norm=qk_norm, **kwargs) + self.x_block = DismantledBlock(*args, pre_only=False, qk_norm=qk_norm, **kwargs) + + def forward(self, *args, **kwargs): + return block_mixing( + *args, context_block=self.context_block, x_block=self.x_block, **kwargs + ) + + +class FinalLayer(nn.Module): + """ + The final layer of DiT. + """ + + def __init__( + self, + hidden_size: int, + patch_size: int, + out_channels: int, + total_out_channels: Optional[int] = None, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.linear = ( + operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) + if (total_out_channels is None) + else operations.Linear(hidden_size, total_out_channels, bias=True, dtype=dtype, device=device) + ) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device) + ) + + def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: + shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) + x = modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + return x + +class SelfAttentionContext(nn.Module): + def __init__(self, dim, heads=8, dim_head=64, dtype=None, device=None, operations=None): + super().__init__() + dim_head = dim // heads + inner_dim = dim + + self.heads = heads + self.dim_head = dim_head + + self.qkv = operations.Linear(dim, dim * 3, bias=True, dtype=dtype, device=device) + + self.proj = operations.Linear(inner_dim, dim, dtype=dtype, device=device) + + def forward(self, x): + qkv = self.qkv(x) + q, k, v = split_qkv(qkv, self.dim_head) + x = optimized_attention((q.reshape(q.shape[0], q.shape[1], -1), k, v), self.heads) + return self.proj(x) + +class ContextProcessorBlock(nn.Module): + def __init__(self, context_size, dtype=None, device=None, operations=None): + super().__init__() + self.norm1 = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.attn = SelfAttentionContext(context_size, dtype=dtype, device=device, operations=operations) + self.norm2 = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.mlp = Mlp(in_features=context_size, hidden_features=(context_size * 4), act_layer=lambda: nn.GELU(approximate="tanh"), drop=0, dtype=dtype, device=device, operations=operations) + + def forward(self, x): + x += self.attn(self.norm1(x)) + x += self.mlp(self.norm2(x)) + return x + +class ContextProcessor(nn.Module): + def __init__(self, context_size, num_layers, dtype=None, device=None, operations=None): + super().__init__() + self.layers = torch.nn.ModuleList([ContextProcessorBlock(context_size, dtype=dtype, device=device, operations=operations) for i in range(num_layers)]) + self.norm = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + + def forward(self, x): + for i, l in enumerate(self.layers): + x = l(x) + return self.norm(x) + +class MMDiT(nn.Module): + """ + Diffusion model with a Transformer backbone. + """ + + def __init__( + self, + input_size: int = 32, + patch_size: int = 2, + in_channels: int = 4, + depth: int = 28, + # hidden_size: Optional[int] = None, + # num_heads: Optional[int] = None, + mlp_ratio: float = 4.0, + learn_sigma: bool = False, + adm_in_channels: Optional[int] = None, + context_embedder_config: Optional[Dict] = None, + compile_core: bool = False, + use_checkpoint: bool = False, + register_length: int = 0, + attn_mode: str = "torch", + rmsnorm: bool = False, + scale_mod_only: bool = False, + swiglu: bool = False, + out_channels: Optional[int] = None, + pos_embed_scaling_factor: Optional[float] = None, + pos_embed_offset: Optional[float] = None, + pos_embed_max_size: Optional[int] = None, + num_patches = None, + qk_norm: Optional[str] = None, + qkv_bias: bool = True, + context_processor_layers = None, + context_size = 4096, + dtype = None, #TODO + device = None, + operations = None, + ): + super().__init__() + self.dtype = dtype + self.learn_sigma = learn_sigma + self.in_channels = in_channels + default_out_channels = in_channels * 2 if learn_sigma else in_channels + self.out_channels = default(out_channels, default_out_channels) + self.patch_size = patch_size + self.pos_embed_scaling_factor = pos_embed_scaling_factor + self.pos_embed_offset = pos_embed_offset + self.pos_embed_max_size = pos_embed_max_size + + # hidden_size = default(hidden_size, 64 * depth) + # num_heads = default(num_heads, hidden_size // 64) + + # apply magic --> this defines a head_size of 64 + self.hidden_size = 64 * depth + num_heads = depth + + self.num_heads = num_heads + + self.x_embedder = PatchEmbed( + input_size, + patch_size, + in_channels, + self.hidden_size, + bias=True, + strict_img_size=self.pos_embed_max_size is None, + dtype=dtype, + device=device, + operations=operations + ) + self.t_embedder = TimestepEmbedder(self.hidden_size, dtype=dtype, device=device, operations=operations) + + self.y_embedder = None + if adm_in_channels is not None: + assert isinstance(adm_in_channels, int) + self.y_embedder = VectorEmbedder(adm_in_channels, self.hidden_size, dtype=dtype, device=device, operations=operations) + + if context_processor_layers is not None: + self.context_processor = ContextProcessor(context_size, context_processor_layers, dtype=dtype, device=device, operations=operations) + else: + self.context_processor = None + + self.context_embedder = nn.Identity() + if context_embedder_config is not None: + if context_embedder_config["target"] == "torch.nn.Linear": + self.context_embedder = operations.Linear(**context_embedder_config["params"], dtype=dtype, device=device) + + self.register_length = register_length + if self.register_length > 0: + self.register = nn.Parameter(torch.randn(1, register_length, self.hidden_size, dtype=dtype, device=device)) + + # num_patches = self.x_embedder.num_patches + # Will use fixed sin-cos embedding: + # just use a buffer already + if num_patches is not None: + self.register_buffer( + "pos_embed", + torch.empty(1, num_patches, self.hidden_size, dtype=dtype, device=device), + ) + else: + self.pos_embed = None + + self.use_checkpoint = use_checkpoint + self.joint_blocks = nn.ModuleList( + [ + JointBlock( + self.hidden_size, + num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + attn_mode=attn_mode, + pre_only=i == depth - 1, + rmsnorm=rmsnorm, + scale_mod_only=scale_mod_only, + swiglu=swiglu, + qk_norm=qk_norm, + dtype=dtype, + device=device, + operations=operations + ) + for i in range(depth) + ] + ) + + self.final_layer = FinalLayer(self.hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations) + # self.initialize_weights() + + if compile_core: + assert False + self.forward_core_with_concat = torch.compile(self.forward_core_with_concat) + + def initialize_weights(self): + # TODO: Init context_embedder? + # Initialize transformer layers: + def _basic_init(module): + if isinstance(module, nn.Linear): + torch.nn.init.xavier_uniform_(module.weight) + if module.bias is not None: + nn.init.constant_(module.bias, 0) + + self.apply(_basic_init) + + # Initialize (and freeze) pos_embed by sin-cos embedding + if self.pos_embed is not None: + pos_embed_grid_size = ( + int(self.x_embedder.num_patches**0.5) + if self.pos_embed_max_size is None + else self.pos_embed_max_size + ) + pos_embed = get_2d_sincos_pos_embed( + self.pos_embed.shape[-1], + int(self.x_embedder.num_patches**0.5), + pos_embed_grid_size, + scaling_factor=self.pos_embed_scaling_factor, + offset=self.pos_embed_offset, + ) + + + pos_embed = get_2d_sincos_pos_embed( + self.pos_embed.shape[-1], + int(self.pos_embed.shape[-2]**0.5), + scaling_factor=self.pos_embed_scaling_factor, + ) + self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0)) + + # Initialize patch_embed like nn.Linear (instead of nn.Conv2d) + w = self.x_embedder.proj.weight.data + nn.init.xavier_uniform_(w.view([w.shape[0], -1])) + nn.init.constant_(self.x_embedder.proj.bias, 0) + + if hasattr(self, "y_embedder"): + nn.init.normal_(self.y_embedder.mlp[0].weight, std=0.02) + nn.init.normal_(self.y_embedder.mlp[2].weight, std=0.02) + + # Initialize timestep embedding MLP: + nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02) + nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02) + + # Zero-out adaLN modulation layers in DiT blocks: + for block in self.joint_blocks: + nn.init.constant_(block.x_block.adaLN_modulation[-1].weight, 0) + nn.init.constant_(block.x_block.adaLN_modulation[-1].bias, 0) + nn.init.constant_(block.context_block.adaLN_modulation[-1].weight, 0) + nn.init.constant_(block.context_block.adaLN_modulation[-1].bias, 0) + + # Zero-out output layers: + nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0) + nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0) + nn.init.constant_(self.final_layer.linear.weight, 0) + nn.init.constant_(self.final_layer.linear.bias, 0) + + def cropped_pos_embed(self, hw, device=None): + p = self.x_embedder.patch_size[0] + h, w = hw + # patched size + h = (h + 1) // p + w = (w + 1) // p + if self.pos_embed is None: + return get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, device=device) + assert self.pos_embed_max_size is not None + assert h <= self.pos_embed_max_size, (h, self.pos_embed_max_size) + assert w <= self.pos_embed_max_size, (w, self.pos_embed_max_size) + top = (self.pos_embed_max_size - h) // 2 + left = (self.pos_embed_max_size - w) // 2 + spatial_pos_embed = rearrange( + self.pos_embed, + "1 (h w) c -> 1 h w c", + h=self.pos_embed_max_size, + w=self.pos_embed_max_size, + ) + spatial_pos_embed = spatial_pos_embed[:, top : top + h, left : left + w, :] + spatial_pos_embed = rearrange(spatial_pos_embed, "1 h w c -> 1 (h w) c") + # print(spatial_pos_embed, top, left, h, w) + # # t = get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, 7.875, 7.875, device=device) #matches exactly for 1024 res + # t = get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, 7.5, 7.5, device=device) #scales better + # # print(t) + # return t + return spatial_pos_embed + + def unpatchify(self, x, hw=None): + """ + x: (N, T, patch_size**2 * C) + imgs: (N, H, W, C) + """ + c = self.out_channels + p = self.x_embedder.patch_size[0] + if hw is None: + h = w = int(x.shape[1] ** 0.5) + else: + h, w = hw + h = (h + 1) // p + w = (w + 1) // p + assert h * w == x.shape[1] + + x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) + x = torch.einsum("nhwpqc->nchpwq", x) + imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p)) + return imgs + + def forward_core_with_concat( + self, + x: torch.Tensor, + c_mod: torch.Tensor, + context: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + if self.register_length > 0: + context = torch.cat( + ( + repeat(self.register, "1 ... -> b ...", b=x.shape[0]), + default(context, torch.Tensor([]).type_as(x)), + ), + 1, + ) + + # context is B, L', D + # x is B, L, D + for block in self.joint_blocks: + context, x = block( + context, + x, + c=c_mod, + use_checkpoint=self.use_checkpoint, + ) + + x = self.final_layer(x, c_mod) # (N, T, patch_size ** 2 * out_channels) + return x + + def forward( + self, + x: torch.Tensor, + t: torch.Tensor, + y: Optional[torch.Tensor] = None, + context: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + """ + Forward pass of DiT. + x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) + t: (N,) tensor of diffusion timesteps + y: (N,) tensor of class labels + """ + + if self.context_processor is not None: + context = self.context_processor(context) + + hw = x.shape[-2:] + x = self.x_embedder(x) + self.cropped_pos_embed(hw, device=x.device).to(dtype=x.dtype) + c = self.t_embedder(t, dtype=x.dtype) # (N, D) + if y is not None and self.y_embedder is not None: + y = self.y_embedder(y) # (N, D) + c = c + y # (N, D) + + if context is not None: + context = self.context_embedder(context) + + x = self.forward_core_with_concat(x, c, context) + + x = self.unpatchify(x, hw=hw) # (N, out_channels, H, W) + return x[:,:,:hw[-2],:hw[-1]] + + +class OpenAISignatureMMDITWrapper(MMDiT): + def forward( + self, + x: torch.Tensor, + timesteps: torch.Tensor, + context: Optional[torch.Tensor] = None, + y: Optional[torch.Tensor] = None, + **kwargs, + ) -> torch.Tensor: + return super().forward(x, timesteps, context=context, y=y) + diff --git a/comfy/model_base.py b/comfy/model_base.py index 841598b7..a26b442b 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -5,11 +5,13 @@ from comfy.ldm.cascade.stage_c import StageC from comfy.ldm.cascade.stage_b import StageB from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation from comfy.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation +from comfy.ldm.modules.diffusionmodules.mmdit import OpenAISignatureMMDITWrapper import comfy.model_management import comfy.conds import comfy.ops from enum import Enum from . import utils +import comfy.latent_formats class ModelType(Enum): EPS = 1 @@ -17,6 +19,7 @@ class ModelType(Enum): V_PREDICTION_EDM = 3 STABLE_CASCADE = 4 EDM = 5 + FLOW = 6 from comfy.model_sampling import EPS, V_PREDICTION, EDM, ModelSamplingDiscrete, ModelSamplingContinuousEDM, StableCascadeSampling @@ -32,6 +35,9 @@ def model_sampling(model_config, model_type): elif model_type == ModelType.V_PREDICTION_EDM: c = V_PREDICTION s = ModelSamplingContinuousEDM + elif model_type == ModelType.FLOW: + c = comfy.model_sampling.CONST + s = comfy.model_sampling.ModelSamplingDiscreteFlow elif model_type == ModelType.STABLE_CASCADE: c = EPS s = StableCascadeSampling @@ -557,3 +563,23 @@ class StableCascade_B(BaseModel): out["effnet"] = comfy.conds.CONDRegular(prior) out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,))) return out + + +class SD3(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=OpenAISignatureMMDITWrapper) + + def encode_adm(self, **kwargs): + return kwargs["pooled_output"] + + def extra_conds(self, **kwargs): + out = {} + adm = self.encode_adm(**kwargs) + if adm is not None: + out['y'] = comfy.conds.CONDRegular(adm) + + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + return out + diff --git a/comfy/model_detection.py b/comfy/model_detection.py index 23358a2c..dfe0ea99 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -1,5 +1,6 @@ import comfy.supported_models import comfy.supported_models_base +import math import logging def count_blocks(state_dict_keys, prefix_string): @@ -26,12 +27,47 @@ def calculate_transformer_depth(prefix, state_dict_keys, state_dict): context_dim = state_dict['{}0.attn2.to_k.weight'.format(transformer_prefix)].shape[1] use_linear_in_transformer = len(state_dict['{}1.proj_in.weight'.format(prefix)].shape) == 2 time_stack = '{}1.time_stack.0.attn1.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn1.to_q.weight'.format(prefix) in state_dict - return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack + time_stack_cross = '{}1.time_stack.0.attn2.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn2.to_q.weight'.format(prefix) in state_dict + return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack, time_stack_cross return None def detect_unet_config(state_dict, key_prefix): state_dict_keys = list(state_dict.keys()) + if '{}joint_blocks.0.context_block.attn.qkv.weight'.format(key_prefix) in state_dict_keys: #mmdit model + unet_config = {} + unet_config["in_channels"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[1] + patch_size = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[2] + unet_config["patch_size"] = patch_size + unet_config["out_channels"] = state_dict['{}final_layer.linear.weight'.format(key_prefix)].shape[0] // (patch_size * patch_size) + + unet_config["depth"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0] // 64 + unet_config["input_size"] = None + y_key = '{}y_embedder.mlp.0.weight'.format(key_prefix) + if y_key in state_dict_keys: + unet_config["adm_in_channels"] = state_dict[y_key].shape[1] + + context_key = '{}context_embedder.weight'.format(key_prefix) + if context_key in state_dict_keys: + in_features = state_dict[context_key].shape[1] + out_features = state_dict[context_key].shape[0] + unet_config["context_embedder_config"] = {"target": "torch.nn.Linear", "params": {"in_features": in_features, "out_features": out_features}} + num_patches_key = '{}pos_embed'.format(key_prefix) + if num_patches_key in state_dict_keys: + num_patches = state_dict[num_patches_key].shape[1] + unet_config["num_patches"] = num_patches + unet_config["pos_embed_max_size"] = round(math.sqrt(num_patches)) + + rms_qk = '{}joint_blocks.0.context_block.attn.ln_q.weight'.format(key_prefix) + if rms_qk in state_dict_keys: + unet_config["qk_norm"] = "rms" + + unet_config["pos_embed_scaling_factor"] = None #unused for inference + context_processor = '{}context_processor.layers.0.attn.qkv.weight'.format(key_prefix) + if context_processor in state_dict_keys: + unet_config["context_processor_layers"] = count_blocks(state_dict_keys, '{}context_processor.layers.'.format(key_prefix) + '{}.') + return unet_config + if '{}clf.1.weight'.format(key_prefix) in state_dict_keys: #stable cascade unet_config = {} text_mapper_name = '{}clip_txt_mapper.weight'.format(key_prefix) @@ -58,7 +94,6 @@ def detect_unet_config(state_dict, key_prefix): unet_config['nhead'] = [-1, 9, 18, 18] unet_config['blocks'] = [[2, 4, 14, 4], [4, 14, 4, 2]] unet_config['block_repeat'] = [[1, 1, 1, 1], [2, 2, 2, 2]] - return unet_config unet_config = { @@ -93,6 +128,7 @@ def detect_unet_config(state_dict, key_prefix): use_linear_in_transformer = False video_model = False + video_model_cross = False current_res = 1 count = 0 @@ -136,6 +172,7 @@ def detect_unet_config(state_dict, key_prefix): context_dim = out[1] use_linear_in_transformer = out[2] video_model = out[3] + video_model_cross = out[4] else: transformer_depth.append(0) @@ -176,6 +213,7 @@ def detect_unet_config(state_dict, key_prefix): unet_config["video_kernel_size"] = [3, 1, 1] unet_config["use_temporal_resblock"] = True unet_config["use_temporal_attention"] = True + unet_config["disable_temporal_crossattention"] = not video_model_cross else: unet_config["use_temporal_resblock"] = False unet_config["use_temporal_attention"] = False diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py index 37976b32..d6120a83 100644 --- a/comfy/model_sampling.py +++ b/comfy/model_sampling.py @@ -33,6 +33,19 @@ class EDM(V_PREDICTION): sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 +class CONST: + def calculate_input(self, sigma, noise): + return noise + + def calculate_denoised(self, sigma, model_output, model_input): + sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) + return model_input - model_output * sigma + + def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): + return sigma * noise + (1.0 - sigma) * latent_image + + def inverse_noise_scaling(self, sigma, latent): + return latent / (1.0 - sigma) class ModelSamplingDiscrete(torch.nn.Module): def __init__(self, model_config=None): @@ -104,6 +117,12 @@ class ModelSamplingDiscrete(torch.nn.Module): percent = 1.0 - percent return self.sigma(torch.tensor(percent * 999.0)).item() +class ModelSamplingDiscreteEDM(ModelSamplingDiscrete): + def timestep(self, sigma): + return 0.25 * sigma.log() + + def sigma(self, timestep): + return (timestep / 0.25).exp() class ModelSamplingContinuousEDM(torch.nn.Module): def __init__(self, model_config=None): @@ -149,6 +168,48 @@ class ModelSamplingContinuousEDM(torch.nn.Module): log_sigma_min = math.log(self.sigma_min) return math.exp((math.log(self.sigma_max) - log_sigma_min) * percent + log_sigma_min) + +def time_snr_shift(alpha, t): + if alpha == 1.0: + return t + return alpha * t / (1 + (alpha - 1) * t) + +class ModelSamplingDiscreteFlow(torch.nn.Module): + def __init__(self, model_config=None): + super().__init__() + if model_config is not None: + sampling_settings = model_config.sampling_settings + else: + sampling_settings = {} + + self.set_parameters(shift=sampling_settings.get("shift", 1.0)) + + def set_parameters(self, shift=1.0, timesteps=1000): + self.shift = shift + ts = self.sigma(torch.arange(1, timesteps + 1, 1)) + self.register_buffer('sigmas', ts) + + @property + def sigma_min(self): + return self.sigmas[0] + + @property + def sigma_max(self): + return self.sigmas[-1] + + def timestep(self, sigma): + return sigma * 1000 + + def sigma(self, timestep): + return time_snr_shift(self.shift, timestep / 1000) + + def percent_to_sigma(self, percent): + if percent <= 0.0: + return 1.0 + if percent >= 1.0: + return 0.0 + return 1.0 - percent + class StableCascadeSampling(ModelSamplingDiscrete): def __init__(self, model_config=None): super().__init__() diff --git a/comfy/sd.py b/comfy/sd.py index 343d2a02..cb147fa4 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -19,6 +19,7 @@ from . import model_detection from . import sd1_clip from . import sd2_clip from . import sdxl_clip +from . import sd3_clip import comfy.model_patcher import comfy.lora @@ -395,9 +396,12 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI else: clip_target.clip = sd1_clip.SD1ClipModel clip_target.tokenizer = sd1_clip.SD1Tokenizer - else: + elif len(clip_data) == 2: clip_target.clip = sdxl_clip.SDXLClipModel clip_target.tokenizer = sdxl_clip.SDXLTokenizer + elif len(clip_data) == 3: + clip_target.clip = sd3_clip.SD3ClipModel + clip_target.tokenizer = sd3_clip.SD3Tokenizer clip = CLIP(clip_target, embedding_directory=embedding_directory) for c in clip_data: diff --git a/comfy/sd3_clip.py b/comfy/sd3_clip.py new file mode 100644 index 00000000..bbbf6aff --- /dev/null +++ b/comfy/sd3_clip.py @@ -0,0 +1,91 @@ +from comfy import sd1_clip +from comfy import sdxl_clip +from transformers import T5TokenizerFast +import comfy.t5 +import torch +import os +import comfy.model_management + +class T5XXLModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None): + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_xxl.json") + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.t5.T5) + +class T5XXLTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") + super().__init__(tokenizer_path, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=77) + +class SDT5XXLTokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None): + super().__init__(embedding_directory=embedding_directory, clip_name="t5xxl", tokenizer=T5XXLTokenizer) + +class SDT5XXLModel(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, **kwargs): + super().__init__(device=device, dtype=dtype, clip_name="t5xxl", clip_model=T5XXLModel, **kwargs) + + + +class SD3Tokenizer: + def __init__(self, embedding_directory=None): + self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory) + self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory) + self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory) + + def tokenize_with_weights(self, text:str, return_word_ids=False): + out = {} + out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids) + out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) + out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids) + return out + + def untokenize(self, token_weight_pair): + return self.clip_g.untokenize(token_weight_pair) + +class SD3ClipModel(torch.nn.Module): + def __init__(self, device="cpu", dtype=None): + super().__init__() + self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False) + self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype) + self.t5xxl = T5XXLModel(device=device, dtype=dtype) + + def set_clip_options(self, options): + self.clip_l.set_clip_options(options) + self.clip_g.set_clip_options(options) + self.t5xxl.set_clip_options(options) + + def reset_clip_options(self): + self.clip_g.reset_clip_options() + self.clip_l.reset_clip_options() + self.t5xxl.reset_clip_options() + + def encode_token_weights(self, token_weight_pairs): + token_weight_pairs_l = token_weight_pairs["l"] + token_weight_pairs_g = token_weight_pairs["g"] + token_weight_pars_t5 = token_weight_pairs["t5xxl"] + lg_out = None + if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0: + l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) + g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g) + lg_out = torch.cat([l_out, g_out], dim=-1) + lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1])) + out = lg_out + pooled = torch.cat((l_pooled, g_pooled), dim=-1) + else: + pooled = torch.zeros((1, 1280 + 768), device=comfy.model_management.intermediate_device()) + + t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pars_t5) + if lg_out is not None: + out = torch.cat([lg_out, t5_out], dim=-2) + else: + out = t5_out + + return out, pooled + + def load_sd(self, sd): + if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: + return self.clip_g.load_sd(sd) + elif "text_model.encoder.layers.1.mlp.fc1.weight" in sd: + return self.clip_l.load_sd(sd) + else: + return self.t5xxl.load_sd(sd) diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 6ca32e8e..6bb76c96 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -5,6 +5,7 @@ from . import utils from . import sd1_clip from . import sd2_clip from . import sdxl_clip +from . import sd3_clip from . import supported_models_base from . import latent_formats @@ -488,6 +489,28 @@ class SDXL_instructpix2pix(SDXL): def get_model(self, state_dict, prefix="", device=None): return model_base.SDXL_instructpix2pix(self, model_type=self.model_type(state_dict, prefix), device=device) -models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p] +class SD3(supported_models_base.BASE): + unet_config = { + "in_channels": 16, + "pos_embed_scaling_factor": None, + } + + sampling_settings = { + "shift": 3.0, + } + + unet_extra_config = {} + latent_format = latent_formats.SD3 + text_encoder_key_prefix = ["text_encoders."] #TODO? + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.SD3(self, device=device) + return out + + def clip_target(self): + return supported_models_base.ClipTarget(sd3_clip.SD3Tokenizer, sd3_clip.SD3ClipModel) #TODO? + + +models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3] models += [SVD_img2vid] diff --git a/comfy/t5.py b/comfy/t5.py new file mode 100644 index 00000000..06dfe476 --- /dev/null +++ b/comfy/t5.py @@ -0,0 +1,231 @@ +import torch +import math +from comfy.ldm.modules.attention import optimized_attention_for_device + +class T5LayerNorm(torch.nn.Module): + def __init__(self, hidden_size, eps=1e-6, dtype=None, device=None, operations=None): + super().__init__() + self.weight = torch.nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device)) + self.variance_epsilon = eps + + def forward(self, x): + variance = x.pow(2).mean(-1, keepdim=True) + x = x * torch.rsqrt(variance + self.variance_epsilon) + return self.weight.to(device=x.device, dtype=x.dtype) * x + +class T5DenseActDense(torch.nn.Module): + def __init__(self, model_dim, ff_dim, dtype, device, operations): + super().__init__() + self.wi = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device) + self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device) + # self.dropout = nn.Dropout(config.dropout_rate) + + def forward(self, x): + x = torch.nn.functional.relu(self.wi(x)) + # x = self.dropout(x) + x = self.wo(x) + return x + +class T5DenseGatedActDense(torch.nn.Module): + def __init__(self, model_dim, ff_dim, dtype, device, operations): + super().__init__() + self.wi_0 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device) + self.wi_1 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device) + self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device) + # self.dropout = nn.Dropout(config.dropout_rate) + + def forward(self, x): + hidden_gelu = torch.nn.functional.gelu(self.wi_0(x), approximate="tanh") + hidden_linear = self.wi_1(x) + x = hidden_gelu * hidden_linear + # x = self.dropout(x) + x = self.wo(x) + return x + +class T5LayerFF(torch.nn.Module): + def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations): + super().__init__() + if ff_activation == "gelu_pytorch_tanh": + self.DenseReluDense = T5DenseGatedActDense(model_dim, ff_dim, dtype, device, operations) + elif ff_activation == "relu": + self.DenseReluDense = T5DenseActDense(model_dim, ff_dim, dtype, device, operations) + + self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations) + # self.dropout = nn.Dropout(config.dropout_rate) + + def forward(self, x): + forwarded_states = self.layer_norm(x) + forwarded_states = self.DenseReluDense(forwarded_states) + # x = x + self.dropout(forwarded_states) + x += forwarded_states + return x + +class T5Attention(torch.nn.Module): + def __init__(self, model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations): + super().__init__() + + # Mesh TensorFlow initialization to avoid scaling before softmax + self.q = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.k = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.v = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.o = operations.Linear(inner_dim, model_dim, bias=False, dtype=dtype, device=device) + self.num_heads = num_heads + + self.relative_attention_bias = None + if relative_attention_bias: + self.relative_attention_num_buckets = 32 + self.relative_attention_max_distance = 128 + self.relative_attention_bias = torch.nn.Embedding(self.relative_attention_num_buckets, self.num_heads, device=device) + + @staticmethod + def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128): + """ + Adapted from Mesh Tensorflow: + https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 + + Translate relative position to a bucket number for relative attention. The relative position is defined as + memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to + position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for + small absolute relative_position and larger buckets for larger absolute relative_positions. All relative + positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. + This should allow for more graceful generalization to longer sequences than the model has been trained on + + Args: + relative_position: an int32 Tensor + bidirectional: a boolean - whether the attention is bidirectional + num_buckets: an integer + max_distance: an integer + + Returns: + a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets) + """ + relative_buckets = 0 + if bidirectional: + num_buckets //= 2 + relative_buckets += (relative_position > 0).to(torch.long) * num_buckets + relative_position = torch.abs(relative_position) + else: + relative_position = -torch.min(relative_position, torch.zeros_like(relative_position)) + # now relative_position is in the range [0, inf) + + # half of the buckets are for exact increments in positions + max_exact = num_buckets // 2 + is_small = relative_position < max_exact + + # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance + relative_position_if_large = max_exact + ( + torch.log(relative_position.float() / max_exact) + / math.log(max_distance / max_exact) + * (num_buckets - max_exact) + ).to(torch.long) + relative_position_if_large = torch.min( + relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1) + ) + + relative_buckets += torch.where(is_small, relative_position, relative_position_if_large) + return relative_buckets + + def compute_bias(self, query_length, key_length, device): + """Compute binned relative position bias""" + context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None] + memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :] + relative_position = memory_position - context_position # shape (query_length, key_length) + relative_position_bucket = self._relative_position_bucket( + relative_position, # shape (query_length, key_length) + bidirectional=True, + num_buckets=self.relative_attention_num_buckets, + max_distance=self.relative_attention_max_distance, + ) + values = self.relative_attention_bias(relative_position_bucket) # shape (query_length, key_length, num_heads) + values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length) + return values + + def forward(self, x, mask=None, past_bias=None, optimized_attention=None): + q = self.q(x) + k = self.k(x) + v = self.v(x) + if self.relative_attention_bias is not None: + past_bias = self.compute_bias(x.shape[1], x.shape[1], x.device) + + if past_bias is not None: + if mask is not None: + mask = mask + past_bias + else: + mask = past_bias + + out = optimized_attention(q, k * ((k.shape[-1] / self.num_heads) ** 0.5), v, self.num_heads, mask) + return self.o(out), past_bias + +class T5LayerSelfAttention(torch.nn.Module): + def __init__(self, model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations): + super().__init__() + self.SelfAttention = T5Attention(model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations) + self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations) + # self.dropout = nn.Dropout(config.dropout_rate) + + def forward(self, x, mask=None, past_bias=None, optimized_attention=None): + normed_hidden_states = self.layer_norm(x) + output, past_bias = self.SelfAttention(self.layer_norm(x), mask=mask, past_bias=past_bias, optimized_attention=optimized_attention) + # x = x + self.dropout(attention_output) + x += output + return x, past_bias + +class T5Block(torch.nn.Module): + def __init__(self, model_dim, inner_dim, ff_dim, ff_activation, num_heads, relative_attention_bias, dtype, device, operations): + super().__init__() + self.layer = torch.nn.ModuleList() + self.layer.append(T5LayerSelfAttention(model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations)) + self.layer.append(T5LayerFF(model_dim, ff_dim, ff_activation, dtype, device, operations)) + + def forward(self, x, mask=None, past_bias=None, optimized_attention=None): + x, past_bias = self.layer[0](x, mask, past_bias, optimized_attention) + x = self.layer[-1](x) + return x, past_bias + +class T5Stack(torch.nn.Module): + def __init__(self, num_layers, model_dim, inner_dim, ff_dim, ff_activation, num_heads, dtype, device, operations): + super().__init__() + + self.block = torch.nn.ModuleList( + [T5Block(model_dim, inner_dim, ff_dim, ff_activation, num_heads, relative_attention_bias=(i == 0), dtype=dtype, device=device, operations=operations) for i in range(num_layers)] + ) + self.final_layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations) + # self.dropout = nn.Dropout(config.dropout_rate) + + def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True): + mask = None + if attention_mask is not None: + mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) + mask = mask.masked_fill(mask.to(torch.bool), float("-inf")) + + intermediate = None + optimized_attention = optimized_attention_for_device(x.device, mask=attention_mask is not None, small_input=True) + past_bias = None + for i, l in enumerate(self.block): + x, past_bias = l(x, mask, past_bias, optimized_attention) + if i == intermediate_output: + intermediate = x.clone() + x = self.final_layer_norm(x) + if intermediate is not None and final_layer_norm_intermediate: + intermediate = self.final_layer_norm(intermediate) + return x, intermediate + +class T5(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + self.num_layers = config_dict["num_layers"] + model_dim = config_dict["d_model"] + + self.encoder = T5Stack(self.num_layers, model_dim, model_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["num_heads"], dtype, device, operations) + self.dtype = dtype + self.shared = torch.nn.Embedding(config_dict["vocab_size"], model_dim, device=device) + + def get_input_embeddings(self): + return self.shared + + def set_input_embeddings(self, embeddings): + self.shared = embeddings + + def forward(self, input_ids, *args, **kwargs): + x = self.shared(input_ids) + return self.encoder(x, *args, **kwargs) diff --git a/comfy/t5_config_base.json b/comfy/t5_config_base.json new file mode 100644 index 00000000..facd85ef --- /dev/null +++ b/comfy/t5_config_base.json @@ -0,0 +1,21 @@ +{ + "d_ff": 3072, + "d_kv": 64, + "d_model": 768, + "decoder_start_token_id": 0, + "dropout_rate": 0.1, + "eos_token_id": 1, + "dense_act_fn": "relu", + "initializer_factor": 1.0, + "is_encoder_decoder": true, + "layer_norm_epsilon": 1e-06, + "model_type": "t5", + "num_decoder_layers": 12, + "num_heads": 12, + "num_layers": 12, + "output_past": true, + "pad_token_id": 0, + "relative_attention_num_buckets": 32, + "tie_word_embeddings": false, + "vocab_size": 32128 +} diff --git a/comfy/t5_config_xxl.json b/comfy/t5_config_xxl.json new file mode 100644 index 00000000..bf4feadc --- /dev/null +++ b/comfy/t5_config_xxl.json @@ -0,0 +1,21 @@ +{ + "d_ff": 10240, + "d_kv": 64, + "d_model": 4096, + "decoder_start_token_id": 0, + "dropout_rate": 0.1, + "eos_token_id": 1, + "dense_act_fn": "gelu_pytorch_tanh", + "initializer_factor": 1.0, + "is_encoder_decoder": true, + "layer_norm_epsilon": 1e-06, + "model_type": "t5", + "num_decoder_layers": 24, + "num_heads": 64, + "num_layers": 24, + "output_past": true, + "pad_token_id": 0, + "relative_attention_num_buckets": 32, + "tie_word_embeddings": false, + "vocab_size": 32128 +} diff --git a/comfy/t5_tokenizer/special_tokens_map.json b/comfy/t5_tokenizer/special_tokens_map.json new file mode 100644 index 00000000..17ade346 --- /dev/null +++ b/comfy/t5_tokenizer/special_tokens_map.json @@ -0,0 +1,125 @@ +{ + "additional_special_tokens": [ + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "" + ], + "eos_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "unk_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + } +} diff --git a/comfy/t5_tokenizer/tokenizer.json b/comfy/t5_tokenizer/tokenizer.json new file mode 100644 index 00000000..b11c92d7 --- /dev/null +++ b/comfy/t5_tokenizer/tokenizer.json @@ -0,0 +1,129428 @@ +{ + "version": "1.0", + "truncation": null, + "padding": null, + "added_tokens": [ + { + "id": 0, + "content": "", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 1, + "content": "", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 2, + "content": "", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 32000, + "content": "", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 32001, + "content": "", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": 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+ "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "" + ], + "clean_up_tokenization_spaces": true, + "eos_token": "", + "extra_ids": 100, + "legacy": false, + "model_max_length": 512, + "pad_token": "", + "sp_model_kwargs": {}, + "tokenizer_class": "T5Tokenizer", + "unk_token": "" +} diff --git a/comfy_extras/nodes_model_advanced.py b/comfy_extras/nodes_model_advanced.py index 21af4b73..64002a8d 100644 --- a/comfy_extras/nodes_model_advanced.py +++ b/comfy_extras/nodes_model_advanced.py @@ -132,6 +132,32 @@ class ModelSamplingStableCascade: m.add_object_patch("model_sampling", model_sampling) return (m, ) +class ModelSamplingSD3: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "shift": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01}), + }} + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "advanced/model" + + def patch(self, model, shift): + m = model.clone() + + sampling_base = comfy.model_sampling.ModelSamplingDiscreteFlow + sampling_type = comfy.model_sampling.CONST + + class ModelSamplingAdvanced(sampling_base, sampling_type): + pass + + model_sampling = ModelSamplingAdvanced(model.model.model_config) + model_sampling.set_parameters(shift=shift) + m.add_object_patch("model_sampling", model_sampling) + return (m, ) + class ModelSamplingContinuousEDM: @classmethod def INPUT_TYPES(s): @@ -213,5 +239,6 @@ NODE_CLASS_MAPPINGS = { "ModelSamplingDiscrete": ModelSamplingDiscrete, "ModelSamplingContinuousEDM": ModelSamplingContinuousEDM, "ModelSamplingStableCascade": ModelSamplingStableCascade, + "ModelSamplingSD3": ModelSamplingSD3, "RescaleCFG": RescaleCFG, } diff --git a/comfy_extras/nodes_sd3.py b/comfy_extras/nodes_sd3.py new file mode 100644 index 00000000..80b8644a --- /dev/null +++ b/comfy_extras/nodes_sd3.py @@ -0,0 +1,87 @@ +import folder_paths +import comfy.sd +import comfy.model_management +import nodes +import torch + +class TripleCLIPLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ), "clip_name2": (folder_paths.get_filename_list("clip"), ), "clip_name3": (folder_paths.get_filename_list("clip"), ) + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "load_clip" + + CATEGORY = "advanced/loaders" + + def load_clip(self, clip_name1, clip_name2, clip_name3): + clip_path1 = folder_paths.get_full_path("clip", clip_name1) + clip_path2 = folder_paths.get_full_path("clip", clip_name2) + clip_path3 = folder_paths.get_full_path("clip", clip_name3) + clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2, clip_path3], embedding_directory=folder_paths.get_folder_paths("embeddings")) + return (clip,) + +class EmptySD3LatentImage: + def __init__(self): + self.device = comfy.model_management.intermediate_device() + + @classmethod + def INPUT_TYPES(s): + return {"required": { "width": ("INT", {"default": 512, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 512, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "generate" + + CATEGORY = "latent/sd3" + + def generate(self, width, height, batch_size=1): + latent = torch.ones([batch_size, 16, height // 8, width // 8], device=self.device) * 0.0609 + return ({"samples":latent}, ) + +class CLIPTextEncodeSD3: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "clip": ("CLIP", ), + "clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "clip_g": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "empty_padding": (["none", "empty_prompt"], ) + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "advanced/conditioning" + + def encode(self, clip, clip_l, clip_g, t5xxl, empty_padding): + no_padding = empty_padding == "none" + + tokens = clip.tokenize(clip_g) + if len(clip_g) == 0 and no_padding: + tokens["g"] = [] + + if len(clip_l) == 0 and no_padding: + tokens["l"] = [] + else: + tokens["l"] = clip.tokenize(clip_l)["l"] + + if len(t5xxl) == 0 and no_padding: + tokens["t5xxl"] = [] + else: + tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"] + if len(tokens["l"]) != len(tokens["g"]): + empty = clip.tokenize("") + while len(tokens["l"]) < len(tokens["g"]): + tokens["l"] += empty["l"] + while len(tokens["l"]) > len(tokens["g"]): + tokens["g"] += empty["g"] + cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) + return ([[cond, {"pooled_output": pooled}]], ) + + +NODE_CLASS_MAPPINGS = { + "TripleCLIPLoader": TripleCLIPLoader, + "EmptySD3LatentImage": EmptySD3LatentImage, + "CLIPTextEncodeSD3": CLIPTextEncodeSD3, +} diff --git a/nodes.py b/nodes.py index b744b53f..ef1f8561 100644 --- a/nodes.py +++ b/nodes.py @@ -1964,6 +1964,7 @@ def init_custom_nodes(): "nodes_attention_multiply.py", "nodes_advanced_samplers.py", "nodes_webcam.py", + "nodes_sd3.py", ] import_failed = [] From a82fae2375744a73d63268bc4e167649e3f026e0 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 10 Jun 2024 16:00:03 -0400 Subject: [PATCH 265/378] Fix bug with cosxl edit model. --- comfy/conds.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/comfy/conds.py b/comfy/conds.py index 23fa4887..660690af 100644 --- a/comfy/conds.py +++ b/comfy/conds.py @@ -29,7 +29,12 @@ class CONDRegular: class CONDNoiseShape(CONDRegular): def process_cond(self, batch_size, device, area, **kwargs): - data = self.cond[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] + data = self.cond + if area is not None: + dims = len(area) // 2 + for i in range(dims): + data = data.narrow(i + 2, area[i + dims], area[i]) + return self._copy_with(comfy.utils.repeat_to_batch_size(data, batch_size).to(device)) From 4134564dc15c5eb40a61e2da4c493cf6786e67d1 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 11 Jun 2024 06:26:13 -0400 Subject: [PATCH 266/378] Require safetensors library to be at least 0.4.2 for fp8 support. --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 906b96ed..8f681f8f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,7 +3,7 @@ torchsde torchvision einops transformers>=4.25.1 -safetensors>=0.3.0 +safetensors>=0.4.2 aiohttp pyyaml Pillow From 73ce178021338e2cea419a00ae61ec0a6630ef19 Mon Sep 17 00:00:00 2001 From: Dango233 Date: Tue, 11 Jun 2024 18:30:25 +0800 Subject: [PATCH 267/378] Remove redundancy in mmdit.py (#3685) --- comfy/ldm/modules/diffusionmodules/mmdit.py | 61 --------------------- 1 file changed, 61 deletions(-) diff --git a/comfy/ldm/modules/diffusionmodules/mmdit.py b/comfy/ldm/modules/diffusionmodules/mmdit.py index 5e7afc8d..be40ab94 100644 --- a/comfy/ldm/modules/diffusionmodules/mmdit.py +++ b/comfy/ldm/modules/diffusionmodules/mmdit.py @@ -835,72 +835,11 @@ class MMDiT(nn.Module): ) self.final_layer = FinalLayer(self.hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations) - # self.initialize_weights() if compile_core: assert False self.forward_core_with_concat = torch.compile(self.forward_core_with_concat) - def initialize_weights(self): - # TODO: Init context_embedder? - # Initialize transformer layers: - def _basic_init(module): - if isinstance(module, nn.Linear): - torch.nn.init.xavier_uniform_(module.weight) - if module.bias is not None: - nn.init.constant_(module.bias, 0) - - self.apply(_basic_init) - - # Initialize (and freeze) pos_embed by sin-cos embedding - if self.pos_embed is not None: - pos_embed_grid_size = ( - int(self.x_embedder.num_patches**0.5) - if self.pos_embed_max_size is None - else self.pos_embed_max_size - ) - pos_embed = get_2d_sincos_pos_embed( - self.pos_embed.shape[-1], - int(self.x_embedder.num_patches**0.5), - pos_embed_grid_size, - scaling_factor=self.pos_embed_scaling_factor, - offset=self.pos_embed_offset, - ) - - - pos_embed = get_2d_sincos_pos_embed( - self.pos_embed.shape[-1], - int(self.pos_embed.shape[-2]**0.5), - scaling_factor=self.pos_embed_scaling_factor, - ) - self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0)) - - # Initialize patch_embed like nn.Linear (instead of nn.Conv2d) - w = self.x_embedder.proj.weight.data - nn.init.xavier_uniform_(w.view([w.shape[0], -1])) - nn.init.constant_(self.x_embedder.proj.bias, 0) - - if hasattr(self, "y_embedder"): - nn.init.normal_(self.y_embedder.mlp[0].weight, std=0.02) - nn.init.normal_(self.y_embedder.mlp[2].weight, std=0.02) - - # Initialize timestep embedding MLP: - nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02) - nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02) - - # Zero-out adaLN modulation layers in DiT blocks: - for block in self.joint_blocks: - nn.init.constant_(block.x_block.adaLN_modulation[-1].weight, 0) - nn.init.constant_(block.x_block.adaLN_modulation[-1].bias, 0) - nn.init.constant_(block.context_block.adaLN_modulation[-1].weight, 0) - nn.init.constant_(block.context_block.adaLN_modulation[-1].bias, 0) - - # Zero-out output layers: - nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0) - nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0) - nn.init.constant_(self.final_layer.linear.weight, 0) - nn.init.constant_(self.final_layer.linear.bias, 0) - def cropped_pos_embed(self, hw, device=None): p = self.x_embedder.patch_size[0] h, w = hw From 9424522ead16a36199c50217ae09093ff8e3223d Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 11 Jun 2024 07:20:26 -0400 Subject: [PATCH 268/378] Reuse code. --- comfy/model_base.py | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/comfy/model_base.py b/comfy/model_base.py index a26b442b..28458bba 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -573,13 +573,8 @@ class SD3(BaseModel): return kwargs["pooled_output"] def extra_conds(self, **kwargs): - out = {} - adm = self.encode_adm(**kwargs) - if adm is not None: - out['y'] = comfy.conds.CONDRegular(adm) - + out = super().extra_conds(**kwargs) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) return out - From 1c34d338d7cc11513023080cd0adedbf9f997356 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 11 Jun 2024 07:37:22 -0400 Subject: [PATCH 269/378] Update EmptySD3LatentImage to use 1024 resolution by default. --- comfy_extras/nodes_sd3.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comfy_extras/nodes_sd3.py b/comfy_extras/nodes_sd3.py index 80b8644a..d0303aec 100644 --- a/comfy_extras/nodes_sd3.py +++ b/comfy_extras/nodes_sd3.py @@ -27,8 +27,8 @@ class EmptySD3LatentImage: @classmethod def INPUT_TYPES(s): - return {"required": { "width": ("INT", {"default": 512, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), - "height": ("INT", {"default": 512, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), + return {"required": { "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} RETURN_TYPES = ("LATENT",) FUNCTION = "generate" From 5889b7ca0ad7b3bf036999330b0c5371ce0da0f3 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 11 Jun 2024 13:14:43 -0400 Subject: [PATCH 270/378] Support multiple text encoder configurations on SD3. --- comfy/sd.py | 2 +- comfy/sd3_clip.py | 89 ++++++++++++++++++++++++++++----------- comfy/supported_models.py | 35 ++++++++++----- 3 files changed, 91 insertions(+), 35 deletions(-) diff --git a/comfy/sd.py b/comfy/sd.py index cb147fa4..11764077 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -482,7 +482,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o vae = VAE(sd=vae_sd) if output_clip: - clip_target = model_config.clip_target() + clip_target = model_config.clip_target(state_dict=sd) if clip_target is not None: clip_sd = model_config.process_clip_state_dict(sd) if len(clip_sd) > 0: diff --git a/comfy/sd3_clip.py b/comfy/sd3_clip.py index bbbf6aff..595381fc 100644 --- a/comfy/sd3_clip.py +++ b/comfy/sd3_clip.py @@ -5,6 +5,7 @@ import comfy.t5 import torch import os import comfy.model_management +import logging class T5XXLModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None): @@ -43,42 +44,82 @@ class SD3Tokenizer: return self.clip_g.untokenize(token_weight_pair) class SD3ClipModel(torch.nn.Module): - def __init__(self, device="cpu", dtype=None): + def __init__(self, clip_l=True, clip_g=True, t5=True, device="cpu", dtype=None): super().__init__() - self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False) - self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype) - self.t5xxl = T5XXLModel(device=device, dtype=dtype) + if clip_l: + self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False) + else: + self.clip_l = None + + if clip_g: + self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype) + else: + self.clip_g = None + + if t5: + self.t5xxl = T5XXLModel(device=device, dtype=dtype) + else: + self.t5xxl = None + + logging.debug("Created SD3 text encoder with: clip_l {}, clip_g {}, t5xxl {}".format(clip_l, clip_g, t5)) def set_clip_options(self, options): - self.clip_l.set_clip_options(options) - self.clip_g.set_clip_options(options) - self.t5xxl.set_clip_options(options) + if self.clip_l is not None: + self.clip_l.set_clip_options(options) + if self.clip_g is not None: + self.clip_g.set_clip_options(options) + if self.t5xxl is not None: + self.t5xxl.set_clip_options(options) def reset_clip_options(self): - self.clip_g.reset_clip_options() - self.clip_l.reset_clip_options() - self.t5xxl.reset_clip_options() + if self.clip_l is not None: + self.clip_l.reset_clip_options() + if self.clip_g is not None: + self.clip_g.reset_clip_options() + if self.t5xxl is not None: + self.t5xxl.reset_clip_options() def encode_token_weights(self, token_weight_pairs): token_weight_pairs_l = token_weight_pairs["l"] token_weight_pairs_g = token_weight_pairs["g"] token_weight_pars_t5 = token_weight_pairs["t5xxl"] lg_out = None - if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0: - l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) - g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g) - lg_out = torch.cat([l_out, g_out], dim=-1) - lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1])) - out = lg_out - pooled = torch.cat((l_pooled, g_pooled), dim=-1) - else: - pooled = torch.zeros((1, 1280 + 768), device=comfy.model_management.intermediate_device()) + pooled = None + out = None - t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pars_t5) - if lg_out is not None: - out = torch.cat([lg_out, t5_out], dim=-2) - else: - out = t5_out + if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0: + if self.clip_l is not None: + lg_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) + else: + l_pooled = torch.zeros((1, 768), device=comfy.model_management.intermediate_device()) + + if self.clip_g is not None: + g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g) + if lg_out is not None: + lg_out = torch.cat([lg_out, g_out], dim=-1) + else: + lg_out = torch.nn.functional.pad(g_out, (768, 0)) + else: + g_out = None + g_pooled = torch.zeros((1, 1280), device=comfy.model_management.intermediate_device()) + + if lg_out is not None: + lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1])) + out = lg_out + pooled = torch.cat((l_pooled, g_pooled), dim=-1) + + if self.t5xxl is not None: + t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pars_t5) + if lg_out is not None: + out = torch.cat([lg_out, t5_out], dim=-2) + else: + out = t5_out + + if out is None: + out = torch.zeros((1, 77, 4096), device=comfy.model_management.intermediate_device()) + + if pooled is None: + pooled = torch.zeros((1, 768 + 1280), device=comfy.model_management.intermediate_device()) return out, pooled diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 6bb76c96..481ecaa6 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -54,7 +54,7 @@ class SD15(supported_models_base.BASE): replace_prefix = {"clip_l.": "cond_stage_model."} return utils.state_dict_prefix_replace(state_dict, replace_prefix) - def clip_target(self): + def clip_target(self, state_dict={}): return supported_models_base.ClipTarget(sd1_clip.SD1Tokenizer, sd1_clip.SD1ClipModel) class SD20(supported_models_base.BASE): @@ -97,7 +97,7 @@ class SD20(supported_models_base.BASE): state_dict = diffusers_convert.convert_text_enc_state_dict_v20(state_dict) return state_dict - def clip_target(self): + def clip_target(self, state_dict={}): return supported_models_base.ClipTarget(sd2_clip.SD2Tokenizer, sd2_clip.SD2ClipModel) class SD21UnclipL(SD20): @@ -159,7 +159,7 @@ class SDXLRefiner(supported_models_base.BASE): state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix) return state_dict_g - def clip_target(self): + def clip_target(self, state_dict={}): return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLRefinerClipModel) class SDXL(supported_models_base.BASE): @@ -228,7 +228,7 @@ class SDXL(supported_models_base.BASE): state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix) return state_dict_g - def clip_target(self): + def clip_target(self, state_dict={}): return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLClipModel) class SSD1B(SDXL): @@ -299,7 +299,7 @@ class SVD_img2vid(supported_models_base.BASE): out = model_base.SVD_img2vid(self, device=device) return out - def clip_target(self): + def clip_target(self, state_dict={}): return None class SV3D_u(SVD_img2vid): @@ -365,7 +365,7 @@ class Stable_Zero123(supported_models_base.BASE): out = model_base.Stable_Zero123(self, device=device, cc_projection_weight=state_dict["cc_projection.weight"], cc_projection_bias=state_dict["cc_projection.bias"]) return out - def clip_target(self): + def clip_target(self, state_dict={}): return None class SD_X4Upscaler(SD20): @@ -439,7 +439,7 @@ class Stable_Cascade_C(supported_models_base.BASE): out = model_base.StableCascade_C(self, device=device) return out - def clip_target(self): + def clip_target(self, state_dict={}): return supported_models_base.ClipTarget(sdxl_clip.StableCascadeTokenizer, sdxl_clip.StableCascadeClipModel) class Stable_Cascade_B(Stable_Cascade_C): @@ -501,14 +501,29 @@ class SD3(supported_models_base.BASE): unet_extra_config = {} latent_format = latent_formats.SD3 - text_encoder_key_prefix = ["text_encoders."] #TODO? + text_encoder_key_prefix = ["text_encoders."] def get_model(self, state_dict, prefix="", device=None): out = model_base.SD3(self, device=device) return out - def clip_target(self): - return supported_models_base.ClipTarget(sd3_clip.SD3Tokenizer, sd3_clip.SD3ClipModel) #TODO? + def clip_target(self, state_dict={}): + clip_l = False + clip_g = False + t5 = False + pref = self.text_encoder_key_prefix[0] + if "{}clip_l.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict: + clip_l = True + if "{}clip_g.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict: + clip_g = True + if "{}t5xxl.transformer.encoder.final_layer_norm.weight".format(pref) in state_dict: + t5 = True + + class SD3ClipModel(sd3_clip.SD3ClipModel): + def __init__(self, device="cpu", dtype=None): + super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, device=device, dtype=dtype) + + return supported_models_base.ClipTarget(sd3_clip.SD3Tokenizer, SD3ClipModel) models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3] From 0e49211a110fff099ebafa927ee7b9416ff9feaa Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 11 Jun 2024 17:03:26 -0400 Subject: [PATCH 271/378] Load the SD3 T5xxl model in the same dtype stored in the checkpoint. --- comfy/model_management.py | 17 +++++++++++++++++ comfy/sd.py | 8 +++++++- comfy/sd1_clip.py | 4 ++++ comfy/sd3_clip.py | 18 +++++++++++++++--- comfy/sdxl_clip.py | 1 + comfy/supported_models.py | 7 +++++-- 6 files changed, 49 insertions(+), 6 deletions(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index 57aa8bca..dbd0dbac 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -639,6 +639,23 @@ def supports_dtype(device, dtype): #TODO return True return False +def supports_cast(device, dtype): #TODO + if dtype == torch.float32: + return True + if dtype == torch.float16: + return True + if is_device_mps(device): + return False + if directml_enabled: #TODO: test this + return False + if dtype == torch.bfloat16: + return True + if dtype == torch.float8_e4m3fn: + return True + if dtype == torch.float8_e5m2: + return True + return False + def device_supports_non_blocking(device): if is_device_mps(device): return False #pytorch bug? mps doesn't support non blocking diff --git a/comfy/sd.py b/comfy/sd.py index 11764077..a7b4dbcf 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -98,13 +98,19 @@ class CLIP: load_device = model_management.text_encoder_device() offload_device = model_management.text_encoder_offload_device() params['device'] = offload_device - params['dtype'] = model_management.text_encoder_dtype(load_device) + dtype = model_management.text_encoder_dtype(load_device) + params['dtype'] = dtype self.cond_stage_model = clip(**(params)) + for dt in self.cond_stage_model.dtypes: + if not model_management.supports_cast(load_device, dt): + load_device = offload_device + self.tokenizer = tokenizer(embedding_directory=embedding_directory) self.patcher = comfy.model_patcher.ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device) self.layer_idx = None + logging.debug("CLIP model load device: {}, offload device: {}".format(load_device, offload_device)) def clone(self): n = CLIP(no_init=True) diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py index 2729f14d..911af0a7 100644 --- a/comfy/sd1_clip.py +++ b/comfy/sd1_clip.py @@ -511,6 +511,10 @@ class SD1ClipModel(torch.nn.Module): self.clip = "clip_{}".format(self.clip_name) setattr(self, self.clip, clip_model(device=device, dtype=dtype, **kwargs)) + self.dtypes = set() + if dtype is not None: + self.dtypes.add(dtype) + def set_clip_options(self, options): getattr(self, self.clip).set_clip_options(options) diff --git a/comfy/sd3_clip.py b/comfy/sd3_clip.py index 595381fc..cbbbe53d 100644 --- a/comfy/sd3_clip.py +++ b/comfy/sd3_clip.py @@ -44,24 +44,36 @@ class SD3Tokenizer: return self.clip_g.untokenize(token_weight_pair) class SD3ClipModel(torch.nn.Module): - def __init__(self, clip_l=True, clip_g=True, t5=True, device="cpu", dtype=None): + def __init__(self, clip_l=True, clip_g=True, t5=True, dtype_t5=None, device="cpu", dtype=None): super().__init__() + self.dtypes = set() if clip_l: self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False) + self.dtypes.add(dtype) else: self.clip_l = None if clip_g: self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype) + self.dtypes.add(dtype) else: self.clip_g = None if t5: - self.t5xxl = T5XXLModel(device=device, dtype=dtype) + if dtype_t5 is None: + dtype_t5 = dtype + elif comfy.model_management.dtype_size(dtype_t5) > comfy.model_management.dtype_size(dtype): + dtype_t5 = dtype + + if not comfy.model_management.supports_cast(device, dtype_t5): + dtype_t5 = dtype + + self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5) + self.dtypes.add(dtype_t5) else: self.t5xxl = None - logging.debug("Created SD3 text encoder with: clip_l {}, clip_g {}, t5xxl {}".format(clip_l, clip_g, t5)) + logging.debug("Created SD3 text encoder with: clip_l {}, clip_g {}, t5xxl {}:{}".format(clip_l, clip_g, t5, dtype_t5)) def set_clip_options(self, options): if self.clip_l is not None: diff --git a/comfy/sdxl_clip.py b/comfy/sdxl_clip.py index e62d1ed8..1257cba1 100644 --- a/comfy/sdxl_clip.py +++ b/comfy/sdxl_clip.py @@ -39,6 +39,7 @@ class SDXLClipModel(torch.nn.Module): super().__init__() self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False) self.clip_g = SDXLClipG(device=device, dtype=dtype) + self.dtypes = set([dtype]) def set_clip_options(self, options): self.clip_l.set_clip_options(options) diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 481ecaa6..a49df7a3 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -511,17 +511,20 @@ class SD3(supported_models_base.BASE): clip_l = False clip_g = False t5 = False + dtype_t5 = None pref = self.text_encoder_key_prefix[0] if "{}clip_l.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict: clip_l = True if "{}clip_g.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict: clip_g = True - if "{}t5xxl.transformer.encoder.final_layer_norm.weight".format(pref) in state_dict: + t5_key = "{}t5xxl.transformer.encoder.final_layer_norm.weight".format(pref) + if t5_key in state_dict: t5 = True + dtype_t5 = state_dict[t5_key].dtype class SD3ClipModel(sd3_clip.SD3ClipModel): def __init__(self, device="cpu", dtype=None): - super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, device=device, dtype=dtype) + super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, device=device, dtype=dtype) return supported_models_base.ClipTarget(sd3_clip.SD3Tokenizer, SD3ClipModel) From 69c8d6d8a60ac6355d02a8a003d55e304fcc702d Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 11 Jun 2024 23:27:39 -0400 Subject: [PATCH 272/378] Single and dual clip loader nodes support SD3. You can use the CLIPLoader to use the t5xxl only or the DualCLIPLoader to use CLIP-L and CLIP-G only for sd3. --- comfy/sd.py | 13 +++++++++++-- comfy/sd3_clip.py | 6 ++++++ comfy/supported_models.py | 6 +----- nodes.py | 17 +++++++++++++---- 4 files changed, 31 insertions(+), 11 deletions(-) diff --git a/comfy/sd.py b/comfy/sd.py index a7b4dbcf..3fd9e0e9 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -370,6 +370,7 @@ def load_style_model(ckpt_path): class CLIPType(Enum): STABLE_DIFFUSION = 1 STABLE_CASCADE = 2 + SD3 = 3 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION): clip_data = [] @@ -399,12 +400,20 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI elif "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data[0]: clip_target.clip = sd2_clip.SD2ClipModel clip_target.tokenizer = sd2_clip.SD2Tokenizer + elif "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in clip_data[0]: + dtype_t5 = clip_data[0]["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"].dtype + clip_target.clip = sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, dtype_t5=dtype_t5) + clip_target.tokenizer = sd3_clip.SD3Tokenizer else: clip_target.clip = sd1_clip.SD1ClipModel clip_target.tokenizer = sd1_clip.SD1Tokenizer elif len(clip_data) == 2: - clip_target.clip = sdxl_clip.SDXLClipModel - clip_target.tokenizer = sdxl_clip.SDXLTokenizer + if clip_type == CLIPType.SD3: + clip_target.clip = sd3_clip.sd3_clip(clip_l=True, clip_g=True, t5=False) + clip_target.tokenizer = sd3_clip.SD3Tokenizer + else: + clip_target.clip = sdxl_clip.SDXLClipModel + clip_target.tokenizer = sdxl_clip.SDXLTokenizer elif len(clip_data) == 3: clip_target.clip = sd3_clip.SD3ClipModel clip_target.tokenizer = sd3_clip.SD3Tokenizer diff --git a/comfy/sd3_clip.py b/comfy/sd3_clip.py index cbbbe53d..0713eb28 100644 --- a/comfy/sd3_clip.py +++ b/comfy/sd3_clip.py @@ -142,3 +142,9 @@ class SD3ClipModel(torch.nn.Module): return self.clip_l.load_sd(sd) else: return self.t5xxl.load_sd(sd) + +def sd3_clip(clip_l=True, clip_g=True, t5=True, dtype_t5=None): + class SD3ClipModel_(SD3ClipModel): + def __init__(self, device="cpu", dtype=None): + super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, device=device, dtype=dtype) + return SD3ClipModel_ diff --git a/comfy/supported_models.py b/comfy/supported_models.py index a49df7a3..c8ddf3e2 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -522,11 +522,7 @@ class SD3(supported_models_base.BASE): t5 = True dtype_t5 = state_dict[t5_key].dtype - class SD3ClipModel(sd3_clip.SD3ClipModel): - def __init__(self, device="cpu", dtype=None): - super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, device=device, dtype=dtype) - - return supported_models_base.ClipTarget(sd3_clip.SD3Tokenizer, SD3ClipModel) + return supported_models_base.ClipTarget(sd3_clip.SD3Tokenizer, sd3_clip.sd3_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5)) models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3] diff --git a/nodes.py b/nodes.py index ef1f8561..6fbeb377 100644 --- a/nodes.py +++ b/nodes.py @@ -818,7 +818,7 @@ class CLIPLoader: @classmethod def INPUT_TYPES(s): return {"required": { "clip_name": (folder_paths.get_filename_list("clip"), ), - "type": (["stable_diffusion", "stable_cascade"], ), + "type": (["stable_diffusion", "stable_cascade", "sd3"], ), }} RETURN_TYPES = ("CLIP",) FUNCTION = "load_clip" @@ -829,6 +829,8 @@ class CLIPLoader: clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION if type == "stable_cascade": clip_type = comfy.sd.CLIPType.STABLE_CASCADE + elif type == "sd3": + clip_type = comfy.sd.CLIPType.SD3 clip_path = folder_paths.get_full_path("clip", clip_name) clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type) @@ -837,17 +839,24 @@ class CLIPLoader: class DualCLIPLoader: @classmethod def INPUT_TYPES(s): - return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ), "clip_name2": (folder_paths.get_filename_list("clip"), ), + return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ), + "clip_name2": (folder_paths.get_filename_list("clip"), ), + "type": (["sdxl", "sd3"], ), }} RETURN_TYPES = ("CLIP",) FUNCTION = "load_clip" CATEGORY = "advanced/loaders" - def load_clip(self, clip_name1, clip_name2): + def load_clip(self, clip_name1, clip_name2, type): clip_path1 = folder_paths.get_full_path("clip", clip_name1) clip_path2 = folder_paths.get_full_path("clip", clip_name2) - clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings")) + if type == "sdxl": + clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION + elif type == "sd3": + clip_type = comfy.sd.CLIPType.SD3 + + clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type) return (clip,) class CLIPVisionLoader: From 694e0b48e0f6d55ddfcbe44a5d2818774241077b Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 12 Jun 2024 00:49:00 -0400 Subject: [PATCH 273/378] SD3 better memory usage estimation. --- comfy/model_base.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/comfy/model_base.py b/comfy/model_base.py index 28458bba..7ef03440 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -578,3 +578,15 @@ class SD3(BaseModel): if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) return out + + def memory_required(self, input_shape): + if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention(): + dtype = self.get_dtype() + if self.manual_cast_dtype is not None: + dtype = self.manual_cast_dtype + #TODO: this probably needs to be tweaked + area = input_shape[0] * input_shape[2] * input_shape[3] + return (area * comfy.model_management.dtype_size(dtype) * 0.012) * (1024 * 1024) + else: + area = input_shape[0] * input_shape[2] * input_shape[3] + return (area * 0.3) * (1024 * 1024) From 32be358213bf14a342073eca6fc45623d07e6267 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 12 Jun 2024 01:02:07 -0400 Subject: [PATCH 274/378] Save SD3 modelspec.architecture in CheckpointSave node. --- comfy_extras/nodes_model_merging.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/comfy_extras/nodes_model_merging.py b/comfy_extras/nodes_model_merging.py index bb15112f..b0d149c6 100644 --- a/comfy_extras/nodes_model_merging.py +++ b/comfy_extras/nodes_model_merging.py @@ -183,6 +183,8 @@ def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefi metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-refiner" elif isinstance(model.model, comfy.model_base.SVD_img2vid): metadata["modelspec.architecture"] = "stable-video-diffusion-img2vid-v1" + elif isinstance(model.model, comfy.model_base.SD3): + metadata["modelspec.architecture"] = "stable-diffusion-v3-medium" #TODO: other SD3 variants else: enable_modelspec = False From 1ddf512fdc69bf8dfb51eb858d3e5ba069570791 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 12 Jun 2024 01:07:58 -0400 Subject: [PATCH 275/378] Don't auto convert clip and vae weights to fp16 when saving checkpoint. --- comfy/model_base.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/comfy/model_base.py b/comfy/model_base.py index 7ef03440..21f884ba 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -207,9 +207,6 @@ class BaseModel(torch.nn.Module): unet_state_dict = self.diffusion_model.state_dict() unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict) - if self.get_dtype() == torch.float16: - extra_sds = map(lambda sd: utils.convert_sd_to(sd, torch.float16), extra_sds) - if self.model_type == ModelType.V_PREDICTION: unet_state_dict["v_pred"] = torch.tensor([]) From c8b5e08dc39171babb5d43f160cc04271591743e Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 12 Jun 2024 02:24:39 -0400 Subject: [PATCH 276/378] Default shift value on SD3 is 3.0 --- comfy_extras/nodes_model_advanced.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy_extras/nodes_model_advanced.py b/comfy_extras/nodes_model_advanced.py index 64002a8d..9bcd3c39 100644 --- a/comfy_extras/nodes_model_advanced.py +++ b/comfy_extras/nodes_model_advanced.py @@ -136,7 +136,7 @@ class ModelSamplingSD3: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), - "shift": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01}), + "shift": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step":0.01}), }} RETURN_TYPES = ("MODEL",) From 321e509e0a8a143095d29969ef9f1b32c9a93c9f Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 12 Jun 2024 09:48:27 -0400 Subject: [PATCH 277/378] Add link to SD3 example page to README. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index de0c062a..a40dd07d 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ This ui will let you design and execute advanced stable diffusion pipelines usin ## Features - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything. -- Fully supports SD1.x, SD2.x, [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/) and [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/) +- Fully supports SD1.x, SD2.x, [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/), [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/) and [SD3](https://comfyanonymous.github.io/ComfyUI_examples/sd3/) - Asynchronous Queue system - Many optimizations: Only re-executes the parts of the workflow that changes between executions. - Command line option: ```--lowvram``` to make it work on GPUs with less than 3GB vram (enabled automatically on GPUs with low vram) From 0eaa34ec5b22fd305159a78ea92b2ef00105ab18 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 12 Jun 2024 10:32:34 -0400 Subject: [PATCH 278/378] Fix regular empty latent image not working with SD3 and custom sampler. --- comfy_extras/nodes_custom_sampler.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index 45ef8cf4..69f1b941 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -380,7 +380,10 @@ class SamplerCustom: def sample(self, model, add_noise, noise_seed, cfg, positive, negative, sampler, sigmas, latent_image): latent = latent_image latent_image = latent["samples"] + latent = latent.copy() latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image) + latent["samples"] = latent_image + if not add_noise: noise = Noise_EmptyNoise().generate_noise(latent) else: @@ -539,7 +542,9 @@ class SamplerCustomAdvanced: def sample(self, noise, guider, sampler, sigmas, latent_image): latent = latent_image latent_image = latent["samples"] + latent = latent.copy() latent_image = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image) + latent["samples"] = latent_image noise_mask = None if "noise_mask" in latent: From 605e64f6d3da44235498bf9103d7aab1c95ef211 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 12 Jun 2024 10:39:33 -0400 Subject: [PATCH 279/378] Fix lowvram issue. --- comfy/ldm/modules/diffusionmodules/mmdit.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/ldm/modules/diffusionmodules/mmdit.py b/comfy/ldm/modules/diffusionmodules/mmdit.py index be40ab94..0cb6bd31 100644 --- a/comfy/ldm/modules/diffusionmodules/mmdit.py +++ b/comfy/ldm/modules/diffusionmodules/mmdit.py @@ -934,7 +934,7 @@ class MMDiT(nn.Module): context = self.context_processor(context) hw = x.shape[-2:] - x = self.x_embedder(x) + self.cropped_pos_embed(hw, device=x.device).to(dtype=x.dtype) + x = self.x_embedder(x) + self.cropped_pos_embed(hw, device=x.device).to(dtype=x.dtype, device=x.device) c = self.t_embedder(t, dtype=x.dtype) # (N, D) if y is not None and self.y_embedder is not None: y = self.y_embedder(y) # (N, D) From 37a08a41b3861e3b52dd69ff6b7fb00ba6b43758 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 13 Jun 2024 17:12:50 -0400 Subject: [PATCH 280/378] Support setting weight offsets in weight patcher. --- comfy/model_patcher.py | 24 ++++++++++++++++++++---- 1 file changed, 20 insertions(+), 4 deletions(-) diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index 84592f93..2f80ae2b 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -209,11 +209,18 @@ class ModelPatcher: p = set() model_sd = self.model.state_dict() for k in patches: - if k in model_sd: + offset = None + if isinstance(k, str): + key = k + else: + offset = k[1] + key = k[0] + + if key in model_sd: p.add(k) - current_patches = self.patches.get(k, []) - current_patches.append((strength_patch, patches[k], strength_model)) - self.patches[k] = current_patches + current_patches = self.patches.get(key, []) + current_patches.append((strength_patch, patches[k], strength_model, offset)) + self.patches[key] = current_patches self.patches_uuid = uuid.uuid4() return list(p) @@ -339,6 +346,12 @@ class ModelPatcher: strength = p[0] v = p[1] strength_model = p[2] + offset = p[3] + + old_weight = None + if offset is not None: + old_weight = weight + weight = weight.narrow(offset[0], offset[1], offset[2]) if strength_model != 1.0: weight *= strength_model @@ -488,6 +501,9 @@ class ModelPatcher: else: logging.warning("patch type not recognized {} {}".format(patch_type, key)) + if old_weight is not None: + weight = old_weight + return weight def unpatch_model(self, device_to=None, unpatch_weights=True): From ac151ac1698624bc4e321addb8c126069aced4b0 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 13 Jun 2024 18:26:01 -0400 Subject: [PATCH 281/378] Support SD3 diffusers lora. --- comfy/lora.py | 22 +++++++++++++++++++++- 1 file changed, 21 insertions(+), 1 deletion(-) diff --git a/comfy/lora.py b/comfy/lora.py index 096285bb..37254b03 100644 --- a/comfy/lora.py +++ b/comfy/lora.py @@ -29,6 +29,7 @@ def load_lora(lora, to_load): regular_lora = "{}.lora_up.weight".format(x) diffusers_lora = "{}_lora.up.weight".format(x) + diffusers2_lora = "{}.lora_B.weight".format(x) transformers_lora = "{}.lora_linear_layer.up.weight".format(x) A_name = None @@ -40,6 +41,10 @@ def load_lora(lora, to_load): A_name = diffusers_lora B_name = "{}_lora.down.weight".format(x) mid_name = None + elif diffusers2_lora in lora.keys(): + A_name = diffusers2_lora + B_name = "{}.lora_A.weight".format(x) + mid_name = None elif transformers_lora in lora.keys(): A_name = transformers_lora B_name ="{}.lora_linear_layer.down.weight".format(x) @@ -164,6 +169,7 @@ def load_lora(lora, to_load): for x in lora.keys(): if x not in loaded_keys: logging.warning("lora key not loaded: {}".format(x)) + return patch_dict def model_lora_keys_clip(model, key_map={}): @@ -217,7 +223,8 @@ def model_lora_keys_clip(model, key_map={}): return key_map def model_lora_keys_unet(model, key_map={}): - sdk = model.state_dict().keys() + sd = model.state_dict() + sdk = sd.keys() for k in sdk: if k.startswith("diffusion_model.") and k.endswith(".weight"): @@ -238,4 +245,17 @@ def model_lora_keys_unet(model, key_map={}): if diffusers_lora_key.endswith(".to_out.0"): diffusers_lora_key = diffusers_lora_key[:-2] key_map[diffusers_lora_key] = unet_key + + if isinstance(model, comfy.model_base.SD3): #Diffusers lora SD3 + for i in range(model.model_config.unet_config.get("depth", 0)): + k = "transformer.transformer_blocks.{}.attn.".format(i) + qkv = "diffusion_model.joint_blocks.{}.x_block.attn.qkv.weight".format(i) + proj = "diffusion_model.joint_blocks.{}.x_block.attn.proj.weight".format(i) + if qkv in sd: + offset = sd[qkv].shape[0] // 3 + key_map["{}to_q".format(k)] = (qkv, (0, 0, offset)) + key_map["{}to_k".format(k)] = (qkv, (0, offset, offset)) + key_map["{}to_v".format(k)] = (qkv, (0, offset * 2, offset)) + key_map["{}to_out.0".format(k)] = proj + return key_map From 5eb98f00927ace00b6b3d01ed9c76b113fc4ec9f Mon Sep 17 00:00:00 2001 From: Simon Lui <502929+simonlui@users.noreply.github.com> Date: Thu, 13 Jun 2024 15:51:14 -0700 Subject: [PATCH 282/378] Exempt IPEX from non_blocking previews fixing segmentation faults. (#3708) --- comfy/model_management.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/comfy/model_management.py b/comfy/model_management.py index dbd0dbac..dbb33d07 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -659,6 +659,8 @@ def supports_cast(device, dtype): #TODO def device_supports_non_blocking(device): if is_device_mps(device): return False #pytorch bug? mps doesn't support non blocking + if is_intel_xpu(): + return False if args.deterministic: #TODO: figure out why deterministic breaks non blocking from gpu to cpu (previews) return False if directml_enabled: From 0e06b370dbd1c6a8aaf13e9c9cede9ec8925ea86 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 14 Jun 2024 18:18:53 -0400 Subject: [PATCH 283/378] Print key names for easier debugging. --- comfy/model_patcher.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index 2f80ae2b..44b82795 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -335,7 +335,7 @@ class ModelPatcher: self.patch_weight_to_device(bias_key, device_to) m.to(device_to) mem_counter += comfy.model_management.module_size(m) - logging.debug("lowvram: loaded module regularly {}".format(m)) + logging.debug("lowvram: loaded module regularly {} {}".format(n, m)) self.model_lowvram = True self.lowvram_patch_counter = patch_counter From 0ec513d8774fc0b14fc3fdbb3f09745244532146 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 15 Jun 2024 01:08:12 -0400 Subject: [PATCH 284/378] Add a --force-channels-last to inference models in channel last mode. --- comfy/cli_args.py | 1 + comfy/model_base.py | 3 +++ comfy/model_management.py | 6 ++++++ 3 files changed, 10 insertions(+) diff --git a/comfy/cli_args.py b/comfy/cli_args.py index b8ac9bc6..fb0d37ce 100644 --- a/comfy/cli_args.py +++ b/comfy/cli_args.py @@ -75,6 +75,7 @@ fpte_group.add_argument("--fp8_e5m2-text-enc", action="store_true", help="Store fpte_group.add_argument("--fp16-text-enc", action="store_true", help="Store text encoder weights in fp16.") fpte_group.add_argument("--fp32-text-enc", action="store_true", help="Store text encoder weights in fp32.") +parser.add_argument("--force-channels-last", action="store_true", help="Force channels last format when inferencing the models.") parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.") diff --git a/comfy/model_base.py b/comfy/model_base.py index 21f884ba..daff6e0f 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -66,6 +66,9 @@ class BaseModel(torch.nn.Module): else: operations = comfy.ops.disable_weight_init self.diffusion_model = unet_model(**unet_config, device=device, operations=operations) + if comfy.model_management.force_channels_last(): + self.diffusion_model.to(memory_format=torch.channels_last) + logging.debug("using channels last mode for diffusion model") self.model_type = model_type self.model_sampling = model_sampling(model_config, model_type) diff --git a/comfy/model_management.py b/comfy/model_management.py index dbb33d07..8b8d3ff0 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -673,6 +673,12 @@ def device_should_use_non_blocking(device): return False # return True #TODO: figure out why this causes memory issues on Nvidia and possibly others +def force_channels_last(): + if args.force_channels_last: + return True + + #TODO + return False def cast_to_device(tensor, device, dtype, copy=False): device_supports_cast = False From f2e844e0542ae98b6bfcd438fbc8d22e66f178c9 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 15 Jun 2024 02:25:03 -0400 Subject: [PATCH 285/378] Optimize some unneeded if conditions in the sampling code. --- comfy/k_diffusion/sampling.py | 26 +++++++++++++++++++++----- 1 file changed, 21 insertions(+), 5 deletions(-) diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py index f9b28189..5bb991e7 100644 --- a/comfy/k_diffusion/sampling.py +++ b/comfy/k_diffusion/sampling.py @@ -129,8 +129,13 @@ def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): - gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. - sigma_hat = sigmas[i] * (gamma + 1) + if s_churn > 0: + gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. + sigma_hat = sigmas[i] * (gamma + 1) + else: + gamma = 0 + sigma_hat = sigmas[i] + if gamma > 0: eps = torch.randn_like(x) * s_noise x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 @@ -170,7 +175,13 @@ def sample_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): - gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. + if s_churn > 0: + gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. + sigma_hat = sigmas[i] * (gamma + 1) + else: + gamma = 0 + sigma_hat = sigmas[i] + sigma_hat = sigmas[i] * (gamma + 1) if gamma > 0: eps = torch.randn_like(x) * s_noise @@ -199,8 +210,13 @@ def sample_dpm_2(model, x, sigmas, extra_args=None, callback=None, disable=None, extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): - gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. - sigma_hat = sigmas[i] * (gamma + 1) + if s_churn > 0: + gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. + sigma_hat = sigmas[i] * (gamma + 1) + else: + gamma = 0 + sigma_hat = sigmas[i] + if gamma > 0: eps = torch.randn_like(x) * s_noise x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 From 1281f933c1c38ac0491ff2f86cbcd2ec90743ce3 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 15 Jun 2024 02:44:38 -0400 Subject: [PATCH 286/378] Small optimization. --- comfy/ldm/modules/diffusionmodules/mmdit.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comfy/ldm/modules/diffusionmodules/mmdit.py b/comfy/ldm/modules/diffusionmodules/mmdit.py index 0cb6bd31..20d3a321 100644 --- a/comfy/ldm/modules/diffusionmodules/mmdit.py +++ b/comfy/ldm/modules/diffusionmodules/mmdit.py @@ -243,9 +243,9 @@ class TimestepEmbedder(nn.Module): half = dim // 2 freqs = torch.exp( -math.log(max_period) - * torch.arange(start=0, end=half, dtype=torch.float32) + * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half - ).to(device=t.device) + ) args = t[:, None].float() * freqs[None] embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) if dim % 2: From bb1969cab7281199fca8c84b11ba2daa63a2e632 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 15 Jun 2024 12:14:56 -0400 Subject: [PATCH 287/378] Initial support for the stable audio open model. --- comfy/latent_formats.py | 3 + comfy/ldm/audio/autoencoder.py | 276 +++++++++ comfy/ldm/audio/dit.py | 888 +++++++++++++++++++++++++++ comfy/ldm/audio/embedders.py | 108 ++++ comfy/ldm/modules/attention.py | 140 +++-- comfy/model_base.py | 43 +- comfy/model_detection.py | 12 + comfy/model_sampling.py | 8 + comfy/ops.py | 41 ++ comfy/sa_t5.py | 22 + comfy/sd.py | 43 +- comfy/supported_models.py | 31 +- comfy_extras/nodes_audio.py | 128 ++++ comfy_extras/nodes_model_advanced.py | 31 + nodes.py | 8 +- requirements.txt | 1 + 16 files changed, 1719 insertions(+), 64 deletions(-) create mode 100644 comfy/ldm/audio/autoencoder.py create mode 100644 comfy/ldm/audio/dit.py create mode 100644 comfy/ldm/audio/embedders.py create mode 100644 comfy/sa_t5.py create mode 100644 comfy_extras/nodes_audio.py diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py index 6a9a9620..92f39d5c 100644 --- a/comfy/latent_formats.py +++ b/comfy/latent_formats.py @@ -135,3 +135,6 @@ class SD3(LatentFormat): def process_out(self, latent): return (latent / self.scale_factor) + self.shift_factor + +class StableAudio1(LatentFormat): + latent_channels = 64 diff --git a/comfy/ldm/audio/autoencoder.py b/comfy/ldm/audio/autoencoder.py new file mode 100644 index 00000000..7363131e --- /dev/null +++ b/comfy/ldm/audio/autoencoder.py @@ -0,0 +1,276 @@ +# code adapted from: https://github.com/Stability-AI/stable-audio-tools + +import torch +from torch import nn +from typing import Literal, Dict, Any +import math +import comfy.ops +ops = comfy.ops.disable_weight_init + +def vae_sample(mean, scale): + stdev = nn.functional.softplus(scale) + 1e-4 + var = stdev * stdev + logvar = torch.log(var) + latents = torch.randn_like(mean) * stdev + mean + + kl = (mean * mean + var - logvar - 1).sum(1).mean() + + return latents, kl + +class VAEBottleneck(nn.Module): + def __init__(self): + super().__init__() + self.is_discrete = False + + def encode(self, x, return_info=False, **kwargs): + info = {} + + mean, scale = x.chunk(2, dim=1) + + x, kl = vae_sample(mean, scale) + + info["kl"] = kl + + if return_info: + return x, info + else: + return x + + def decode(self, x): + return x + + +def snake_beta(x, alpha, beta): + return x + (1.0 / (beta + 0.000000001)) * pow(torch.sin(x * alpha), 2) + +# Adapted from https://github.com/NVIDIA/BigVGAN/blob/main/activations.py under MIT license +class SnakeBeta(nn.Module): + + def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True): + super(SnakeBeta, self).__init__() + self.in_features = in_features + + # initialize alpha + self.alpha_logscale = alpha_logscale + if self.alpha_logscale: # log scale alphas initialized to zeros + self.alpha = nn.Parameter(torch.zeros(in_features) * alpha) + self.beta = nn.Parameter(torch.zeros(in_features) * alpha) + else: # linear scale alphas initialized to ones + self.alpha = nn.Parameter(torch.ones(in_features) * alpha) + self.beta = nn.Parameter(torch.ones(in_features) * alpha) + + # self.alpha.requires_grad = alpha_trainable + # self.beta.requires_grad = alpha_trainable + + self.no_div_by_zero = 0.000000001 + + def forward(self, x): + alpha = self.alpha.unsqueeze(0).unsqueeze(-1).to(x.device) # line up with x to [B, C, T] + beta = self.beta.unsqueeze(0).unsqueeze(-1).to(x.device) + if self.alpha_logscale: + alpha = torch.exp(alpha) + beta = torch.exp(beta) + x = snake_beta(x, alpha, beta) + + return x + +def WNConv1d(*args, **kwargs): + return torch.nn.utils.weight_norm(ops.Conv1d(*args, **kwargs)) + +def WNConvTranspose1d(*args, **kwargs): + return torch.nn.utils.weight_norm(ops.ConvTranspose1d(*args, **kwargs)) + +def get_activation(activation: Literal["elu", "snake", "none"], antialias=False, channels=None) -> nn.Module: + if activation == "elu": + act = torch.nn.ELU() + elif activation == "snake": + act = SnakeBeta(channels) + elif activation == "none": + act = torch.nn.Identity() + else: + raise ValueError(f"Unknown activation {activation}") + + if antialias: + act = Activation1d(act) + + return act + + +class ResidualUnit(nn.Module): + def __init__(self, in_channels, out_channels, dilation, use_snake=False, antialias_activation=False): + super().__init__() + + self.dilation = dilation + + padding = (dilation * (7-1)) // 2 + + self.layers = nn.Sequential( + get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels), + WNConv1d(in_channels=in_channels, out_channels=out_channels, + kernel_size=7, dilation=dilation, padding=padding), + get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels), + WNConv1d(in_channels=out_channels, out_channels=out_channels, + kernel_size=1) + ) + + def forward(self, x): + res = x + + #x = checkpoint(self.layers, x) + x = self.layers(x) + + return x + res + +class EncoderBlock(nn.Module): + def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False): + super().__init__() + + self.layers = nn.Sequential( + ResidualUnit(in_channels=in_channels, + out_channels=in_channels, dilation=1, use_snake=use_snake), + ResidualUnit(in_channels=in_channels, + out_channels=in_channels, dilation=3, use_snake=use_snake), + ResidualUnit(in_channels=in_channels, + out_channels=in_channels, dilation=9, use_snake=use_snake), + get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels), + WNConv1d(in_channels=in_channels, out_channels=out_channels, + kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2)), + ) + + def forward(self, x): + return self.layers(x) + +class DecoderBlock(nn.Module): + def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False, use_nearest_upsample=False): + super().__init__() + + if use_nearest_upsample: + upsample_layer = nn.Sequential( + nn.Upsample(scale_factor=stride, mode="nearest"), + WNConv1d(in_channels=in_channels, + out_channels=out_channels, + kernel_size=2*stride, + stride=1, + bias=False, + padding='same') + ) + else: + upsample_layer = WNConvTranspose1d(in_channels=in_channels, + out_channels=out_channels, + kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2)) + + self.layers = nn.Sequential( + get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels), + upsample_layer, + ResidualUnit(in_channels=out_channels, out_channels=out_channels, + dilation=1, use_snake=use_snake), + ResidualUnit(in_channels=out_channels, out_channels=out_channels, + dilation=3, use_snake=use_snake), + ResidualUnit(in_channels=out_channels, out_channels=out_channels, + dilation=9, use_snake=use_snake), + ) + + def forward(self, x): + return self.layers(x) + +class OobleckEncoder(nn.Module): + def __init__(self, + in_channels=2, + channels=128, + latent_dim=32, + c_mults = [1, 2, 4, 8], + strides = [2, 4, 8, 8], + use_snake=False, + antialias_activation=False + ): + super().__init__() + + c_mults = [1] + c_mults + + self.depth = len(c_mults) + + layers = [ + WNConv1d(in_channels=in_channels, out_channels=c_mults[0] * channels, kernel_size=7, padding=3) + ] + + for i in range(self.depth-1): + layers += [EncoderBlock(in_channels=c_mults[i]*channels, out_channels=c_mults[i+1]*channels, stride=strides[i], use_snake=use_snake)] + + layers += [ + get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[-1] * channels), + WNConv1d(in_channels=c_mults[-1]*channels, out_channels=latent_dim, kernel_size=3, padding=1) + ] + + self.layers = nn.Sequential(*layers) + + def forward(self, x): + return self.layers(x) + + +class OobleckDecoder(nn.Module): + def __init__(self, + out_channels=2, + channels=128, + latent_dim=32, + c_mults = [1, 2, 4, 8], + strides = [2, 4, 8, 8], + use_snake=False, + antialias_activation=False, + use_nearest_upsample=False, + final_tanh=True): + super().__init__() + + c_mults = [1] + c_mults + + self.depth = len(c_mults) + + layers = [ + WNConv1d(in_channels=latent_dim, out_channels=c_mults[-1]*channels, kernel_size=7, padding=3), + ] + + for i in range(self.depth-1, 0, -1): + layers += [DecoderBlock( + in_channels=c_mults[i]*channels, + out_channels=c_mults[i-1]*channels, + stride=strides[i-1], + use_snake=use_snake, + antialias_activation=antialias_activation, + use_nearest_upsample=use_nearest_upsample + ) + ] + + layers += [ + get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[0] * channels), + WNConv1d(in_channels=c_mults[0] * channels, out_channels=out_channels, kernel_size=7, padding=3, bias=False), + nn.Tanh() if final_tanh else nn.Identity() + ] + + self.layers = nn.Sequential(*layers) + + def forward(self, x): + return self.layers(x) + + +class AudioOobleckVAE(nn.Module): + def __init__(self, + in_channels=2, + channels=128, + latent_dim=64, + c_mults = [1, 2, 4, 8, 16], + strides = [2, 4, 4, 8, 8], + use_snake=True, + antialias_activation=False, + use_nearest_upsample=False, + final_tanh=False): + super().__init__() + self.encoder = OobleckEncoder(in_channels, channels, latent_dim * 2, c_mults, strides, use_snake, antialias_activation) + self.decoder = OobleckDecoder(in_channels, channels, latent_dim, c_mults, strides, use_snake, antialias_activation, + use_nearest_upsample=use_nearest_upsample, final_tanh=final_tanh) + self.bottleneck = VAEBottleneck() + + def encode(self, x): + return self.bottleneck.encode(self.encoder(x)) + + def decode(self, x): + return self.decoder(self.bottleneck.decode(x)) + diff --git a/comfy/ldm/audio/dit.py b/comfy/ldm/audio/dit.py new file mode 100644 index 00000000..1c1112c5 --- /dev/null +++ b/comfy/ldm/audio/dit.py @@ -0,0 +1,888 @@ +# code adapted from: https://github.com/Stability-AI/stable-audio-tools + +from comfy.ldm.modules.attention import optimized_attention +import typing as tp + +import torch + +from einops import rearrange +from torch import nn +from torch.nn import functional as F +import math + +class FourierFeatures(nn.Module): + def __init__(self, in_features, out_features, std=1., dtype=None, device=None): + super().__init__() + assert out_features % 2 == 0 + self.weight = nn.Parameter(torch.empty( + [out_features // 2, in_features], dtype=dtype, device=device)) + + def forward(self, input): + f = 2 * math.pi * input @ self.weight.T.to(dtype=input.dtype, device=input.device) + return torch.cat([f.cos(), f.sin()], dim=-1) + +# norms +class LayerNorm(nn.Module): + def __init__(self, dim, bias=False, fix_scale=False, dtype=None, device=None): + """ + bias-less layernorm has been shown to be more stable. most newer models have moved towards rmsnorm, also bias-less + """ + super().__init__() + + self.gamma = nn.Parameter(torch.empty(dim, dtype=dtype, device=device)) + + if bias: + self.beta = nn.Parameter(torch.empty(dim, dtype=dtype, device=device)) + else: + self.beta = None + + def forward(self, x): + beta = self.beta + if self.beta is not None: + beta = beta.to(dtype=x.dtype, device=x.device) + return F.layer_norm(x, x.shape[-1:], weight=self.gamma.to(dtype=x.dtype, device=x.device), bias=beta) + +class GLU(nn.Module): + def __init__( + self, + dim_in, + dim_out, + activation, + use_conv = False, + conv_kernel_size = 3, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + self.act = activation + self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device) if not use_conv else operations.Conv1d(dim_in, dim_out * 2, conv_kernel_size, padding = (conv_kernel_size // 2), dtype=dtype, device=device) + self.use_conv = use_conv + + def forward(self, x): + if self.use_conv: + x = rearrange(x, 'b n d -> b d n') + x = self.proj(x) + x = rearrange(x, 'b d n -> b n d') + else: + x = self.proj(x) + + x, gate = x.chunk(2, dim = -1) + return x * self.act(gate) + +class AbsolutePositionalEmbedding(nn.Module): + def __init__(self, dim, max_seq_len): + super().__init__() + self.scale = dim ** -0.5 + self.max_seq_len = max_seq_len + self.emb = nn.Embedding(max_seq_len, dim) + + def forward(self, x, pos = None, seq_start_pos = None): + seq_len, device = x.shape[1], x.device + assert seq_len <= self.max_seq_len, f'you are passing in a sequence length of {seq_len} but your absolute positional embedding has a max sequence length of {self.max_seq_len}' + + if pos is None: + pos = torch.arange(seq_len, device = device) + + if seq_start_pos is not None: + pos = (pos - seq_start_pos[..., None]).clamp(min = 0) + + pos_emb = self.emb(pos) + pos_emb = pos_emb * self.scale + return pos_emb + +class ScaledSinusoidalEmbedding(nn.Module): + def __init__(self, dim, theta = 10000): + super().__init__() + assert (dim % 2) == 0, 'dimension must be divisible by 2' + self.scale = nn.Parameter(torch.ones(1) * dim ** -0.5) + + half_dim = dim // 2 + freq_seq = torch.arange(half_dim).float() / half_dim + inv_freq = theta ** -freq_seq + self.register_buffer('inv_freq', inv_freq, persistent = False) + + def forward(self, x, pos = None, seq_start_pos = None): + seq_len, device = x.shape[1], x.device + + if pos is None: + pos = torch.arange(seq_len, device = device) + + if seq_start_pos is not None: + pos = pos - seq_start_pos[..., None] + + emb = torch.einsum('i, j -> i j', pos, self.inv_freq) + emb = torch.cat((emb.sin(), emb.cos()), dim = -1) + return emb * self.scale + +class RotaryEmbedding(nn.Module): + def __init__( + self, + dim, + use_xpos = False, + scale_base = 512, + interpolation_factor = 1., + base = 10000, + base_rescale_factor = 1. + ): + super().__init__() + # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning + # has some connection to NTK literature + # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/ + base *= base_rescale_factor ** (dim / (dim - 2)) + + inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim)) + self.register_buffer('inv_freq', inv_freq) + + assert interpolation_factor >= 1. + self.interpolation_factor = interpolation_factor + + if not use_xpos: + self.register_buffer('scale', None) + return + + scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim) + + self.scale_base = scale_base + self.register_buffer('scale', scale) + + def forward_from_seq_len(self, seq_len, device, dtype): + # device = self.inv_freq.device + + t = torch.arange(seq_len, device=device, dtype=dtype) + return self.forward(t) + + def forward(self, t): + # device = self.inv_freq.device + device = t.device + dtype = t.dtype + + # t = t.to(torch.float32) + + t = t / self.interpolation_factor + + freqs = torch.einsum('i , j -> i j', t, self.inv_freq.to(dtype=dtype, device=device)) + freqs = torch.cat((freqs, freqs), dim = -1) + + if self.scale is None: + return freqs, 1. + + power = (torch.arange(seq_len, device = device) - (seq_len // 2)) / self.scale_base + scale = self.scale.to(dtype=dtype, device=device) ** rearrange(power, 'n -> n 1') + scale = torch.cat((scale, scale), dim = -1) + + return freqs, scale + +def rotate_half(x): + x = rearrange(x, '... (j d) -> ... j d', j = 2) + x1, x2 = x.unbind(dim = -2) + return torch.cat((-x2, x1), dim = -1) + +def apply_rotary_pos_emb(t, freqs, scale = 1): + out_dtype = t.dtype + + # cast to float32 if necessary for numerical stability + dtype = t.dtype #reduce(torch.promote_types, (t.dtype, freqs.dtype, torch.float32)) + rot_dim, seq_len = freqs.shape[-1], t.shape[-2] + freqs, t = freqs.to(dtype), t.to(dtype) + freqs = freqs[-seq_len:, :] + + if t.ndim == 4 and freqs.ndim == 3: + freqs = rearrange(freqs, 'b n d -> b 1 n d') + + # partial rotary embeddings, Wang et al. GPT-J + t, t_unrotated = t[..., :rot_dim], t[..., rot_dim:] + t = (t * freqs.cos() * scale) + (rotate_half(t) * freqs.sin() * scale) + + t, t_unrotated = t.to(out_dtype), t_unrotated.to(out_dtype) + + return torch.cat((t, t_unrotated), dim = -1) + +class FeedForward(nn.Module): + def __init__( + self, + dim, + dim_out = None, + mult = 4, + no_bias = False, + glu = True, + use_conv = False, + conv_kernel_size = 3, + zero_init_output = True, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + inner_dim = int(dim * mult) + + # Default to SwiGLU + + activation = nn.SiLU() + + dim_out = dim if dim_out is None else dim_out + + if glu: + linear_in = GLU(dim, inner_dim, activation, dtype=dtype, device=device, operations=operations) + else: + linear_in = nn.Sequential( + Rearrange('b n d -> b d n') if use_conv else nn.Identity(), + operations.Linear(dim, inner_dim, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(dim, inner_dim, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device), + Rearrange('b n d -> b d n') if use_conv else nn.Identity(), + activation + ) + + linear_out = operations.Linear(inner_dim, dim_out, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(inner_dim, dim_out, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device) + + # # init last linear layer to 0 + # if zero_init_output: + # nn.init.zeros_(linear_out.weight) + # if not no_bias: + # nn.init.zeros_(linear_out.bias) + + + self.ff = nn.Sequential( + linear_in, + Rearrange('b d n -> b n d') if use_conv else nn.Identity(), + linear_out, + Rearrange('b n d -> b d n') if use_conv else nn.Identity(), + ) + + def forward(self, x): + return self.ff(x) + +class Attention(nn.Module): + def __init__( + self, + dim, + dim_heads = 64, + dim_context = None, + causal = False, + zero_init_output=True, + qk_norm = False, + natten_kernel_size = None, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + self.dim = dim + self.dim_heads = dim_heads + self.causal = causal + + dim_kv = dim_context if dim_context is not None else dim + + self.num_heads = dim // dim_heads + self.kv_heads = dim_kv // dim_heads + + if dim_context is not None: + self.to_q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + self.to_kv = operations.Linear(dim_kv, dim_kv * 2, bias=False, dtype=dtype, device=device) + else: + self.to_qkv = operations.Linear(dim, dim * 3, bias=False, dtype=dtype, device=device) + + self.to_out = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + + # if zero_init_output: + # nn.init.zeros_(self.to_out.weight) + + self.qk_norm = qk_norm + + + def forward( + self, + x, + context = None, + mask = None, + context_mask = None, + rotary_pos_emb = None, + causal = None + ): + h, kv_h, has_context = self.num_heads, self.kv_heads, context is not None + + kv_input = context if has_context else x + + if hasattr(self, 'to_q'): + # Use separate linear projections for q and k/v + q = self.to_q(x) + q = rearrange(q, 'b n (h d) -> b h n d', h = h) + + k, v = self.to_kv(kv_input).chunk(2, dim=-1) + + k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = kv_h), (k, v)) + else: + # Use fused linear projection + q, k, v = self.to_qkv(x).chunk(3, dim=-1) + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v)) + + # Normalize q and k for cosine sim attention + if self.qk_norm: + q = F.normalize(q, dim=-1) + k = F.normalize(k, dim=-1) + + if rotary_pos_emb is not None and not has_context: + freqs, _ = rotary_pos_emb + + q_dtype = q.dtype + k_dtype = k.dtype + + q = q.to(torch.float32) + k = k.to(torch.float32) + freqs = freqs.to(torch.float32) + + q = apply_rotary_pos_emb(q, freqs) + k = apply_rotary_pos_emb(k, freqs) + + q = q.to(q_dtype) + k = k.to(k_dtype) + + input_mask = context_mask + + if input_mask is None and not has_context: + input_mask = mask + + # determine masking + masks = [] + final_attn_mask = None # The mask that will be applied to the attention matrix, taking all masks into account + + if input_mask is not None: + input_mask = rearrange(input_mask, 'b j -> b 1 1 j') + masks.append(~input_mask) + + # Other masks will be added here later + + if len(masks) > 0: + final_attn_mask = ~or_reduce(masks) + + n, device = q.shape[-2], q.device + + causal = self.causal if causal is None else causal + + if n == 1 and causal: + causal = False + + if h != kv_h: + # Repeat interleave kv_heads to match q_heads + heads_per_kv_head = h // kv_h + k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v)) + + out = optimized_attention(q, k, v, h, skip_reshape=True) + out = self.to_out(out) + + if mask is not None: + mask = rearrange(mask, 'b n -> b n 1') + out = out.masked_fill(~mask, 0.) + + return out + +class ConformerModule(nn.Module): + def __init__( + self, + dim, + norm_kwargs = {}, + ): + + super().__init__() + + self.dim = dim + + self.in_norm = LayerNorm(dim, **norm_kwargs) + self.pointwise_conv = nn.Conv1d(dim, dim, kernel_size=1, bias=False) + self.glu = GLU(dim, dim, nn.SiLU()) + self.depthwise_conv = nn.Conv1d(dim, dim, kernel_size=17, groups=dim, padding=8, bias=False) + self.mid_norm = LayerNorm(dim, **norm_kwargs) # This is a batch norm in the original but I don't like batch norm + self.swish = nn.SiLU() + self.pointwise_conv_2 = nn.Conv1d(dim, dim, kernel_size=1, bias=False) + + def forward(self, x): + x = self.in_norm(x) + x = rearrange(x, 'b n d -> b d n') + x = self.pointwise_conv(x) + x = rearrange(x, 'b d n -> b n d') + x = self.glu(x) + x = rearrange(x, 'b n d -> b d n') + x = self.depthwise_conv(x) + x = rearrange(x, 'b d n -> b n d') + x = self.mid_norm(x) + x = self.swish(x) + x = rearrange(x, 'b n d -> b d n') + x = self.pointwise_conv_2(x) + x = rearrange(x, 'b d n -> b n d') + + return x + +class TransformerBlock(nn.Module): + def __init__( + self, + dim, + dim_heads = 64, + cross_attend = False, + dim_context = None, + global_cond_dim = None, + causal = False, + zero_init_branch_outputs = True, + conformer = False, + layer_ix = -1, + remove_norms = False, + attn_kwargs = {}, + ff_kwargs = {}, + norm_kwargs = {}, + dtype=None, + device=None, + operations=None, + ): + + super().__init__() + self.dim = dim + self.dim_heads = dim_heads + self.cross_attend = cross_attend + self.dim_context = dim_context + self.causal = causal + + self.pre_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity() + + self.self_attn = Attention( + dim, + dim_heads = dim_heads, + causal = causal, + zero_init_output=zero_init_branch_outputs, + dtype=dtype, + device=device, + operations=operations, + **attn_kwargs + ) + + if cross_attend: + self.cross_attend_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity() + self.cross_attn = Attention( + dim, + dim_heads = dim_heads, + dim_context=dim_context, + causal = causal, + zero_init_output=zero_init_branch_outputs, + dtype=dtype, + device=device, + operations=operations, + **attn_kwargs + ) + + self.ff_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity() + self.ff = FeedForward(dim, zero_init_output=zero_init_branch_outputs, dtype=dtype, device=device, operations=operations,**ff_kwargs) + + self.layer_ix = layer_ix + + self.conformer = ConformerModule(dim, norm_kwargs=norm_kwargs) if conformer else None + + self.global_cond_dim = global_cond_dim + + if global_cond_dim is not None: + self.to_scale_shift_gate = nn.Sequential( + nn.SiLU(), + nn.Linear(global_cond_dim, dim * 6, bias=False) + ) + + nn.init.zeros_(self.to_scale_shift_gate[1].weight) + #nn.init.zeros_(self.to_scale_shift_gate_self[1].bias) + + def forward( + self, + x, + context = None, + global_cond=None, + mask = None, + context_mask = None, + rotary_pos_emb = None + ): + if self.global_cond_dim is not None and self.global_cond_dim > 0 and global_cond is not None: + + scale_self, shift_self, gate_self, scale_ff, shift_ff, gate_ff = self.to_scale_shift_gate(global_cond).unsqueeze(1).chunk(6, dim = -1) + + # self-attention with adaLN + residual = x + x = self.pre_norm(x) + x = x * (1 + scale_self) + shift_self + x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb) + x = x * torch.sigmoid(1 - gate_self) + x = x + residual + + if context is not None: + x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask) + + if self.conformer is not None: + x = x + self.conformer(x) + + # feedforward with adaLN + residual = x + x = self.ff_norm(x) + x = x * (1 + scale_ff) + shift_ff + x = self.ff(x) + x = x * torch.sigmoid(1 - gate_ff) + x = x + residual + + else: + x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb) + + if context is not None: + x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask) + + if self.conformer is not None: + x = x + self.conformer(x) + + x = x + self.ff(self.ff_norm(x)) + + return x + +class ContinuousTransformer(nn.Module): + def __init__( + self, + dim, + depth, + *, + dim_in = None, + dim_out = None, + dim_heads = 64, + cross_attend=False, + cond_token_dim=None, + global_cond_dim=None, + causal=False, + rotary_pos_emb=True, + zero_init_branch_outputs=True, + conformer=False, + use_sinusoidal_emb=False, + use_abs_pos_emb=False, + abs_pos_emb_max_length=10000, + dtype=None, + device=None, + operations=None, + **kwargs + ): + + super().__init__() + + self.dim = dim + self.depth = depth + self.causal = causal + self.layers = nn.ModuleList([]) + + self.project_in = operations.Linear(dim_in, dim, bias=False, dtype=dtype, device=device) if dim_in is not None else nn.Identity() + self.project_out = operations.Linear(dim, dim_out, bias=False, dtype=dtype, device=device) if dim_out is not None else nn.Identity() + + if rotary_pos_emb: + self.rotary_pos_emb = RotaryEmbedding(max(dim_heads // 2, 32)) + else: + self.rotary_pos_emb = None + + self.use_sinusoidal_emb = use_sinusoidal_emb + if use_sinusoidal_emb: + self.pos_emb = ScaledSinusoidalEmbedding(dim) + + self.use_abs_pos_emb = use_abs_pos_emb + if use_abs_pos_emb: + self.pos_emb = AbsolutePositionalEmbedding(dim, abs_pos_emb_max_length) + + for i in range(depth): + self.layers.append( + TransformerBlock( + dim, + dim_heads = dim_heads, + cross_attend = cross_attend, + dim_context = cond_token_dim, + global_cond_dim = global_cond_dim, + causal = causal, + zero_init_branch_outputs = zero_init_branch_outputs, + conformer=conformer, + layer_ix=i, + dtype=dtype, + device=device, + operations=operations, + **kwargs + ) + ) + + def forward( + self, + x, + mask = None, + prepend_embeds = None, + prepend_mask = None, + global_cond = None, + return_info = False, + **kwargs + ): + batch, seq, device = *x.shape[:2], x.device + + info = { + "hidden_states": [], + } + + x = self.project_in(x) + + if prepend_embeds is not None: + prepend_length, prepend_dim = prepend_embeds.shape[1:] + + assert prepend_dim == x.shape[-1], 'prepend dimension must match sequence dimension' + + x = torch.cat((prepend_embeds, x), dim = -2) + + if prepend_mask is not None or mask is not None: + mask = mask if mask is not None else torch.ones((batch, seq), device = device, dtype = torch.bool) + prepend_mask = prepend_mask if prepend_mask is not None else torch.ones((batch, prepend_length), device = device, dtype = torch.bool) + + mask = torch.cat((prepend_mask, mask), dim = -1) + + # Attention layers + + if self.rotary_pos_emb is not None: + rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1], dtype=x.dtype, device=x.device) + else: + rotary_pos_emb = None + + if self.use_sinusoidal_emb or self.use_abs_pos_emb: + x = x + self.pos_emb(x) + + # Iterate over the transformer layers + for layer in self.layers: + x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs) + # x = checkpoint(layer, x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs) + + if return_info: + info["hidden_states"].append(x) + + x = self.project_out(x) + + if return_info: + return x, info + + return x + +class AudioDiffusionTransformer(nn.Module): + def __init__(self, + io_channels=64, + patch_size=1, + embed_dim=1536, + cond_token_dim=768, + project_cond_tokens=False, + global_cond_dim=1536, + project_global_cond=True, + input_concat_dim=0, + prepend_cond_dim=0, + depth=24, + num_heads=24, + transformer_type: tp.Literal["continuous_transformer"] = "continuous_transformer", + global_cond_type: tp.Literal["prepend", "adaLN"] = "prepend", + audio_model="", + dtype=None, + device=None, + operations=None, + **kwargs): + + super().__init__() + + self.dtype = dtype + self.cond_token_dim = cond_token_dim + + # Timestep embeddings + timestep_features_dim = 256 + + self.timestep_features = FourierFeatures(1, timestep_features_dim, dtype=dtype, device=device) + + self.to_timestep_embed = nn.Sequential( + operations.Linear(timestep_features_dim, embed_dim, bias=True, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device), + ) + + if cond_token_dim > 0: + # Conditioning tokens + + cond_embed_dim = cond_token_dim if not project_cond_tokens else embed_dim + self.to_cond_embed = nn.Sequential( + operations.Linear(cond_token_dim, cond_embed_dim, bias=False, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(cond_embed_dim, cond_embed_dim, bias=False, dtype=dtype, device=device) + ) + else: + cond_embed_dim = 0 + + if global_cond_dim > 0: + # Global conditioning + global_embed_dim = global_cond_dim if not project_global_cond else embed_dim + self.to_global_embed = nn.Sequential( + operations.Linear(global_cond_dim, global_embed_dim, bias=False, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(global_embed_dim, global_embed_dim, bias=False, dtype=dtype, device=device) + ) + + if prepend_cond_dim > 0: + # Prepend conditioning + self.to_prepend_embed = nn.Sequential( + operations.Linear(prepend_cond_dim, embed_dim, bias=False, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device) + ) + + self.input_concat_dim = input_concat_dim + + dim_in = io_channels + self.input_concat_dim + + self.patch_size = patch_size + + # Transformer + + self.transformer_type = transformer_type + + self.global_cond_type = global_cond_type + + if self.transformer_type == "continuous_transformer": + + global_dim = None + + if self.global_cond_type == "adaLN": + # The global conditioning is projected to the embed_dim already at this point + global_dim = embed_dim + + self.transformer = ContinuousTransformer( + dim=embed_dim, + depth=depth, + dim_heads=embed_dim // num_heads, + dim_in=dim_in * patch_size, + dim_out=io_channels * patch_size, + cross_attend = cond_token_dim > 0, + cond_token_dim = cond_embed_dim, + global_cond_dim=global_dim, + dtype=dtype, + device=device, + operations=operations, + **kwargs + ) + else: + raise ValueError(f"Unknown transformer type: {self.transformer_type}") + + self.preprocess_conv = operations.Conv1d(dim_in, dim_in, 1, bias=False, dtype=dtype, device=device) + self.postprocess_conv = operations.Conv1d(io_channels, io_channels, 1, bias=False, dtype=dtype, device=device) + + def _forward( + self, + x, + t, + mask=None, + cross_attn_cond=None, + cross_attn_cond_mask=None, + input_concat_cond=None, + global_embed=None, + prepend_cond=None, + prepend_cond_mask=None, + return_info=False, + **kwargs): + + if cross_attn_cond is not None: + cross_attn_cond = self.to_cond_embed(cross_attn_cond) + + if global_embed is not None: + # Project the global conditioning to the embedding dimension + global_embed = self.to_global_embed(global_embed) + + prepend_inputs = None + prepend_mask = None + prepend_length = 0 + if prepend_cond is not None: + # Project the prepend conditioning to the embedding dimension + prepend_cond = self.to_prepend_embed(prepend_cond) + + prepend_inputs = prepend_cond + if prepend_cond_mask is not None: + prepend_mask = prepend_cond_mask + + if input_concat_cond is not None: + + # Interpolate input_concat_cond to the same length as x + if input_concat_cond.shape[2] != x.shape[2]: + input_concat_cond = F.interpolate(input_concat_cond, (x.shape[2], ), mode='nearest') + + x = torch.cat([x, input_concat_cond], dim=1) + + # Get the batch of timestep embeddings + timestep_embed = self.to_timestep_embed(self.timestep_features(t[:, None]).to(x.dtype)) # (b, embed_dim) + + # Timestep embedding is considered a global embedding. Add to the global conditioning if it exists + if global_embed is not None: + global_embed = global_embed + timestep_embed + else: + global_embed = timestep_embed + + # Add the global_embed to the prepend inputs if there is no global conditioning support in the transformer + if self.global_cond_type == "prepend": + if prepend_inputs is None: + # Prepend inputs are just the global embed, and the mask is all ones + prepend_inputs = global_embed.unsqueeze(1) + prepend_mask = torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool) + else: + # Prepend inputs are the prepend conditioning + the global embed + prepend_inputs = torch.cat([prepend_inputs, global_embed.unsqueeze(1)], dim=1) + prepend_mask = torch.cat([prepend_mask, torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)], dim=1) + + prepend_length = prepend_inputs.shape[1] + + x = self.preprocess_conv(x) + x + + x = rearrange(x, "b c t -> b t c") + + extra_args = {} + + if self.global_cond_type == "adaLN": + extra_args["global_cond"] = global_embed + + if self.patch_size > 1: + x = rearrange(x, "b (t p) c -> b t (c p)", p=self.patch_size) + + if self.transformer_type == "x-transformers": + output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, **extra_args, **kwargs) + elif self.transformer_type == "continuous_transformer": + output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, return_info=return_info, **extra_args, **kwargs) + + if return_info: + output, info = output + elif self.transformer_type == "mm_transformer": + output = self.transformer(x, context=cross_attn_cond, mask=mask, context_mask=cross_attn_cond_mask, **extra_args, **kwargs) + + output = rearrange(output, "b t c -> b c t")[:,:,prepend_length:] + + if self.patch_size > 1: + output = rearrange(output, "b (c p) t -> b c (t p)", p=self.patch_size) + + output = self.postprocess_conv(output) + output + + if return_info: + return output, info + + return output + + def forward( + self, + x, + timestep, + context=None, + context_mask=None, + input_concat_cond=None, + global_embed=None, + negative_global_embed=None, + prepend_cond=None, + prepend_cond_mask=None, + mask=None, + return_info=False, + control=None, + transformer_options={}, + **kwargs): + return self._forward( + x, + timestep, + cross_attn_cond=context, + cross_attn_cond_mask=context_mask, + input_concat_cond=input_concat_cond, + global_embed=global_embed, + prepend_cond=prepend_cond, + prepend_cond_mask=prepend_cond_mask, + mask=mask, + return_info=return_info, + **kwargs + ) diff --git a/comfy/ldm/audio/embedders.py b/comfy/ldm/audio/embedders.py new file mode 100644 index 00000000..82a3210c --- /dev/null +++ b/comfy/ldm/audio/embedders.py @@ -0,0 +1,108 @@ +# code adapted from: https://github.com/Stability-AI/stable-audio-tools + +import torch +import torch.nn as nn +from torch import Tensor, einsum +from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, TypeVar, Union +from einops import rearrange +import math +import comfy.ops + +class LearnedPositionalEmbedding(nn.Module): + """Used for continuous time""" + + def __init__(self, dim: int): + super().__init__() + assert (dim % 2) == 0 + half_dim = dim // 2 + self.weights = nn.Parameter(torch.empty(half_dim)) + + def forward(self, x: Tensor) -> Tensor: + x = rearrange(x, "b -> b 1") + freqs = x * rearrange(self.weights, "d -> 1 d") * 2 * math.pi + fouriered = torch.cat((freqs.sin(), freqs.cos()), dim=-1) + fouriered = torch.cat((x, fouriered), dim=-1) + return fouriered + +def TimePositionalEmbedding(dim: int, out_features: int) -> nn.Module: + return nn.Sequential( + LearnedPositionalEmbedding(dim), + comfy.ops.manual_cast.Linear(in_features=dim + 1, out_features=out_features), + ) + + +class NumberEmbedder(nn.Module): + def __init__( + self, + features: int, + dim: int = 256, + ): + super().__init__() + self.features = features + self.embedding = TimePositionalEmbedding(dim=dim, out_features=features) + + def forward(self, x: Union[List[float], Tensor]) -> Tensor: + if not torch.is_tensor(x): + device = next(self.embedding.parameters()).device + x = torch.tensor(x, device=device) + assert isinstance(x, Tensor) + shape = x.shape + x = rearrange(x, "... -> (...)") + embedding = self.embedding(x) + x = embedding.view(*shape, self.features) + return x # type: ignore + + +class Conditioner(nn.Module): + def __init__( + self, + dim: int, + output_dim: int, + project_out: bool = False + ): + + super().__init__() + + self.dim = dim + self.output_dim = output_dim + self.proj_out = nn.Linear(dim, output_dim) if (dim != output_dim or project_out) else nn.Identity() + + def forward(self, x): + raise NotImplementedError() + +class NumberConditioner(Conditioner): + ''' + Conditioner that takes a list of floats, normalizes them for a given range, and returns a list of embeddings + ''' + def __init__(self, + output_dim: int, + min_val: float=0, + max_val: float=1 + ): + super().__init__(output_dim, output_dim) + + self.min_val = min_val + self.max_val = max_val + + self.embedder = NumberEmbedder(features=output_dim) + + def forward(self, floats, device=None): + # Cast the inputs to floats + floats = [float(x) for x in floats] + + if device is None: + device = next(self.embedder.parameters()).device + + floats = torch.tensor(floats).to(device) + + floats = floats.clamp(self.min_val, self.max_val) + + normalized_floats = (floats - self.min_val) / (self.max_val - self.min_val) + + # Cast floats to same type as embedder + embedder_dtype = next(self.embedder.parameters()).dtype + normalized_floats = normalized_floats.to(embedder_dtype) + + float_embeds = self.embedder(normalized_floats).unsqueeze(1) + + return [float_embeds, torch.ones(float_embeds.shape[0], 1).to(device)] diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py index da9f7aab..65a8bcf4 100644 --- a/comfy/ldm/modules/attention.py +++ b/comfy/ldm/modules/attention.py @@ -86,22 +86,32 @@ class FeedForward(nn.Module): def Normalize(in_channels, dtype=None, device=None): return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device) -def attention_basic(q, k, v, heads, mask=None, attn_precision=None): +def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False): attn_precision = get_attn_precision(attn_precision) - b, _, dim_head = q.shape - dim_head //= heads + if skip_reshape: + b, _, _, dim_head = q.shape + else: + b, _, dim_head = q.shape + dim_head //= heads + scale = dim_head ** -0.5 h = heads - q, k, v = map( - lambda t: t.unsqueeze(3) - .reshape(b, -1, heads, dim_head) - .permute(0, 2, 1, 3) - .reshape(b * heads, -1, dim_head) - .contiguous(), - (q, k, v), - ) + if skip_reshape: + q, k, v = map( + lambda t: t.reshape(b * heads, -1, dim_head), + (q, k, v), + ) + else: + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, -1, heads, dim_head) + .permute(0, 2, 1, 3) + .reshape(b * heads, -1, dim_head) + .contiguous(), + (q, k, v), + ) # force cast to fp32 to avoid overflowing if attn_precision == torch.float32: @@ -138,17 +148,26 @@ def attention_basic(q, k, v, heads, mask=None, attn_precision=None): return out -def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None): +def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False): attn_precision = get_attn_precision(attn_precision) - b, _, dim_head = query.shape - dim_head //= heads + if skip_reshape: + b, _, _, dim_head = query.shape + else: + b, _, dim_head = query.shape + dim_head //= heads scale = dim_head ** -0.5 - query = query.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head) - value = value.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head) - key = key.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1) + if skip_reshape: + query = query.reshape(b * heads, -1, dim_head) + value = value.reshape(b * heads, -1, dim_head) + key = key.reshape(b * heads, -1, dim_head).movedim(1, 2) + else: + query = query.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head) + value = value.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head) + key = key.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1) + dtype = query.dtype upcast_attention = attn_precision == torch.float32 and query.dtype != torch.float32 @@ -200,22 +219,32 @@ def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None) hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2) return hidden_states -def attention_split(q, k, v, heads, mask=None, attn_precision=None): +def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False): attn_precision = get_attn_precision(attn_precision) - b, _, dim_head = q.shape - dim_head //= heads + if skip_reshape: + b, _, _, dim_head = q.shape + else: + b, _, dim_head = q.shape + dim_head //= heads + scale = dim_head ** -0.5 h = heads - q, k, v = map( - lambda t: t.unsqueeze(3) - .reshape(b, -1, heads, dim_head) - .permute(0, 2, 1, 3) - .reshape(b * heads, -1, dim_head) - .contiguous(), - (q, k, v), - ) + if skip_reshape: + q, k, v = map( + lambda t: t.reshape(b * heads, -1, dim_head), + (q, k, v), + ) + else: + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, -1, heads, dim_head) + .permute(0, 2, 1, 3) + .reshape(b * heads, -1, dim_head) + .contiguous(), + (q, k, v), + ) r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype) @@ -311,9 +340,12 @@ try: except: pass -def attention_xformers(q, k, v, heads, mask=None, attn_precision=None): - b, _, dim_head = q.shape - dim_head //= heads +def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False): + if skip_reshape: + b, _, _, dim_head = q.shape + else: + b, _, dim_head = q.shape + dim_head //= heads disabled_xformers = False @@ -328,10 +360,16 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None): if disabled_xformers: return attention_pytorch(q, k, v, heads, mask) - q, k, v = map( - lambda t: t.reshape(b, -1, heads, dim_head), - (q, k, v), - ) + if skip_reshape: + q, k, v = map( + lambda t: t.reshape(b * heads, -1, dim_head), + (q, k, v), + ) + else: + q, k, v = map( + lambda t: t.reshape(b, -1, heads, dim_head), + (q, k, v), + ) if mask is not None: pad = 8 - q.shape[1] % 8 @@ -341,18 +379,30 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None): out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask) - out = ( - out.reshape(b, -1, heads * dim_head) - ) + if skip_reshape: + out = ( + out.unsqueeze(0) + .reshape(b, heads, -1, dim_head) + .permute(0, 2, 1, 3) + .reshape(b, -1, heads * dim_head) + ) + else: + out = ( + out.reshape(b, -1, heads * dim_head) + ) + return out -def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None): - b, _, dim_head = q.shape - dim_head //= heads - q, k, v = map( - lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2), - (q, k, v), - ) +def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False): + if skip_reshape: + b, _, _, dim_head = q.shape + else: + b, _, dim_head = q.shape + dim_head //= heads + q, k, v = map( + lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2), + (q, k, v), + ) out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False) out = ( diff --git a/comfy/model_base.py b/comfy/model_base.py index daff6e0f..f45b375d 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -6,12 +6,15 @@ from comfy.ldm.cascade.stage_b import StageB from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation from comfy.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation from comfy.ldm.modules.diffusionmodules.mmdit import OpenAISignatureMMDITWrapper +import comfy.ldm.audio.dit +import comfy.ldm.audio.embedders import comfy.model_management import comfy.conds import comfy.ops from enum import Enum from . import utils import comfy.latent_formats +import math class ModelType(Enum): EPS = 1 @@ -20,9 +23,10 @@ class ModelType(Enum): STABLE_CASCADE = 4 EDM = 5 FLOW = 6 + V_PREDICTION_CONTINUOUS = 7 -from comfy.model_sampling import EPS, V_PREDICTION, EDM, ModelSamplingDiscrete, ModelSamplingContinuousEDM, StableCascadeSampling +from comfy.model_sampling import EPS, V_PREDICTION, EDM, ModelSamplingDiscrete, ModelSamplingContinuousEDM, StableCascadeSampling, ModelSamplingContinuousV def model_sampling(model_config, model_type): @@ -44,6 +48,9 @@ def model_sampling(model_config, model_type): elif model_type == ModelType.EDM: c = EDM s = ModelSamplingContinuousEDM + elif model_type == ModelType.V_PREDICTION_CONTINUOUS: + c = V_PREDICTION + s = ModelSamplingContinuousV class ModelSampling(s, c): pass @@ -236,11 +243,11 @@ class BaseModel(torch.nn.Module): if self.manual_cast_dtype is not None: dtype = self.manual_cast_dtype #TODO: this needs to be tweaked - area = input_shape[0] * input_shape[2] * input_shape[3] + area = input_shape[0] * math.prod(input_shape[2:]) return (area * comfy.model_management.dtype_size(dtype) / 50) * (1024 * 1024) else: #TODO: this formula might be too aggressive since I tweaked the sub-quad and split algorithms to use less memory. - area = input_shape[0] * input_shape[2] * input_shape[3] + area = input_shape[0] * math.prod(input_shape[2:]) return (((area * 0.6) / 0.9) + 1024) * (1024 * 1024) @@ -590,3 +597,33 @@ class SD3(BaseModel): else: area = input_shape[0] * input_shape[2] * input_shape[3] return (area * 0.3) * (1024 * 1024) + + +class StableAudio1(BaseModel): + def __init__(self, model_config, seconds_start_embedder_weights, seconds_total_embedder_weights, model_type=ModelType.V_PREDICTION_CONTINUOUS, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.audio.dit.AudioDiffusionTransformer) + self.seconds_start_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512) + self.seconds_total_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512) + self.seconds_start_embedder.load_state_dict(seconds_start_embedder_weights) + self.seconds_total_embedder.load_state_dict(seconds_total_embedder_weights) + + def extra_conds(self, **kwargs): + out = {} + + noise = kwargs.get("noise", None) + device = kwargs["device"] + + seconds_start = kwargs.get("seconds_start", 0) + seconds_total = kwargs.get("seconds_total", int(noise.shape[-1] / 21.53)) + + seconds_start_embed = self.seconds_start_embedder([seconds_start])[0].to(device) + seconds_total_embed = self.seconds_total_embedder([seconds_total])[0].to(device) + + global_embed = torch.cat([seconds_start_embed, seconds_total_embed], dim=-1).reshape((1, -1)) + out['global_embed'] = comfy.conds.CONDRegular(global_embed) + + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + cross_attn = torch.cat([cross_attn.to(device), seconds_start_embed.repeat((cross_attn.shape[0], 1, 1)), seconds_total_embed.repeat((cross_attn.shape[0], 1, 1))], dim=1) + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + return out diff --git a/comfy/model_detection.py b/comfy/model_detection.py index dfe0ea99..4843e6a4 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -96,6 +96,11 @@ def detect_unet_config(state_dict, key_prefix): unet_config['block_repeat'] = [[1, 1, 1, 1], [2, 2, 2, 2]] return unet_config + if '{}transformer.rotary_pos_emb.inv_freq'.format(key_prefix) in state_dict_keys: #stable audio dit + unet_config = {} + unet_config["audio_model"] = "dit1.0" + return unet_config + unet_config = { "use_checkpoint": False, "image_size": 32, @@ -236,6 +241,13 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=Fal else: return model_config +def unet_prefix_from_state_dict(state_dict): + if "model.model.postprocess_conv.weight" in state_dict: #audio models + unet_key_prefix = "model.model." + else: + unet_key_prefix = "model.diffusion_model." + return unet_key_prefix + def convert_config(unet_config): new_config = unet_config.copy() num_res_blocks = new_config.get("num_res_blocks", None) diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py index d6120a83..6bd3a5d7 100644 --- a/comfy/model_sampling.py +++ b/comfy/model_sampling.py @@ -169,6 +169,14 @@ class ModelSamplingContinuousEDM(torch.nn.Module): return math.exp((math.log(self.sigma_max) - log_sigma_min) * percent + log_sigma_min) +class ModelSamplingContinuousV(ModelSamplingContinuousEDM): + def timestep(self, sigma): + return sigma.atan() / math.pi * 2 + + def sigma(self, timestep): + return (timestep * math.pi / 2).tan() + + def time_snr_shift(alpha, t): if alpha == 1.0: return t diff --git a/comfy/ops.py b/comfy/ops.py index 7ebb3dd2..0f1ceb57 100644 --- a/comfy/ops.py +++ b/comfy/ops.py @@ -51,6 +51,20 @@ class disable_weight_init: else: return super().forward(*args, **kwargs) + class Conv1d(torch.nn.Conv1d, CastWeightBiasOp): + def reset_parameters(self): + return None + + def forward_comfy_cast_weights(self, input): + weight, bias = cast_bias_weight(self, input) + return self._conv_forward(input, weight, bias) + + def forward(self, *args, **kwargs): + if self.comfy_cast_weights: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + class Conv2d(torch.nn.Conv2d, CastWeightBiasOp): def reset_parameters(self): return None @@ -133,6 +147,27 @@ class disable_weight_init: else: return super().forward(*args, **kwargs) + class ConvTranspose1d(torch.nn.ConvTranspose1d, CastWeightBiasOp): + def reset_parameters(self): + return None + + def forward_comfy_cast_weights(self, input, output_size=None): + num_spatial_dims = 1 + output_padding = self._output_padding( + input, output_size, self.stride, self.padding, self.kernel_size, + num_spatial_dims, self.dilation) + + weight, bias = cast_bias_weight(self, input) + return torch.nn.functional.conv_transpose1d( + input, weight, bias, self.stride, self.padding, + output_padding, self.groups, self.dilation) + + def forward(self, *args, **kwargs): + if self.comfy_cast_weights: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + @classmethod def conv_nd(s, dims, *args, **kwargs): if dims == 2: @@ -147,6 +182,9 @@ class manual_cast(disable_weight_init): class Linear(disable_weight_init.Linear): comfy_cast_weights = True + class Conv1d(disable_weight_init.Conv1d): + comfy_cast_weights = True + class Conv2d(disable_weight_init.Conv2d): comfy_cast_weights = True @@ -161,3 +199,6 @@ class manual_cast(disable_weight_init): class ConvTranspose2d(disable_weight_init.ConvTranspose2d): comfy_cast_weights = True + + class ConvTranspose1d(disable_weight_init.ConvTranspose1d): + comfy_cast_weights = True diff --git a/comfy/sa_t5.py b/comfy/sa_t5.py new file mode 100644 index 00000000..37be5287 --- /dev/null +++ b/comfy/sa_t5.py @@ -0,0 +1,22 @@ +from comfy import sd1_clip +from transformers import T5TokenizerFast +import comfy.t5 +import os + +class T5BaseModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None): + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_base.json") + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.t5.T5, enable_attention_masks=True, zero_out_masked=True) + +class T5BaseTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") + super().__init__(tokenizer_path, pad_with_end=False, embedding_size=768, embedding_key='t5base', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128) + +class SAT5Tokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None): + super().__init__(embedding_directory=embedding_directory, clip_name="t5base", tokenizer=T5BaseTokenizer) + +class SAT5Model(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, **kwargs): + super().__init__(device=device, dtype=dtype, clip_name="t5base", clip_model=T5BaseModel, **kwargs) diff --git a/comfy/sd.py b/comfy/sd.py index 3fd9e0e9..f1e48713 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -6,7 +6,7 @@ from comfy import model_management from .ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine from .ldm.cascade.stage_a import StageA from .ldm.cascade.stage_c_coder import StageC_coder - +from .ldm.audio.autoencoder import AudioOobleckVAE import yaml import comfy.utils @@ -20,6 +20,7 @@ from . import sd1_clip from . import sd2_clip from . import sdxl_clip from . import sd3_clip +from . import sa_t5 import comfy.model_patcher import comfy.lora @@ -174,6 +175,7 @@ class VAE: self.downscale_ratio = 8 self.upscale_ratio = 8 self.latent_channels = 4 + self.output_channels = 3 self.process_input = lambda image: image * 2.0 - 1.0 self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0) @@ -232,6 +234,16 @@ class VAE: self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig}, decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig}) + elif "decoder.layers.0.weight_v" in sd: + self.first_stage_model = AudioOobleckVAE() + self.memory_used_encode = lambda shape, dtype: (1767 * shape[2]) * model_management.dtype_size(dtype) #TODO: tweak for the audio VAE + self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * 64) * model_management.dtype_size(dtype) + self.latent_channels = 64 + self.output_channels = 2 + self.upscale_ratio = 2048 + self.downscale_ratio = 2048 + self.process_output = lambda audio: audio + self.process_input = lambda audio: audio else: logging.warning("WARNING: No VAE weights detected, VAE not initalized.") self.first_stage_model = None @@ -260,12 +272,12 @@ class VAE: self.patcher = comfy.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device) def vae_encode_crop_pixels(self, pixels): - x = (pixels.shape[1] // self.downscale_ratio) * self.downscale_ratio - y = (pixels.shape[2] // self.downscale_ratio) * self.downscale_ratio - if pixels.shape[1] != x or pixels.shape[2] != y: - x_offset = (pixels.shape[1] % self.downscale_ratio) // 2 - y_offset = (pixels.shape[2] % self.downscale_ratio) // 2 - pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :] + dims = pixels.shape[1:-1] + for d in range(len(dims)): + x = (dims[d] // self.downscale_ratio) * self.downscale_ratio + x_offset = (dims[d] % self.downscale_ratio) // 2 + if x != dims[d]: + pixels = pixels.narrow(d + 1, x_offset, x) return pixels def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16): @@ -303,7 +315,7 @@ class VAE: batch_number = int(free_memory / memory_used) batch_number = max(1, batch_number) - pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * self.upscale_ratio), round(samples_in.shape[3] * self.upscale_ratio)), device=self.output_device) + pixel_samples = torch.empty((samples_in.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples_in.shape[2:])), device=self.output_device) for x in range(0, samples_in.shape[0], batch_number): samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device) pixel_samples[x:x+batch_number] = self.process_output(self.first_stage_model.decode(samples).to(self.output_device).float()) @@ -328,7 +340,7 @@ class VAE: free_memory = model_management.get_free_memory(self.device) batch_number = int(free_memory / memory_used) batch_number = max(1, batch_number) - samples = torch.empty((pixel_samples.shape[0], self.latent_channels, round(pixel_samples.shape[2] // self.downscale_ratio), round(pixel_samples.shape[3] // self.downscale_ratio)), device=self.output_device) + samples = torch.empty((pixel_samples.shape[0], self.latent_channels) + tuple(map(lambda a: a // self.downscale_ratio, pixel_samples.shape[2:])), device=self.output_device) for x in range(0, pixel_samples.shape[0], batch_number): pixels_in = self.process_input(pixel_samples[x:x+batch_number]).to(self.vae_dtype).to(self.device) samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).to(self.output_device).float() @@ -371,6 +383,7 @@ class CLIPType(Enum): STABLE_DIFFUSION = 1 STABLE_CASCADE = 2 SD3 = 3 + STABLE_AUDIO = 4 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION): clip_data = [] @@ -404,6 +417,9 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI dtype_t5 = clip_data[0]["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"].dtype clip_target.clip = sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, dtype_t5=dtype_t5) clip_target.tokenizer = sd3_clip.SD3Tokenizer + elif "encoder.block.0.layer.0.SelfAttention.k.weight" in clip_data[0]: + clip_target.clip = sa_t5.SAT5Model + clip_target.tokenizer = sa_t5.SAT5Tokenizer else: clip_target.clip = sd1_clip.SD1ClipModel clip_target.tokenizer = sd1_clip.SD1Tokenizer @@ -470,10 +486,11 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o model_patcher = None clip_target = None - parameters = comfy.utils.calculate_parameters(sd, "model.diffusion_model.") + diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd) + parameters = comfy.utils.calculate_parameters(sd, diffusion_model_prefix) load_device = model_management.get_torch_device() - model_config = model_detection.model_config_from_unet(sd, "model.diffusion_model.") + model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix) unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes) manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes) model_config.set_inference_dtype(unet_dtype, manual_cast_dtype) @@ -488,8 +505,8 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o if output_model: inital_load_device = model_management.unet_inital_load_device(parameters, unet_dtype) offload_device = model_management.unet_offload_device() - model = model_config.get_model(sd, "model.diffusion_model.", device=inital_load_device) - model.load_model_weights(sd, "model.diffusion_model.") + model = model_config.get_model(sd, diffusion_model_prefix, device=inital_load_device) + model.load_model_weights(sd, diffusion_model_prefix) if output_vae: vae_sd = comfy.utils.state_dict_prefix_replace(sd, {k: "" for k in model_config.vae_key_prefix}, filter_keys=True) diff --git a/comfy/supported_models.py b/comfy/supported_models.py index c8ddf3e2..761498db 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -6,6 +6,7 @@ from . import sd1_clip from . import sd2_clip from . import sdxl_clip from . import sd3_clip +from . import sa_t5 from . import supported_models_base from . import latent_formats @@ -524,7 +525,35 @@ class SD3(supported_models_base.BASE): return supported_models_base.ClipTarget(sd3_clip.SD3Tokenizer, sd3_clip.sd3_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5)) +class StableAudio(supported_models_base.BASE): + unet_config = { + "audio_model": "dit1.0", + } -models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3] + sampling_settings = {"sigma_max": 500.0, "sigma_min": 0.03} + + unet_extra_config = {} + latent_format = latent_formats.StableAudio1 + + text_encoder_key_prefix = ["text_encoders."] + vae_key_prefix = ["pretransform.model."] + + def get_model(self, state_dict, prefix="", device=None): + seconds_start_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_start.": ""}, filter_keys=True) + seconds_total_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_total.": ""}, filter_keys=True) + return model_base.StableAudio1(self, seconds_start_embedder_weights=seconds_start_sd, seconds_total_embedder_weights=seconds_total_sd, device=device) + + + def process_unet_state_dict(self, state_dict): + for k in list(state_dict.keys()): + if k.endswith(".cross_attend_norm.beta") or k.endswith(".ff_norm.beta") or k.endswith(".pre_norm.beta"): #These weights are all zero + state_dict.pop(k) + return state_dict + + def clip_target(self, state_dict={}): + return supported_models_base.ClipTarget(sa_t5.SAT5Tokenizer, sa_t5.SAT5Model) + + +models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio] models += [SVD_img2vid] diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py new file mode 100644 index 00000000..5f4bd354 --- /dev/null +++ b/comfy_extras/nodes_audio.py @@ -0,0 +1,128 @@ +import torchaudio +import torch +import comfy.model_management +import folder_paths +import os + +class EmptyLatentAudio: + def __init__(self): + self.device = comfy.model_management.intermediate_device() + + @classmethod + def INPUT_TYPES(s): + return {"required": {}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "generate" + + CATEGORY = "_for_testing/audio" + + def generate(self): + batch_size = 1 + latent = torch.zeros([batch_size, 64, 1024], device=self.device) + return ({"samples":latent, "type": "audio"}, ) + +class VAEEncodeAudio: + @classmethod + def INPUT_TYPES(s): + return {"required": { "audio": ("AUDIO", ), "vae": ("VAE", )}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "encode" + + CATEGORY = "_for_testing/audio" + + def encode(self, vae, audio): + t = vae.encode(audio["waveform"].movedim(1, -1)) + return ({"samples":t}, ) + +class VAEDecodeAudio: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}} + RETURN_TYPES = ("AUDIO",) + FUNCTION = "decode" + + CATEGORY = "_for_testing/audio" + + def decode(self, vae, samples): + audio = vae.decode(samples["samples"]).movedim(-1, 1) + return ({"waveform": audio, "sample_rate": 44100}, ) + +class SaveAudio: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" + self.compress_level = 4 + + @classmethod + def INPUT_TYPES(s): + return {"required": { "audio": ("AUDIO", ), + "filename_prefix": ("STRING", {"default": "audio/ComfyUI"})}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = () + FUNCTION = "save_audio" + + OUTPUT_NODE = True + + CATEGORY = "_for_testing/audio" + + def save_audio(self, audio, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): + filename_prefix += self.prefix_append + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) + results = list() + for (batch_number, waveform) in enumerate(audio["waveform"]): + #TODO: metadata + filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) + file = f"{filename_with_batch_num}_{counter:05}_.flac" + torchaudio.save(os.path.join(full_output_folder, file), waveform, audio["sample_rate"], format="FLAC") + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + counter += 1 + + return { "ui": { "audio": results } } + +class LoadAudio: + @classmethod + def INPUT_TYPES(s): + input_dir = folder_paths.get_input_directory() + files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] + return {"required": {"audio": [sorted(files), ]}, } + + CATEGORY = "_for_testing/audio" + + RETURN_TYPES = ("AUDIO", ) + FUNCTION = "load" + + def load(self, audio): + audio_path = folder_paths.get_annotated_filepath(audio) + waveform, sample_rate = torchaudio.load(audio_path) + multiplier = 1.0 + audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate} + return (audio, ) + + @classmethod + def IS_CHANGED(s, audio): + image_path = folder_paths.get_annotated_filepath(audio) + m = hashlib.sha256() + with open(image_path, 'rb') as f: + m.update(f.read()) + return m.digest().hex() + + @classmethod + def VALIDATE_INPUTS(s, audio): + if not folder_paths.exists_annotated_filepath(audio): + return "Invalid audio file: {}".format(audio) + return True + +NODE_CLASS_MAPPINGS = { + "EmptyLatentAudio": EmptyLatentAudio, + "VAEEncodeAudio": VAEEncodeAudio, + "VAEDecodeAudio": VAEDecodeAudio, + "SaveAudio": SaveAudio, + "LoadAudio": LoadAudio, +} diff --git a/comfy_extras/nodes_model_advanced.py b/comfy_extras/nodes_model_advanced.py index 9bcd3c39..97559cf5 100644 --- a/comfy_extras/nodes_model_advanced.py +++ b/comfy_extras/nodes_model_advanced.py @@ -196,6 +196,36 @@ class ModelSamplingContinuousEDM: m.add_object_patch("latent_format", latent_format) return (m, ) +class ModelSamplingContinuousV: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "sampling": (["v_prediction"],), + "sigma_max": ("FLOAT", {"default": 500.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), + "sigma_min": ("FLOAT", {"default": 0.03, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), + }} + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "advanced/model" + + def patch(self, model, sampling, sigma_max, sigma_min): + m = model.clone() + + latent_format = None + sigma_data = 1.0 + if sampling == "v_prediction": + sampling_type = comfy.model_sampling.V_PREDICTION + + class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingContinuousV, sampling_type): + pass + + model_sampling = ModelSamplingAdvanced(model.model.model_config) + model_sampling.set_parameters(sigma_min, sigma_max, sigma_data) + m.add_object_patch("model_sampling", model_sampling) + return (m, ) + class RescaleCFG: @classmethod def INPUT_TYPES(s): @@ -238,6 +268,7 @@ class RescaleCFG: NODE_CLASS_MAPPINGS = { "ModelSamplingDiscrete": ModelSamplingDiscrete, "ModelSamplingContinuousEDM": ModelSamplingContinuousEDM, + "ModelSamplingContinuousV": ModelSamplingContinuousV, "ModelSamplingStableCascade": ModelSamplingStableCascade, "ModelSamplingSD3": ModelSamplingSD3, "RescaleCFG": RescaleCFG, diff --git a/nodes.py b/nodes.py index 6fbeb377..0b2a96f7 100644 --- a/nodes.py +++ b/nodes.py @@ -818,7 +818,7 @@ class CLIPLoader: @classmethod def INPUT_TYPES(s): return {"required": { "clip_name": (folder_paths.get_filename_list("clip"), ), - "type": (["stable_diffusion", "stable_cascade", "sd3"], ), + "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio"], ), }} RETURN_TYPES = ("CLIP",) FUNCTION = "load_clip" @@ -826,11 +826,14 @@ class CLIPLoader: CATEGORY = "advanced/loaders" def load_clip(self, clip_name, type="stable_diffusion"): - clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION if type == "stable_cascade": clip_type = comfy.sd.CLIPType.STABLE_CASCADE elif type == "sd3": clip_type = comfy.sd.CLIPType.SD3 + elif type == "stable_audio": + clip_type = comfy.sd.CLIPType.STABLE_AUDIO + else: + clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION clip_path = folder_paths.get_full_path("clip", clip_name) clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type) @@ -1973,6 +1976,7 @@ def init_custom_nodes(): "nodes_attention_multiply.py", "nodes_advanced_samplers.py", "nodes_webcam.py", + "nodes_audio.py", "nodes_sd3.py", ] diff --git a/requirements.txt b/requirements.txt index 8f681f8f..85e1dc9b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,7 @@ torch torchsde torchvision +torchaudio einops transformers>=4.25.1 safetensors>=0.4.2 From df7db0e0279f58d9f2f3f33ddb60bb238b6d0dc8 Mon Sep 17 00:00:00 2001 From: "Dr.Lt.Data" <128333288+ltdrdata@users.noreply.github.com> Date: Sun, 16 Jun 2024 15:03:53 +0900 Subject: [PATCH 288/378] support TAESD3 (#3738) --- comfy/latent_formats.py | 1 + comfy/sd.py | 6 +++--- comfy/taesd/taesd.py | 15 ++++++++------- latent_preview.py | 2 +- nodes.py | 17 +++++++++++++++-- 5 files changed, 28 insertions(+), 13 deletions(-) diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py index 92f39d5c..4b4a9eda 100644 --- a/comfy/latent_formats.py +++ b/comfy/latent_formats.py @@ -129,6 +129,7 @@ class SD3(LatentFormat): [-0.0749, -0.0634, -0.0456], [-0.1418, -0.1457, -0.1259] ] + self.taesd_decoder_name = "taesd3_decoder" def process_in(self, latent): return (latent - self.shift_factor) * self.scale_factor diff --git a/comfy/sd.py b/comfy/sd.py index f1e48713..d2720ec1 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -166,7 +166,7 @@ class CLIP: return self.patcher.get_key_patches() class VAE: - def __init__(self, sd=None, device=None, config=None, dtype=None): + def __init__(self, sd=None, device=None, config=None, dtype=None, latent_channels=4): if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format sd = diffusers_convert.convert_vae_state_dict(sd) @@ -174,7 +174,7 @@ class VAE: self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype) self.downscale_ratio = 8 self.upscale_ratio = 8 - self.latent_channels = 4 + self.latent_channels = latent_channels self.output_channels = 3 self.process_input = lambda image: image * 2.0 - 1.0 self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0) @@ -189,7 +189,7 @@ class VAE: encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config}, decoder_config={'target': "comfy.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config}) elif "taesd_decoder.1.weight" in sd: - self.first_stage_model = comfy.taesd.taesd.TAESD() + self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=self.latent_channels) elif "vquantizer.codebook.weight" in sd: #VQGan: stage a of stable cascade self.first_stage_model = StageA() self.downscale_ratio = 4 diff --git a/comfy/taesd/taesd.py b/comfy/taesd/taesd.py index 8f96c54e..74031c60 100644 --- a/comfy/taesd/taesd.py +++ b/comfy/taesd/taesd.py @@ -25,18 +25,19 @@ class Block(nn.Module): def forward(self, x): return self.fuse(self.conv(x) + self.skip(x)) -def Encoder(): +def Encoder(latent_channels=4): return nn.Sequential( conv(3, 64), Block(64, 64), conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), - conv(64, 4), + conv(64, latent_channels), ) -def Decoder(): + +def Decoder(latent_channels=4): return nn.Sequential( - Clamp(), conv(4, 64), nn.ReLU(), + Clamp(), conv(latent_channels, 64), nn.ReLU(), Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), @@ -47,11 +48,11 @@ class TAESD(nn.Module): latent_magnitude = 3 latent_shift = 0.5 - def __init__(self, encoder_path=None, decoder_path=None): + def __init__(self, encoder_path=None, decoder_path=None, latent_channels=4): """Initialize pretrained TAESD on the given device from the given checkpoints.""" super().__init__() - self.taesd_encoder = Encoder() - self.taesd_decoder = Decoder() + self.taesd_encoder = Encoder(latent_channels=latent_channels) + self.taesd_decoder = Decoder(latent_channels=latent_channels) self.vae_scale = torch.nn.Parameter(torch.tensor(1.0)) if encoder_path is not None: self.taesd_encoder.load_state_dict(comfy.utils.load_torch_file(encoder_path, safe_load=True)) diff --git a/latent_preview.py b/latent_preview.py index 54aa233f..ae6c106e 100644 --- a/latent_preview.py +++ b/latent_preview.py @@ -64,7 +64,7 @@ def get_previewer(device, latent_format): if method == LatentPreviewMethod.TAESD: if taesd_decoder_path: - taesd = TAESD(None, taesd_decoder_path).to(device) + taesd = TAESD(None, taesd_decoder_path, latent_channels=latent_format.latent_channels).to(device) previewer = TAESDPreviewerImpl(taesd) else: logging.warning("Warning: TAESD previews enabled, but could not find models/vae_approx/{}".format(latent_format.taesd_decoder_name)) diff --git a/nodes.py b/nodes.py index 0b2a96f7..ca10ca32 100644 --- a/nodes.py +++ b/nodes.py @@ -634,6 +634,8 @@ class VAELoader: sdxl_taesd_dec = False sd1_taesd_enc = False sd1_taesd_dec = False + sd3_taesd_enc = False + sd3_taesd_dec = False for v in approx_vaes: if v.startswith("taesd_decoder."): @@ -644,10 +646,16 @@ class VAELoader: sdxl_taesd_dec = True elif v.startswith("taesdxl_encoder."): sdxl_taesd_enc = True + elif v.startswith("taesd3_decoder."): + sd3_taesd_dec = True + elif v.startswith("taesd3_encoder."): + sd3_taesd_enc = True if sd1_taesd_dec and sd1_taesd_enc: vaes.append("taesd") if sdxl_taesd_dec and sdxl_taesd_enc: vaes.append("taesdxl") + if sd3_taesd_dec and sd3_taesd_enc: + vaes.append("taesd3") return vaes @staticmethod @@ -670,6 +678,8 @@ class VAELoader: sd["vae_scale"] = torch.tensor(0.18215) elif name == "taesdxl": sd["vae_scale"] = torch.tensor(0.13025) + elif name == "taesd3": + sd["vae_scale"] = torch.tensor(1.5305) return sd @classmethod @@ -682,12 +692,15 @@ class VAELoader: #TODO: scale factor? def load_vae(self, vae_name): - if vae_name in ["taesd", "taesdxl"]: + if vae_name in ["taesd", "taesdxl", "taesd3"]: sd = self.load_taesd(vae_name) else: vae_path = folder_paths.get_full_path("vae", vae_name) sd = comfy.utils.load_torch_file(vae_path) - vae = comfy.sd.VAE(sd=sd) + + latent_channels = 16 if vae_name == 'taesd3' else 4 + + vae = comfy.sd.VAE(sd=sd, latent_channels=latent_channels) return (vae,) class ControlNetLoader: From 04e8798c37d958d74ea6bda506b86f51356d6caf Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 16 Jun 2024 02:04:24 -0400 Subject: [PATCH 289/378] Improvements to the TAESD3 implementation. --- comfy/sd.py | 6 +++--- comfy/taesd/taesd.py | 5 +++-- nodes.py | 8 ++++---- 3 files changed, 10 insertions(+), 9 deletions(-) diff --git a/comfy/sd.py b/comfy/sd.py index d2720ec1..8d90dd06 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -166,7 +166,7 @@ class CLIP: return self.patcher.get_key_patches() class VAE: - def __init__(self, sd=None, device=None, config=None, dtype=None, latent_channels=4): + def __init__(self, sd=None, device=None, config=None, dtype=None): if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format sd = diffusers_convert.convert_vae_state_dict(sd) @@ -174,7 +174,7 @@ class VAE: self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype) self.downscale_ratio = 8 self.upscale_ratio = 8 - self.latent_channels = latent_channels + self.latent_channels = 4 self.output_channels = 3 self.process_input = lambda image: image * 2.0 - 1.0 self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0) @@ -189,7 +189,7 @@ class VAE: encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config}, decoder_config={'target': "comfy.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config}) elif "taesd_decoder.1.weight" in sd: - self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=self.latent_channels) + self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=sd["taesd_decoder.1.weight"].shape[1]) elif "vquantizer.codebook.weight" in sd: #VQGan: stage a of stable cascade self.first_stage_model = StageA() self.downscale_ratio = 4 diff --git a/comfy/taesd/taesd.py b/comfy/taesd/taesd.py index 74031c60..ce36f1a8 100644 --- a/comfy/taesd/taesd.py +++ b/comfy/taesd/taesd.py @@ -54,6 +54,7 @@ class TAESD(nn.Module): self.taesd_encoder = Encoder(latent_channels=latent_channels) self.taesd_decoder = Decoder(latent_channels=latent_channels) self.vae_scale = torch.nn.Parameter(torch.tensor(1.0)) + self.vae_shift = torch.nn.Parameter(torch.tensor(0.0)) if encoder_path is not None: self.taesd_encoder.load_state_dict(comfy.utils.load_torch_file(encoder_path, safe_load=True)) if decoder_path is not None: @@ -70,9 +71,9 @@ class TAESD(nn.Module): return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude) def decode(self, x): - x_sample = self.taesd_decoder(x * self.vae_scale) + x_sample = self.taesd_decoder((x - self.vae_shift) * self.vae_scale) x_sample = x_sample.sub(0.5).mul(2) return x_sample def encode(self, x): - return self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale + return (self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale) + self.vae_shift diff --git a/nodes.py b/nodes.py index ca10ca32..06ea4621 100644 --- a/nodes.py +++ b/nodes.py @@ -676,10 +676,13 @@ class VAELoader: if name == "taesd": sd["vae_scale"] = torch.tensor(0.18215) + sd["vae_shift"] = torch.tensor(0.0) elif name == "taesdxl": sd["vae_scale"] = torch.tensor(0.13025) + sd["vae_shift"] = torch.tensor(0.0) elif name == "taesd3": sd["vae_scale"] = torch.tensor(1.5305) + sd["vae_shift"] = torch.tensor(0.0609) return sd @classmethod @@ -697,10 +700,7 @@ class VAELoader: else: vae_path = folder_paths.get_full_path("vae", vae_name) sd = comfy.utils.load_torch_file(vae_path) - - latent_channels = 16 if vae_name == 'taesd3' else 4 - - vae = comfy.sd.VAE(sd=sd, latent_channels=latent_channels) + vae = comfy.sd.VAE(sd=sd) return (vae,) class ControlNetLoader: From 746a0410d43b02dcaf91ac5abc2c1803e420203d Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 16 Jun 2024 03:10:04 -0400 Subject: [PATCH 290/378] Fix VAEEncode with taesd3. --- comfy/sd.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/comfy/sd.py b/comfy/sd.py index 8d90dd06..82f9aeab 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -189,7 +189,8 @@ class VAE: encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config}, decoder_config={'target': "comfy.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config}) elif "taesd_decoder.1.weight" in sd: - self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=sd["taesd_decoder.1.weight"].shape[1]) + self.latent_channels = sd["taesd_decoder.1.weight"].shape[1] + self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=self.latent_channels) elif "vquantizer.codebook.weight" in sd: #VQGan: stage a of stable cascade self.first_stage_model = StageA() self.downscale_ratio = 4 From ca9d300a804fd1bfc67b3de9200c2d09b78899d0 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 16 Jun 2024 11:47:32 -0400 Subject: [PATCH 291/378] Better estimation for memory usage during audio VAE encoding/decoding. --- comfy/sd.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comfy/sd.py b/comfy/sd.py index 82f9aeab..e16cd8e5 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -237,8 +237,8 @@ class VAE: decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig}) elif "decoder.layers.0.weight_v" in sd: self.first_stage_model = AudioOobleckVAE() - self.memory_used_encode = lambda shape, dtype: (1767 * shape[2]) * model_management.dtype_size(dtype) #TODO: tweak for the audio VAE - self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * 64) * model_management.dtype_size(dtype) + self.memory_used_encode = lambda shape, dtype: (1000 * shape[2]) * model_management.dtype_size(dtype) + self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * 2048) * model_management.dtype_size(dtype) self.latent_channels = 64 self.output_channels = 2 self.upscale_ratio = 2048 From 8ddc151a4ce7f59de75b24ea8349f0e406ba0da5 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 16 Jun 2024 13:06:23 -0400 Subject: [PATCH 292/378] Squash depreciation warning on new pytorch. --- comfy/ldm/audio/autoencoder.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/comfy/ldm/audio/autoencoder.py b/comfy/ldm/audio/autoencoder.py index 7363131e..8123e66a 100644 --- a/comfy/ldm/audio/autoencoder.py +++ b/comfy/ldm/audio/autoencoder.py @@ -75,10 +75,16 @@ class SnakeBeta(nn.Module): return x def WNConv1d(*args, **kwargs): - return torch.nn.utils.weight_norm(ops.Conv1d(*args, **kwargs)) + try: + return torch.nn.utils.parametrizations.weight_norm(ops.Conv1d(*args, **kwargs)) + except: + return torch.nn.utils.weight_norm(ops.Conv1d(*args, **kwargs)) #support pytorch 2.1 and older def WNConvTranspose1d(*args, **kwargs): - return torch.nn.utils.weight_norm(ops.ConvTranspose1d(*args, **kwargs)) + try: + return torch.nn.utils.parametrizations.weight_norm(ops.ConvTranspose1d(*args, **kwargs)) + except: + return torch.nn.utils.weight_norm(ops.ConvTranspose1d(*args, **kwargs)) #support pytorch 2.1 and older def get_activation(activation: Literal["elu", "snake", "none"], antialias=False, channels=None) -> nn.Module: if activation == "elu": From 6425252c4f2f6acd8f4ad59a2135f5bdae3452e4 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 16 Jun 2024 13:12:54 -0400 Subject: [PATCH 293/378] Use fp16 as the default vae dtype for the audio VAE. --- comfy/model_management.py | 35 ++++++++++++++++++++--------------- comfy/sd.py | 5 ++++- 2 files changed, 24 insertions(+), 16 deletions(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index 8b8d3ff0..04719329 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -167,7 +167,7 @@ if args.use_pytorch_cross_attention: ENABLE_PYTORCH_ATTENTION = True XFORMERS_IS_AVAILABLE = False -VAE_DTYPE = torch.float32 +VAE_DTYPES = [torch.float32] try: if is_nvidia(): @@ -176,7 +176,7 @@ try: if ENABLE_PYTORCH_ATTENTION == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False: ENABLE_PYTORCH_ATTENTION = True if torch.cuda.is_bf16_supported() and torch.cuda.get_device_properties(torch.cuda.current_device()).major >= 8: - VAE_DTYPE = torch.bfloat16 + VAE_DTYPES = [torch.bfloat16] + VAE_DTYPES if is_intel_xpu(): if args.use_split_cross_attention == False and args.use_quad_cross_attention == False: ENABLE_PYTORCH_ATTENTION = True @@ -184,17 +184,10 @@ except: pass if is_intel_xpu(): - VAE_DTYPE = torch.bfloat16 + VAE_DTYPES = [torch.bfloat16] + VAE_DTYPES if args.cpu_vae: - VAE_DTYPE = torch.float32 - -if args.fp16_vae: - VAE_DTYPE = torch.float16 -elif args.bf16_vae: - VAE_DTYPE = torch.bfloat16 -elif args.fp32_vae: - VAE_DTYPE = torch.float32 + VAE_DTYPES = [torch.float32] if ENABLE_PYTORCH_ATTENTION: @@ -258,7 +251,6 @@ try: except: logging.warning("Could not pick default device.") -logging.info("VAE dtype: {}".format(VAE_DTYPE)) current_loaded_models = [] @@ -619,9 +611,22 @@ def vae_offload_device(): else: return torch.device("cpu") -def vae_dtype(): - global VAE_DTYPE - return VAE_DTYPE +def vae_dtype(device=None, allowed_dtypes=[]): + global VAE_DTYPES + if args.fp16_vae: + return torch.float16 + elif args.bf16_vae: + return torch.bfloat16 + elif args.fp32_vae: + return torch.float32 + + for d in allowed_dtypes: + if d == torch.float16 and should_use_fp16(device, prioritize_performance=False): + return d + if d in VAE_DTYPES: + return d + + return VAE_DTYPES[0] def get_autocast_device(dev): if hasattr(dev, 'type'): diff --git a/comfy/sd.py b/comfy/sd.py index e16cd8e5..58a858aa 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -178,6 +178,7 @@ class VAE: self.output_channels = 3 self.process_input = lambda image: image * 2.0 - 1.0 self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0) + self.working_dtypes = [torch.bfloat16, torch.float32] if config is None: if "decoder.mid.block_1.mix_factor" in sd: @@ -245,6 +246,7 @@ class VAE: self.downscale_ratio = 2048 self.process_output = lambda audio: audio self.process_input = lambda audio: audio + self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32] else: logging.warning("WARNING: No VAE weights detected, VAE not initalized.") self.first_stage_model = None @@ -265,12 +267,13 @@ class VAE: self.device = device offload_device = model_management.vae_offload_device() if dtype is None: - dtype = model_management.vae_dtype() + dtype = model_management.vae_dtype(self.device, self.working_dtypes) self.vae_dtype = dtype self.first_stage_model.to(self.vae_dtype) self.output_device = model_management.intermediate_device() self.patcher = comfy.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device) + logging.debug("VAE load device: {}, offload device: {}, dtype: {}".format(self.device, offload_device, self.vae_dtype)) def vae_encode_crop_pixels(self, pixels): dims = pixels.shape[1:-1] From b7c473d1abda165e1ff6f90dfacc6f589c3d1c38 Mon Sep 17 00:00:00 2001 From: Janek Mann Date: Mon, 17 Jun 2024 12:55:06 +0100 Subject: [PATCH 294/378] Fix lora keys for SimpleTuner (#3759) --- comfy/lora.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/comfy/lora.py b/comfy/lora.py index 37254b03..082a8b3c 100644 --- a/comfy/lora.py +++ b/comfy/lora.py @@ -30,6 +30,7 @@ def load_lora(lora, to_load): regular_lora = "{}.lora_up.weight".format(x) diffusers_lora = "{}_lora.up.weight".format(x) diffusers2_lora = "{}.lora_B.weight".format(x) + diffusers3_lora = "{}.lora.up.weight".format(x) transformers_lora = "{}.lora_linear_layer.up.weight".format(x) A_name = None @@ -45,6 +46,10 @@ def load_lora(lora, to_load): A_name = diffusers2_lora B_name = "{}.lora_A.weight".format(x) mid_name = None + elif diffusers3_lora in lora.keys(): + A_name = diffusers3_lora + B_name = "{}.lora.down.weight".format(x) + mid_name = None elif transformers_lora in lora.keys(): A_name = transformers_lora B_name ="{}.lora_linear_layer.down.weight".format(x) From 379ff92e9e872a588ad2ceeabdd08b909590b25b Mon Sep 17 00:00:00 2001 From: Juanjuan <472185568@qq.com> Date: Mon, 17 Jun 2024 19:56:53 +0800 Subject: [PATCH 295/378] fix app.js no graph defined (#3754) * local test * fix "graph" not found * fix --------- Co-authored-by: Xiujuan Li --- web/scripts/app.js | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/web/scripts/app.js b/web/scripts/app.js index f96d197a..58fb765c 100644 --- a/web/scripts/app.js +++ b/web/scripts/app.js @@ -2239,7 +2239,7 @@ export class ComfyApp { const node = LiteGraph.createNode(data.class_type); node.id = isNaN(+id) ? id : +id; node.title = data._meta?.title ?? node.title - graph.add(node); + app.graph.add(node); } for (const id of ids) { From a45df695706e8b80a66ff6815789582d88571326 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 17 Jun 2024 22:48:23 -0400 Subject: [PATCH 296/378] Basic tiled decoding for audio VAE. --- comfy/sd.py | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/comfy/sd.py b/comfy/sd.py index 58a858aa..cfbf8fa4 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -298,6 +298,17 @@ class VAE: / 3.0) return output + def decode_tiled_1d(self, samples, tile_x=128, overlap=64): + output = torch.empty((samples.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples.shape[2:])), device=self.output_device) + + for j in range(samples.shape[0]): + for i in range(0, samples.shape[-1], tile_x - overlap): + f = i + t = i + tile_x + output[j:j+1,:,f * self.upscale_ratio:t * self.upscale_ratio] = self.first_stage_model.decode(samples[j:j+1,:,f:t].to(self.vae_dtype).to(self.device)).float() + + return output + def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap) steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap) @@ -325,7 +336,10 @@ class VAE: pixel_samples[x:x+batch_number] = self.process_output(self.first_stage_model.decode(samples).to(self.output_device).float()) except model_management.OOM_EXCEPTION as e: logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.") - pixel_samples = self.decode_tiled_(samples_in) + if len(samples_in.shape) == 3: + pixel_samples = self.decode_tiled_1d(samples_in) + else: + pixel_samples = self.decode_tiled_(samples_in) pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1) return pixel_samples From 55f0dc124ed4352ac52b884c13f41ff9a9736102 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 18 Jun 2024 09:57:40 -0400 Subject: [PATCH 297/378] Add soundfile dependency so that windows can save audio. --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index 85e1dc9b..108958d2 100644 --- a/requirements.txt +++ b/requirements.txt @@ -15,3 +15,4 @@ psutil #non essential dependencies: kornia>=0.7.1 spandrel +soundfile From 3914d5a2ae5c755556c9f15e70d2b4104984da87 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 19 Jun 2024 10:01:43 -0400 Subject: [PATCH 298/378] Support full SD3 loras. --- comfy/lora.py | 19 +++++++------- comfy/utils.py | 70 ++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 79 insertions(+), 10 deletions(-) diff --git a/comfy/lora.py b/comfy/lora.py index 082a8b3c..9e3e7a8b 100644 --- a/comfy/lora.py +++ b/comfy/lora.py @@ -252,15 +252,14 @@ def model_lora_keys_unet(model, key_map={}): key_map[diffusers_lora_key] = unet_key if isinstance(model, comfy.model_base.SD3): #Diffusers lora SD3 - for i in range(model.model_config.unet_config.get("depth", 0)): - k = "transformer.transformer_blocks.{}.attn.".format(i) - qkv = "diffusion_model.joint_blocks.{}.x_block.attn.qkv.weight".format(i) - proj = "diffusion_model.joint_blocks.{}.x_block.attn.proj.weight".format(i) - if qkv in sd: - offset = sd[qkv].shape[0] // 3 - key_map["{}to_q".format(k)] = (qkv, (0, 0, offset)) - key_map["{}to_k".format(k)] = (qkv, (0, offset, offset)) - key_map["{}to_v".format(k)] = (qkv, (0, offset * 2, offset)) - key_map["{}to_out.0".format(k)] = proj + diffusers_keys = comfy.utils.mmdit_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.") + for k in diffusers_keys: + if k.endswith(".weight"): + to = diffusers_keys[k] + key_lora = "transformer.{}".format(k[:-len(".weight")]) #regular diffusers sd3 lora format + key_map[key_lora] = to + + key_lora = "base_model.model.{}".format(k[:-len(".weight")]) #format for flash-sd3 lora and others? + key_map[key_lora] = to return key_map diff --git a/comfy/utils.py b/comfy/utils.py index 884404cc..0d24fe73 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -249,6 +249,76 @@ def unet_to_diffusers(unet_config): return diffusers_unet_map +MMDIT_MAP_BASIC = { + ("context_embedder.bias", "context_embedder.bias"), + ("context_embedder.weight", "context_embedder.weight"), + ("t_embedder.mlp.0.bias", "time_text_embed.timestep_embedder.linear_1.bias"), + ("t_embedder.mlp.0.weight", "time_text_embed.timestep_embedder.linear_1.weight"), + ("t_embedder.mlp.2.bias", "time_text_embed.timestep_embedder.linear_2.bias"), + ("t_embedder.mlp.2.weight", "time_text_embed.timestep_embedder.linear_2.weight"), + ("x_embedder.proj.bias", "pos_embed.proj.bias"), + ("x_embedder.proj.weight", "pos_embed.proj.weight"), + ("y_embedder.mlp.0.bias", "time_text_embed.text_embedder.linear_1.bias"), + ("y_embedder.mlp.0.weight", "time_text_embed.text_embedder.linear_1.weight"), + ("y_embedder.mlp.2.bias", "time_text_embed.text_embedder.linear_2.bias"), + ("y_embedder.mlp.2.weight", "time_text_embed.text_embedder.linear_2.weight"), + ("pos_embed", "pos_embed.pos_embed"), + ("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias"), + ("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight"), + ("final_layer.linear.bias", "proj_out.bias"), + ("final_layer.linear.weight", "proj_out.weight"), +} + +MMDIT_MAP_BLOCK = { + ("context_block.adaLN_modulation.1.bias", "norm1_context.linear.bias"), + ("context_block.adaLN_modulation.1.weight", "norm1_context.linear.weight"), + ("context_block.attn.proj.bias", "attn.to_add_out.bias"), + ("context_block.attn.proj.weight", "attn.to_add_out.weight"), + ("context_block.mlp.fc1.bias", "ff_context.net.0.proj.bias"), + ("context_block.mlp.fc1.weight", "ff_context.net.0.proj.weight"), + ("context_block.mlp.fc2.bias", "ff_context.net.2.bias"), + ("context_block.mlp.fc2.weight", "ff_context.net.2.weight"), + ("x_block.adaLN_modulation.1.bias", "norm1.linear.bias"), + ("x_block.adaLN_modulation.1.weight", "norm1.linear.weight"), + ("x_block.attn.proj.bias", "attn.to_out.0.bias"), + ("x_block.attn.proj.weight", "attn.to_out.0.weight"), + ("x_block.mlp.fc1.bias", "ff.net.0.proj.bias"), + ("x_block.mlp.fc1.weight", "ff.net.0.proj.weight"), + ("x_block.mlp.fc2.bias", "ff.net.2.bias"), + ("x_block.mlp.fc2.weight", "ff.net.2.weight"), + ("", ""), +} + +def mmdit_to_diffusers(mmdit_config, output_prefix=""): + key_map = {} + + depth = mmdit_config.get("depth", 0) + for i in range(depth): + block_from = "transformer_blocks.{}".format(i) + block_to = "{}joint_blocks.{}".format(output_prefix, i) + + offset = depth * 64 + + for end in ("weight", "bias"): + k = "{}.attn.".format(block_from) + qkv = "{}.x_block.attn.qkv.{}".format(block_to, end) + key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, offset)) + key_map["{}to_k.{}".format(k, end)] = (qkv, (0, offset, offset)) + key_map["{}to_v.{}".format(k, end)] = (qkv, (0, offset * 2, offset)) + + qkv = "{}.context_block.attn.qkv.{}".format(block_to, end) + key_map["{}add_q_proj.{}".format(k, end)] = (qkv, (0, 0, offset)) + key_map["{}add_k_proj.{}".format(k, end)] = (qkv, (0, offset, offset)) + key_map["{}add_v_proj.{}".format(k, end)] = (qkv, (0, offset * 2, offset)) + + for k in MMDIT_MAP_BLOCK: + key_map["{}.{}".format(block_from, k[1])] = "{}.{}".format(block_to, k[0]) + + for k in MMDIT_MAP_BASIC: + key_map[k[1]] = "{}{}".format(output_prefix, k[0]) + + return key_map + def repeat_to_batch_size(tensor, batch_size, dim=0): if tensor.shape[dim] > batch_size: return tensor.narrow(dim, 0, batch_size) From 97ae6ef46001ed444ef1f2bd085beae98c17c240 Mon Sep 17 00:00:00 2001 From: Chenlei Hu Date: Wed, 19 Jun 2024 10:39:17 -0400 Subject: [PATCH 299/378] Add api/ prefix to api endpoints (#3779) --- server.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/server.py b/server.py index bab3b060..59090258 100644 --- a/server.py +++ b/server.py @@ -527,9 +527,20 @@ class PromptServer(): self.prompt_queue.delete_history_item(id_to_delete) return web.Response(status=200) - + def add_routes(self): self.user_manager.add_routes(self.routes) + + # Prefix every route with /api for easier matching for delegation. + # This is very useful for frontend dev server, which need to forward + # everything except serving of static files. + # Currently both the old endpoints without prefix and new endpoints with + # prefix are supported. + api_routes = web.RouteTableDef() + for route in self.routes: + assert isinstance(route, web.RouteDef) + api_routes.route(route.method, "/api" + route.path)(route.handler, **route.kwargs) + self.app.add_routes(api_routes) self.app.add_routes(self.routes) for name, dir in nodes.EXTENSION_WEB_DIRS.items(): From e11052afcfd1e8aa3190d47c44f79b72a7a7a78a Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 19 Jun 2024 16:32:30 -0400 Subject: [PATCH 300/378] Add ipndm sampler. --- comfy/k_diffusion/sampling.py | 41 +++++++++++++++++++++++++++++++++++ comfy/samplers.py | 3 ++- 2 files changed, 43 insertions(+), 1 deletion(-) diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py index 5bb991e7..21419926 100644 --- a/comfy/k_diffusion/sampling.py +++ b/comfy/k_diffusion/sampling.py @@ -841,3 +841,44 @@ def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=Non d_prime = w1 * d + w2 * d_2 + w3 * d_3 x = x + d_prime * dt return x + + +#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py +#under Apache 2 license +def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4): + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + + x_next = x + + buffer_model = [] + for i in trange(len(sigmas) - 1, disable=disable): + t_cur = sigmas[i] + t_next = sigmas[i + 1] + + x_cur = x_next + + denoised = model(x_cur, t_cur * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + + d_cur = (x_cur - denoised) / t_cur + + order = min(max_order, i+1) + if order == 1: # First Euler step. + x_next = x_cur + (t_next - t_cur) * d_cur + elif order == 2: # Use one history point. + x_next = x_cur + (t_next - t_cur) * (3 * d_cur - buffer_model[-1]) / 2 + elif order == 3: # Use two history points. + x_next = x_cur + (t_next - t_cur) * (23 * d_cur - 16 * buffer_model[-1] + 5 * buffer_model[-2]) / 12 + elif order == 4: # Use three history points. + x_next = x_cur + (t_next - t_cur) * (55 * d_cur - 59 * buffer_model[-1] + 37 * buffer_model[-2] - 9 * buffer_model[-3]) / 24 + + if len(buffer_model) == max_order - 1: + for k in range(max_order - 2): + buffer_model[k] = buffer_model[k+1] + buffer_model[-1] = d_cur + else: + buffer_model.append(d_cur) + + return x_next diff --git a/comfy/samplers.py b/comfy/samplers.py index 656e0a28..4de377cc 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -539,7 +539,8 @@ class Sampler: KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral", "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu", - "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm"] + "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm", + "ipndm"] class KSAMPLER(Sampler): def __init__(self, sampler_function, extra_options={}, inpaint_options={}): From eee815ec99c8ff69d9b31b9eba096442119cb3f8 Mon Sep 17 00:00:00 2001 From: Mario Klingemann Date: Wed, 19 Jun 2024 22:42:41 +0200 Subject: [PATCH 301/378] Update sd1_clip.py (#3684) Made token instance check more flexible so it also works with integers from numpy arrays or long tensors --- comfy/sd1_clip.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py index 911af0a7..78e556b5 100644 --- a/comfy/sd1_clip.py +++ b/comfy/sd1_clip.py @@ -9,6 +9,7 @@ from . import model_management import comfy.clip_model import json import logging +import numbers def gen_empty_tokens(special_tokens, length): start_token = special_tokens.get("start", None) @@ -130,10 +131,10 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): for x in tokens: tokens_temp = [] for y in x: - if isinstance(y, int): + if isinstance(y, numbers.Integral): if y == token_dict_size: #EOS token y = -1 - tokens_temp += [y] + tokens_temp += [int(y)] else: if y.shape[0] == current_embeds.weight.shape[1]: embedding_weights += [y] From b08a9dd04b725b46d0fabc4a48b4ddd659677267 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 19 Jun 2024 20:20:35 -0400 Subject: [PATCH 302/378] Remove empty line. --- comfy/utils.py | 1 - 1 file changed, 1 deletion(-) diff --git a/comfy/utils.py b/comfy/utils.py index 0d24fe73..877f14b0 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -286,7 +286,6 @@ MMDIT_MAP_BLOCK = { ("x_block.mlp.fc1.weight", "ff.net.0.proj.weight"), ("x_block.mlp.fc2.bias", "ff.net.2.bias"), ("x_block.mlp.fc2.weight", "ff.net.2.weight"), - ("", ""), } def mmdit_to_diffusers(mmdit_config, output_prefix=""): From 0d6a57938e4fdb5b22b473dfe5a71af8150009e6 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 19 Jun 2024 21:46:37 -0400 Subject: [PATCH 303/378] Support loading diffusers SD3 model format with UNETLoader node. --- comfy/model_detection.py | 37 +++++++++++++++++++ comfy/sd.py | 9 ++++- comfy/utils.py | 20 ++++++++-- .../nodes_model_merging_model_specific.py | 23 ++++++++++++ 4 files changed, 84 insertions(+), 5 deletions(-) diff --git a/comfy/model_detection.py b/comfy/model_detection.py index 4843e6a4..e09dd381 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -1,7 +1,9 @@ import comfy.supported_models import comfy.supported_models_base +import comfy.utils import math import logging +import torch def count_blocks(state_dict_keys, prefix_string): count = 0 @@ -431,3 +433,38 @@ def model_config_from_diffusers_unet(state_dict): if unet_config is not None: return model_config_from_unet_config(unet_config) return None + +def convert_diffusers_mmdit(state_dict, output_prefix=""): + depth = count_blocks(state_dict, 'transformer_blocks.{}.') + if depth > 0: + out_sd = {} + sd_map = comfy.utils.mmdit_to_diffusers({"depth": depth}, output_prefix=output_prefix) + for k in sd_map: + weight = state_dict.get(k, None) + if weight is not None: + t = sd_map[k] + + if not isinstance(t, str): + if len(t) > 2: + fun = t[2] + else: + fun = lambda a: a + offset = t[1] + if offset is not None: + old_weight = out_sd.get(t[0], None) + if old_weight is None: + old_weight = torch.empty_like(weight) + old_weight = old_weight.repeat([3] + [1] * (len(old_weight.shape) - 1)) + + w = old_weight.narrow(offset[0], offset[1], offset[2]) + else: + old_weight = weight + w = weight + w[:] = fun(weight) + t = t[0] + out_sd[t] = old_weight + else: + out_sd[t] = weight + state_dict.pop(k) + + return out_sd diff --git a/comfy/sd.py b/comfy/sd.py index cfbf8fa4..178f52e8 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -568,7 +568,14 @@ def load_unet_state_dict(sd): #load unet in diffusers format unet_dtype = model_management.unet_dtype(model_params=parameters) load_device = model_management.get_torch_device() - if "input_blocks.0.0.weight" in sd or 'clf.1.weight' in sd: #ldm or stable cascade + if 'transformer_blocks.0.attn.add_q_proj.weight' in sd: #MMDIT SD3 + new_sd = model_detection.convert_diffusers_mmdit(sd, "") + if new_sd is None: + return None + model_config = model_detection.model_config_from_unet(new_sd, "") + if model_config is None: + return None + elif "input_blocks.0.0.weight" in sd or 'clf.1.weight' in sd: #ldm or stable cascade model_config = model_detection.model_config_from_unet(sd, "") if model_config is None: return None diff --git a/comfy/utils.py b/comfy/utils.py index 877f14b0..f0d67500 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -249,6 +249,11 @@ def unet_to_diffusers(unet_config): return diffusers_unet_map +def swap_scale_shift(weight): + shift, scale = weight.chunk(2, dim=0) + new_weight = torch.cat([scale, shift], dim=0) + return new_weight + MMDIT_MAP_BASIC = { ("context_embedder.bias", "context_embedder.bias"), ("context_embedder.weight", "context_embedder.weight"), @@ -263,8 +268,8 @@ MMDIT_MAP_BASIC = { ("y_embedder.mlp.2.bias", "time_text_embed.text_embedder.linear_2.bias"), ("y_embedder.mlp.2.weight", "time_text_embed.text_embedder.linear_2.weight"), ("pos_embed", "pos_embed.pos_embed"), - ("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias"), - ("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight"), + ("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias", swap_scale_shift), + ("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight", swap_scale_shift), ("final_layer.linear.bias", "proj_out.bias"), ("final_layer.linear.weight", "proj_out.weight"), } @@ -313,8 +318,15 @@ def mmdit_to_diffusers(mmdit_config, output_prefix=""): for k in MMDIT_MAP_BLOCK: key_map["{}.{}".format(block_from, k[1])] = "{}.{}".format(block_to, k[0]) - for k in MMDIT_MAP_BASIC: - key_map[k[1]] = "{}{}".format(output_prefix, k[0]) + map_basic = MMDIT_MAP_BASIC.copy() + map_basic.add(("joint_blocks.{}.context_block.adaLN_modulation.1.bias".format(depth - 1), "transformer_blocks.{}.norm1_context.linear.bias".format(depth - 1), swap_scale_shift)) + map_basic.add(("joint_blocks.{}.context_block.adaLN_modulation.1.weight".format(depth - 1), "transformer_blocks.{}.norm1_context.linear.weight".format(depth - 1), swap_scale_shift)) + + for k in map_basic: + if len(k) > 2: + key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2]) + else: + key_map[k[1]] = "{}{}".format(output_prefix, k[0]) return key_map diff --git a/comfy_extras/nodes_model_merging_model_specific.py b/comfy_extras/nodes_model_merging_model_specific.py index f2d008d8..74ab6814 100644 --- a/comfy_extras/nodes_model_merging_model_specific.py +++ b/comfy_extras/nodes_model_merging_model_specific.py @@ -52,9 +52,32 @@ class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks): return {"required": arg_dict} +class ModelMergeSD3(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["pos_embed."] = argument + arg_dict["x_embedder."] = argument + arg_dict["context_embedder."] = argument + arg_dict["y_embedder."] = argument + arg_dict["t_embedder."] = argument + + for i in range(38): + arg_dict["joint_blocks.{}.".format(i)] = argument + + arg_dict["final_layer."] = argument + + return {"required": arg_dict} NODE_CLASS_MAPPINGS = { "ModelMergeSD1": ModelMergeSD1, "ModelMergeSD2": ModelMergeSD1, #SD1 and SD2 have the same blocks "ModelMergeSDXL": ModelMergeSDXL, + "ModelMergeSD3": ModelMergeSD3, } From 028a583befa6dab7d60a10d780474f58e945bc05 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 19 Jun 2024 22:32:04 -0400 Subject: [PATCH 304/378] Fix issue with full diffusers SD3 loras. --- comfy/model_patcher.py | 26 ++++++++++++++++---------- 1 file changed, 16 insertions(+), 10 deletions(-) diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index 44b82795..bf878776 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -210,16 +210,19 @@ class ModelPatcher: model_sd = self.model.state_dict() for k in patches: offset = None + function = None if isinstance(k, str): key = k else: offset = k[1] key = k[0] + if len(k) > 2: + function = k[2] if key in model_sd: p.add(k) current_patches = self.patches.get(key, []) - current_patches.append((strength_patch, patches[k], strength_model, offset)) + current_patches.append((strength_patch, patches[k], strength_model, offset, function)) self.patches[key] = current_patches self.patches_uuid = uuid.uuid4() @@ -347,6 +350,9 @@ class ModelPatcher: v = p[1] strength_model = p[2] offset = p[3] + function = p[4] + if function is None: + function = lambda a: a old_weight = None if offset is not None: @@ -371,7 +377,7 @@ class ModelPatcher: if w1.shape != weight.shape: logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape)) else: - weight += strength * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype) + weight += function(strength * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype)) elif patch_type == "lora": #lora/locon mat1 = comfy.model_management.cast_to_device(v[0], weight.device, torch.float32) mat2 = comfy.model_management.cast_to_device(v[1], weight.device, torch.float32) @@ -389,9 +395,9 @@ class ModelPatcher: try: lora_diff = torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1)).reshape(weight.shape) if dora_scale is not None: - weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength) + weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength)) else: - weight += ((strength * alpha) * lora_diff).type(weight.dtype) + weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) except Exception as e: logging.error("ERROR {} {} {}".format(patch_type, key, e)) elif patch_type == "lokr": @@ -435,9 +441,9 @@ class ModelPatcher: try: lora_diff = torch.kron(w1, w2).reshape(weight.shape) if dora_scale is not None: - weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength) + weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength)) else: - weight += ((strength * alpha) * lora_diff).type(weight.dtype) + weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) except Exception as e: logging.error("ERROR {} {} {}".format(patch_type, key, e)) elif patch_type == "loha": @@ -472,9 +478,9 @@ class ModelPatcher: try: lora_diff = (m1 * m2).reshape(weight.shape) if dora_scale is not None: - weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength) + weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength)) else: - weight += ((strength * alpha) * lora_diff).type(weight.dtype) + weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) except Exception as e: logging.error("ERROR {} {} {}".format(patch_type, key, e)) elif patch_type == "glora": @@ -493,9 +499,9 @@ class ModelPatcher: try: lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)).reshape(weight.shape) if dora_scale is not None: - weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength) + weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength)) else: - weight += ((strength * alpha) * lora_diff).type(weight.dtype) + weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) except Exception as e: logging.error("ERROR {} {} {}".format(patch_type, key, e)) else: From d7f0964266170d1070eac9823bbb340e89371282 Mon Sep 17 00:00:00 2001 From: Chenlei Hu Date: Wed, 19 Jun 2024 22:36:31 -0400 Subject: [PATCH 305/378] Fix routes (#3790) --- server.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/server.py b/server.py index 59090258..30ea90c6 100644 --- a/server.py +++ b/server.py @@ -538,8 +538,10 @@ class PromptServer(): # prefix are supported. api_routes = web.RouteTableDef() for route in self.routes: - assert isinstance(route, web.RouteDef) - api_routes.route(route.method, "/api" + route.path)(route.handler, **route.kwargs) + # Custom nodes might add extra static routes. Only process non-static + # routes to add /api prefix. + if isinstance(route, web.RouteDef): + api_routes.route(route.method, "/api" + route.path)(route.handler, **route.kwargs) self.app.add_routes(api_routes) self.app.add_routes(self.routes) From 45e10cac19de5c819674ef3df39bd22fe1397ef9 Mon Sep 17 00:00:00 2001 From: Zhenyu Zhou <79033747+zhyzhouu@users.noreply.github.com> Date: Thu, 20 Jun 2024 20:12:15 +0800 Subject: [PATCH 306/378] feat: add gits scheduler (#3769) --- comfy_extras/nodes_gits.py | 369 +++++++++++++++++++++++++++++++++++++ nodes.py | 1 + 2 files changed, 370 insertions(+) create mode 100644 comfy_extras/nodes_gits.py diff --git a/comfy_extras/nodes_gits.py b/comfy_extras/nodes_gits.py new file mode 100644 index 00000000..7bfae4ce --- /dev/null +++ b/comfy_extras/nodes_gits.py @@ -0,0 +1,369 @@ +# from https://github.com/zju-pi/diff-sampler/tree/main/gits-main +import numpy as np +import torch + +def loglinear_interp(t_steps, num_steps): + """ + Performs log-linear interpolation of a given array of decreasing numbers. + """ + xs = np.linspace(0, 1, len(t_steps)) + ys = np.log(t_steps[::-1]) + + new_xs = np.linspace(0, 1, num_steps) + new_ys = np.interp(new_xs, xs, ys) + + interped_ys = np.exp(new_ys)[::-1].copy() + return interped_ys + +NOISE_LEVELS = { + 0.80: [ + [14.61464119, 7.49001646, 0.02916753], + [14.61464119, 11.54541874, 6.77309084, 0.02916753], + [14.61464119, 11.54541874, 7.49001646, 3.07277966, 0.02916753], + [14.61464119, 11.54541874, 7.49001646, 5.85520077, 2.05039096, 0.02916753], + [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 2.05039096, 0.02916753], + [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], + [14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], + [14.61464119, 13.76078796, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], + [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], + [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], + [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753], + [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753], + [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753], + [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 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0.57119018, 0.4783645, 0.43325692, 0.38853383, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], + ], +} + +class GITSScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"coeff": ("FLOAT", {"default": 1.20, "min": 0.80, "max": 1.50, "step": 0.05}), + "steps": ("INT", {"default": 10, "min": 2, "max": 1000}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, coeff, steps, denoise): + total_steps = steps + if denoise < 1.0: + if denoise <= 0.0: + return (torch.FloatTensor([]),) + total_steps = round(steps * denoise) + + if steps <= 20: + sigmas = NOISE_LEVELS[round(coeff, 2)][steps-2][:] + else: + sigmas = NOISE_LEVELS[round(coeff, 2)][-1][:] + sigmas = loglinear_interp(sigmas, steps + 1) + + sigmas = sigmas[-(total_steps + 1):] + sigmas[-1] = 0 + return (torch.FloatTensor(sigmas), ) + +NODE_CLASS_MAPPINGS = { + "GITSScheduler": GITSScheduler, +} diff --git a/nodes.py b/nodes.py index 06ea4621..99645b81 100644 --- a/nodes.py +++ b/nodes.py @@ -1991,6 +1991,7 @@ def init_custom_nodes(): "nodes_webcam.py", "nodes_audio.py", "nodes_sd3.py", + "nodes_gits.py", ] import_failed = [] From d5efde89b76c40c72668daec052c07c71a737908 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 20 Jun 2024 08:51:49 -0400 Subject: [PATCH 307/378] Add ipndm_v sampler, works best with the exponential scheduler. --- comfy/k_diffusion/sampling.py | 63 +++++++++++++++++++++++++++++++++++ comfy/samplers.py | 2 +- 2 files changed, 64 insertions(+), 1 deletion(-) diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py index 21419926..78896b8b 100644 --- a/comfy/k_diffusion/sampling.py +++ b/comfy/k_diffusion/sampling.py @@ -882,3 +882,66 @@ def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None, buffer_model.append(d_cur) return x_next + +#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py +#under Apache 2 license +def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4): + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + + x_next = x + t_steps = sigmas + + buffer_model = [] + for i in trange(len(sigmas) - 1, disable=disable): + t_cur = sigmas[i] + t_next = sigmas[i + 1] + + x_cur = x_next + + denoised = model(x_cur, t_cur * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + + d_cur = (x_cur - denoised) / t_cur + + order = min(max_order, i+1) + if order == 1: # First Euler step. + x_next = x_cur + (t_next - t_cur) * d_cur + elif order == 2: # Use one history point. + h_n = (t_next - t_cur) + h_n_1 = (t_cur - t_steps[i-1]) + coeff1 = (2 + (h_n / h_n_1)) / 2 + coeff2 = -(h_n / h_n_1) / 2 + x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1]) + elif order == 3: # Use two history points. + h_n = (t_next - t_cur) + h_n_1 = (t_cur - t_steps[i-1]) + h_n_2 = (t_steps[i-1] - t_steps[i-2]) + temp = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2 + coeff1 = (2 + (h_n / h_n_1)) / 2 + temp + coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp + coeff3 = temp * h_n_1 / h_n_2 + x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2]) + elif order == 4: # Use three history points. + h_n = (t_next - t_cur) + h_n_1 = (t_cur - t_steps[i-1]) + h_n_2 = (t_steps[i-1] - t_steps[i-2]) + h_n_3 = (t_steps[i-2] - t_steps[i-3]) + temp1 = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2 + temp2 = ((1 - h_n / (3 * (h_n + h_n_1))) / 2 + (1 - h_n / (2 * (h_n + h_n_1))) * h_n / (6 * (h_n + h_n_1 + h_n_2))) \ + * (h_n * (h_n + h_n_1) * (h_n + h_n_1 + h_n_2)) / (h_n_1 * (h_n_1 + h_n_2) * (h_n_1 + h_n_2 + h_n_3)) + coeff1 = (2 + (h_n / h_n_1)) / 2 + temp1 + temp2 + coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp1 - (1 + (h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3)))) * temp2 + coeff3 = temp1 * h_n_1 / h_n_2 + ((h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * (1 + h_n_2 / h_n_3)) * temp2 + coeff4 = -temp2 * (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * h_n_1 / h_n_2 + x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2] + coeff4 * buffer_model[-3]) + + if len(buffer_model) == max_order - 1: + for k in range(max_order - 2): + buffer_model[k] = buffer_model[k+1] + buffer_model[-1] = d_cur.detach() + else: + buffer_model.append(d_cur.detach()) + + return x_next diff --git a/comfy/samplers.py b/comfy/samplers.py index 4de377cc..55b1486b 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -540,7 +540,7 @@ class Sampler: KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral", "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu", "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm", - "ipndm"] + "ipndm", "ipndm_v"] class KSAMPLER(Sampler): def __init__(self, sampler_function, extra_options={}, inpaint_options={}): From 1e2839f4d904b686cea2686b2855279572df8022 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 20 Jun 2024 16:50:31 -0400 Subject: [PATCH 308/378] More proper tiled audio decoding. --- comfy/sd.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/comfy/sd.py b/comfy/sd.py index 178f52e8..470bd717 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -299,15 +299,24 @@ class VAE: return output def decode_tiled_1d(self, samples, tile_x=128, overlap=64): - output = torch.empty((samples.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples.shape[2:])), device=self.output_device) + output = torch.zeros((samples.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples.shape[2:])), device=self.output_device) + output_mult = torch.zeros((samples.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples.shape[2:])), device=self.output_device) for j in range(samples.shape[0]): for i in range(0, samples.shape[-1], tile_x - overlap): f = i t = i + tile_x - output[j:j+1,:,f * self.upscale_ratio:t * self.upscale_ratio] = self.first_stage_model.decode(samples[j:j+1,:,f:t].to(self.vae_dtype).to(self.device)).float() + o = output[j:j+1,:,f * self.upscale_ratio:t * self.upscale_ratio] + m = torch.ones_like(o) + l = m.shape[-1] + for x in range(overlap): + c = ((x + 1) / overlap) + m[:,:,x:x+1] *= c + m[:,:,l-x-1:l-x] *= c + o += self.first_stage_model.decode(samples[j:j+1,:,f:t].to(self.vae_dtype).to(self.device)).float().to(self.output_device) * m + output_mult[j:j+1,:,f * self.upscale_ratio:t * self.upscale_ratio] += m - return output + return output / output_mult def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap) From 887a6341ed14c9904230cf55a0eabe95cd0218d3 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 21 Jun 2024 08:41:31 -0400 Subject: [PATCH 309/378] Proper ModelMergeSD3_2B node. --- comfy_extras/nodes_model_merging_model_specific.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/comfy_extras/nodes_model_merging_model_specific.py b/comfy_extras/nodes_model_merging_model_specific.py index 74ab6814..df111bd6 100644 --- a/comfy_extras/nodes_model_merging_model_specific.py +++ b/comfy_extras/nodes_model_merging_model_specific.py @@ -52,7 +52,7 @@ class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks): return {"required": arg_dict} -class ModelMergeSD3(comfy_extras.nodes_model_merging.ModelMergeBlocks): +class ModelMergeSD3_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks): CATEGORY = "advanced/model_merging/model_specific" @classmethod @@ -68,7 +68,7 @@ class ModelMergeSD3(comfy_extras.nodes_model_merging.ModelMergeBlocks): arg_dict["y_embedder."] = argument arg_dict["t_embedder."] = argument - for i in range(38): + for i in range(24): arg_dict["joint_blocks.{}.".format(i)] = argument arg_dict["final_layer."] = argument @@ -79,5 +79,5 @@ NODE_CLASS_MAPPINGS = { "ModelMergeSD1": ModelMergeSD1, "ModelMergeSD2": ModelMergeSD1, #SD1 and SD2 have the same blocks "ModelMergeSDXL": ModelMergeSDXL, - "ModelMergeSD3": ModelMergeSD3, + "ModelMergeSD3_2B": ModelMergeSD3_2B, } From 4ef1479dcde5bbb51aabe99a5e0c295a453b5bb8 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 22 Jun 2024 11:45:58 -0400 Subject: [PATCH 310/378] Multi dimension tiled scale function and tiled VAE audio encoding fallback. --- comfy/sd.py | 31 +++++++++---------------- comfy/utils.py | 61 +++++++++++++++++++++++++++++++++----------------- 2 files changed, 51 insertions(+), 41 deletions(-) diff --git a/comfy/sd.py b/comfy/sd.py index 470bd717..ea6e9b66 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -298,25 +298,9 @@ class VAE: / 3.0) return output - def decode_tiled_1d(self, samples, tile_x=128, overlap=64): - output = torch.zeros((samples.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples.shape[2:])), device=self.output_device) - output_mult = torch.zeros((samples.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples.shape[2:])), device=self.output_device) - - for j in range(samples.shape[0]): - for i in range(0, samples.shape[-1], tile_x - overlap): - f = i - t = i + tile_x - o = output[j:j+1,:,f * self.upscale_ratio:t * self.upscale_ratio] - m = torch.ones_like(o) - l = m.shape[-1] - for x in range(overlap): - c = ((x + 1) / overlap) - m[:,:,x:x+1] *= c - m[:,:,l-x-1:l-x] *= c - o += self.first_stage_model.decode(samples[j:j+1,:,f:t].to(self.vae_dtype).to(self.device)).float().to(self.output_device) * m - output_mult[j:j+1,:,f * self.upscale_ratio:t * self.upscale_ratio] += m - - return output / output_mult + def decode_tiled_1d(self, samples, tile_x=128, overlap=32): + decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float() + return comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device) def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap) @@ -331,6 +315,10 @@ class VAE: samples /= 3.0 return samples + def encode_tiled_1d(self, samples, tile_x=128 * 2048, overlap=32 * 2048): + encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float() + return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=(1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device) + def decode(self, samples_in): try: memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype) @@ -374,7 +362,10 @@ class VAE: except model_management.OOM_EXCEPTION as e: logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.") - samples = self.encode_tiled_(pixel_samples) + if len(pixel_samples.shape) == 3: + samples = self.encode_tiled_1d(pixel_samples) + else: + samples = self.encode_tiled_(pixel_samples) return samples diff --git a/comfy/utils.py b/comfy/utils.py index f0d67500..ed6c58a6 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -6,6 +6,7 @@ import safetensors.torch import numpy as np from PIL import Image import logging +import itertools def load_torch_file(ckpt, safe_load=False, device=None): if device is None: @@ -506,34 +507,52 @@ def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap): return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap))) @torch.inference_mode() -def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None): - output = torch.empty((samples.shape[0], out_channels, round(samples.shape[2] * upscale_amount), round(samples.shape[3] * upscale_amount)), device=output_device) +def tiled_scale_multidim(samples, function, tile=(64, 64), overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None): + dims = len(tile) + output = torch.empty([samples.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), samples.shape[2:])), device=output_device) + for b in range(samples.shape[0]): s = samples[b:b+1] - out = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device) - out_div = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device) - for y in range(0, s.shape[2], tile_y - overlap): - for x in range(0, s.shape[3], tile_x - overlap): - x = max(0, min(s.shape[-1] - overlap, x)) - y = max(0, min(s.shape[-2] - overlap, y)) - s_in = s[:,:,y:y+tile_y,x:x+tile_x] + out = torch.zeros([s.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), s.shape[2:])), device=output_device) + out_div = torch.zeros([s.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), s.shape[2:])), device=output_device) - ps = function(s_in).to(output_device) - mask = torch.ones_like(ps) - feather = round(overlap * upscale_amount) - for t in range(feather): - mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1)) - mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1)) - mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1)) - mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1)) - out[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += ps * mask - out_div[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += mask - if pbar is not None: - pbar.update(1) + for it in itertools.product(*map(lambda a: range(0, a[0], a[1] - overlap), zip(s.shape[2:], tile))): + s_in = s + upscaled = [] + + for d in range(dims): + pos = max(0, min(s.shape[d + 2] - overlap, it[d])) + l = min(tile[d], s.shape[d + 2] - pos) + s_in = s_in.narrow(d + 2, pos, l) + upscaled.append(round(pos * upscale_amount)) + ps = function(s_in).to(output_device) + mask = torch.ones_like(ps) + feather = round(overlap * upscale_amount) + for t in range(feather): + for d in range(2, dims + 2): + m = mask.narrow(d, t, 1) + m *= ((1.0/feather) * (t + 1)) + m = mask.narrow(d, mask.shape[d] -1 -t, 1) + m *= ((1.0/feather) * (t + 1)) + + o = out + o_d = out_div + for d in range(dims): + o = o.narrow(d + 2, upscaled[d], mask.shape[d + 2]) + o_d = o_d.narrow(d + 2, upscaled[d], mask.shape[d + 2]) + + o += ps * mask + o_d += mask + + if pbar is not None: + pbar.update(1) output[b:b+1] = out/out_div return output +def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None): + return tiled_scale_multidim(samples, function, (tile_y, tile_x), overlap, upscale_amount, out_channels, output_device, pbar) + PROGRESS_BAR_ENABLED = True def set_progress_bar_enabled(enabled): global PROGRESS_BAR_ENABLED From 2f360ae89853bfe40cc281428a3bafde06f114d5 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 22 Jun 2024 13:08:04 -0400 Subject: [PATCH 311/378] Support OneTrainer SD3 lora format. --- comfy/lora.py | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/comfy/lora.py b/comfy/lora.py index 9e3e7a8b..03743177 100644 --- a/comfy/lora.py +++ b/comfy/lora.py @@ -218,12 +218,21 @@ def model_lora_keys_clip(model, key_map={}): lora_key = "lora_prior_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #cascade lora: TODO put lora key prefix in the model config key_map[lora_key] = k + for k in sdk: #OneTrainer SD3 lora + if k.startswith("t5xxl.transformer.") and k.endswith(".weight"): + l_key = k[len("t5xxl.transformer."):-len(".weight")] + lora_key = "lora_te3_{}".format(l_key.replace(".", "_")) + key_map[lora_key] = k k = "clip_g.transformer.text_projection.weight" if k in sdk: key_map["lora_prior_te_text_projection"] = k #cascade lora? # key_map["text_encoder.text_projection"] = k #TODO: check if other lora have the text_projection too - # key_map["lora_te_text_projection"] = k + key_map["lora_te2_text_projection"] = k #OneTrainer SD3 lora + + k = "clip_l.transformer.text_projection.weight" + if k in sdk: + key_map["lora_te1_text_projection"] = k #OneTrainer SD3 lora, not necessary but omits warning return key_map @@ -262,4 +271,7 @@ def model_lora_keys_unet(model, key_map={}): key_lora = "base_model.model.{}".format(k[:-len(".weight")]) #format for flash-sd3 lora and others? key_map[key_lora] = to + key_lora = "lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_")) #OneTrainer lora + key_map[key_lora] = to + return key_map From 73ca7800192f6bc2e2194966b316b7cc3028e09a Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 23 Jun 2024 13:21:18 -0400 Subject: [PATCH 312/378] Add SamplerEulerCFG++ node. This node should match the DDIM implementation of CFG++ when "regular" is selected. "alternative" is a slightly different take on CFG++ --- comfy/model_patcher.py | 10 +++- comfy_extras/nodes_advanced_samplers.py | 74 +++++++++++++++++++++++++ 2 files changed, 81 insertions(+), 3 deletions(-) diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index bf878776..b949031e 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -51,6 +51,12 @@ def set_model_options_patch_replace(model_options, patch, name, block_name, numb model_options["transformer_options"] = to return model_options +def set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=False): + model_options["sampler_post_cfg_function"] = model_options.get("sampler_post_cfg_function", []) + [post_cfg_function] + if disable_cfg1_optimization: + model_options["disable_cfg1_optimization"] = True + return model_options + class ModelPatcher: def __init__(self, model, load_device, offload_device, size=0, current_device=None, weight_inplace_update=False): self.size = size @@ -122,9 +128,7 @@ class ModelPatcher: self.model_options["disable_cfg1_optimization"] = True def set_model_sampler_post_cfg_function(self, post_cfg_function, disable_cfg1_optimization=False): - self.model_options["sampler_post_cfg_function"] = self.model_options.get("sampler_post_cfg_function", []) + [post_cfg_function] - if disable_cfg1_optimization: - self.model_options["disable_cfg1_optimization"] = True + self.model_options = set_model_options_post_cfg_function(self.model_options, post_cfg_function, disable_cfg1_optimization) def set_model_unet_function_wrapper(self, unet_wrapper_function: UnetWrapperFunction): self.model_options["model_function_wrapper"] = unet_wrapper_function diff --git a/comfy_extras/nodes_advanced_samplers.py b/comfy_extras/nodes_advanced_samplers.py index d973def8..7aa89c64 100644 --- a/comfy_extras/nodes_advanced_samplers.py +++ b/comfy_extras/nodes_advanced_samplers.py @@ -56,6 +56,80 @@ class SamplerLCMUpscale: sampler = comfy.samplers.KSAMPLER(sample_lcm_upscale, extra_options={"total_upscale": scale_ratio, "upscale_steps": scale_steps, "upscale_method": upscale_method}) return (sampler, ) +from comfy.k_diffusion.sampling import to_d +import comfy.model_patcher + +@torch.no_grad() +def sample_euler_cfgpp(model, x, sigmas, extra_args=None, callback=None, disable=None): + extra_args = {} if extra_args is None else extra_args + + temp = [0] + def post_cfg_function(args): + temp[0] = args["uncond_denoised"] + return args["denoised"] + + model_options = extra_args.get("model_options", {}).copy() + extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) + + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + sigma_hat = sigmas[i] + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x, sigma_hat, temp[0]) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + dt = sigmas[i + 1] - sigma_hat + x = denoised + sigmas[i + 1] * d + return x + +@torch.no_grad() +def sample_euler_cfgpp_alt(model, x, sigmas, extra_args=None, callback=None, disable=None): + extra_args = {} if extra_args is None else extra_args + + temp = [0] + def post_cfg_function(args): + temp[0] = args["uncond_denoised"] + return args["denoised"] + + model_options = extra_args.get("model_options", {}).copy() + extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) + + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + sigma_hat = sigmas[i] + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x - denoised + temp[0], sigma_hat, denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + dt = sigmas[i + 1] - sigma_hat + # Euler method + x = x + d * dt + return x + +class SamplerEulerCFGpp: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"version": (["regular", "alternative"],),} + } + RETURN_TYPES = ("SAMPLER",) + # CATEGORY = "sampling/custom_sampling/samplers" + CATEGORY = "_for_testing" + + FUNCTION = "get_sampler" + + def get_sampler(self, version): + if version == "regular": + sampler = comfy.samplers.KSAMPLER(sample_euler_cfgpp) + else: + sampler = comfy.samplers.KSAMPLER(sample_euler_cfgpp_alt) + return (sampler, ) + NODE_CLASS_MAPPINGS = { "SamplerLCMUpscale": SamplerLCMUpscale, + "SamplerEulerCFGpp": SamplerEulerCFGpp, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "SamplerEulerCFGpp": "SamplerEulerCFG++", } From 866f54da8d7eed41a0554648aef8cf213551ee59 Mon Sep 17 00:00:00 2001 From: Chenlei Hu Date: Mon, 24 Jun 2024 14:47:28 -0400 Subject: [PATCH 313/378] Add browser test action synced with TS repo (#3852) * Add browser test action * Add npm install task --- .github/workflows/test-browser.yml | 61 ++++++++++++++++++++++++++++++ 1 file changed, 61 insertions(+) create mode 100644 .github/workflows/test-browser.yml diff --git a/.github/workflows/test-browser.yml b/.github/workflows/test-browser.yml new file mode 100644 index 00000000..049b1cde --- /dev/null +++ b/.github/workflows/test-browser.yml @@ -0,0 +1,61 @@ +# This is a temporary action during frontend TS migration. +# This file should be removed after TS migration is completed. +# The browser test is here to ensure TS repo is working the same way as the +# current JS code. +# If you are adding UI feature, please sync your changes to the TS repo: +# huchenlei/ComfyUI_frontend and update test expectation files accordingly. +name: Playwright Browser Tests CI + +on: + pull_request: + branches: [ main, master ] + +jobs: + test: + runs-on: ubuntu-latest + steps: + - name: Checkout ComfyUI + uses: actions/checkout@v4 + with: + repository: "comfyanonymous/ComfyUI" + path: "ComfyUI" + - name: Checkout ComfyUI_frontend + uses: actions/checkout@v4 + with: + repository: "huchenlei/ComfyUI_frontend" + path: "ComfyUI_frontend" + ref: "23a4ee83144d9bf22e7218b9c78c71f8544c8b27" + - uses: actions/setup-node@v3 + with: + node-version: lts/* + - uses: actions/setup-python@v4 + with: + python-version: '3.10' + - name: Install requirements + run: | + python -m pip install --upgrade pip + pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu + pip install -r requirements.txt + pip install wait-for-it + working-directory: ComfyUI + - name: Start ComfyUI server + run: | + python main.py --cpu & + wait-for-it --service 127.0.0.1:8188 -t 600 + working-directory: ComfyUI + - name: Install ComfyUI_frontend dependencies + run: | + npm ci + working-directory: ComfyUI_frontend + - name: Install Playwright Browsers + run: npx playwright install --with-deps + working-directory: ComfyUI_frontend + - name: Run Playwright tests + run: npx playwright test + working-directory: ComfyUI_frontend + - uses: actions/upload-artifact@v4 + if: always() + with: + name: playwright-report + path: ComfyUI_frontend/playwright-report/ + retention-days: 30 From eab211bb1e79fbda0b8eef00e44249993c040ae0 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 24 Jun 2024 16:55:20 -0400 Subject: [PATCH 314/378] Resample audio to 44100 when VAE encoding it. --- comfy_extras/nodes_audio.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py index 5f4bd354..57d0a20a 100644 --- a/comfy_extras/nodes_audio.py +++ b/comfy_extras/nodes_audio.py @@ -31,7 +31,13 @@ class VAEEncodeAudio: CATEGORY = "_for_testing/audio" def encode(self, vae, audio): - t = vae.encode(audio["waveform"].movedim(1, -1)) + sample_rate = audio["sample_rate"] + if 44100 != sample_rate: + waveform = torchaudio.functional.resample(audio["waveform"], sample_rate, 44100) + else: + waveform = audio["waveform"] + + t = vae.encode(waveform.movedim(1, -1)) return ({"samples":t}, ) class VAEDecodeAudio: From 90aebb6c868d713c3e6457d1474c53808cb5f6a2 Mon Sep 17 00:00:00 2001 From: pythongosssss <125205205+pythongosssss@users.noreply.github.com> Date: Tue, 25 Jun 2024 11:49:25 +0100 Subject: [PATCH 315/378] New Menu & Workflow Management (#3112) * menu * wip * wip * wip * wip * wip * workflow saving/loading * Support inserting workflows Move buttosn to top of lists * fix session storage implement renaming * temp * refactor, better workflow instance management * wip * progress on progress * added send to workflow various fixes * Support multiple image loaders * Support dynamic size breakpoints based on content * various fixes add close unsaved warning * Add filtering tree * prevent renaming unsaved * fix zindex on hover * fix top offset * use filename as workflow name * resize on setting change * hide element until it is drawn * remove glow * Fix export name * Fix test, revert accidental changes to groupNode * Fix colors on all themes * show hover items on smaller screen (mobile) * remove debugging code * dialog fix * Dont reorder open workflows Allow elements around canvas * Toggle body display on setting change * Fix menu disappearing on chrome * Increase delay when typing, remove margin on Safari, fix dialog location * Fix overflow issue on iOS * Add reset view button Prevent view changes causing history entries * Bottom menu wip * Various fixes * Fix merge * Fix breaking old menu position * Fix merge adding restore view to loadGraphData --- app/user_manager.py | 91 ++- tests-ui/utils/setup.js | 1 + web/extensions/core/colorPalette.js | 30 +- web/extensions/core/groupNode.js | 2 +- web/extensions/core/undoRedo.js | 177 ----- web/fonts/materialdesignicons-webfont.woff2 | Bin 0 -> 403216 bytes web/index.html | 1 + web/jsconfig.json | 4 +- web/lib/materialdesignicons.min.css | 3 + web/scripts/api.js | 75 +- web/scripts/app.js | 223 ++++-- web/scripts/changeTracker.js | 242 +++++++ web/scripts/domWidget.js | 14 +- web/scripts/ui.js | 34 +- web/scripts/ui/components/asyncDialog.js | 64 ++ web/scripts/ui/components/button.js | 163 +++++ web/scripts/ui/components/buttonGroup.js | 45 ++ web/scripts/ui/components/popup.js | 128 ++++ web/scripts/ui/components/splitButton.js | 43 ++ web/scripts/ui/dialog.js | 28 +- web/scripts/ui/menu/index.js | 302 ++++++++ web/scripts/ui/menu/interruptButton.js | 23 + web/scripts/ui/menu/menu.css | 701 ++++++++++++++++++ web/scripts/ui/menu/queueButton.js | 93 +++ web/scripts/ui/menu/queueOptions.js | 77 ++ web/scripts/ui/menu/viewHistory.js | 27 + web/scripts/ui/menu/viewList.js | 203 ++++++ web/scripts/ui/menu/viewQueue.js | 55 ++ web/scripts/ui/menu/workflows.js | 764 ++++++++++++++++++++ web/scripts/ui/settings.js | 20 +- web/scripts/ui/utils.js | 56 ++ web/scripts/utils.js | 68 ++ web/scripts/workflows.js | 450 ++++++++++++ web/style.css | 75 ++ web/types/comfy.d.ts | 16 +- 35 files changed, 3986 insertions(+), 312 deletions(-) delete mode 100644 web/extensions/core/undoRedo.js create mode 100644 web/fonts/materialdesignicons-webfont.woff2 create mode 100644 web/lib/materialdesignicons.min.css create mode 100644 web/scripts/changeTracker.js create mode 100644 web/scripts/ui/components/asyncDialog.js create mode 100644 web/scripts/ui/components/button.js create mode 100644 web/scripts/ui/components/buttonGroup.js create mode 100644 web/scripts/ui/components/popup.js create mode 100644 web/scripts/ui/components/splitButton.js create mode 100644 web/scripts/ui/menu/index.js create mode 100644 web/scripts/ui/menu/interruptButton.js create mode 100644 web/scripts/ui/menu/menu.css create mode 100644 web/scripts/ui/menu/queueButton.js create mode 100644 web/scripts/ui/menu/queueOptions.js create mode 100644 web/scripts/ui/menu/viewHistory.js create mode 100644 web/scripts/ui/menu/viewList.js create mode 100644 web/scripts/ui/menu/viewQueue.js create mode 100644 web/scripts/ui/menu/workflows.js create mode 100644 web/scripts/ui/utils.js create mode 100644 web/scripts/workflows.js diff --git a/app/user_manager.py b/app/user_manager.py index 209094af..53dff18b 100644 --- a/app/user_manager.py +++ b/app/user_manager.py @@ -2,6 +2,8 @@ import json import os import re import uuid +import glob +import shutil from aiohttp import web from comfy.cli_args import args from folder_paths import user_directory @@ -56,16 +58,16 @@ class UserManager(): if os.path.commonpath((root_dir, user_root)) != root_dir: return None - parent = user_root - if file is not None: # prevent leaving /{type}/{user} path = os.path.abspath(os.path.join(user_root, file)) if os.path.commonpath((user_root, path)) != user_root: return None + parent = os.path.split(path)[0] + if create_dir and not os.path.exists(parent): - os.mkdir(parent) + os.makedirs(parent, exist_ok=True) return path @@ -108,33 +110,96 @@ class UserManager(): user_id = self.add_user(username) return web.json_response(user_id) - @routes.get("/userdata/{file}") - async def getuserdata(request): - file = request.match_info.get("file", None) - if not file: + @routes.get("/userdata") + async def listuserdata(request): + directory = request.rel_url.query.get('dir', '') + if not directory: return web.Response(status=400) - path = self.get_request_user_filepath(request, file) + path = self.get_request_user_filepath(request, directory) if not path: return web.Response(status=403) if not os.path.exists(path): return web.Response(status=404) - return web.FileResponse(path) + recurse = request.rel_url.query.get('recurse', '').lower() == "true" + results = glob.glob(os.path.join( + glob.escape(path), '**/*'), recursive=recurse) + results = [os.path.relpath(x, path) for x in results if os.path.isfile(x)] + + split_path = request.rel_url.query.get('split', '').lower() == "true" + if split_path: + results = [[x] + x.split(os.sep) for x in results] - @routes.post("/userdata/{file}") - async def post_userdata(request): - file = request.match_info.get("file", None) + return web.json_response(results) + + def get_user_data_path(request, check_exists = False, param = "file"): + file = request.match_info.get(param, None) if not file: return web.Response(status=400) path = self.get_request_user_filepath(request, file) if not path: return web.Response(status=403) + + if check_exists and not os.path.exists(path): + return web.Response(status=404) + + return path + + @routes.get("/userdata/{file}") + async def getuserdata(request): + path = get_user_data_path(request, check_exists=True) + if not isinstance(path, str): + return path + + return web.FileResponse(path) + + @routes.post("/userdata/{file}") + async def post_userdata(request): + path = get_user_data_path(request) + if not isinstance(path, str): + return path + + overwrite = request.query["overwrite"] != "false" + if not overwrite and os.path.exists(path): + return web.Response(status=409) body = await request.read() + with open(path, "wb") as f: f.write(body) - return web.Response(status=200) + resp = os.path.relpath(path, self.get_request_user_filepath(request, None)) + return web.json_response(resp) + + @routes.delete("/userdata/{file}") + async def delete_userdata(request): + path = get_user_data_path(request, check_exists=True) + if not isinstance(path, str): + return path + + os.remove(path) + + return web.Response(status=204) + + @routes.post("/userdata/{file}/move/{dest}") + async def move_userdata(request): + source = get_user_data_path(request, check_exists=True) + if not isinstance(source, str): + return source + + dest = get_user_data_path(request, check_exists=False, param="dest") + if not isinstance(source, str): + return dest + + overwrite = request.query["overwrite"] != "false" + if not overwrite and os.path.exists(dest): + return web.Response(status=409) + + print(f"moving '{source}' -> '{dest}'") + shutil.move(source, dest) + + resp = os.path.relpath(dest, self.get_request_user_filepath(request, None)) + return web.json_response(resp) diff --git a/tests-ui/utils/setup.js b/tests-ui/utils/setup.js index e4625894..3a6d69b7 100644 --- a/tests-ui/utils/setup.js +++ b/tests-ui/utils/setup.js @@ -72,6 +72,7 @@ export function mockApi(config = {}) { storeUserData: jest.fn((file, data) => { userData[file] = data; }), + listUserData: jest.fn(() => []) }; jest.mock("../../web/scripts/api", () => ({ get api() { diff --git a/web/extensions/core/colorPalette.js b/web/extensions/core/colorPalette.js index 02546782..245f3b93 100644 --- a/web/extensions/core/colorPalette.js +++ b/web/extensions/core/colorPalette.js @@ -63,6 +63,10 @@ const colorPalettes = { "border-color": "#4e4e4e", "tr-even-bg-color": "#222", "tr-odd-bg-color": "#353535", + "content-bg": "#4e4e4e", + "content-fg": "#fff", + "content-hover-bg": "#222", + "content-hover-fg": "#fff" } }, }, @@ -120,6 +124,10 @@ const colorPalettes = { "border-color": "#888", "tr-even-bg-color": "#f9f9f9", "tr-odd-bg-color": "#fff", + "content-bg": "#e0e0e0", + "content-fg": "#222", + "content-hover-bg": "#adadad", + "content-hover-fg": "#222" } }, }, @@ -176,6 +184,10 @@ const colorPalettes = { "border-color": "#657b83", // Base00 "tr-even-bg-color": "#002b36", "tr-odd-bg-color": "#073642", + "content-bg": "#657b83", + "content-fg": "#fdf6e3", + "content-hover-bg": "#002b36", + "content-hover-fg": "#fdf6e3" } }, }, @@ -244,7 +256,11 @@ const colorPalettes = { "error-text": "#ff4444", "border-color": "#6e7581", "tr-even-bg-color": "#2b2f38", - "tr-odd-bg-color": "#242730" + "tr-odd-bg-color": "#242730", + "content-bg": "#6e7581", + "content-fg": "#fff", + "content-hover-bg": "#2b2f38", + "content-hover-fg": "#fff" } }, }, @@ -313,7 +329,11 @@ const colorPalettes = { "error-text": "#ff4444", "border-color": "#545d70", "tr-even-bg-color": "#2e3440", - "tr-odd-bg-color": "#161b22" + "tr-odd-bg-color": "#161b22", + "content-bg": "#545d70", + "content-fg": "#e5eaf0", + "content-hover-bg": "#2e3440", + "content-hover-fg": "#e5eaf0" } }, }, @@ -382,7 +402,11 @@ const colorPalettes = { "error-text": "#ff4444", "border-color": "#30363d", "tr-even-bg-color": "#161b22", - "tr-odd-bg-color": "#13171d" + "tr-odd-bg-color": "#13171d", + "content-bg": "#30363d", + "content-fg": "#e5eaf0", + "content-hover-bg": "#161b22", + "content-hover-fg": "#e5eaf0" } }, } diff --git a/web/extensions/core/groupNode.js b/web/extensions/core/groupNode.js index 0b0763d1..9a223890 100644 --- a/web/extensions/core/groupNode.js +++ b/web/extensions/core/groupNode.js @@ -1278,4 +1278,4 @@ const ext = { } }; -app.registerExtension(ext); +app.registerExtension(ext); \ No newline at end of file diff --git a/web/extensions/core/undoRedo.js b/web/extensions/core/undoRedo.js deleted file mode 100644 index 900eed2a..00000000 --- a/web/extensions/core/undoRedo.js +++ /dev/null @@ -1,177 +0,0 @@ -import { app } from "../../scripts/app.js"; -import { api } from "../../scripts/api.js" - -const MAX_HISTORY = 50; - -let undo = []; -let redo = []; -let activeState = null; -let isOurLoad = false; -function checkState() { - const currentState = app.graph.serialize(); - if (!graphEqual(activeState, currentState)) { - undo.push(activeState); - if (undo.length > MAX_HISTORY) { - undo.shift(); - } - activeState = clone(currentState); - redo.length = 0; - api.dispatchEvent(new CustomEvent("graphChanged", { detail: activeState })); - } -} - -const loadGraphData = app.loadGraphData; -app.loadGraphData = async function () { - const v = await loadGraphData.apply(this, arguments); - if (isOurLoad) { - isOurLoad = false; - } else { - checkState(); - } - return v; -}; - -function clone(obj) { - try { - if (typeof structuredClone !== "undefined") { - return structuredClone(obj); - } - } catch (error) { - // structuredClone is stricter than using JSON.parse/stringify so fallback to that - } - - return JSON.parse(JSON.stringify(obj)); -} - -function graphEqual(a, b, root = true) { - if (a === b) return true; - - if (typeof a == "object" && a && typeof b == "object" && b) { - const keys = Object.getOwnPropertyNames(a); - - if (keys.length != Object.getOwnPropertyNames(b).length) { - return false; - } - - for (const key of keys) { - let av = a[key]; - let bv = b[key]; - if (root && key === "nodes") { - // Nodes need to be sorted as the order changes when selecting nodes - av = [...av].sort((a, b) => a.id - b.id); - bv = [...bv].sort((a, b) => a.id - b.id); - } - if (!graphEqual(av, bv, false)) { - return false; - } - } - - return true; - } - - return false; -} - -const undoRedo = async (e) => { - const updateState = async (source, target) => { - const prevState = source.pop(); - if (prevState) { - target.push(activeState); - isOurLoad = true; - await app.loadGraphData(prevState, false); - activeState = prevState; - } - } - if (e.ctrlKey || e.metaKey) { - if (e.key === "y") { - updateState(redo, undo); - return true; - } else if (e.key === "z") { - updateState(undo, redo); - return true; - } - } -}; - -const bindInput = (activeEl) => { - if (activeEl && activeEl.tagName !== "CANVAS" && activeEl.tagName !== "BODY") { - for (const evt of ["change", "input", "blur"]) { - if (`on${evt}` in activeEl) { - const listener = () => { - checkState(); - activeEl.removeEventListener(evt, listener); - }; - activeEl.addEventListener(evt, listener); - return true; - } - } - } -}; - -let keyIgnored = false; -window.addEventListener( - "keydown", - (e) => { - requestAnimationFrame(async () => { - let activeEl; - // If we are auto queue in change mode then we do want to trigger on inputs - if (!app.ui.autoQueueEnabled || app.ui.autoQueueMode === "instant") { - activeEl = document.activeElement; - if (activeEl?.tagName === "INPUT" || activeEl?.type === "textarea") { - // Ignore events on inputs, they have their native history - return; - } - } - - keyIgnored = e.key === "Control" || e.key === "Shift" || e.key === "Alt" || e.key === "Meta"; - if (keyIgnored) return; - - // Check if this is a ctrl+z ctrl+y - if (await undoRedo(e)) return; - - // If our active element is some type of input then handle changes after they're done - if (bindInput(activeEl)) return; - checkState(); - }); - }, - true -); - -window.addEventListener("keyup", (e) => { - if (keyIgnored) { - keyIgnored = false; - checkState(); - } -}); - -// Handle clicking DOM elements (e.g. widgets) -window.addEventListener("mouseup", () => { - checkState(); -}); - -// Handle prompt queue event for dynamic widget changes -api.addEventListener("promptQueued", () => { - checkState(); -}); - -// Handle litegraph clicks -const processMouseUp = LGraphCanvas.prototype.processMouseUp; -LGraphCanvas.prototype.processMouseUp = function (e) { - const v = processMouseUp.apply(this, arguments); - checkState(); - return v; -}; -const processMouseDown = LGraphCanvas.prototype.processMouseDown; -LGraphCanvas.prototype.processMouseDown = function (e) { - const v = processMouseDown.apply(this, arguments); - checkState(); - return v; -}; - -// Handle 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::before{content:"\F02E5"}.mdi-pot-mix::before{content:"\F065B"}.mdi-pot-mix-outline::before{content:"\F0677"}.mdi-pot-outline::before{content:"\F02FF"}.mdi-pot-steam::before{content:"\F065A"}.mdi-pot-steam-outline::before{content:"\F0326"}.mdi-pound::before{content:"\F0423"}.mdi-pound-box::before{content:"\F0424"}.mdi-pound-box-outline::before{content:"\F117F"}.mdi-power::before{content:"\F0425"}.mdi-power-cycle::before{content:"\F0901"}.mdi-power-off::before{content:"\F0902"}.mdi-power-on::before{content:"\F0903"}.mdi-power-plug::before{content:"\F06A5"}.mdi-power-plug-battery::before{content:"\F1C3B"}.mdi-power-plug-battery-outline::before{content:"\F1C3C"}.mdi-power-plug-off::before{content:"\F06A6"}.mdi-power-plug-off-outline::before{content:"\F1424"}.mdi-power-plug-outline::before{content:"\F1425"}.mdi-power-settings::before{content:"\F0426"}.mdi-power-sleep::before{content:"\F0904"}.mdi-power-socket::before{content:"\F0427"}.mdi-power-socket-au::before{content:"\F0905"}.mdi-power-socket-ch::before{content:"\F0FB3"}.mdi-power-socket-de::before{content:"\F1107"}.mdi-power-socket-eu::before{content:"\F07E7"}.mdi-power-socket-fr::before{content:"\F1108"}.mdi-power-socket-it::before{content:"\F14FF"}.mdi-power-socket-jp::before{content:"\F1109"}.mdi-power-socket-uk::before{content:"\F07E8"}.mdi-power-socket-us::before{content:"\F07E9"}.mdi-power-standby::before{content:"\F0906"}.mdi-powershell::before{content:"\F0A0A"}.mdi-prescription::before{content:"\F0706"}.mdi-presentation::before{content:"\F0428"}.mdi-presentation-play::before{content:"\F0429"}.mdi-pretzel::before{content:"\F1562"}.mdi-printer::before{content:"\F042A"}.mdi-printer-3d::before{content:"\F042B"}.mdi-printer-3d-nozzle::before{content:"\F0E5B"}.mdi-printer-3d-nozzle-alert::before{content:"\F11C0"}.mdi-printer-3d-nozzle-alert-outline::before{content:"\F11C1"}.mdi-printer-3d-nozzle-heat::before{content:"\F18B8"}.mdi-printer-3d-nozzle-heat-outline::before{content:"\F18B9"}.mdi-printer-3d-nozzle-off::before{content:"\F1B19"}.mdi-printer-3d-nozzle-off-outline::before{content:"\F1B1A"}.mdi-printer-3d-nozzle-outline::before{content:"\F0E5C"}.mdi-printer-3d-off::before{content:"\F1B0E"}.mdi-printer-alert::before{content:"\F042C"}.mdi-printer-check::before{content:"\F1146"}.mdi-printer-eye::before{content:"\F1458"}.mdi-printer-off::before{content:"\F0E5D"}.mdi-printer-off-outline::before{content:"\F1785"}.mdi-printer-outline::before{content:"\F1786"}.mdi-printer-pos::before{content:"\F1057"}.mdi-printer-pos-alert::before{content:"\F1BBC"}.mdi-printer-pos-alert-outline::before{content:"\F1BBD"}.mdi-printer-pos-cancel::before{content:"\F1BBE"}.mdi-printer-pos-cancel-outline::before{content:"\F1BBF"}.mdi-printer-pos-check::before{content:"\F1BC0"}.mdi-printer-pos-check-outline::before{content:"\F1BC1"}.mdi-printer-pos-cog::before{content:"\F1BC2"}.mdi-printer-pos-cog-outline::before{content:"\F1BC3"}.mdi-printer-pos-edit::before{content:"\F1BC4"}.mdi-printer-pos-edit-outline::before{content:"\F1BC5"}.mdi-printer-pos-minus::before{content:"\F1BC6"}.mdi-printer-pos-minus-outline::before{content:"\F1BC7"}.mdi-printer-pos-network::before{content:"\F1BC8"}.mdi-printer-pos-network-outline::before{content:"\F1BC9"}.mdi-printer-pos-off::before{content:"\F1BCA"}.mdi-printer-pos-off-outline::before{content:"\F1BCB"}.mdi-printer-pos-outline::before{content:"\F1BCC"}.mdi-printer-pos-pause::before{content:"\F1BCD"}.mdi-printer-pos-pause-outline::before{content:"\F1BCE"}.mdi-printer-pos-play::before{content:"\F1BCF"}.mdi-printer-pos-play-outline::before{content:"\F1BD0"}.mdi-printer-pos-plus::before{content:"\F1BD1"}.mdi-printer-pos-plus-outline::before{content:"\F1BD2"}.mdi-printer-pos-refresh::before{content:"\F1BD3"}.mdi-printer-pos-refresh-outline::before{content:"\F1BD4"}.mdi-printer-pos-remove::before{content:"\F1BD5"}.mdi-printer-pos-remove-outline::before{content:"\F1BD6"}.mdi-printer-pos-star::before{content:"\F1BD7"}.mdi-printer-pos-star-outline::before{content:"\F1BD8"}.mdi-printer-pos-stop::before{content:"\F1BD9"}.mdi-printer-pos-stop-outline::before{content:"\F1BDA"}.mdi-printer-pos-sync::before{content:"\F1BDB"}.mdi-printer-pos-sync-outline::before{content:"\F1BDC"}.mdi-printer-pos-wrench::before{content:"\F1BDD"}.mdi-printer-pos-wrench-outline::before{content:"\F1BDE"}.mdi-printer-search::before{content:"\F1457"}.mdi-printer-settings::before{content:"\F0707"}.mdi-printer-wireless::before{content:"\F0A0B"}.mdi-priority-high::before{content:"\F0603"}.mdi-priority-low::before{content:"\F0604"}.mdi-professional-hexagon::before{content:"\F042D"}.mdi-progress-alert::before{content:"\F0CBC"}.mdi-progress-check::before{content:"\F0995"}.mdi-progress-clock::before{content:"\F0996"}.mdi-progress-close::before{content:"\F110A"}.mdi-progress-download::before{content:"\F0997"}.mdi-progress-helper::before{content:"\F1BA2"}.mdi-progress-pencil::before{content:"\F1787"}.mdi-progress-question::before{content:"\F1522"}.mdi-progress-star::before{content:"\F1788"}.md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2s infinite linear;animation:mdi-spin 2s infinite linear}@-webkit-keyframes mdi-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}100%{-webkit-transform:rotate(359deg);transform:rotate(359deg)}}@keyframes mdi-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}100%{-webkit-transform:rotate(359deg);transform:rotate(359deg)}} + +/*# sourceMappingURL=materialdesignicons.css.map */ \ No newline at end of file diff --git a/web/scripts/api.js b/web/scripts/api.js index 8c8155be..39f0a9bb 100644 --- a/web/scripts/api.js +++ b/web/scripts/api.js @@ -327,7 +327,7 @@ class ComfyApi extends EventTarget { /** * Gets user configuration data and where data should be stored - * @returns { Promise<{ storage: "server" | "browser", users?: Promise, migrated?: boolean }> } + * @returns { Promise<{ storage: "server" | "browser", users?: Promise, migrated?: boolean }> } */ async getUserConfig() { return (await this.fetchApi("/users")).json(); @@ -335,7 +335,7 @@ class ComfyApi extends EventTarget { /** * Creates a new user - * @param { string } username + * @param { string } username * @returns The fetch response */ createUser(username) { @@ -394,7 +394,7 @@ class ComfyApi extends EventTarget { * Gets a user data file for the current user * @param { string } file The name of the userdata file to load * @param { RequestInit } [options] - * @returns { Promise } The fetch response object + * @returns { Promise } The fetch response object */ async getUserData(file, options) { return this.fetchApi(`/userdata/${encodeURIComponent(file)}`, options); @@ -404,18 +404,75 @@ class ComfyApi extends EventTarget { * Stores a user data file for the current user * @param { string } file The name of the userdata file to save * @param { unknown } data The data to save to the file - * @param { RequestInit & { stringify?: boolean, throwOnError?: boolean } } [options] - * @returns { Promise } + * @param { RequestInit & { overwrite?: boolean, stringify?: boolean, throwOnError?: boolean } } [options] + * @returns { Promise } */ - async storeUserData(file, data, options = { stringify: true, throwOnError: true }) { - const resp = await this.fetchApi(`/userdata/${encodeURIComponent(file)}`, { + async storeUserData(file, data, options = { overwrite: true, stringify: true, throwOnError: true }) { + const resp = await this.fetchApi(`/userdata/${encodeURIComponent(file)}?overwrite=${options?.overwrite}`, { method: "POST", body: options?.stringify ? JSON.stringify(data) : data, ...options, - }); - if (resp.status !== 200) { + }); + if (resp.status !== 200 && options?.throwOnError !== false) { throw new Error(`Error storing user data file '${file}': ${resp.status} ${(await resp).statusText}`); } + return resp; + } + + /** + * Deletes a user data file for the current user + * @param { string } file The name of the userdata file to delete + */ + async deleteUserData(file) { + const resp = await this.fetchApi(`/userdata/${encodeURIComponent(file)}`, { + method: "DELETE", + }); + if (resp.status !== 204) { + throw new Error(`Error removing user data file '${file}': ${resp.status} ${(resp).statusText}`); + } + } + + /** + * Move a user data file for the current user + * @param { string } source The userdata file to move + * @param { string } dest The destination for the file + */ + async moveUserData(source, dest, options = { overwrite: false }) { + const resp = await this.fetchApi(`/userdata/${encodeURIComponent(source)}/move/${encodeURIComponent(dest)}?overwrite=${options?.overwrite}`, { + method: "POST", + }); + return resp; + } + + /** + * @overload + * Lists user data files for the current user + * @param { string } dir The directory in which to list files + * @param { boolean } [recurse] If the listing should be recursive + * @param { true } [split] If the paths should be split based on the os path separator + * @returns { Promise> } The list of split file paths in the format [fullPath, ...splitPath] + */ + /** + * @overload + * Lists user data files for the current user + * @param { string } dir The directory in which to list files + * @param { boolean } [recurse] If the listing should be recursive + * @param { false | undefined } [split] If the paths should be split based on the os path separator + * @returns { Promise> } The list of files + */ + async listUserData(dir, recurse, split) { + const resp = await this.fetchApi( + `/userdata?${new URLSearchParams({ + recurse, + dir, + split, + })}` + ); + if (resp.status === 404) return []; + if (resp.status !== 200) { + throw new Error(`Error getting user data list '${dir}': ${resp.status} ${resp.statusText}`); + } + return resp.json(); } } diff --git a/web/scripts/app.js b/web/scripts/app.js index 58fb765c..72553856 100644 --- a/web/scripts/app.js +++ b/web/scripts/app.js @@ -5,9 +5,11 @@ import { api } from "./api.js"; import { defaultGraph } from "./defaultGraph.js"; import { getPngMetadata, getWebpMetadata, importA1111, getLatentMetadata } from "./pnginfo.js"; import { addDomClippingSetting } from "./domWidget.js"; -import { createImageHost, calculateImageGrid } from "./ui/imagePreview.js" - -export const ANIM_PREVIEW_WIDGET = "$$comfy_animation_preview" +import { createImageHost, calculateImageGrid } from "./ui/imagePreview.js"; +import { ComfyAppMenu } from "./ui/menu/index.js"; +import { getStorageValue, setStorageValue } from "./utils.js"; +import { ComfyWorkflowManager } from "./workflows.js"; +export const ANIM_PREVIEW_WIDGET = "$$comfy_animation_preview"; function sanitizeNodeName(string) { let entityMap = { @@ -52,6 +54,12 @@ export class ComfyApp { constructor() { this.ui = new ComfyUI(this); this.logging = new ComfyLogging(this); + this.workflowManager = new ComfyWorkflowManager(this); + this.bodyTop = $el("div.comfyui-body-top", { parent: document.body }); + this.bodyLeft = $el("div.comfyui-body-left", { parent: document.body }); + this.bodyRight = $el("div.comfyui-body-right", { parent: document.body }); + this.bodyBottom = $el("div.comfyui-body-bottom", { parent: document.body }); + this.menu = new ComfyAppMenu(this); /** * List of extensions that are registered with the app @@ -1313,11 +1321,15 @@ export class ComfyApp { }); api.addEventListener("progress", ({ detail }) => { + if (this.workflowManager.activePrompt?.workflow + && this.workflowManager.activePrompt.workflow !== this.workflowManager.activeWorkflow) return; this.progress = detail; this.graph.setDirtyCanvas(true, false); }); api.addEventListener("executing", ({ detail }) => { + if (this.workflowManager.activePrompt ?.workflow + && this.workflowManager.activePrompt.workflow !== this.workflowManager.activeWorkflow) return; this.progress = null; this.runningNodeId = detail; this.graph.setDirtyCanvas(true, false); @@ -1325,6 +1337,8 @@ export class ComfyApp { }); api.addEventListener("executed", ({ detail }) => { + if (this.workflowManager.activePrompt ?.workflow + && this.workflowManager.activePrompt.workflow !== this.workflowManager.activeWorkflow) return; const output = this.nodeOutputs[detail.node]; if (detail.merge && output) { for (const k in detail.output ?? {}) { @@ -1433,6 +1447,11 @@ export class ComfyApp { }); await Promise.all(extensionPromises); + try { + this.menu.workflows.registerExtension(this); + } catch (error) { + console.error(error); + } } async #migrateSettings() { @@ -1520,15 +1539,17 @@ export class ComfyApp { */ async setup() { await this.#setUser(); - await this.ui.settings.load(); - await this.#loadExtensions(); // Create and mount the LiteGraph in the DOM const mainCanvas = document.createElement("canvas") mainCanvas.style.touchAction = "none" const canvasEl = (this.canvasEl = Object.assign(mainCanvas, { id: "graph-canvas" })); canvasEl.tabIndex = "1"; - document.body.prepend(canvasEl); + document.body.append(canvasEl); + this.resizeCanvas(); + + await Promise.all([this.workflowManager.loadWorkflows(), this.ui.settings.load()]); + await this.#loadExtensions(); addDomClippingSetting(); this.#addProcessMouseHandler(); @@ -1541,7 +1562,7 @@ export class ComfyApp { this.#addAfterConfigureHandler(); - const canvas = (this.canvas = new LGraphCanvas(canvasEl, this.graph)); + this.canvas = new LGraphCanvas(canvasEl, this.graph); this.ctx = canvasEl.getContext("2d"); LiteGraph.release_link_on_empty_shows_menu = true; @@ -1549,19 +1570,14 @@ export class ComfyApp { this.graph.start(); - function resizeCanvas() { - // Limit minimal scale to 1, see https://github.com/comfyanonymous/ComfyUI/pull/845 - const scale = Math.max(window.devicePixelRatio, 1); - const { width, height } = canvasEl.getBoundingClientRect(); - canvasEl.width = Math.round(width * scale); - canvasEl.height = Math.round(height * scale); - canvasEl.getContext("2d").scale(scale, scale); - canvas.draw(true, true); - } - // Ensure the canvas fills the window - resizeCanvas(); - window.addEventListener("resize", resizeCanvas); + this.resizeCanvas(); + window.addEventListener("resize", () => this.resizeCanvas()); + const ro = new ResizeObserver(() => this.resizeCanvas()); + ro.observe(this.bodyTop); + ro.observe(this.bodyLeft); + ro.observe(this.bodyRight); + ro.observe(this.bodyBottom); await this.#invokeExtensionsAsync("init"); await this.registerNodes(); @@ -1573,7 +1589,8 @@ export class ComfyApp { const loadWorkflow = async (json) => { if (json) { const workflow = JSON.parse(json); - await this.loadGraphData(workflow); + const workflowName = getStorageValue("Comfy.PreviousWorkflow"); + await this.loadGraphData(workflow, true, workflowName); return true; } }; @@ -1609,6 +1626,19 @@ export class ComfyApp { await this.#invokeExtensionsAsync("setup"); } + resizeCanvas() { + // Limit minimal scale to 1, see https://github.com/comfyanonymous/ComfyUI/pull/845 + const scale = Math.max(window.devicePixelRatio, 1); + + // Clear fixed width and height while calculating rect so it uses 100% instead + this.canvasEl.height = this.canvasEl.width = ""; + const { width, height } = this.canvasEl.getBoundingClientRect(); + this.canvasEl.width = Math.round(width * scale); + this.canvasEl.height = Math.round(height * scale); + this.canvasEl.getContext("2d").scale(scale, scale); + this.canvas?.draw(true, true); + } + /** * Registers nodes with the graph */ @@ -1795,12 +1825,29 @@ export class ComfyApp { }); } + async changeWorkflow(callback, workflow = null) { + try { + this.workflowManager.activeWorkflow?.changeTracker?.store() + } catch (error) { + console.error(error); + } + await callback(); + try { + this.workflowManager.setWorkflow(workflow); + this.workflowManager.activeWorkflow?.track() + } catch (error) { + console.error(error); + } + } + /** * Populates the graph with the specified workflow data * @param {*} graphData A serialized graph object * @param { boolean } clean If the graph state, e.g. images, should be cleared + * @param { boolean } restore_view If the graph position should be restored + * @param { import("./workflows.js").ComfyWorkflowInstance | null } workflow The workflow */ - async loadGraphData(graphData, clean = true, restore_view = true) { + async loadGraphData(graphData, clean = true, restore_view = true, workflow = null) { if (clean !== false) { this.clean(); } @@ -1818,6 +1865,12 @@ export class ComfyApp { { graphData = structuredClone(graphData); } + + try { + this.workflowManager.setWorkflow(workflow); + } catch (error) { + console.error(error); + } const missingNodeTypes = []; await this.#invokeExtensionsAsync("beforeConfigureGraph", graphData, missingNodeTypes); @@ -1840,6 +1893,11 @@ export class ComfyApp { this.canvas.ds.offset = graphData.extra.ds.offset; this.canvas.ds.scale = graphData.extra.ds.scale; } + + try { + this.workflowManager.activeWorkflow?.track() + } catch (error) { + } } catch (error) { let errorHint = []; // Try extracting filename to see if it was caused by an extension script @@ -1927,14 +1985,17 @@ export class ComfyApp { this.showMissingNodesError(missingNodeTypes); } await this.#invokeExtensionsAsync("afterConfigureGraph", missingNodeTypes); + requestAnimationFrame(() => { + this.graph.setDirtyCanvas(true, true); + }); } /** * Converts the current graph workflow for sending to the API * @returns The workflow and node links */ - async graphToPrompt() { - for (const outerNode of this.graph.computeExecutionOrder(false)) { + async graphToPrompt(graph = this.graph, clean = true) { + for (const outerNode of graph.computeExecutionOrder(false)) { if (outerNode.widgets) { for (const widget of outerNode.widgets) { // Allow widgets to run callbacks before a prompt has been queued @@ -1954,10 +2015,10 @@ export class ComfyApp { } } - const workflow = this.graph.serialize(); + const workflow = graph.serialize(); const output = {}; // Process nodes in order of execution - for (const outerNode of this.graph.computeExecutionOrder(false)) { + for (const outerNode of graph.computeExecutionOrder(false)) { const skipNode = outerNode.mode === 2 || outerNode.mode === 4; const innerNodes = (!skipNode && outerNode.getInnerNodes) ? outerNode.getInnerNodes() : [outerNode]; for (const node of innerNodes) { @@ -2049,13 +2110,14 @@ export class ComfyApp { } // Remove inputs connected to removed nodes - - for (const o in output) { - for (const i in output[o].inputs) { - if (Array.isArray(output[o].inputs[i]) - && output[o].inputs[i].length === 2 - && !output[output[o].inputs[i][0]]) { - delete output[o].inputs[i]; + if(clean) { + for (const o in output) { + for (const i in output[o].inputs) { + if (Array.isArray(output[o].inputs[i]) + && output[o].inputs[i].length === 2 + && !output[output[o].inputs[i][0]]) { + delete output[o].inputs[i]; + } } } } @@ -2123,6 +2185,14 @@ export class ComfyApp { this.lastNodeErrors = res.node_errors; if (this.lastNodeErrors.length > 0) { this.canvas.draw(true, true); + } else { + try { + this.workflowManager.storePrompt({ + id: res.prompt_id, + nodes: Object.keys(p.output) + }); + } catch (error) { + } } } catch (error) { const formattedError = this.#formatPromptError(error) @@ -2155,6 +2225,7 @@ export class ComfyApp { this.#processingQueue = false; } api.dispatchEvent(new CustomEvent("promptQueued", { detail: { number, batchCount } })); + return !this.lastNodeErrors; } showErrorOnFileLoad(file) { @@ -2170,14 +2241,24 @@ export class ComfyApp { * @param {File} file */ async handleFile(file) { + const removeExt = f => { + if(!f) return f; + const p = f.lastIndexOf("."); + if(p === -1) return f; + return f.substring(0, p); + }; + + const fileName = removeExt(file.name); if (file.type === "image/png") { const pngInfo = await getPngMetadata(file); if (pngInfo?.workflow) { - await this.loadGraphData(JSON.parse(pngInfo.workflow)); + await this.loadGraphData(JSON.parse(pngInfo.workflow), true, true, fileName); } else if (pngInfo?.prompt) { - this.loadApiJson(JSON.parse(pngInfo.prompt)); + this.loadApiJson(JSON.parse(pngInfo.prompt), fileName); } else if (pngInfo?.parameters) { - importA1111(this.graph, pngInfo.parameters); + this.changeWorkflow(() => { + importA1111(this.graph, pngInfo.parameters); + }, fileName) } else { this.showErrorOnFileLoad(file); } @@ -2188,9 +2269,9 @@ export class ComfyApp { const prompt = pngInfo?.prompt || pngInfo?.Prompt; if (workflow) { - this.loadGraphData(JSON.parse(workflow)); + this.loadGraphData(JSON.parse(workflow), true, true, fileName); } else if (prompt) { - this.loadApiJson(JSON.parse(prompt)); + this.loadApiJson(JSON.parse(prompt), fileName); } else { this.showErrorOnFileLoad(file); } @@ -2201,16 +2282,16 @@ export class ComfyApp { if (jsonContent?.templates) { this.loadTemplateData(jsonContent); } else if(this.isApiJson(jsonContent)) { - this.loadApiJson(jsonContent); + this.loadApiJson(jsonContent, fileName); } else { - await this.loadGraphData(jsonContent); + await this.loadGraphData(jsonContent, true, fileName); } }; reader.readAsText(file); } else if (file.name?.endsWith(".latent") || file.name?.endsWith(".safetensors")) { const info = await getLatentMetadata(file); if (info.workflow) { - await this.loadGraphData(JSON.parse(info.workflow)); + await this.loadGraphData(JSON.parse(info.workflow), true, fileName); } else if (info.prompt) { this.loadApiJson(JSON.parse(info.prompt)); } else { @@ -2225,7 +2306,7 @@ export class ComfyApp { return Object.values(data).every((v) => v.class_type); } - loadApiJson(apiData) { + loadApiJson(apiData, fileName) { const missingNodeTypes = Object.values(apiData).filter((n) => !LiteGraph.registered_node_types[n.class_type]); if (missingNodeTypes.length) { this.showMissingNodesError(missingNodeTypes.map(t => t.class_type), false); @@ -2240,40 +2321,42 @@ export class ComfyApp { node.id = isNaN(+id) ? id : +id; node.title = data._meta?.title ?? node.title app.graph.add(node); + graph.add(node); } - for (const id of ids) { - const data = apiData[id]; - const node = app.graph.getNodeById(id); - for (const input in data.inputs ?? {}) { - const value = data.inputs[input]; - if (value instanceof Array) { - const [fromId, fromSlot] = value; - const fromNode = app.graph.getNodeById(fromId); - let toSlot = node.inputs?.findIndex((inp) => inp.name === input); - if (toSlot == null || toSlot === -1) { - try { - // Target has no matching input, most likely a converted widget - const widget = node.widgets?.find((w) => w.name === input); - if (widget && node.convertWidgetToInput?.(widget)) { - toSlot = node.inputs?.length - 1; - } - } catch (error) {} - } - if (toSlot != null || toSlot !== -1) { - fromNode.connect(fromSlot, node, toSlot); - } - } else { - const widget = node.widgets?.find((w) => w.name === input); - if (widget) { - widget.value = value; - widget.callback?.(value); + this.changeWorkflow(() => { + for (const id of ids) { + const data = apiData[id]; + const node = app.graph.getNodeById(id); + for (const input in data.inputs ?? {}) { + const value = data.inputs[input]; + if (value instanceof Array) { + const [fromId, fromSlot] = value; + const fromNode = app.graph.getNodeById(fromId); + let toSlot = node.inputs?.findIndex((inp) => inp.name === input); + if (toSlot == null || toSlot === -1) { + try { + // Target has no matching input, most likely a converted widget + const widget = node.widgets?.find((w) => w.name === input); + if (widget && node.convertWidgetToInput?.(widget)) { + toSlot = node.inputs?.length - 1; + } + } catch (error) {} + } + if (toSlot != null || toSlot !== -1) { + fromNode.connect(fromSlot, node, toSlot); + } + } else { + const widget = node.widgets?.find((w) => w.name === input); + if (widget) { + widget.value = value; + widget.callback?.(value); + } } } } - } - - app.graph.arrange(); + app.graph.arrange(); + }, fileName); } /** diff --git a/web/scripts/changeTracker.js b/web/scripts/changeTracker.js new file mode 100644 index 00000000..59901d5f --- /dev/null +++ b/web/scripts/changeTracker.js @@ -0,0 +1,242 @@ +// @ts-check + +import { api } from "./api.js"; +import { clone } from "./utils.js"; + + +export class ChangeTracker { + static MAX_HISTORY = 50; + #app; + undo = []; + redo = []; + activeState = null; + isOurLoad = false; + /** @type { import("./workflows").ComfyWorkflow | null } */ + workflow; + + ds; + nodeOutputs; + + get app() { + return this.#app ?? this.workflow.manager.app; + } + + constructor(workflow) { + this.workflow = workflow; + } + + #setApp(app) { + this.#app = app; + } + + store() { + this.ds = { scale: this.app.canvas.ds.scale, offset: [...this.app.canvas.ds.offset] }; + } + + restore() { + if (this.ds) { + this.app.canvas.ds.scale = this.ds.scale; + this.app.canvas.ds.offset = this.ds.offset; + } + if (this.nodeOutputs) { + this.app.nodeOutputs = this.nodeOutputs; + } + } + + checkState() { + if (!this.app.graph) return; + + const currentState = this.app.graph.serialize(); + if (!this.activeState) { + this.activeState = clone(currentState); + return; + } + if (!ChangeTracker.graphEqual(this.activeState, currentState)) { + this.undo.push(this.activeState); + if (this.undo.length > ChangeTracker.MAX_HISTORY) { + this.undo.shift(); + } + this.activeState = clone(currentState); + this.redo.length = 0; + this.workflow.unsaved = true; + api.dispatchEvent(new CustomEvent("graphChanged", { detail: this.activeState })); + } + } + + async updateState(source, target) { + const prevState = source.pop(); + if (prevState) { + target.push(this.activeState); + this.isOurLoad = true; + await this.app.loadGraphData(prevState, false, false, this.workflow); + this.activeState = prevState; + } + } + + async undoRedo(e) { + if (e.ctrlKey || e.metaKey) { + if (e.key === "y") { + this.updateState(this.redo, this.undo); + return true; + } else if (e.key === "z") { + this.updateState(this.undo, this.redo); + return true; + } + } + } + + /** @param { import("./app.js").ComfyApp } app */ + static init(app) { + const changeTracker = () => app.workflowManager.activeWorkflow?.changeTracker ?? globalTracker; + globalTracker.#setApp(app); + + const loadGraphData = app.loadGraphData; + app.loadGraphData = async function () { + const v = await loadGraphData.apply(this, arguments); + const ct = changeTracker(); + if (ct.isOurLoad) { + ct.isOurLoad = false; + } else { + ct.checkState(); + } + return v; + }; + + let keyIgnored = false; + window.addEventListener( + "keydown", + (e) => { + requestAnimationFrame(async () => { + let activeEl; + // If we are auto queue in change mode then we do want to trigger on inputs + if (!app.ui.autoQueueEnabled || app.ui.autoQueueMode === "instant") { + activeEl = document.activeElement; + if (activeEl?.tagName === "INPUT" || activeEl?.["type"] === "textarea") { + // Ignore events on inputs, they have their native history + return; + } + } + + keyIgnored = e.key === "Control" || e.key === "Shift" || e.key === "Alt" || e.key === "Meta"; + if (keyIgnored) return; + + // Check if this is a ctrl+z ctrl+y + if (await changeTracker().undoRedo(e)) return; + + // If our active element is some type of input then handle changes after they're done + if (ChangeTracker.bindInput(activeEl)) return; + changeTracker().checkState(); + }); + }, + true + ); + + window.addEventListener("keyup", (e) => { + if (keyIgnored) { + keyIgnored = false; + changeTracker().checkState(); + } + }); + + // Handle clicking DOM elements (e.g. widgets) + window.addEventListener("mouseup", () => { + changeTracker().checkState(); + }); + + // Handle prompt queue event for dynamic widget changes + api.addEventListener("promptQueued", () => { + changeTracker().checkState(); + }); + + // Handle litegraph clicks + const processMouseUp = LGraphCanvas.prototype.processMouseUp; + LGraphCanvas.prototype.processMouseUp = function (e) { + const v = processMouseUp.apply(this, arguments); + changeTracker().checkState(); + return v; + }; + const processMouseDown = LGraphCanvas.prototype.processMouseDown; + LGraphCanvas.prototype.processMouseDown = function (e) { + const v = processMouseDown.apply(this, arguments); + changeTracker().checkState(); + return v; + }; + + // Handle litegraph context menu for COMBO widgets + const close = LiteGraph.ContextMenu.prototype.close; + LiteGraph.ContextMenu.prototype.close = function (e) { + const v = close.apply(this, arguments); + changeTracker().checkState(); + return v; + }; + + // Store node outputs + api.addEventListener("executed", ({ detail }) => { + const prompt = app.workflowManager.queuedPrompts[detail.prompt_id]; + if (!prompt?.workflow) return; + const nodeOutputs = (prompt.workflow.changeTracker.nodeOutputs ??= {}); + const output = nodeOutputs[detail.node]; + if (detail.merge && output) { + for (const k in detail.output ?? {}) { + const v = output[k]; + if (v instanceof Array) { + output[k] = v.concat(detail.output[k]); + } else { + output[k] = detail.output[k]; + } + } + } else { + nodeOutputs[detail.node] = detail.output; + } + }); + } + + static bindInput(app, activeEl) { + if (activeEl && activeEl.tagName !== "CANVAS" && activeEl.tagName !== "BODY") { + for (const evt of ["change", "input", "blur"]) { + if (`on${evt}` in activeEl) { + const listener = () => { + app.workflowManager.activeWorkflow.changeTracker.checkState(); + activeEl.removeEventListener(evt, listener); + }; + activeEl.addEventListener(evt, listener); + return true; + } + } + } + } + + static graphEqual(a, b, path = "") { + if (a === b) return true; + + if (typeof a == "object" && a && typeof b == "object" && b) { + const keys = Object.getOwnPropertyNames(a); + + if (keys.length != Object.getOwnPropertyNames(b).length) { + return false; + } + + for (const key of keys) { + let av = a[key]; + let bv = b[key]; + if (!path && key === "nodes") { + // Nodes need to be sorted as the order changes when selecting nodes + av = [...av].sort((a, b) => a.id - b.id); + bv = [...bv].sort((a, b) => a.id - b.id); + } else if (path === "extra.ds") { + // Ignore view changes + continue; + } + if (!ChangeTracker.graphEqual(av, bv, path + (path ? "." : "") + key)) { + return false; + } + } + + return true; + } + + return false; + } +} + +const globalTracker = new ChangeTracker({}); \ No newline at end of file diff --git a/web/scripts/domWidget.js b/web/scripts/domWidget.js index b7f437ad..d97122f9 100644 --- a/web/scripts/domWidget.js +++ b/web/scripts/domWidget.js @@ -34,8 +34,8 @@ function getClipPath(node, element) { } const widgetRect = element.getBoundingClientRect(); - const clipX = intersection[0] - widgetRect.x / scale + "px"; - const clipY = intersection[1] - widgetRect.y / scale + "px"; + const clipX = elRect.left + intersection[0] - widgetRect.x / scale + "px"; + const clipY = elRect.top + intersection[1] - widgetRect.y / scale + "px"; const clipWidth = intersection[2] + "px"; const clipHeight = intersection[3] + "px"; const path = `polygon(0% 0%, 0% 100%, ${clipX} 100%, ${clipX} ${clipY}, calc(${clipX} + ${clipWidth}) ${clipY}, calc(${clipX} + ${clipWidth}) calc(${clipY} + ${clipHeight}), ${clipX} calc(${clipY} + ${clipHeight}), ${clipX} 100%, 100% 100%, 100% 0%)`; @@ -210,7 +210,9 @@ LGraphNode.prototype.addDOMWidget = function (name, type, element, options) { if (!element.parentElement) { document.body.append(element); } - + element.hidden = true; + element.style.display = "none"; + let mouseDownHandler; if (element.blur) { mouseDownHandler = (event) => { @@ -254,15 +256,15 @@ LGraphNode.prototype.addDOMWidget = function (name, type, element, options) { const transform = new DOMMatrix() .scaleSelf(elRect.width / ctx.canvas.width, elRect.height / ctx.canvas.height) .multiplySelf(ctx.getTransform()) - .translateSelf(margin, margin + y); + .translateSelf(margin, margin + y ); const scale = new DOMMatrix().scaleSelf(transform.a, transform.d); Object.assign(element.style, { transformOrigin: "0 0", transform: scale, - left: `${transform.a + transform.e}px`, - top: `${transform.d + transform.f}px`, + left: `${transform.a + transform.e + elRect.left}px`, + top: `${transform.d + transform.f + elRect.top}px`, width: `${widgetWidth - margin * 2}px`, height: `${(widget.computedHeight ?? 50) - margin * 2}px`, position: "absolute", diff --git a/web/scripts/ui.js b/web/scripts/ui.js index 72e43d35..87b4887e 100644 --- a/web/scripts/ui.js +++ b/web/scripts/ui.js @@ -6,17 +6,22 @@ import { ComfySettingsDialog } from "./ui/settings.js"; export const ComfyDialog = _ComfyDialog; /** - * - * @param { string } tag HTML Element Tag and optional classes e.g. div.class1.class2 - * @param { string | Element | Element[] | { + * @template { string | (keyof HTMLElementTagNameMap) } K + * @typedef { K extends keyof HTMLElementTagNameMap ? HTMLElementTagNameMap[K] : HTMLElement } ElementType + */ + +/** + * @template { string | (keyof HTMLElementTagNameMap) } K + * @param { K } tag HTML Element Tag and optional classes e.g. div.class1.class2 + * @param { string | Element | Element[] | ({ * parent?: Element, - * $?: (el: Element) => void, + * $?: (el: ElementType) => void, * dataset?: DOMStringMap, - * style?: CSSStyleDeclaration, + * style?: Partial, * for?: string - * } | undefined } propsOrChildren - * @param { Element[] | undefined } [children] - * @returns + * } & Omit>, "style">) | undefined } [propsOrChildren] + * @param { string | Element | Element[] | undefined } [children] + * @returns { ElementType } */ export function $el(tag, propsOrChildren, children) { const split = tag.split("."); @@ -54,7 +59,7 @@ export function $el(tag, propsOrChildren, children) { Object.assign(element, propsOrChildren); if (children) { - element.append(...(children instanceof Array ? children : [children])); + element.append(...(children instanceof Array ? children.filter(Boolean) : [children])); } if (parent) { @@ -102,6 +107,8 @@ function dragElement(dragEl, settings) { } function positionElement() { + if(dragEl.style.display === "none") return; + const halfWidth = document.body.clientWidth / 2; const anchorRight = newPosX + dragEl.clientWidth / 2 > halfWidth; @@ -191,6 +198,8 @@ function dragElement(dragEl, settings) { document.onmouseup = null; document.onmousemove = null; } + + return restorePos; } class ComfyList { @@ -372,6 +381,8 @@ export class ComfyUI { }, }); + this.loadFile = () => fileInput.click(); + const autoQueueModeEl = toggleSwitch( "autoQueueMode", [ @@ -592,6 +603,7 @@ export class ComfyUI { onclick: () => app.refreshComboInNodes() }), $el("button", {id: "comfy-clipspace-button", textContent: "Clipspace", onclick: () => app.openClipspace()}), + $el("button", {id: "comfy-reset-view-button", textContent: "Reset View", onclick: () => app.resetView()}), $el("button", { id: "comfy-clear-button", textContent: "Clear", onclick: () => { if (!confirmClear.value || confirm("Clear workflow?")) { @@ -621,10 +633,10 @@ export class ComfyUI { name: "Enable Dev mode Options", type: "boolean", defaultValue: false, - onChange: function(value) { document.getElementById("comfy-dev-save-api-button").style.display = value ? "block" : "none"}, + onChange: function(value) { document.getElementById("comfy-dev-save-api-button").style.display = value ? "flex" : "none"}, }); - dragElement(this.menuContainer, this.settings); + this.restoreMenuPosition = dragElement(this.menuContainer, this.settings); this.setStatus({exec_info: {queue_remaining: "X"}}); } diff --git a/web/scripts/ui/components/asyncDialog.js b/web/scripts/ui/components/asyncDialog.js new file mode 100644 index 00000000..434ce4b3 --- /dev/null +++ b/web/scripts/ui/components/asyncDialog.js @@ -0,0 +1,64 @@ +import { ComfyDialog } from "../dialog.js"; +import { $el } from "../../ui.js"; + +export class ComfyAsyncDialog extends ComfyDialog { + #resolve; + + constructor(actions) { + super( + "dialog.comfy-dialog.comfyui-dialog", + actions?.map((opt) => { + if (typeof opt === "string") { + opt = { text: opt }; + } + return $el("button.comfyui-button", { + type: "button", + textContent: opt.text, + onclick: () => this.close(opt.value ?? opt.text), + }); + }) + ); + } + + show(html) { + this.element.addEventListener("close", () => { + this.close(); + }); + + super.show(html); + + return new Promise((resolve) => { + this.#resolve = resolve; + }); + } + + showModal(html) { + this.element.addEventListener("close", () => { + this.close(); + }); + + super.show(html); + this.element.showModal(); + + return new Promise((resolve) => { + this.#resolve = resolve; + }); + } + + close(result = null) { + this.#resolve(result); + this.element.close(); + super.close(); + } + + static async prompt({ title = null, message, actions }) { + const dialog = new ComfyAsyncDialog(actions); + const content = [$el("span", message)]; + if (title) { + content.unshift($el("h3", title)); + } + const res = await dialog.showModal(content); + dialog.element.remove(); + return res; + } +} diff --git a/web/scripts/ui/components/button.js b/web/scripts/ui/components/button.js new file mode 100644 index 00000000..25e5aeeb --- /dev/null +++ b/web/scripts/ui/components/button.js @@ -0,0 +1,163 @@ +// @ts-check + +import { $el } from "../../ui.js"; +import { applyClasses, toggleElement } from "../utils.js"; +import { prop } from "../../utils.js"; + +/** + * @typedef {{ + * icon?: string; + * overIcon?: string; + * iconSize?: number; + * content?: string | HTMLElement; + * tooltip?: string; + * enabled?: boolean; + * action?: (e: Event, btn: ComfyButton) => void, + * classList?: import("../utils.js").ClassList, + * visibilitySetting?: { id: string, showValue: any }, + * app?: import("../../app.js").ComfyApp + * }} ComfyButtonProps + */ +export class ComfyButton { + #over = 0; + #popupOpen = false; + isOver = false; + iconElement = $el("i.mdi"); + contentElement = $el("span"); + /** + * @type {import("./popup.js").ComfyPopup} + */ + popup; + + /** + * @param {ComfyButtonProps} opts + */ + constructor({ + icon, + overIcon, + iconSize, + content, + tooltip, + action, + classList = "comfyui-button", + visibilitySetting, + app, + enabled = true, + }) { + this.element = $el("button", { + onmouseenter: () => { + this.isOver = true; + if(this.overIcon) { + this.updateIcon(); + } + }, + onmouseleave: () => { + this.isOver = false; + if(this.overIcon) { + this.updateIcon(); + } + } + + }, [this.iconElement, this.contentElement]); + + this.icon = prop(this, "icon", icon, toggleElement(this.iconElement, { onShow: this.updateIcon })); + this.overIcon = prop(this, "overIcon", overIcon, () => { + if(this.isOver) { + this.updateIcon(); + } + }); + this.iconSize = prop(this, "iconSize", iconSize, this.updateIcon); + this.content = prop( + this, + "content", + content, + toggleElement(this.contentElement, { + onShow: (el, v) => { + if (typeof v === "string") { + el.textContent = v; + } else { + el.replaceChildren(v); + } + }, + }) + ); + + this.tooltip = prop(this, "tooltip", tooltip, (v) => { + if (v) { + this.element.title = v; + } else { + this.element.removeAttribute("title"); + } + }); + this.classList = prop(this, "classList", classList, this.updateClasses); + this.hidden = prop(this, "hidden", false, this.updateClasses); + this.enabled = prop(this, "enabled", enabled, () => { + this.updateClasses(); + this.element.disabled = !this.enabled; + }); + this.action = prop(this, "action", action); + this.element.addEventListener("click", (e) => { + if (this.popup) { + // we are either a touch device or triggered by click not hover + if (!this.#over) { + this.popup.toggle(); + } + } + this.action?.(e, this); + }); + + if (visibilitySetting?.id) { + const settingUpdated = () => { + this.hidden = app.ui.settings.getSettingValue(visibilitySetting.id) !== visibilitySetting.showValue; + }; + app.ui.settings.addEventListener(visibilitySetting.id + ".change", settingUpdated); + settingUpdated(); + } + } + + updateIcon = () => (this.iconElement.className = `mdi mdi-${(this.isOver && this.overIcon) || this.icon}${this.iconSize ? " mdi-" + this.iconSize + "px" : ""}`); + updateClasses = () => { + const internalClasses = []; + if (this.hidden) { + internalClasses.push("hidden"); + } + if (!this.enabled) { + internalClasses.push("disabled"); + } + if (this.popup) { + if (this.#popupOpen) { + internalClasses.push("popup-open"); + } else { + internalClasses.push("popup-closed"); + } + } + applyClasses(this.element, this.classList, ...internalClasses); + }; + + /** + * + * @param { import("./popup.js").ComfyPopup } popup + * @param { "click" | "hover" } mode + */ + withPopup(popup, mode = "click") { + this.popup = popup; + + if (mode === "hover") { + for (const el of [this.element, this.popup.element]) { + el.addEventListener("mouseenter", () => { + this.popup.open = !!++this.#over; + }); + el.addEventListener("mouseleave", () => { + this.popup.open = !!--this.#over; + }); + } + } + + popup.addEventListener("change", () => { + this.#popupOpen = popup.open; + this.updateClasses(); + }); + + return this; + } +} diff --git a/web/scripts/ui/components/buttonGroup.js b/web/scripts/ui/components/buttonGroup.js new file mode 100644 index 00000000..573572fd --- /dev/null +++ b/web/scripts/ui/components/buttonGroup.js @@ -0,0 +1,45 @@ +// @ts-check + +import { $el } from "../../ui.js"; +import { ComfyButton } from "./button.js"; +import { prop } from "../../utils.js"; + +export class ComfyButtonGroup { + element = $el("div.comfyui-button-group"); + + /** @param {Array} buttons */ + constructor(...buttons) { + this.buttons = prop(this, "buttons", buttons, () => this.update()); + } + + /** + * @param {ComfyButton} button + * @param {number} index + */ + insert(button, index) { + this.buttons.splice(index, 0, button); + this.update(); + } + + /** @param {ComfyButton} button */ + append(button) { + this.buttons.push(button); + this.update(); + } + + /** @param {ComfyButton|number} indexOrButton */ + remove(indexOrButton) { + if (typeof indexOrButton !== "number") { + indexOrButton = this.buttons.indexOf(indexOrButton); + } + if (indexOrButton > -1) { + const r = this.buttons.splice(indexOrButton, 1); + this.update(); + return r; + } + } + + update() { + this.element.replaceChildren(...this.buttons.map((b) => b["element"] ?? b)); + } +} diff --git a/web/scripts/ui/components/popup.js b/web/scripts/ui/components/popup.js new file mode 100644 index 00000000..ee59b35d --- /dev/null +++ b/web/scripts/ui/components/popup.js @@ -0,0 +1,128 @@ +// @ts-check + +import { prop } from "../../utils.js"; +import { $el } from "../../ui.js"; +import { applyClasses } from "../utils.js"; + +export class ComfyPopup extends EventTarget { + element = $el("div.comfyui-popup"); + + /** + * @param {{ + * target: HTMLElement, + * container?: HTMLElement, + * classList?: import("../utils.js").ClassList, + * ignoreTarget?: boolean, + * closeOnEscape?: boolean, + * position?: "absolute" | "relative", + * horizontal?: "left" | "right" + * }} param0 + * @param {...HTMLElement} children + */ + constructor( + { + target, + container = document.body, + classList = "", + ignoreTarget = true, + closeOnEscape = true, + position = "absolute", + horizontal = "left", + }, + ...children + ) { + super(); + this.target = target; + this.ignoreTarget = ignoreTarget; + this.container = container; + this.position = position; + this.closeOnEscape = closeOnEscape; + this.horizontal = horizontal; + + container.append(this.element); + + this.children = prop(this, "children", children, () => { + this.element.replaceChildren(...this.children); + this.update(); + }); + this.classList = prop(this, "classList", classList, () => applyClasses(this.element, this.classList, "comfyui-popup", horizontal)); + this.open = prop(this, "open", false, (v, o) => { + if (v === o) return; + if (v) { + this.#show(); + } else { + this.#hide(); + } + }); + } + + toggle() { + this.open = !this.open; + } + + #hide() { + this.element.classList.remove("open"); + window.removeEventListener("resize", this.update); + window.removeEventListener("click", this.#clickHandler, { capture: true }); + window.removeEventListener("keydown", this.#escHandler, { capture: true }); + + this.dispatchEvent(new CustomEvent("close")); + this.dispatchEvent(new CustomEvent("change")); + } + + #show() { + this.element.classList.add("open"); + this.update(); + + window.addEventListener("resize", this.update); + window.addEventListener("click", this.#clickHandler, { capture: true }); + if (this.closeOnEscape) { + window.addEventListener("keydown", this.#escHandler, { capture: true }); + } + + this.dispatchEvent(new CustomEvent("open")); + this.dispatchEvent(new CustomEvent("change")); + } + + #escHandler = (e) => { + if (e.key === "Escape") { + this.open = false; + e.preventDefault(); + e.stopImmediatePropagation(); + } + }; + + #clickHandler = (e) => { + /** @type {any} */ + const target = e.target; + if (!this.element.contains(target) && this.ignoreTarget && !this.target.contains(target)) { + this.open = false; + } + }; + + update = () => { + const rect = this.target.getBoundingClientRect(); + this.element.style.setProperty("--bottom", "unset"); + if (this.position === "absolute") { + if (this.horizontal === "left") { + this.element.style.setProperty("--left", rect.left + "px"); + } else { + this.element.style.setProperty("--left", rect.right - this.element.clientWidth + "px"); + } + this.element.style.setProperty("--top", rect.bottom + "px"); + this.element.style.setProperty("--limit", rect.bottom + "px"); + } else { + this.element.style.setProperty("--left", 0 + "px"); + this.element.style.setProperty("--top", rect.height + "px"); + this.element.style.setProperty("--limit", rect.height + "px"); + } + + const thisRect = this.element.getBoundingClientRect(); + if (thisRect.height < 30) { + // Move up instead + this.element.style.setProperty("--top", "unset"); + this.element.style.setProperty("--bottom", rect.height + 5 + "px"); + this.element.style.setProperty("--limit", rect.height + 5 + "px"); + } + }; +} diff --git a/web/scripts/ui/components/splitButton.js b/web/scripts/ui/components/splitButton.js new file mode 100644 index 00000000..2b4e6d9f --- /dev/null +++ b/web/scripts/ui/components/splitButton.js @@ -0,0 +1,43 @@ +// @ts-check + +import { $el } from "../../ui.js"; +import { ComfyButton } from "./button.js"; +import { prop } from "../../utils.js"; +import { ComfyPopup } from "./popup.js"; + +export class ComfySplitButton { + /** + * @param {{ + * primary: ComfyButton, + * mode?: "hover" | "click", + * horizontal?: "left" | "right", + * position?: "relative" | "absolute" + * }} param0 + * @param {Array | Array} items + */ + constructor({ primary, mode, horizontal = "left", position = "relative" }, ...items) { + this.arrow = new ComfyButton({ + icon: "chevron-down", + }); + this.element = $el("div.comfyui-split-button" + (mode === "hover" ? ".hover" : ""), [ + $el("div.comfyui-split-primary", primary.element), + $el("div.comfyui-split-arrow", this.arrow.element), + ]); + this.popup = new ComfyPopup({ + target: this.element, + container: position === "relative" ? this.element : document.body, + classList: "comfyui-split-button-popup" + (mode === "hover" ? " hover" : ""), + closeOnEscape: mode === "click", + position, + horizontal, + }); + + this.arrow.withPopup(this.popup, mode); + + this.items = prop(this, "items", items, () => this.update()); + } + + update() { + this.popup.element.replaceChildren(...this.items.map((b) => b.element ?? b)); + } +} diff --git a/web/scripts/ui/dialog.js b/web/scripts/ui/dialog.js index aee93b3c..803a97a2 100644 --- a/web/scripts/ui/dialog.js +++ b/web/scripts/ui/dialog.js @@ -1,20 +1,26 @@ import { $el } from "../ui.js"; -export class ComfyDialog { - constructor() { - this.element = $el("div.comfy-modal", { parent: document.body }, [ +export class ComfyDialog extends EventTarget { + #buttons; + + constructor(type = "div", buttons = null) { + super(); + this.#buttons = buttons; + this.element = $el(type + ".comfy-modal", { parent: document.body }, [ $el("div.comfy-modal-content", [$el("p", { $: (p) => (this.textElement = p) }), ...this.createButtons()]), ]); } createButtons() { - return [ - $el("button", { - type: "button", - textContent: "Close", - onclick: () => this.close(), - }), - ]; + return ( + this.#buttons ?? [ + $el("button", { + type: "button", + textContent: "Close", + onclick: () => this.close(), + }), + ] + ); } close() { @@ -25,7 +31,7 @@ export class ComfyDialog { if (typeof html === "string") { this.textElement.innerHTML = html; } else { - this.textElement.replaceChildren(html); + this.textElement.replaceChildren(...(html instanceof Array ? html : [html])); } this.element.style.display = "flex"; } diff --git a/web/scripts/ui/menu/index.js b/web/scripts/ui/menu/index.js new file mode 100644 index 00000000..1e00b3d2 --- /dev/null +++ b/web/scripts/ui/menu/index.js @@ -0,0 +1,302 @@ +// @ts-check + +import { $el } from "../../ui.js"; +import { downloadBlob } from "../../utils.js"; +import { ComfyButton } from "../components/button.js"; +import { ComfyButtonGroup } from "../components/buttonGroup.js"; +import { ComfySplitButton } from "../components/splitButton.js"; +import { ComfyViewHistoryButton } from "./viewHistory.js"; +import { ComfyQueueButton } from "./queueButton.js"; +import { ComfyWorkflowsMenu } from "./workflows.js"; +import { ComfyViewQueueButton } from "./viewQueue.js"; +import { getInteruptButton } from "./interruptButton.js"; + +const collapseOnMobile = (t) => { + (t.element ?? t).classList.add("comfyui-menu-mobile-collapse"); + return t; +}; +const showOnMobile = (t) => { + (t.element ?? t).classList.add("lt-lg-show"); + return t; +}; + +export class ComfyAppMenu { + #sizeBreak = "lg"; + #lastSizeBreaks = { + lg: null, + md: null, + sm: null, + xs: null, + }; + #sizeBreaks = Object.keys(this.#lastSizeBreaks); + #cachedInnerSize = null; + #cacheTimeout = null; + + /** + * @param { import("../../app.js").ComfyApp } app + */ + constructor(app) { + this.app = app; + + this.workflows = new ComfyWorkflowsMenu(app); + const getSaveButton = (t) => + new ComfyButton({ + icon: "content-save", + tooltip: "Save the current workflow", + action: () => app.workflowManager.activeWorkflow.save(), + content: t, + }); + + this.logo = $el("h1.comfyui-logo.nlg-hide", { title: "ComfyUI" }, "ComfyUI"); + this.saveButton = new ComfySplitButton( + { + primary: getSaveButton(), + mode: "hover", + position: "absolute", + }, + getSaveButton("Save"), + new ComfyButton({ + icon: "content-save-edit", + content: "Save As", + tooltip: "Save the current graph as a new workflow", + action: () => app.workflowManager.activeWorkflow.save(true), + }), + new ComfyButton({ + icon: "download", + content: "Export", + tooltip: "Export the current workflow as JSON", + action: () => this.exportWorkflow("workflow", "workflow"), + }), + new ComfyButton({ + icon: "api", + content: "Export (API Format)", + tooltip: "Export the current workflow as JSON for use with the ComfyUI API", + action: () => this.exportWorkflow("workflow_api", "output"), + visibilitySetting: { id: "Comfy.DevMode", showValue: true }, + app, + }) + ); + this.actionsGroup = new ComfyButtonGroup( + new ComfyButton({ + icon: "refresh", + content: "Refresh", + tooltip: "Refresh widgets in nodes to find new models or files", + action: () => app.refreshComboInNodes(), + }), + new ComfyButton({ + icon: "clipboard-edit-outline", + content: "Clipspace", + tooltip: "Open Clipspace window", + action: () => app["openClipspace"](), + }), + new ComfyButton({ + icon: "fit-to-page-outline", + content: "Reset View", + tooltip: "Reset the canvas view", + action: () => app.resetView(), + }), + new ComfyButton({ + icon: "cancel", + content: "Clear", + tooltip: "Clears current workflow", + action: () => { + if (!app.ui.settings.getSettingValue("Comfy.ConfirmClear", true) || confirm("Clear workflow?")) { + app.clean(); + app.graph.clear(); + } + }, + }) + ); + this.settingsGroup = new ComfyButtonGroup( + new ComfyButton({ + icon: "cog", + content: "Settings", + tooltip: "Open settings", + action: () => { + app.ui.settings.show(); + }, + }) + ); + this.viewGroup = new ComfyButtonGroup( + new ComfyViewHistoryButton(app).element, + new ComfyViewQueueButton(app).element, + getInteruptButton("nlg-hide").element + ); + this.mobileMenuButton = new ComfyButton({ + icon: "menu", + action: (_, btn) => { + btn.icon = this.element.classList.toggle("expanded") ? "menu-open" : "menu"; + window.dispatchEvent(new Event("resize")); + }, + classList: "comfyui-button comfyui-menu-button", + }); + + this.element = $el("nav.comfyui-menu.lg", { style: { display: "none" } }, [ + this.logo, + this.workflows.element, + this.saveButton.element, + collapseOnMobile(this.actionsGroup).element, + $el("section.comfyui-menu-push"), + collapseOnMobile(this.settingsGroup).element, + collapseOnMobile(this.viewGroup).element, + + getInteruptButton("lt-lg-show").element, + new ComfyQueueButton(app).element, + showOnMobile(this.mobileMenuButton).element, + ]); + + let resizeHandler; + this.menuPositionSetting = app.ui.settings.addSetting({ + id: "Comfy.UseNewMenu", + defaultValue: "Disabled", + name: "[Beta] Use new menu and workflow management. Note: On small screens the menu will always be at the top.", + type: "combo", + options: ["Disabled", "Top", "Bottom"], + onChange: async (v) => { + if (v && v !== "Disabled") { + if (!resizeHandler) { + resizeHandler = () => { + this.calculateSizeBreak(); + }; + window.addEventListener("resize", resizeHandler); + } + this.updatePosition(v); + } else { + if (resizeHandler) { + window.removeEventListener("resize", resizeHandler); + resizeHandler = null; + } + document.body.style.removeProperty("display"); + app.ui.menuContainer.style.removeProperty("display"); + this.element.style.display = "none"; + app.ui.restoreMenuPosition(); + } + window.dispatchEvent(new Event("resize")); + }, + }); + } + + updatePosition(v) { + document.body.style.display = "grid"; + this.app.ui.menuContainer.style.display = "none"; + this.element.style.removeProperty("display"); + this.position = v; + if (v === "Bottom") { + this.app.bodyBottom.append(this.element); + } else { + this.app.bodyTop.prepend(this.element); + } + this.calculateSizeBreak(); + } + + updateSizeBreak(idx, prevIdx, direction) { + const newSize = this.#sizeBreaks[idx]; + if (newSize === this.#sizeBreak) return; + this.#cachedInnerSize = null; + clearTimeout(this.#cacheTimeout); + + this.#sizeBreak = this.#sizeBreaks[idx]; + for (let i = 0; i < this.#sizeBreaks.length; i++) { + const sz = this.#sizeBreaks[i]; + if (sz === this.#sizeBreak) { + this.element.classList.add(sz); + } else { + this.element.classList.remove(sz); + } + if (i < idx) { + this.element.classList.add("lt-" + sz); + } else { + this.element.classList.remove("lt-" + sz); + } + } + + if (idx) { + // We're on a small screen, force the menu at the top + if (this.position !== "Top") { + this.updatePosition("Top"); + } + } else if (this.position != this.menuPositionSetting.value) { + // Restore user position + this.updatePosition(this.menuPositionSetting.value); + } + + // Allow multiple updates, but prevent bouncing + if (!direction) { + direction = prevIdx - idx; + } else if (direction != prevIdx - idx) { + return; + } + this.calculateSizeBreak(direction); + } + + calculateSizeBreak(direction = 0) { + let idx = this.#sizeBreaks.indexOf(this.#sizeBreak); + const currIdx = idx; + const innerSize = this.calculateInnerSize(idx); + if (window.innerWidth >= this.#lastSizeBreaks[this.#sizeBreaks[idx - 1]]) { + if (idx > 0) { + idx--; + } + } else if (innerSize > this.element.clientWidth) { + this.#lastSizeBreaks[this.#sizeBreak] = Math.max(window.innerWidth, innerSize); + // We need to shrink + if (idx < this.#sizeBreaks.length - 1) { + idx++; + } + } + + this.updateSizeBreak(idx, currIdx, direction); + } + + calculateInnerSize(idx) { + // Cache the inner size to prevent too much calculation when resizing the window + clearTimeout(this.#cacheTimeout); + if (this.#cachedInnerSize) { + // Extend cache time + this.#cacheTimeout = setTimeout(() => (this.#cachedInnerSize = null), 100); + } else { + let innerSize = 0; + let count = 1; + for (const c of this.element.children) { + if (c.classList.contains("comfyui-menu-push")) continue; // ignore right push + if (idx && c.classList.contains("comfyui-menu-mobile-collapse")) continue; // ignore collapse items + innerSize += c.clientWidth; + count++; + } + innerSize += 8 * count; + this.#cachedInnerSize = innerSize; + this.#cacheTimeout = setTimeout(() => (this.#cachedInnerSize = null), 100); + } + return this.#cachedInnerSize; + } + + /** + * @param {string} defaultName + */ + getFilename(defaultName) { + if (this.app.ui.settings.getSettingValue("Comfy.PromptFilename", true)) { + defaultName = prompt("Save workflow as:", defaultName); + if (!defaultName) return; + if (!defaultName.toLowerCase().endsWith(".json")) { + defaultName += ".json"; + } + } + return defaultName; + } + + /** + * @param {string} [filename] + * @param { "workflow" | "output" } [promptProperty] + */ + async exportWorkflow(filename, promptProperty) { + if (this.app.workflowManager.activeWorkflow?.path) { + filename = this.app.workflowManager.activeWorkflow.name; + } + const p = await this.app.graphToPrompt(); + const json = JSON.stringify(p[promptProperty], null, 2); + const blob = new Blob([json], { type: "application/json" }); + const file = this.getFilename(filename); + if (!file) return; + downloadBlob(file, blob); + } +} diff --git a/web/scripts/ui/menu/interruptButton.js b/web/scripts/ui/menu/interruptButton.js new file mode 100644 index 00000000..4db3328d --- /dev/null +++ b/web/scripts/ui/menu/interruptButton.js @@ -0,0 +1,23 @@ +// @ts-check + +import { api } from "../../api.js"; +import { ComfyButton } from "../components/button.js"; + +export function getInteruptButton(visibility) { + const btn = new ComfyButton({ + icon: "close", + tooltip: "Cancel current generation", + enabled: false, + action: () => { + api.interrupt(); + }, + classList: ["comfyui-button", "comfyui-interrupt-button", visibility], + }); + + api.addEventListener("status", ({ detail }) => { + const sz = detail?.exec_info?.queue_remaining; + btn.enabled = sz > 0; + }); + + return btn; +} diff --git a/web/scripts/ui/menu/menu.css b/web/scripts/ui/menu/menu.css new file mode 100644 index 00000000..20eeab2c --- /dev/null +++ b/web/scripts/ui/menu/menu.css @@ -0,0 +1,701 @@ +.relative { + position: relative; +} +.hidden { + display: none !important; +} +.mdi.rotate270::before { + transform: rotate(270deg); +} + +/* Generic */ +.comfyui-button { + display: flex; + align-items: center; + gap: 0.5em; + cursor: pointer; + border: none; + border-radius: 4px; + padding: 4px 8px; + box-sizing: border-box; + margin: 0; +} + +.comfyui-button:disabled { + opacity: 0.5; + cursor: not-allowed; +} +.primary .comfyui-button, +.primary.comfyui-button { + background-color: var(--primary-bg) !important; + color: var(--primary-fg) !important; +} + +.primary .comfyui-button:not(:disabled):hover, +.primary.comfyui-button:not(:disabled):hover { + background-color: var(--primary-hover-bg) !important; + color: var(--primary-hover-fg) !important; +} + +/* Popup */ +.comfyui-popup { + position: absolute; + left: var(--left); + right: var(--right); + top: var(--top); + bottom: var(--bottom); + z-index: 2000; + max-height: calc(100vh - var(--limit) - 10px); + box-shadow: 3px 3px 5px 0px rgba(0, 0, 0, 0.3); +} + +.comfyui-popup:not(.open) { + display: none; +} + +.comfyui-popup.right.open { + border-top-left-radius: 4px; + border-bottom-right-radius: 4px; + border-bottom-left-radius: 4px; + overflow: hidden; +} +/* Split button */ +.comfyui-split-button { + position: relative; + display: flex; +} + +.comfyui-split-primary { + flex: auto; +} + +.comfyui-split-primary .comfyui-button { + border-top-right-radius: 0; + border-bottom-right-radius: 0; + border-right: 1px solid var(--comfy-menu-bg); + width: 100%; +} + +.comfyui-split-arrow .comfyui-button { + border-top-left-radius: 0; + border-bottom-left-radius: 0; + padding-left: 2px; + padding-right: 2px; +} + +.comfyui-split-button-popup { + white-space: nowrap; + background-color: var(--content-bg); + color: var(--content-fg); + display: flex; + flex-direction: column; + overflow: auto; +} + +.comfyui-split-button-popup.hover { + z-index: 2001; +} +.comfyui-split-button-popup > .comfyui-button { + border: none; + background-color: transparent; + color: var(--fg-color); + padding: 8px 12px 8px 8px; +} + +.comfyui-split-button-popup > .comfyui-button:not(:disabled):hover { + background-color: var(--comfy-input-bg); +} + +/* Button group */ +.comfyui-button-group { + display: flex; + border-radius: 4px; + overflow: hidden; +} + +.comfyui-button-group > .comfyui-button, +.comfyui-button-group > .comfyui-button-wrapper > .comfyui-button { + padding: 4px 10px; + border-radius: 0; +} + +/* Menu */ +.comfyui-menu { + width: 100vw; + background: var(--comfy-menu-bg); + color: var(--fg-color); + font-family: Arial, Helvetica, sans-serif; + font-size: 0.8em; + display: flex; + padding: 4px 8px; + align-items: center; + gap: 8px; + box-sizing: border-box; + z-index: 1000; + order: 0; + grid-column: 1/-1; + overflow: auto; + max-height: 90vh; +} + +.comfyui-menu>* { + flex-shrink: 0; +} +.comfyui-menu .mdi::before { + font-size: 18px; +} + +.comfyui-menu .comfyui-button { + background: var(--comfy-input-bg); + color: var(--fg-color); + white-space: nowrap; +} + +.comfyui-menu .comfyui-button:not(:disabled):hover { + background: var(--border-color); + color: var(--content-fg); +} + +.comfyui-menu .comfyui-split-button-popup > .comfyui-button { + border-radius: 0; + background-color: transparent; +} + +.comfyui-menu .comfyui-split-button-popup > .comfyui-button:not(:disabled):hover { + background-color: var(--comfy-input-bg); +} + +.comfyui-menu .comfyui-split-button-popup.left { + border-top-right-radius: 4px; + border-bottom-left-radius: 4px; + border-bottom-right-radius: 4px; +} + +.comfyui-menu .comfyui-button.popup-open { + background-color: var(--content-bg); + color: var(--content-fg); +} + +.comfyui-menu-push { + margin-left: -0.8em; + flex: auto; +} + +.comfyui-logo { + font-size: 1.2em; + margin: 0; + user-select: none; + cursor: default; +} + +/* Workflows */ +.comfyui-workflows-button { + flex-direction: row-reverse; + max-width: 200px; + position: relative; + z-index: 0; +} + +.comfyui-workflows-button.popup-open { + border-bottom-left-radius: 0; + border-bottom-right-radius: 0; +} +.comfyui-workflows-button.unsaved { + font-style: italic; +} +.comfyui-workflows-button-progress { + position: absolute; + top: 0; + left: 0; + background-color: green; + height: 100%; + border-radius: 4px; + z-index: -1; +} + +.comfyui-workflows-button > span { + flex: auto; + text-align: left; + overflow: hidden; +} +.comfyui-workflows-button-inner { + display: flex; + align-items: center; + gap: 7px; + width: 150px; +} +.comfyui-workflows-label { + overflow: hidden; + text-overflow: ellipsis; + direction: rtl; + flex: auto; + position: relative; +} + +.comfyui-workflows-button.unsaved .comfyui-workflows-label { + padding-left: 8px; +} + +.comfyui-workflows-button.unsaved .comfyui-workflows-label:after { + content: "*"; + position: absolute; + top: 0; + left: 0; +} +.comfyui-workflows-button-inner .mdi-graph::before { + transform: rotate(-90deg); +} + +.comfyui-workflows-popup { + font-family: Arial, Helvetica, sans-serif; + font-size: 0.8em; + padding: 10px; + overflow: auto; + background-color: var(--content-bg); + color: var(--content-fg); + border-top-right-radius: 4px; + border-bottom-right-radius: 4px; + border-bottom-left-radius: 4px; + z-index: 400; +} + +.comfyui-workflows-panel { + min-height: 150px; +} + +.comfyui-workflows-panel .lds-ring { + transform: translate(-50%); + position: absolute; + left: 50%; + top: 75px; +} + +.comfyui-workflows-panel h3 { + margin: 10px 0 10px 0; + font-size: 11px; + opacity: 0.8; +} + +.comfyui-workflows-panel section header { + display: flex; + justify-content: space-between; + align-items: center; +} +.comfy-ui-workflows-search .mdi { + position: relative; + top: 2px; + pointer-events: none; +} +.comfy-ui-workflows-search input { + background-color: var(--comfy-input-bg); + color: var(--input-text); + border: none; + border-radius: 4px; + padding: 4px 10px; + margin-left: -24px; + text-indent: 18px; +} +.comfy-ui-workflows-search input:placeholder-shown { + width: 10px; +} +.comfy-ui-workflows-search input:placeholder-shown:focus { + width: auto; +} +.comfyui-workflows-actions { + display: flex; + gap: 10px; + margin-bottom: 10px; +} + +.comfyui-workflows-actions .comfyui-button { + background: var(--comfy-input-bg); + color: var(--input-text); +} + +.comfyui-workflows-actions .comfyui-button:not(:disabled):hover { + background: var(--primary-bg); + color: var(--primary-fg); +} + +.comfyui-workflows-favorites, +.comfyui-workflows-open { + border-bottom: 1px solid var(--comfy-input-bg); + padding-bottom: 5px; + margin-bottom: 5px; +} + +.comfyui-workflows-open .active { + font-weight: bold; +} + +.comfyui-workflows-favorites:empty { + display: none; +} + +.comfyui-workflows-tree { + padding: 0; + margin: 0; +} + +.comfyui-workflows-tree:empty::after { + content: "No saved workflows"; + display: block; + text-align: center; +} +.comfyui-workflows-tree > ul { + padding: 0; +} + +.comfyui-workflows-tree > ul ul { + margin: 0; + padding: 0 0 0 25px; +} + +.comfyui-workflows-tree:not(.filtered) .closed > ul { + display: none; +} + +.comfyui-workflows-tree li, +.comfyui-workflows-tree-file { + --item-height: 32px; + list-style-type: none; + height: var(--item-height); + display: flex; + align-items: center; + gap: 5px; + cursor: pointer; + user-select: none; +} + +.comfyui-workflows-tree-file.active::before, +.comfyui-workflows-tree li:hover::before, +.comfyui-workflows-tree-file:hover::before { + content: ""; + position: absolute; + width: 100%; + left: 0; + height: var(--item-height); + background-color: var(--content-hover-bg); + color: var(--content-hover-fg); + z-index: -1; +} + +.comfyui-workflows-tree-file.active::before { + background-color: var(--primary-bg); + color: var(--primary-fg); +} + +.comfyui-workflows-tree-file.running:not(:hover)::before { + content: ""; + position: absolute; + width: var(--progress, 0); + left: 0; + height: var(--item-height); + background-color: green; + z-index: -1; +} + +.comfyui-workflows-tree-file.unsaved span { + font-style: italic; +} + +.comfyui-workflows-tree-file span { + flex: auto; +} + +.comfyui-workflows-tree-file span + .comfyui-workflows-file-action { + margin-left: 10px; +} + +.comfyui-workflows-tree-file .comfyui-workflows-file-action { + background-color: transparent; + color: var(--fg-color); + padding: 2px 4px; +} + +.lg ~ .comfyui-workflows-popup .comfyui-workflows-tree-file:not(:hover) .comfyui-workflows-file-action { + opacity: 0; +} + +.comfyui-workflows-tree-file .comfyui-workflows-file-action:hover { + background-color: var(--primary-bg); + color: var(--primary-fg); +} + +.comfyui-workflows-tree-file .comfyui-workflows-file-action-primary { + background-color: transparent; + color: var(--fg-color); + padding: 2px 4px; + margin: 0 -4px; +} + +.comfyui-workflows-file-action-favorite .mdi-star { + color: orange; +} + +/* View List */ +.comfyui-view-list-popup { + padding: 10px; + background-color: var(--content-bg); + color: var(--content-fg); + min-width: 170px; + min-height: 435px; + display: flex; + flex-direction: column; + align-items: center; + box-sizing: border-box; +} +.comfyui-view-list-popup h3 { + margin: 0 0 5px 0; +} +.comfyui-view-list-items { + width: 100%; + background: var(--comfy-menu-bg); + border-radius: 5px; + display: flex; + justify-content: center; + flex: auto; + align-items: center; + flex-direction: column; +} +.comfyui-view-list-items section { + max-height: 400px; + overflow: auto; + width: 100%; + display: grid; + grid-template-columns: auto auto auto; + align-items: center; + justify-content: center; + gap: 5px; + padding: 5px 0; +} +.comfyui-view-list-items section + section { + border-top: 1px solid var(--border-color); + margin-top: 10px; + padding-top: 5px; +} +.comfyui-view-list-items section h5 { + grid-column: 1 / 4; + text-align: center; + margin: 5px; +} +.comfyui-view-list-items span { + text-align: center; + padding: 0 2px; +} +.comfyui-view-list-popup header { + margin-bottom: 10px; + display: flex; + gap: 5px; +} +.comfyui-view-list-popup header .comfyui-button { + border: 1px solid transparent; +} +.comfyui-view-list-popup header .comfyui-button:not(:disabled):hover { + border: 1px solid var(--comfy-menu-bg); +} +/* Queue button */ +.comfyui-queue-button .comfyui-split-primary .comfyui-button { + padding-right: 12px; +} +.comfyui-queue-count { + margin-left: 5px; + border-radius: 10px; + background-color: rgb(8, 80, 153); + padding: 2px 4px; + font-size: 10px; + min-width: 1em; + display: inline-block; +} +/* Queue options*/ +.comfyui-queue-options { + padding: 10px; + font-family: Arial, Helvetica, sans-serif; + font-size: 12px; + display: flex; + gap: 10px; +} + +.comfyui-queue-batch { + display: flex; + flex-direction: column; + border-right: 1px solid var(--comfy-menu-bg); + padding-right: 10px; + gap: 5px; +} + +.comfyui-queue-batch input { + width: 145px; +} + +.comfyui-queue-batch .comfyui-queue-batch-value { + width: 70px; +} + +.comfyui-queue-mode { + display: flex; + flex-direction: column; +} + +.comfyui-queue-mode span { + font-weight: bold; + margin-bottom: 2px; +} + +.comfyui-queue-mode label { + display: flex; + flex-direction: row-reverse; + justify-content: start; + gap: 5px; + padding: 2px 0; +} + +.comfyui-queue-mode label input { + padding: 0; + margin: 0; +} + +/** Send to workflow widget selection dialog */ +.comfy-widget-selection-dialog { + border: none; +} + +.comfy-widget-selection-dialog div { + color: var(--fg-color); + font-family: Arial, Helvetica, sans-serif; +} + +.comfy-widget-selection-dialog h2 { + margin-top: 0; +} + +.comfy-widget-selection-dialog section { + width: fit-content; + display: flex; + flex-direction: column; +} + +.comfy-widget-selection-item { + display: flex; + gap: 10px; + align-items: center; +} + +.comfy-widget-selection-item span { + margin-right: auto; +} + +.comfy-widget-selection-item span::before { + content: '#' attr(data-id); + opacity: 0.5; + margin-right: 5px; +} + +.comfy-modal .comfy-widget-selection-item button { + font-size: 1em; +} + +/***** Responsive *****/ +.lg.comfyui-menu .lt-lg-show { + display: none !important; +} +.comfyui-menu:not(.lg) .nlg-hide { + display: none !important; +} +/** Large screen */ +.lg.comfyui-menu>.comfyui-menu-mobile-collapse .comfyui-button span, +.lg.comfyui-menu>.comfyui-menu-mobile-collapse.comfyui-button span { + display: none; +} +.lg.comfyui-menu>.comfyui-menu-mobile-collapse .comfyui-popup .comfyui-button span { + display: unset; +} + +/** Non large screen */ +.lt-lg.comfyui-menu { + flex-wrap: wrap; +} + +.lt-lg.comfyui-menu > *:not(.comfyui-menu-mobile-collapse) { + order: 1; +} + +.lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse { + order: 9999; + width: 100%; +} + +.comfyui-body-bottom .lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse { + order: -1; +} + +.comfyui-body-bottom .lt-lg.comfyui-menu > .comfyui-menu-button { + top: unset; + bottom: 4px; +} + +.lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse.comfyui-button-group { + flex-wrap: wrap; +} + +.lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse .comfyui-button, +.lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse.comfyui-button { + padding: 10px; +} +.lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse .comfyui-button, +.lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse .comfyui-button-wrapper { + width: 100%; +} + +.lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse .comfyui-popup { + position: static; + background-color: var(--comfy-input-bg); + max-width: unset; + max-height: 50vh; + overflow: auto; +} + +.lt-lg.comfyui-menu:not(.expanded) > .comfyui-menu-mobile-collapse { + display: none; +} + +.lt-lg .comfyui-queue-button { + margin-right: 44px; +} + +.lt-lg .comfyui-menu-button { + position: absolute; + top: 4px; + right: 8px; +} + +.lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse .comfyui-view-list-popup { + border-radius: 0; +} + +.lt-lg.comfyui-menu .comfyui-workflows-popup { + width: 100vw; +} + +/** Small */ +.lt-md .comfyui-workflows-button-inner { + width: unset !important; +} +.lt-md .comfyui-workflows-label { + display: none; +} + +/** Extra small */ +.lt-sm .comfyui-queue-button { + margin-right: 0; + width: 100%; +} +.lt-sm .comfyui-queue-button .comfyui-button { + justify-content: center; +} +.lt-sm .comfyui-interrupt-button { + margin-right: 45px; +} +.comfyui-body-bottom .lt-sm.comfyui-menu > .comfyui-menu-button{ + bottom: 41px; +} \ No newline at end of file diff --git a/web/scripts/ui/menu/queueButton.js b/web/scripts/ui/menu/queueButton.js new file mode 100644 index 00000000..3c29ab09 --- /dev/null +++ b/web/scripts/ui/menu/queueButton.js @@ -0,0 +1,93 @@ +// @ts-check + +import { ComfyButton } from "../components/button.js"; +import { $el } from "../../ui.js"; +import { api } from "../../api.js"; +import { ComfySplitButton } from "../components/splitButton.js"; +import { ComfyQueueOptions } from "./queueOptions.js"; +import { prop } from "../../utils.js"; + +export class ComfyQueueButton { + element = $el("div.comfyui-queue-button"); + #internalQueueSize = 0; + + queuePrompt = async (e) => { + this.#internalQueueSize += this.queueOptions.batchCount; + // Hold shift to queue front + await this.app.queuePrompt(-e.shiftKey, this.queueOptions.batchCount); + }; + + constructor(app) { + this.app = app; + this.queueSizeElement = $el("span.comfyui-queue-count", { + textContent: "?", + }); + + const queue = new ComfyButton({ + content: $el("div", [ + $el("span", { + textContent: "Queue", + }), + this.queueSizeElement, + ]), + icon: "play", + classList: "comfyui-button", + action: this.queuePrompt, + }); + + this.queueOptions = new ComfyQueueOptions(app); + + const btn = new ComfySplitButton( + { + primary: queue, + mode: "click", + position: "absolute", + horizontal: "right", + }, + this.queueOptions.element + ); + btn.element.classList.add("primary"); + this.element.append(btn.element); + + this.autoQueueMode = prop(this, "autoQueueMode", "", () => { + switch (this.autoQueueMode) { + case "instant": + queue.icon = "infinity"; + break; + case "change": + queue.icon = "auto-mode"; + break; + default: + queue.icon = "play"; + break; + } + }); + + this.queueOptions.addEventListener("autoQueueMode", (e) => (this.autoQueueMode = e["detail"])); + + api.addEventListener("graphChanged", () => { + if (this.autoQueueMode === "change") { + if (this.#internalQueueSize) { + this.graphHasChanged = true; + } else { + this.graphHasChanged = false; + this.queuePrompt(); + } + } + }); + + api.addEventListener("status", ({ detail }) => { + this.#internalQueueSize = detail?.exec_info?.queue_remaining; + if (this.#internalQueueSize != null) { + this.queueSizeElement.textContent = this.#internalQueueSize > 99 ? "99+" : this.#internalQueueSize + ""; + this.queueSizeElement.title = `${this.#internalQueueSize} prompts in queue`; + if (!this.#internalQueueSize && !app.lastExecutionError) { + if (this.autoQueueMode === "instant" || (this.autoQueueMode === "change" && this.graphHasChanged)) { + this.graphHasChanged = false; + this.queuePrompt(); + } + } + } + }); + } +} diff --git a/web/scripts/ui/menu/queueOptions.js b/web/scripts/ui/menu/queueOptions.js new file mode 100644 index 00000000..f3d34e74 --- /dev/null +++ b/web/scripts/ui/menu/queueOptions.js @@ -0,0 +1,77 @@ +// @ts-check + +import { $el } from "../../ui.js"; +import { prop } from "../../utils.js"; + +export class ComfyQueueOptions extends EventTarget { + element = $el("div.comfyui-queue-options"); + + constructor(app) { + super(); + this.app = app; + + this.batchCountInput = $el("input", { + className: "comfyui-queue-batch-value", + type: "number", + min: "1", + value: "1", + oninput: () => (this.batchCount = +this.batchCountInput.value), + }); + + this.batchCountRange = $el("input", { + type: "range", + min: "1", + max: "100", + value: "1", + oninput: () => (this.batchCount = +this.batchCountRange.value), + }); + + this.element.append( + $el("div.comfyui-queue-batch", [ + $el( + "label", + { + textContent: "Batch count: ", + }, + this.batchCountInput + ), + this.batchCountRange, + ]) + ); + + const createOption = (text, value, checked = false) => + $el( + "label", + { textContent: text }, + $el("input", { + type: "radio", + name: "AutoQueueMode", + checked, + value, + oninput: (e) => (this.autoQueueMode = e.target["value"]), + }) + ); + + this.autoQueueEl = $el("div.comfyui-queue-mode", [ + $el("span", "Auto Queue:"), + createOption("Disabled", "", true), + createOption("Instant", "instant"), + createOption("On Change", "change"), + ]); + + this.element.append(this.autoQueueEl); + + this.batchCount = prop(this, "batchCount", 1, () => { + this.batchCountInput.value = this.batchCount + ""; + this.batchCountRange.value = this.batchCount + ""; + }); + + this.autoQueueMode = prop(this, "autoQueueMode", "Disabled", () => { + this.dispatchEvent( + new CustomEvent("autoQueueMode", { + detail: this.autoQueueMode, + }) + ); + }); + } +} diff --git a/web/scripts/ui/menu/viewHistory.js b/web/scripts/ui/menu/viewHistory.js new file mode 100644 index 00000000..de6b343d --- /dev/null +++ b/web/scripts/ui/menu/viewHistory.js @@ -0,0 +1,27 @@ +// @ts-check + +import { ComfyButton } from "../components/button.js"; +import { ComfyViewList, ComfyViewListButton } from "./viewList.js"; + +export class ComfyViewHistoryButton extends ComfyViewListButton { + constructor(app) { + super(app, { + button: new ComfyButton({ + content: "View History", + icon: "history", + tooltip: "View history", + classList: "comfyui-button comfyui-history-button", + }), + list: ComfyViewHistoryList, + mode: "History", + }); + } +} + +export class ComfyViewHistoryList extends ComfyViewList { + async loadItems() { + const items = await super.loadItems(); + items["History"].reverse(); + return items; + } +} diff --git a/web/scripts/ui/menu/viewList.js b/web/scripts/ui/menu/viewList.js new file mode 100644 index 00000000..693ca335 --- /dev/null +++ b/web/scripts/ui/menu/viewList.js @@ -0,0 +1,203 @@ +// @ts-check + +import { ComfyButton } from "../components/button.js"; +import { $el } from "../../ui.js"; +import { api } from "../../api.js"; +import { ComfyPopup } from "../components/popup.js"; + +export class ComfyViewListButton { + get open() { + return this.popup.open; + } + + set open(open) { + this.popup.open = open; + } + + constructor(app, { button, list, mode }) { + this.app = app; + this.button = button; + this.element = $el("div.comfyui-button-wrapper", this.button.element); + this.popup = new ComfyPopup({ + target: this.element, + container: this.element, + horizontal: "right", + }); + this.list = new (list ?? ComfyViewList)(app, mode, this.popup); + this.popup.children = [this.list.element]; + this.popup.addEventListener("open", () => { + this.list.update(); + }); + this.popup.addEventListener("close", () => { + this.list.close(); + }); + this.button.withPopup(this.popup); + + api.addEventListener("status", () => { + if (this.popup.open) { + this.popup.update(); + } + }); + } +} + +export class ComfyViewList { + popup; + + constructor(app, mode, popup) { + this.app = app; + this.mode = mode; + this.popup = popup; + this.type = mode.toLowerCase(); + + this.items = $el(`div.comfyui-${this.type}-items.comfyui-view-list-items`); + this.clear = new ComfyButton({ + icon: "cancel", + content: "Clear", + action: async () => { + this.showSpinner(false); + await api.clearItems(this.type); + await this.update(); + }, + }); + + this.refresh = new ComfyButton({ + icon: "refresh", + content: "Refresh", + action: async () => { + await this.update(false); + }, + }); + + this.element = $el(`div.comfyui-${this.type}-popup.comfyui-view-list-popup`, [ + $el("h3", mode), + $el("header", [this.clear.element, this.refresh.element]), + this.items, + ]); + + api.addEventListener("status", () => { + if (this.popup.open) { + this.update(); + } + }); + } + + async close() { + this.items.replaceChildren(); + } + + async update(resize = true) { + this.showSpinner(resize); + const res = await this.loadItems(); + let any = false; + + const names = Object.keys(res); + const sections = names + .map((section) => { + const items = res[section]; + if (items?.length) { + any = true; + } else { + return; + } + + const rows = []; + if (names.length > 1) { + rows.push($el("h5", section)); + } + rows.push(...items.flatMap((item) => this.createRow(item, section))); + return $el("section", rows); + }) + .filter(Boolean); + + if (any) { + this.items.replaceChildren(...sections); + } else { + this.items.replaceChildren($el("h5", "None")); + } + + this.popup.update(); + this.clear.enabled = this.refresh.enabled = true; + this.element.style.removeProperty("height"); + } + + showSpinner(resize = true) { + // if (!this.spinner) { + // this.spinner = createSpinner(); + // } + // if (!resize) { + // this.element.style.height = this.element.clientHeight + "px"; + // } + // this.clear.enabled = this.refresh.enabled = false; + // this.items.replaceChildren( + // $el( + // "div", + // { + // style: { + // fontSize: "18px", + // }, + // }, + // this.spinner + // ) + // ); + // this.popup.update(); + } + + async loadItems() { + return await api.getItems(this.type); + } + + getRow(item, section) { + return { + text: item.prompt[0] + "", + actions: [ + { + text: "Load", + action: async () => { + try { + await this.app.loadGraphData(item.prompt[3].extra_pnginfo.workflow); + if (item.outputs) { + this.app.nodeOutputs = item.outputs; + } + } catch (error) { + alert("Error loading workflow: " + error.message); + console.error(error); + } + }, + }, + { + text: "Delete", + action: async () => { + try { + await api.deleteItem(this.type, item.prompt[1]); + this.update(); + } catch (error) {} + }, + }, + ], + }; + } + + createRow = (item, section) => { + const row = this.getRow(item, section); + return [ + $el("span", row.text), + ...row.actions.map( + (a) => + new ComfyButton({ + content: a.text, + action: async (e, btn) => { + btn.enabled = false; + try { + await a.action(); + } catch (error) { + throw error; + } finally { + btn.enabled = true; + } + }, + }).element + ), + ]; + }; +} diff --git a/web/scripts/ui/menu/viewQueue.js b/web/scripts/ui/menu/viewQueue.js new file mode 100644 index 00000000..97d01298 --- /dev/null +++ b/web/scripts/ui/menu/viewQueue.js @@ -0,0 +1,55 @@ +// @ts-check + +import { ComfyButton } from "../components/button.js"; +import { ComfyViewList, ComfyViewListButton } from "./viewList.js"; +import { api } from "../../api.js"; + +export class ComfyViewQueueButton extends ComfyViewListButton { + constructor(app) { + super(app, { + button: new ComfyButton({ + content: "View Queue", + icon: "format-list-numbered", + tooltip: "View queue", + classList: "comfyui-button comfyui-queue-button", + }), + list: ComfyViewQueueList, + mode: "Queue", + }); + } +} + +export class ComfyViewQueueList extends ComfyViewList { + getRow = (item, section) => { + if (section !== "Running") { + return super.getRow(item, section); + } + return { + text: item.prompt[0] + "", + actions: [ + { + text: "Load", + action: async () => { + try { + await this.app.loadGraphData(item.prompt[3].extra_pnginfo.workflow); + if (item.outputs) { + this.app.nodeOutputs = item.outputs; + } + } catch (error) { + alert("Error loading workflow: " + error.message); + console.error(error); + } + }, + }, + { + text: "Cancel", + action: async () => { + try { + await api.interrupt(); + } catch (error) {} + }, + }, + ], + }; + } +} diff --git a/web/scripts/ui/menu/workflows.js b/web/scripts/ui/menu/workflows.js new file mode 100644 index 00000000..afdff538 --- /dev/null +++ b/web/scripts/ui/menu/workflows.js @@ -0,0 +1,764 @@ +// @ts-check + +import { ComfyButton } from "../components/button.js"; +import { prop, getStorageValue, setStorageValue } from "../../utils.js"; +import { $el } from "../../ui.js"; +import { api } from "../../api.js"; +import { ComfyPopup } from "../components/popup.js"; +import { createSpinner } from "../spinner.js"; +import { ComfyWorkflow, trimJsonExt } from "../../workflows.js"; +import { ComfyAsyncDialog } from "../components/asyncDialog.js"; + +export class ComfyWorkflowsMenu { + #first = true; + element = $el("div.comfyui-workflows"); + + get open() { + return this.popup.open; + } + + set open(open) { + this.popup.open = open; + } + + /** + * @param {import("../../app.js").ComfyApp} app + */ + constructor(app) { + this.app = app; + this.#bindEvents(); + + const classList = { + "comfyui-workflows-button": true, + "comfyui-button": true, + unsaved: getStorageValue("Comfy.PreviousWorkflowUnsaved") === "true", + running: false, + }; + this.buttonProgress = $el("div.comfyui-workflows-button-progress"); + this.workflowLabel = $el("span.comfyui-workflows-label", ""); + this.button = new ComfyButton({ + content: $el("div.comfyui-workflows-button-inner", [$el("i.mdi.mdi-graph"), this.workflowLabel, this.buttonProgress]), + icon: "chevron-down", + classList, + }); + + this.element.append(this.button.element); + + this.popup = new ComfyPopup({ target: this.element, classList: "comfyui-workflows-popup" }); + this.content = new ComfyWorkflowsContent(app, this.popup); + this.popup.children = [this.content.element]; + this.popup.addEventListener("change", () => { + this.button.icon = "chevron-" + (this.popup.open ? "up" : "down"); + }); + this.button.withPopup(this.popup); + + this.unsaved = prop(this, "unsaved", classList.unsaved, (v) => { + classList.unsaved = v; + this.button.classList = classList; + setStorageValue("Comfy.PreviousWorkflowUnsaved", v); + }); + } + + #updateProgress = () => { + const prompt = this.app.workflowManager.activePrompt; + let percent = 0; + if (this.app.workflowManager.activeWorkflow === prompt?.workflow) { + const total = Object.values(prompt.nodes); + const done = total.filter(Boolean); + percent = (done.length / total.length) * 100; + } + this.buttonProgress.style.width = percent + "%"; + }; + + #updateActive = () => { + const active = this.app.workflowManager.activeWorkflow; + this.button.tooltip = active.path; + this.workflowLabel.textContent = active.name; + this.unsaved = active.unsaved; + + if (this.#first) { + this.#first = false; + this.content.load(); + } + + this.#updateProgress(); + }; + + #bindEvents() { + this.app.workflowManager.addEventListener("changeWorkflow", this.#updateActive); + this.app.workflowManager.addEventListener("rename", this.#updateActive); + this.app.workflowManager.addEventListener("delete", this.#updateActive); + + this.app.workflowManager.addEventListener("save", () => { + this.unsaved = this.app.workflowManager.activeWorkflow.unsaved; + }); + + this.app.workflowManager.addEventListener("execute", (e) => { + this.#updateProgress(); + }); + + api.addEventListener("graphChanged", () => { + this.unsaved = true; + }); + } + + #getMenuOptions(callback) { + const menu = []; + const directories = new Map(); + for (const workflow of this.app.workflowManager.workflows || []) { + const path = workflow.pathParts; + if (!path) continue; + let parent = menu; + let currentPath = ""; + for (let i = 0; i < path.length - 1; i++) { + currentPath += "/" + path[i]; + let newParent = directories.get(currentPath); + if (!newParent) { + newParent = { + title: path[i], + has_submenu: true, + submenu: { + options: [], + }, + }; + parent.push(newParent); + newParent = newParent.submenu.options; + directories.set(currentPath, newParent); + } + parent = newParent; + } + parent.push({ + title: trimJsonExt(path[path.length - 1]), + callback: () => callback(workflow), + }); + } + return menu; + } + + #getFavoriteMenuOptions(callback) { + const menu = []; + for (const workflow of this.app.workflowManager.workflows || []) { + if (workflow.isFavorite) { + menu.push({ + title: "⭐ " + workflow.name, + callback: () => callback(workflow), + }); + } + } + return menu; + } + + /** + * @param {import("../../app.js").ComfyApp} app + */ + registerExtension(app) { + const self = this; + app.registerExtension({ + name: "Comfy.Workflows", + async beforeRegisterNodeDef(nodeType) { + function getImageWidget(node) { + const inputs = { ...node.constructor?.nodeData?.input?.required, ...node.constructor?.nodeData?.input?.optional }; + for (const input in inputs) { + if (inputs[input][0] === "IMAGEUPLOAD") { + const imageWidget = node.widgets.find((w) => w.name === (inputs[input]?.[1]?.widget ?? "image")); + if (imageWidget) return imageWidget; + } + } + } + + function setWidgetImage(node, widget, img) { + const url = new URL(img.src); + const filename = url.searchParams.get("filename"); + const subfolder = url.searchParams.get("subfolder"); + const type = url.searchParams.get("type"); + const imageId = `${subfolder ? subfolder + "/" : ""}${filename} [${type}]`; + widget.value = imageId; + node.imgs = [img]; + app.graph.setDirtyCanvas(true, true); + } + + /** + * @param {HTMLImageElement} img + * @param {ComfyWorkflow} workflow + */ + async function sendToWorkflow(img, workflow) { + await workflow.load(); + let options = []; + const nodes = app.graph.computeExecutionOrder(false); + for (const node of nodes) { + const widget = getImageWidget(node); + if (widget == null) continue; + + if (node.title?.toLowerCase().includes("input")) { + options = [{ widget, node }]; + break; + } else { + options.push({ widget, node }); + } + } + + if (!options.length) { + alert("No image nodes have been found in this workflow!"); + return; + } else if (options.length > 1) { + const dialog = new WidgetSelectionDialog(options); + const res = await dialog.show(app); + if (!res) return; + options = [res]; + } + + setWidgetImage(options[0].node, options[0].widget, img); + } + + const getExtraMenuOptions = nodeType.prototype["getExtraMenuOptions"]; + nodeType.prototype["getExtraMenuOptions"] = function (_, options) { + const r = getExtraMenuOptions?.apply?.(this, arguments); + + if (app.ui.settings.getSettingValue("Comfy.UseNewMenu", false) === true) { + const t = /** @type { {imageIndex?: number, overIndex?: number, imgs: string[]} } */ /** @type {any} */ (this); + let img; + if (t.imageIndex != null) { + // An image is selected so select that + img = t.imgs?.[t.imageIndex]; + } else if (t.overIndex != null) { + // No image is selected but one is hovered + img = t.img?.s[t.overIndex]; + } + + if (img) { + let pos = options.findIndex((o) => o.content === "Save Image"); + if (pos === -1) { + pos = 0; + } else { + pos++; + } + + options.splice(pos, 0, { + content: "Send to workflow", + has_submenu: true, + submenu: { + options: [ + { + callback: () => sendToWorkflow(img, app.workflowManager.activeWorkflow), + title: "[Current workflow]", + }, + ...self.#getFavoriteMenuOptions(sendToWorkflow.bind(null, img)), + null, + ...self.#getMenuOptions(sendToWorkflow.bind(null, img)), + ], + }, + }); + } + } + + return r; + }; + }, + }); + } +} + +export class ComfyWorkflowsContent { + element = $el("div.comfyui-workflows-panel"); + treeState = {}; + treeFiles = {}; + /** @type { Map } */ + openFiles = new Map(); + /** @type {WorkflowElement} */ + activeElement = null; + + /** + * @param {import("../../app.js").ComfyApp} app + * @param {ComfyPopup} popup + */ + constructor(app, popup) { + this.app = app; + this.popup = popup; + this.actions = $el("div.comfyui-workflows-actions", [ + new ComfyButton({ + content: "Default", + icon: "file-code", + iconSize: 18, + classList: "comfyui-button primary", + tooltip: "Load default workflow", + action: () => { + popup.open = false; + app.loadGraphData(); + app.resetView(); + }, + }).element, + new ComfyButton({ + content: "Browse", + icon: "folder", + iconSize: 18, + tooltip: "Browse for an image or exported workflow", + action: () => { + popup.open = false; + app.ui.loadFile(); + }, + }).element, + new ComfyButton({ + content: "Blank", + icon: "plus-thick", + iconSize: 18, + tooltip: "Create a new blank workflow", + action: () => { + app.workflowManager.setWorkflow(null); + app.clean(); + app.graph.clear(); + app.workflowManager.activeWorkflow.track(); + popup.open = false; + }, + }).element, + ]); + + this.spinner = createSpinner(); + this.element.replaceChildren(this.actions, this.spinner); + + this.popup.addEventListener("open", () => this.load()); + this.popup.addEventListener("close", () => this.element.replaceChildren(this.actions, this.spinner)); + + this.app.workflowManager.addEventListener("favorite", (e) => { + const workflow = e["detail"]; + const button = this.treeFiles[workflow.path]?.primary; + if (!button) return; // Can happen when a workflow is renamed + button.icon = this.#getFavoriteIcon(workflow); + button.overIcon = this.#getFavoriteOverIcon(workflow); + this.updateFavorites(); + }); + + for (const e of ["save", "open", "close", "changeWorkflow"]) { + // TODO: dont be lazy and just update the specific element + app.workflowManager.addEventListener(e, () => this.updateOpen()); + } + this.app.workflowManager.addEventListener("rename", () => this.load()); + this.app.workflowManager.addEventListener("execute", (e) => this.#updateActive()); + } + + async load() { + await this.app.workflowManager.loadWorkflows(); + this.updateTree(); + this.updateFavorites(); + this.updateOpen(); + this.element.replaceChildren(this.actions, this.openElement, this.favoritesElement, this.treeElement); + } + + updateOpen() { + const current = this.openElement; + this.openFiles.clear(); + + this.openElement = $el("div.comfyui-workflows-open", [ + $el("h3", "Open"), + ...this.app.workflowManager.openWorkflows.map((w) => { + const wrapper = new WorkflowElement(this, w, { + primary: { element: $el("i.mdi.mdi-18px.mdi-progress-pencil") }, + buttons: [ + this.#getRenameButton(w), + new ComfyButton({ + icon: "close", + iconSize: 18, + classList: "comfyui-button comfyui-workflows-file-action", + tooltip: "Close workflow", + action: (e) => { + e.stopImmediatePropagation(); + this.app.workflowManager.closeWorkflow(w); + }, + }), + ], + }); + if (w.unsaved) { + wrapper.element.classList.add("unsaved"); + } + if(w === this.app.workflowManager.activeWorkflow) { + wrapper.element.classList.add("active"); + } + + this.openFiles.set(w, wrapper); + return wrapper.element; + }), + ]); + + this.#updateActive(); + current?.replaceWith(this.openElement); + } + + updateFavorites() { + const current = this.favoritesElement; + const favorites = [...this.app.workflowManager.workflows.filter((w) => w.isFavorite)]; + + this.favoritesElement = $el("div.comfyui-workflows-favorites", [ + $el("h3", "Favorites"), + ...favorites + .map((w) => { + return this.#getWorkflowElement(w).element; + }) + .filter(Boolean), + ]); + + current?.replaceWith(this.favoritesElement); + } + + filterTree() { + if (!this.filterText) { + this.treeRoot.classList.remove("filtered"); + // Unfilter whole tree + for (const item of Object.values(this.treeFiles)) { + item.element.parentElement.style.removeProperty("display"); + this.showTreeParents(item.element.parentElement); + } + return; + } + this.treeRoot.classList.add("filtered"); + const searchTerms = this.filterText.toLocaleLowerCase().split(" "); + for (const item of Object.values(this.treeFiles)) { + const parts = item.workflow.pathParts; + let termIndex = 0; + let valid = false; + for (const part of parts) { + let currentIndex = 0; + do { + currentIndex = part.indexOf(searchTerms[termIndex], currentIndex); + if (currentIndex > -1) currentIndex += searchTerms[termIndex].length; + } while (currentIndex !== -1 && ++termIndex < searchTerms.length); + + if (termIndex >= searchTerms.length) { + valid = true; + break; + } + } + if (valid) { + item.element.parentElement.style.removeProperty("display"); + this.showTreeParents(item.element.parentElement); + } else { + item.element.parentElement.style.display = "none"; + this.hideTreeParents(item.element.parentElement); + } + } + } + + hideTreeParents(element) { + // Hide all parents if no children are visible + if (element.parentElement?.classList.contains("comfyui-workflows-tree") === false) { + for (let i = 1; i < element.parentElement.children.length; i++) { + const c = element.parentElement.children[i]; + if (c.style.display !== "none") { + return; + } + } + element.parentElement.style.display = "none"; + this.hideTreeParents(element.parentElement); + } + } + + showTreeParents(element) { + if (element.parentElement?.classList.contains("comfyui-workflows-tree") === false) { + element.parentElement.style.removeProperty("display"); + this.showTreeParents(element.parentElement); + } + } + + updateTree() { + const current = this.treeElement; + const nodes = {}; + let typingTimeout; + + this.treeFiles = {}; + this.treeRoot = $el("ul.comfyui-workflows-tree"); + this.treeElement = $el("section", [ + $el("header", [ + $el("h3", "Browse"), + $el("div.comfy-ui-workflows-search", [ + $el("i.mdi.mdi-18px.mdi-magnify"), + $el("input", { + placeholder: "Search", + value: this.filterText ?? "", + oninput: (e) => { + this.filterText = e.target["value"]?.trim(); + clearTimeout(typingTimeout); + typingTimeout = setTimeout(() => this.filterTree(), 250); + }, + }), + ]), + ]), + this.treeRoot, + ]); + + for (const workflow of this.app.workflowManager.workflows) { + if (!workflow.pathParts) continue; + + let currentPath = ""; + let currentRoot = this.treeRoot; + + for (let i = 0; i < workflow.pathParts.length; i++) { + currentPath += (currentPath ? "\\" : "") + workflow.pathParts[i]; + const parentNode = nodes[currentPath] ?? this.#createNode(currentPath, workflow, i, currentRoot); + + nodes[currentPath] = parentNode; + currentRoot = parentNode; + } + } + + current?.replaceWith(this.treeElement); + this.filterTree(); + } + + #expandNode(el, workflow, thisPath, i) { + const expanded = !el.classList.toggle("closed"); + if (expanded) { + let c = ""; + for (let j = 0; j <= i; j++) { + c += (c ? "\\" : "") + workflow.pathParts[j]; + this.treeState[c] = true; + } + } else { + let c = thisPath; + for (let j = i + 1; j < workflow.pathParts.length; j++) { + c += (c ? "\\" : "") + workflow.pathParts[j]; + delete this.treeState[c]; + } + delete this.treeState[thisPath]; + } + } + + #updateActive() { + this.#removeActive(); + + const active = this.app.workflowManager.activePrompt; + if (!active?.workflow) return; + + const open = this.openFiles.get(active.workflow); + if (!open) return; + + this.activeElement = open; + + const total = Object.values(active.nodes); + const done = total.filter(Boolean); + const percent = done.length / total.length; + open.element.classList.add("running"); + open.element.style.setProperty("--progress", percent * 100 + "%"); + open.primary.element.classList.remove("mdi-progress-pencil"); + open.primary.element.classList.add("mdi-play"); + } + + #removeActive() { + if (!this.activeElement) return; + this.activeElement.element.classList.remove("running"); + this.activeElement.element.style.removeProperty("--progress"); + this.activeElement.primary.element.classList.add("mdi-progress-pencil"); + this.activeElement.primary.element.classList.remove("mdi-play"); + } + + /** @param {ComfyWorkflow} workflow */ + #getFavoriteIcon(workflow) { + return workflow.isFavorite ? "star" : "file-outline"; + } + + /** @param {ComfyWorkflow} workflow */ + #getFavoriteOverIcon(workflow) { + return workflow.isFavorite ? "star-off" : "star-outline"; + } + + /** @param {ComfyWorkflow} workflow */ + #getFavoriteTooltip(workflow) { + return workflow.isFavorite ? "Remove this workflow from your favorites" : "Add this workflow to your favorites"; + } + + /** @param {ComfyWorkflow} workflow */ + #getFavoriteButton(workflow, primary) { + return new ComfyButton({ + icon: this.#getFavoriteIcon(workflow), + overIcon: this.#getFavoriteOverIcon(workflow), + iconSize: 18, + classList: "comfyui-button comfyui-workflows-file-action-favorite" + (primary ? " comfyui-workflows-file-action-primary" : ""), + tooltip: this.#getFavoriteTooltip(workflow), + action: (e) => { + e.stopImmediatePropagation(); + workflow.favorite(!workflow.isFavorite); + }, + }); + } + + /** @param {ComfyWorkflow} workflow */ + #getDeleteButton(workflow) { + const deleteButton = new ComfyButton({ + icon: "delete", + tooltip: "Delete this workflow", + classList: "comfyui-button comfyui-workflows-file-action", + iconSize: 18, + action: async (e, btn) => { + e.stopImmediatePropagation(); + + if (btn.icon === "delete-empty") { + btn.enabled = false; + await workflow.delete(); + await this.load(); + } else { + btn.icon = "delete-empty"; + btn.element.style.background = "red"; + } + }, + }); + deleteButton.element.addEventListener("mouseleave", () => { + deleteButton.icon = "delete"; + deleteButton.element.style.removeProperty("background"); + }); + return deleteButton; + } + + /** @param {ComfyWorkflow} workflow */ + #getInsertButton(workflow) { + return new ComfyButton({ + icon: "file-move-outline", + iconSize: 18, + tooltip: "Insert this workflow into the current workflow", + classList: "comfyui-button comfyui-workflows-file-action", + action: (e) => { + if (!this.app.shiftDown) { + this.popup.open = false; + } + e.stopImmediatePropagation(); + if (!this.app.shiftDown) { + this.popup.open = false; + } + workflow.insert(); + }, + }); + } + + /** @param {ComfyWorkflow} workflow */ + #getRenameButton(workflow) { + return new ComfyButton({ + icon: "pencil", + tooltip: workflow.path ? "Rename this workflow" : "This workflow can't be renamed as it hasn't been saved.", + classList: "comfyui-button comfyui-workflows-file-action", + iconSize: 18, + enabled: !!workflow.path, + action: async (e) => { + e.stopImmediatePropagation(); + const newName = prompt("Enter new name", workflow.path); + if (newName) { + await workflow.rename(newName); + } + }, + }); + } + + /** @param {ComfyWorkflow} workflow */ + #getWorkflowElement(workflow) { + return new WorkflowElement(this, workflow, { + primary: this.#getFavoriteButton(workflow, true), + buttons: [this.#getInsertButton(workflow), this.#getRenameButton(workflow), this.#getDeleteButton(workflow)], + }); + } + + /** @param {ComfyWorkflow} workflow */ + #createLeafNode(workflow) { + const fileNode = this.#getWorkflowElement(workflow); + this.treeFiles[workflow.path] = fileNode; + return fileNode; + } + + #createNode(currentPath, workflow, i, currentRoot) { + const part = workflow.pathParts[i]; + + const parentNode = $el("ul" + (this.treeState[currentPath] ? "" : ".closed"), { + $: (el) => { + el.onclick = (e) => { + this.#expandNode(el, workflow, currentPath, i); + e.stopImmediatePropagation(); + }; + }, + }); + currentRoot.append(parentNode); + + // Create a node for the current part and an inner UL for its children if it isnt a leaf node + const leaf = i === workflow.pathParts.length - 1; + let nodeElement; + if (leaf) { + nodeElement = this.#createLeafNode(workflow).element; + } else { + nodeElement = $el("li", [$el("i.mdi.mdi-18px.mdi-folder"), $el("span", part)]); + } + parentNode.append(nodeElement); + return parentNode; + } +} + +class WorkflowElement { + /** + * @param { ComfyWorkflowsContent } parent + * @param { ComfyWorkflow } workflow + */ + constructor(parent, workflow, { tagName = "li", primary, buttons }) { + this.parent = parent; + this.workflow = workflow; + this.primary = primary; + this.buttons = buttons; + + this.element = $el( + tagName + ".comfyui-workflows-tree-file", + { + onclick: () => { + workflow.load(); + this.parent.popup.open = false; + }, + title: this.workflow.path, + }, + [this.primary?.element, $el("span", workflow.name), ...buttons.map((b) => b.element)] + ); + } +} + +class WidgetSelectionDialog extends ComfyAsyncDialog { + #options; + + /** + * @param {Array<{widget: {name: string}, node: {pos: [number, number], title: string, id: string, type: string}}>} options + */ + constructor(options) { + super(); + this.#options = options; + } + + show(app) { + this.element.classList.add("comfy-widget-selection-dialog"); + return super.show( + $el("div", [ + $el("h2", "Select image target"), + $el( + "p", + "This workflow has multiple image loader nodes, you can rename a node to include 'input' in the title for it to be automatically selected, or select one below." + ), + $el( + "section", + this.#options.map((opt) => { + return $el("div.comfy-widget-selection-item", [ + $el("span", { dataset: { id: opt.node.id } }, `${opt.node.title ?? opt.node.type} ${opt.widget.name}`), + $el( + "button.comfyui-button", + { + onclick: () => { + app.canvas.ds.offset[0] = -opt.node.pos[0] + 50; + app.canvas.ds.offset[1] = -opt.node.pos[1] + 50; + app.canvas.selectNode(opt.node); + app.graph.setDirtyCanvas(true, true); + }, + }, + "Show" + ), + $el( + "button.comfyui-button.primary", + { + onclick: () => { + this.close(opt); + }, + }, + "Select" + ), + ]); + }) + ), + ]) + ); + } +} \ No newline at end of file diff --git a/web/scripts/ui/settings.js b/web/scripts/ui/settings.js index 9e9d13af..819e4e7d 100644 --- a/web/scripts/ui/settings.js +++ b/web/scripts/ui/settings.js @@ -47,6 +47,17 @@ export class ComfySettingsDialog extends ComfyDialog { return Object.values(this.settingsLookup); } + #dispatchChange(id, value, oldValue) { + this.dispatchEvent( + new CustomEvent(id + ".change", { + detail: { + value, + oldValue + }, + }) + ); + } + async load() { if (this.app.storageLocation === "browser") { this.settingsValues = localStorage; @@ -56,7 +67,9 @@ export class ComfySettingsDialog extends ComfyDialog { // Trigger onChange for any settings added before load for (const id in this.settingsLookup) { - this.settingsLookup[id].onChange?.(this.settingsValues[this.getId(id)]); + const value = this.settingsValues[this.getId(id)]; + this.settingsLookup[id].onChange?.(value); + this.#dispatchChange(id, value); } } @@ -90,6 +103,7 @@ export class ComfySettingsDialog extends ComfyDialog { if (id in this.settingsLookup) { this.settingsLookup[id].onChange?.(value, oldValue); } + this.#dispatchChange(id, value, oldValue); await api.storeSetting(id, value); } @@ -136,6 +150,8 @@ export class ComfySettingsDialog extends ComfyDialog { onChange, name, render: () => { + if (type === "hidden") return; + const setter = (v) => { if (onChange) { onChange(v, value); @@ -310,7 +326,7 @@ export class ComfySettingsDialog extends ComfyDialog { }, [$el("th"), $el("th", { style: { width: "33%" } })] ), - ...this.settings.sort((a, b) => a.name.localeCompare(b.name)).map((s) => s.render()) + ...this.settings.sort((a, b) => a.name.localeCompare(b.name)).map((s) => s.render()).filter(Boolean) ); this.element.showModal(); } diff --git a/web/scripts/ui/utils.js b/web/scripts/ui/utils.js new file mode 100644 index 00000000..e37d8b41 --- /dev/null +++ b/web/scripts/ui/utils.js @@ -0,0 +1,56 @@ +/** + * @typedef { string | string[] | Record } ClassList + */ + +/** + * @param { HTMLElement } element + * @param { ClassList } classList + * @param { string[] } requiredClasses + */ +export function applyClasses(element, classList, ...requiredClasses) { + classList ??= ""; + + let str; + if (typeof classList === "string") { + str = classList; + } else if (classList instanceof Array) { + str = classList.join(" "); + } else { + str = Object.entries(classList).reduce((p, c) => { + if (c[1]) { + p += (p.length ? " " : "") + c[0]; + } + return p; + }, ""); + } + element.className = str; + if (requiredClasses) { + element.classList.add(...requiredClasses); + } +} + +/** + * @param { HTMLElement } element + * @param { { onHide?: (el: HTMLElement) => void, onShow?: (el: HTMLElement, value) => void } } [param1] + * @returns + */ +export function toggleElement(element, { onHide, onShow } = {}) { + let placeholder; + let hidden; + return (value) => { + if (value) { + if (hidden) { + hidden = false; + placeholder.replaceWith(element); + } + onShow?.(element, value); + } else { + if (!placeholder) { + placeholder = document.createComment(""); + } + hidden = true; + element.replaceWith(placeholder); + onHide?.(element); + } + }; +} diff --git a/web/scripts/utils.js b/web/scripts/utils.js index 01b98846..cda7600f 100644 --- a/web/scripts/utils.js +++ b/web/scripts/utils.js @@ -1,4 +1,5 @@ import { $el } from "./ui.js"; +import { api } from "./api.js"; // Simple date formatter const parts = { @@ -25,6 +26,19 @@ function formatDate(text, date) { }); } + +export function clone(obj) { + try { + if (typeof structuredClone !== "undefined") { + return structuredClone(obj); + } + } catch (error) { + // structuredClone is stricter than using JSON.parse/stringify so fallback to that + } + + return JSON.parse(JSON.stringify(obj)); +} + export function applyTextReplacements(app, value) { return value.replace(/%([^%]+)%/g, function (match, text) { const split = text.split("."); @@ -86,3 +100,57 @@ export async function addStylesheet(urlOrFile, relativeTo) { }); }); } + +/** + * @param { string } filename + * @param { Blob } blob + */ +export function downloadBlob(filename, blob) { + const url = URL.createObjectURL(blob); + const a = $el("a", { + href: url, + download: filename, + style: { display: "none" }, + parent: document.body, + }); + a.click(); + setTimeout(function () { + a.remove(); + window.URL.revokeObjectURL(url); + }, 0); +} + +/** + * @template T + * @param {string} name + * @param {T} [defaultValue] + * @param {(currentValue: any, previousValue: any)=>void} [onChanged] + * @returns {T} + */ +export function prop(target, name, defaultValue, onChanged) { + let currentValue; + Object.defineProperty(target, name, { + get() { + return currentValue; + }, + set(newValue) { + const prevValue = currentValue; + currentValue = newValue; + onChanged?.(currentValue, prevValue, target, name); + }, + }); + return defaultValue; +} + +export function getStorageValue(id) { + const clientId = api.clientId ?? api.initialClientId; + return (clientId && sessionStorage.getItem(`${id}:${clientId}`)) ?? localStorage.getItem(id); +} + +export function setStorageValue(id, value) { + const clientId = api.clientId ?? api.initialClientId; + if (clientId) { + sessionStorage.setItem(`${id}:${clientId}`, value); + } + localStorage.setItem(id, value); +} \ No newline at end of file diff --git a/web/scripts/workflows.js b/web/scripts/workflows.js new file mode 100644 index 00000000..16bbc997 --- /dev/null +++ b/web/scripts/workflows.js @@ -0,0 +1,450 @@ +// @ts-check + +import { api } from "./api.js"; +import { ChangeTracker } from "./changeTracker.js"; +import { ComfyAsyncDialog } from "./ui/components/asyncDialog.js"; +import { getStorageValue, setStorageValue } from "./utils.js"; + +function appendJsonExt(path) { + if (!path.toLowerCase().endsWith(".json")) { + path += ".json"; + } + return path; +} + +export function trimJsonExt(path) { + return path?.replace(/\.json$/, ""); +} + +export class ComfyWorkflowManager extends EventTarget { + /** @type {string | null} */ + #activePromptId = null; + #unsavedCount = 0; + #activeWorkflow; + + /** @type {Record} */ + workflowLookup = {}; + /** @type {Array} */ + workflows = []; + /** @type {Array} */ + openWorkflows = []; + /** @type {Record}>} */ + queuedPrompts = {}; + + get activeWorkflow() { + return this.#activeWorkflow ?? this.openWorkflows[0]; + } + + get activePromptId() { + return this.#activePromptId; + } + + get activePrompt() { + return this.queuedPrompts[this.#activePromptId]; + } + + /** + * @param {import("./app.js").ComfyApp} app + */ + constructor(app) { + super(); + this.app = app; + ChangeTracker.init(app); + + this.#bindExecutionEvents(); + } + + #bindExecutionEvents() { + // TODO: on reload, set active prompt based on the latest ws message + + const emit = () => this.dispatchEvent(new CustomEvent("execute", { detail: this.activePrompt })); + let executing = null; + api.addEventListener("execution_start", (e) => { + this.#activePromptId = e.detail.prompt_id; + + // This event can fire before the event is stored, so put a placeholder + this.queuedPrompts[this.#activePromptId] ??= { nodes: {} }; + emit(); + }); + api.addEventListener("execution_cached", (e) => { + if (!this.activePrompt) return; + for (const n of e.detail.nodes) { + this.activePrompt.nodes[n] = true; + } + emit(); + }); + api.addEventListener("executed", (e) => { + if (!this.activePrompt) return; + this.activePrompt.nodes[e.detail.node] = true; + emit(); + }); + api.addEventListener("executing", (e) => { + if (!this.activePrompt) return; + + if (executing) { + // Seems sometimes nodes that are cached fire executing but not executed + this.activePrompt.nodes[executing] = true; + } + executing = e.detail; + if (!executing) { + delete this.queuedPrompts[this.#activePromptId]; + this.#activePromptId = null; + } + emit(); + }); + } + + async loadWorkflows() { + try { + let favorites; + const resp = await api.getUserData("workflows/.index.json"); + let info; + if (resp.status === 200) { + info = await resp.json(); + favorites = new Set(info?.favorites ?? []); + } else { + favorites = new Set(); + } + + const workflows = (await api.listUserData("workflows", true, true)).map((w) => { + let workflow = this.workflowLookup[w[0]]; + if (!workflow) { + workflow = new ComfyWorkflow(this, w[0], w.slice(1), favorites.has(w[0])); + this.workflowLookup[workflow.path] = workflow; + } + return workflow; + }); + + this.workflows = workflows; + } catch (error) { + alert("Error loading workflows: " + (error.message ?? error)); + this.workflows = []; + } + } + + async saveWorkflowMetadata() { + await api.storeUserData("workflows/.index.json", { + favorites: [...this.workflows.filter((w) => w.isFavorite).map((w) => w.path)], + }); + } + + /** + * @param {string | ComfyWorkflow | null} workflow + */ + setWorkflow(workflow) { + if (workflow && typeof workflow === "string") { + // Selected by path, i.e. on reload of last workflow + const found = this.workflows.find((w) => w.path === workflow); + if (found) { + workflow = found; + workflow.unsaved = !workflow || getStorageValue("Comfy.PreviousWorkflowUnsaved") === "true"; + } + } + + if (!(workflow instanceof ComfyWorkflow)) { + // Still not found, either reloading a deleted workflow or blank + workflow = new ComfyWorkflow(this, workflow || "Unsaved Workflow" + (this.#unsavedCount++ ? ` (${this.#unsavedCount})` : "")); + } + + const index = this.openWorkflows.indexOf(workflow); + if (index === -1) { + // Opening a new workflow + this.openWorkflows.push(workflow); + } + + this.#activeWorkflow = workflow; + + setStorageValue("Comfy.PreviousWorkflow", this.activeWorkflow.path ?? ""); + this.dispatchEvent(new CustomEvent("changeWorkflow")); + } + + storePrompt({ nodes, id }) { + this.queuedPrompts[id] ??= {}; + this.queuedPrompts[id].nodes = { + ...nodes.reduce((p, n) => { + p[n] = false; + return p; + }, {}), + ...this.queuedPrompts[id].nodes, + }; + this.queuedPrompts[id].workflow = this.activeWorkflow; + } + + /** + * @param {ComfyWorkflow} workflow + */ + async closeWorkflow(workflow, warnIfUnsaved = true) { + if (!workflow.isOpen) { + return true; + } + if (workflow.unsaved && warnIfUnsaved) { + const res = await ComfyAsyncDialog.prompt({ + title: "Save Changes?", + message: `Do you want to save changes to "${workflow.path ?? workflow.name}" before closing?`, + actions: ["Yes", "No", "Cancel"], + }); + if (res === "Yes") { + const active = this.activeWorkflow; + if (active !== workflow) { + // We need to switch to the workflow to save it + await workflow.load(); + } + + if (!(await workflow.save())) { + // Save was canceled, restore the previous workflow + if (active !== workflow) { + await active.load(); + } + return; + } + } else if (res === "Cancel") { + return; + } + } + workflow.changeTracker = null; + this.openWorkflows.splice(this.openWorkflows.indexOf(workflow), 1); + if (this.openWorkflows.length) { + this.#activeWorkflow = this.openWorkflows[0]; + await this.#activeWorkflow.load(); + } else { + // Load default + await this.app.loadGraphData(); + } + } +} + +export class ComfyWorkflow { + #name; + #path; + #pathParts; + #isFavorite = false; + /** @type {ChangeTracker | null} */ + changeTracker = null; + unsaved = false; + + get name() { + return this.#name; + } + + get path() { + return this.#path; + } + + get pathParts() { + return this.#pathParts; + } + + get isFavorite() { + return this.#isFavorite; + } + + get isOpen() { + return !!this.changeTracker; + } + + /** + * @overload + * @param {ComfyWorkflowManager} manager + * @param {string} path + */ + /** + * @overload + * @param {ComfyWorkflowManager} manager + * @param {string} path + * @param {string[]} pathParts + * @param {boolean} isFavorite + */ + /** + * @param {ComfyWorkflowManager} manager + * @param {string} path + * @param {string[]} [pathParts] + * @param {boolean} [isFavorite] + */ + constructor(manager, path, pathParts, isFavorite) { + this.manager = manager; + if (pathParts) { + this.#updatePath(path, pathParts); + this.#isFavorite = isFavorite; + } else { + this.#name = path; + this.unsaved = true; + } + } + + /** + * @param {string} path + * @param {string[]} [pathParts] + */ + #updatePath(path, pathParts) { + this.#path = path; + + if (!pathParts) { + if (!path.includes("\\")) { + pathParts = path.split("/"); + } else { + pathParts = path.split("\\"); + } + } + + this.#pathParts = pathParts; + this.#name = trimJsonExt(pathParts[pathParts.length - 1]); + } + + async getWorkflowData() { + const resp = await api.getUserData("workflows/" + this.path); + if (resp.status !== 200) { + alert(`Error loading workflow file '${this.path}': ${resp.status} ${resp.statusText}`); + return; + } + return await resp.json(); + } + + load = async () => { + if (this.isOpen) { + await this.manager.app.loadGraphData(this.changeTracker.activeState, true, this); + } else { + const data = await this.getWorkflowData(); + if (!data) return; + await this.manager.app.loadGraphData(data, true, this); + } + }; + + async save(saveAs = false) { + if (!this.path || saveAs) { + return !!(await this.#save(null, false)); + } else { + return !!(await this.#save(this.path, true)); + } + } + + /** + * @param {boolean} value + */ + async favorite(value) { + try { + if (this.#isFavorite === value) return; + this.#isFavorite = value; + await this.manager.saveWorkflowMetadata(); + this.manager.dispatchEvent(new CustomEvent("favorite", { detail: this })); + } catch (error) { + alert("Error favoriting workflow " + this.path + "\n" + (error.message ?? error)); + } + } + + /** + * @param {string} path + */ + async rename(path) { + path = appendJsonExt(path); + let resp = await api.moveUserData("workflows/" + this.path, "workflows/" + path); + + if (resp.status === 409) { + if (!confirm(`Workflow '${path}' already exists, do you want to overwrite it?`)) return resp; + resp = await api.moveUserData("workflows/" + this.path, "workflows/" + path, { overwrite: true }); + } + + if (resp.status !== 200) { + alert(`Error renaming workflow file '${this.path}': ${resp.status} ${resp.statusText}`); + return; + } + + const isFav = this.isFavorite; + if (isFav) { + await this.favorite(false); + } + path = (await resp.json()).substring("workflows/".length); + this.#updatePath(path, null); + if (isFav) { + await this.favorite(true); + } + this.manager.dispatchEvent(new CustomEvent("rename", { detail: this })); + setStorageValue("Comfy.PreviousWorkflow", this.path ?? ""); + } + + async insert() { + const data = await this.getWorkflowData(); + if (!data) return; + + const old = localStorage.getItem("litegrapheditor_clipboard"); + const graph = new LGraph(data); + const canvas = new LGraphCanvas(null, graph, { skip_events: true, skip_render: true }); + canvas.selectNodes(); + canvas.copyToClipboard(); + this.manager.app.canvas.pasteFromClipboard(); + localStorage.setItem("litegrapheditor_clipboard", old); + } + + async delete() { + // TODO: fix delete of current workflow - should mark workflow as unsaved and when saving use old name by default + + try { + if (this.isFavorite) { + await this.favorite(false); + } + await api.deleteUserData("workflows/" + this.path); + this.unsaved = true; + this.#path = null; + this.#pathParts = null; + this.manager.workflows.splice(this.manager.workflows.indexOf(this), 1); + this.manager.dispatchEvent(new CustomEvent("delete", { detail: this })); + } catch (error) { + alert(`Error deleting workflow: ${error.message || error}`); + } + } + + track() { + if (this.changeTracker) { + this.changeTracker.restore(); + } else { + this.changeTracker = new ChangeTracker(this); + } + } + + /** + * @param {string|null} path + * @param {boolean} overwrite + */ + async #save(path, overwrite) { + if (!path) { + path = prompt("Save workflow as:", trimJsonExt(this.path) ?? this.name ?? "workflow"); + if (!path) return; + } + + path = appendJsonExt(path); + + const p = await this.manager.app.graphToPrompt(); + const json = JSON.stringify(p.workflow, null, 2); + let resp = await api.storeUserData("workflows/" + path, json, { stringify: false, throwOnError: false, overwrite }); + if (resp.status === 409) { + if (!confirm(`Workflow '${path}' already exists, do you want to overwrite it?`)) return; + resp = await api.storeUserData("workflows/" + path, json, { stringify: false }); + } + + if (resp.status !== 200) { + alert(`Error saving workflow '${this.path}': ${resp.status} ${resp.statusText}`); + return; + } + + path = (await resp.json()).substring("workflows/".length); + + if (!this.path) { + // Saved new workflow, patch this instance + this.#updatePath(path, null); + await this.manager.loadWorkflows(); + this.unsaved = false; + this.manager.dispatchEvent(new CustomEvent("rename", { detail: this })); + setStorageValue("Comfy.PreviousWorkflow", this.path ?? ""); + } else if (path !== this.path) { + // Saved as, open the new copy + await this.manager.loadWorkflows(); + const workflow = this.manager.workflowLookup[path]; + await workflow.load(); + } else { + // Normal save + this.unsaved = false; + this.manager.dispatchEvent(new CustomEvent("save", { detail: this })); + } + + return true; + } +} diff --git a/web/style.css b/web/style.css index cf7a8b9e..8091a489 100644 --- a/web/style.css +++ b/web/style.css @@ -1,3 +1,5 @@ +@import url("scripts/ui/menu/menu.css"); + :root { --fg-color: #000; --bg-color: #fff; @@ -10,12 +12,24 @@ --border-color: #4e4e4e; --tr-even-bg-color: #222; --tr-odd-bg-color: #353535; + --primary-bg: #236692; + --primary-fg: #ffffff; + --primary-hover-bg: #3485bb; + --primary-hover-fg: #ffffff; + --content-bg: #e0e0e0; + --content-fg: #000; + --content-hover-bg: #adadad; + --content-hover-fg: #000; } @media (prefers-color-scheme: dark) { :root { --fg-color: #fff; --bg-color: #202020; + --content-bg: #4e4e4e; + --content-fg: #fff; + --content-hover-bg: #222; + --content-hover-fg: #fff; } } @@ -26,11 +40,41 @@ body { overflow: hidden; background-color: var(--bg-color); color: var(--fg-color); + grid-template-columns: auto 1fr auto; + grid-template-rows: auto auto 1fr auto; + min-height: -webkit-fill-available; + max-height: -webkit-fill-available; + min-width: -webkit-fill-available; + max-width: -webkit-fill-available; +} + +.comfyui-body-top { + order: 0; + grid-column: 1/-1; + z-index: 10; +} + +.comfyui-body-left { + order: 1; + z-index: 10; } #graph-canvas { width: 100%; height: 100%; + order: 2; + grid-column: 1/-1; +} + +.comfyui-body-right { + order: 3; + z-index: 10; +} + +.comfyui-body-bottom { + order: 4; + grid-column: 1/-1; + z-index: 10; } .comfy-multiline-input { @@ -364,6 +408,37 @@ dialog::backdrop { background: rgba(0, 0, 0, 0.5); } +.comfy-dialog.comfyui-dialog { + top: 0; +} + +.comfy-dialog.comfy-modal { + font-family: Arial, sans-serif; + border-color: var(--bg-color); + box-shadow: none; + border: 2px solid var(--border-color); +} + +.comfy-dialog .comfy-modal-content { + flex-direction: row; + flex-wrap: wrap; + gap: 10px; + color: var(--fg-color); +} + +.comfy-dialog .comfy-modal-content h3 { + margin-top: 0; +} + +.comfy-dialog .comfy-modal-content > p { + width: 100%; +} + +.comfy-dialog .comfy-modal-content > .comfyui-button { + flex: 1; + justify-content: center; +} + #comfy-settings-dialog { padding: 0; width: 41rem; diff --git a/web/types/comfy.d.ts b/web/types/comfy.d.ts index f7129b55..9a338b34 100644 --- a/web/types/comfy.d.ts +++ b/web/types/comfy.d.ts @@ -10,24 +10,24 @@ export interface ComfyExtension { * Allows any initialisation, e.g. loading resources. Called after the canvas is created but before nodes are added * @param app The ComfyUI app instance */ - init(app: ComfyApp): Promise; + init?(app: ComfyApp): Promise; /** * Allows any additonal setup, called after the application is fully set up and running * @param app The ComfyUI app instance */ - setup(app: ComfyApp): Promise; + setup?(app: ComfyApp): Promise; /** * Called before nodes are registered with the graph * @param defs The collection of node definitions, add custom ones or edit existing ones * @param app The ComfyUI app instance */ - addCustomNodeDefs(defs: Record, app: ComfyApp): Promise; + addCustomNodeDefs?(defs: Record, app: ComfyApp): Promise; /** * Allows the extension to add custom widgets * @param app The ComfyUI app instance * @returns An array of {[widget name]: widget data} */ - getCustomWidgets( + getCustomWidgets?( app: ComfyApp ): Promise< Record { widget?: IWidget; minWidth?: number; minHeight?: number }> @@ -38,12 +38,12 @@ export interface ComfyExtension { * @param nodeData The original node object info config object * @param app The ComfyUI app instance */ - beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyObjectInfo, app: ComfyApp): Promise; + beforeRegisterNodeDef?(nodeType: typeof LGraphNode, nodeData: ComfyObjectInfo, app: ComfyApp): Promise; /** * Allows the extension to register additional nodes with LGraph after standard nodes are added * @param app The ComfyUI app instance */ - registerCustomNodes(app: ComfyApp): Promise; + registerCustomNodes?(app: ComfyApp): Promise; /** * Allows the extension to modify a node that has been reloaded onto the graph. * If you break something in the backend and want to patch workflows in the frontend @@ -51,13 +51,13 @@ export interface ComfyExtension { * @param node The node that has been loaded * @param app The ComfyUI app instance */ - loadedGraphNode(node: LGraphNode, app: ComfyApp); + loadedGraphNode?(node: LGraphNode, app: ComfyApp); /** * Allows the extension to run code after the constructor of the node * @param node The node that has been created * @param app The ComfyUI app instance */ - nodeCreated(node: LGraphNode, app: ComfyApp); + nodeCreated?(node: LGraphNode, app: ComfyApp); } export type ComfyObjectInfo = { From 69d710e40f43b87dd7124b9ee1e8982f987827e9 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 25 Jun 2024 07:41:52 -0400 Subject: [PATCH 316/378] Implement my alternative take on CFG++ as the euler_pp sampler. Add euler_ancestral_pp which is the ancestral version of euler with the same modification. --- comfy/k_diffusion/sampling.py | 55 ++++++++++++++++++++++++- comfy/samplers.py | 2 +- comfy_extras/nodes_advanced_samplers.py | 25 +---------- 3 files changed, 56 insertions(+), 26 deletions(-) diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py index 78896b8b..9b58cb1d 100644 --- a/comfy/k_diffusion/sampling.py +++ b/comfy/k_diffusion/sampling.py @@ -7,7 +7,7 @@ import torchsde from tqdm.auto import trange, tqdm from . import utils - +import comfy.model_patcher def append_zero(x): return torch.cat([x, x.new_zeros([1])]) @@ -945,3 +945,56 @@ def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=Non buffer_model.append(d_cur.detach()) return x_next + + +@torch.no_grad() +def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=None): + extra_args = {} if extra_args is None else extra_args + + temp = [0] + def post_cfg_function(args): + temp[0] = args["uncond_denoised"] + return args["denoised"] + + model_options = extra_args.get("model_options", {}).copy() + extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) + + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + sigma_hat = sigmas[i] + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x - denoised + temp[0], sigma_hat, denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + dt = sigmas[i + 1] - sigma_hat + # Euler method + x = x + d * dt + return x + +@torch.no_grad() +def sample_euler_ancestral_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + """Ancestral sampling with Euler method steps.""" + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + + temp = [0] + def post_cfg_function(args): + temp[0] = args["uncond_denoised"] + return args["denoised"] + + model_options = extra_args.get("model_options", {}).copy() + extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) + + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + d = to_d(x - denoised + temp[0], sigmas[i], denoised) + # Euler method + dt = sigma_down - sigmas[i] + x = x + d * dt + if sigmas[i + 1] > 0: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + return x diff --git a/comfy/samplers.py b/comfy/samplers.py index 55b1486b..c3a80dde 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -537,7 +537,7 @@ class Sampler: sigma = float(sigmas[0]) return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma -KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral", +KSAMPLER_NAMES = ["euler", "euler_pp", "euler_ancestral", "euler_ancestral_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral", "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu", "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm", "ipndm", "ipndm_v"] diff --git a/comfy_extras/nodes_advanced_samplers.py b/comfy_extras/nodes_advanced_samplers.py index 7aa89c64..cee3a10c 100644 --- a/comfy_extras/nodes_advanced_samplers.py +++ b/comfy_extras/nodes_advanced_samplers.py @@ -82,29 +82,6 @@ def sample_euler_cfgpp(model, x, sigmas, extra_args=None, callback=None, disable x = denoised + sigmas[i + 1] * d return x -@torch.no_grad() -def sample_euler_cfgpp_alt(model, x, sigmas, extra_args=None, callback=None, disable=None): - extra_args = {} if extra_args is None else extra_args - - temp = [0] - def post_cfg_function(args): - temp[0] = args["uncond_denoised"] - return args["denoised"] - - model_options = extra_args.get("model_options", {}).copy() - extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) - - s_in = x.new_ones([x.shape[0]]) - for i in trange(len(sigmas) - 1, disable=disable): - sigma_hat = sigmas[i] - denoised = model(x, sigma_hat * s_in, **extra_args) - d = to_d(x - denoised + temp[0], sigma_hat, denoised) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) - dt = sigmas[i + 1] - sigma_hat - # Euler method - x = x + d * dt - return x class SamplerEulerCFGpp: @classmethod @@ -122,7 +99,7 @@ class SamplerEulerCFGpp: if version == "regular": sampler = comfy.samplers.KSAMPLER(sample_euler_cfgpp) else: - sampler = comfy.samplers.KSAMPLER(sample_euler_cfgpp_alt) + sampler = comfy.samplers.ksampler("euler_pp") return (sampler, ) NODE_CLASS_MAPPINGS = { From e99d97a9d943c3601ccdf411608bffb771db5bf1 Mon Sep 17 00:00:00 2001 From: Chenlei Hu Date: Wed, 26 Jun 2024 01:23:55 -0400 Subject: [PATCH 317/378] Remove duplicated Reset View button (#3865) * Remove duplicated Reset View button * Disable flaky test --- .github/workflows/test-browser.yml | 2 +- web/scripts/ui.js | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/test-browser.yml b/.github/workflows/test-browser.yml index 049b1cde..323c96cf 100644 --- a/.github/workflows/test-browser.yml +++ b/.github/workflows/test-browser.yml @@ -24,7 +24,7 @@ jobs: with: repository: "huchenlei/ComfyUI_frontend" path: "ComfyUI_frontend" - ref: "23a4ee83144d9bf22e7218b9c78c71f8544c8b27" + ref: "577aea9dfb680a14b3ae4fa626a85098c3207030" - uses: actions/setup-node@v3 with: node-version: lts/* diff --git a/web/scripts/ui.js b/web/scripts/ui.js index 87b4887e..bb094d8b 100644 --- a/web/scripts/ui.js +++ b/web/scripts/ui.js @@ -603,7 +603,6 @@ export class ComfyUI { onclick: () => app.refreshComboInNodes() }), $el("button", {id: "comfy-clipspace-button", textContent: "Clipspace", onclick: () => app.openClipspace()}), - $el("button", {id: "comfy-reset-view-button", textContent: "Reset View", onclick: () => app.resetView()}), $el("button", { id: "comfy-clear-button", textContent: "Clear", onclick: () => { if (!confirmClear.value || confirm("Clear workflow?")) { From edfce78c86c7615039dd8d74e89957a0eebbdc03 Mon Sep 17 00:00:00 2001 From: "Alex \"mcmonkey\" Goodwin" <4000772+mcmonkey4eva@users.noreply.github.com> Date: Tue, 25 Jun 2024 22:37:27 -0700 Subject: [PATCH 318/378] add issue templates for ComfyUI Issues Page (#3868) --- .github/ISSUE_TEMPLATE/bug-report.yml | 45 ++++++++++++++++++++++ .github/ISSUE_TEMPLATE/config.yml | 8 ++++ .github/ISSUE_TEMPLATE/feature-request.yml | 32 +++++++++++++++ .github/ISSUE_TEMPLATE/user-support.yml | 32 +++++++++++++++ 4 files changed, 117 insertions(+) create mode 100644 .github/ISSUE_TEMPLATE/bug-report.yml create mode 100644 .github/ISSUE_TEMPLATE/config.yml create mode 100644 .github/ISSUE_TEMPLATE/feature-request.yml create mode 100644 .github/ISSUE_TEMPLATE/user-support.yml diff --git a/.github/ISSUE_TEMPLATE/bug-report.yml b/.github/ISSUE_TEMPLATE/bug-report.yml new file mode 100644 index 00000000..87bef368 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug-report.yml @@ -0,0 +1,45 @@ +name: Bug Report +description: "Something is broken inside of ComfyUI. (Do not use this if you're just having issues and need help, or if the issue relates to a custom node)" +labels: [ "Potential Bug" ] +body: + - type: markdown + attributes: + value: | + Before submitting a **Bug Report**, please ensure the following: + + **1:** You are running the latest version of ComfyUI. + **2:** You have looked at the existing bug reports and made sure this isn't already reported. + **3:** This is an actual bug in ComfyUI, not just a support question and not caused by an custom node. A bug is when you can specify exact steps to replicate what went wrong and others will be able to repeat your steps and see the same issue happen. + + If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first. + - type: textarea + attributes: + label: Expected Behavior + description: "What you expected to happen." + validations: + required: true + - type: textarea + attributes: + label: Actual Behavior + description: "What actually happened. Please include a screenshot of the issue if possible." + validations: + required: true + - type: textarea + attributes: + label: Steps to Reproduce + description: "Describe how to reproduce the issue. Please be sure to attach a workflow JSON or PNG, ideally one that doesn't require custom nodes to test. If the bug open happens when certain custom nodes are used, most likely that custom node is what has the bug rather than ComfyUI, in which case it should be reported to the node's author." + validations: + required: true + - type: textarea + attributes: + label: Debug Logs + description: "Please copy the output from your terminal logs here." + render: powershell + validations: + required: true + - type: textarea + attributes: + label: Other + description: "Any other additional information you think might be helpful." + validations: + required: false diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 00000000..2c519ede --- /dev/null +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,8 @@ +blank_issues_enabled: true +contact_links: + - name: ComfyUI Matrix Space + url: https://app.element.io/#/room/%23comfyui_space%3Amatrix.org + about: The ComfyUI Matrix Space is available for support and general discussion related to ComfyUI (Matrix is like Discord but open source). + - name: Comfy Org Discord + url: https://discord.gg/comfyorg + about: The Comfy Org Discord is available for support and general discussion related to ComfyUI. diff --git a/.github/ISSUE_TEMPLATE/feature-request.yml b/.github/ISSUE_TEMPLATE/feature-request.yml new file mode 100644 index 00000000..419721b6 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature-request.yml @@ -0,0 +1,32 @@ +name: Feature Request +description: "You have an idea for something new you would like to see added to ComfyUI's core." +labels: [ "Feature" ] +body: + - type: markdown + attributes: + value: | + Before submitting a **Feature Request**, please ensure the following: + + **1:** You are running the latest version of ComfyUI. + **2:** You have looked to make sure there is not already a feature that does what you need, and there is not already a Feature Request listed for the same idea. + **3:** This is something that makes sense to add to ComfyUI Core, and wouldn't make more sense as a custom node. + + If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first. + - type: textarea + attributes: + label: Feature Idea + description: "Describe the feature you want to see." + validations: + required: true + - type: textarea + attributes: + label: Existing Solutions + description: "Please search through available custom nodes / extensions to see if there are existing custom solutions for this. If so, please link the options you found here as a reference." + validations: + required: false + - type: textarea + attributes: + label: Other + description: "Any other additional information you think might be helpful." + validations: + required: false diff --git a/.github/ISSUE_TEMPLATE/user-support.yml b/.github/ISSUE_TEMPLATE/user-support.yml new file mode 100644 index 00000000..df28804c --- /dev/null +++ b/.github/ISSUE_TEMPLATE/user-support.yml @@ -0,0 +1,32 @@ +name: User Support +description: "Use this if you need help with something, or you're experiencing an issue." +labels: [ "User Support" ] +body: + - type: markdown + attributes: + value: | + Before submitting a **User Report** issue, please ensure the following: + + **1:** You are running the latest version of ComfyUI. + **2:** You have made an effort to find public answers to your question before asking here. In other words, you googled it first, and scrolled through recent help topics. + + If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first. + - type: textarea + attributes: + label: Your question + description: "Post your question here. Please be as detailed as possible." + validations: + required: true + - type: textarea + attributes: + label: Logs + description: "If your question relates to an issue you're experiencing, please go to `Server` -> `Logs` -> potentially set `View Type` to `Debug` as well, then copypaste all the text into here." + render: powershell + validations: + required: false + - type: textarea + attributes: + label: Other + description: "Any other additional information you think might be helpful." + validations: + required: false From e3579f33600fea742611cc62503b446833d45735 Mon Sep 17 00:00:00 2001 From: pythongosssss <125205205+pythongosssss@users.noreply.github.com> Date: Wed, 26 Jun 2024 14:08:48 +0100 Subject: [PATCH 319/378] Fix merge issue breaking api json loading (#3876) --- web/scripts/app.js | 1 - 1 file changed, 1 deletion(-) diff --git a/web/scripts/app.js b/web/scripts/app.js index 72553856..a354ff80 100644 --- a/web/scripts/app.js +++ b/web/scripts/app.js @@ -2321,7 +2321,6 @@ export class ComfyApp { node.id = isNaN(+id) ? id : +id; node.title = data._meta?.title ?? node.title app.graph.add(node); - graph.add(node); } this.changeWorkflow(() => { From f12fa1d8d7b42888b6491aa6aa4956aefca3aba5 Mon Sep 17 00:00:00 2001 From: Chenlei Hu Date: Wed, 26 Jun 2024 09:09:21 -0400 Subject: [PATCH 320/378] Enable browser tests on push (#3878) --- .github/workflows/test-browser.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/test-browser.yml b/.github/workflows/test-browser.yml index 323c96cf..987a72ae 100644 --- a/.github/workflows/test-browser.yml +++ b/.github/workflows/test-browser.yml @@ -7,6 +7,8 @@ name: Playwright Browser Tests CI on: + push: + branches: [ main, master ] pull_request: branches: [ main, master ] From a3e83f695d6588bc06fdd40e039cd1e5b5e169dd Mon Sep 17 00:00:00 2001 From: Chenlei Hu Date: Wed, 26 Jun 2024 19:58:56 -0400 Subject: [PATCH 321/378] Update test ref (#3882) * Update ref * Disable some tests --- .github/workflows/test-browser.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/test-browser.yml b/.github/workflows/test-browser.yml index 987a72ae..65483b00 100644 --- a/.github/workflows/test-browser.yml +++ b/.github/workflows/test-browser.yml @@ -26,7 +26,7 @@ jobs: with: repository: "huchenlei/ComfyUI_frontend" path: "ComfyUI_frontend" - ref: "577aea9dfb680a14b3ae4fa626a85098c3207030" + ref: "fcc54d803e5b6a9b08a462a1d94899318c96dcbb" - uses: actions/setup-node@v3 with: node-version: lts/* From bc5a0f10db34a5c942fa3c531864da9665faaaa5 Mon Sep 17 00:00:00 2001 From: Chenlei Hu Date: Wed, 26 Jun 2024 19:59:09 -0400 Subject: [PATCH 322/378] Ignore *.log (#3880) --- .gitignore | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 9f038924..9473f43a 100644 --- a/.gitignore +++ b/.gitignore @@ -15,4 +15,5 @@ venv/ !/web/extensions/logging.js.example !/web/extensions/core/ /tests-ui/data/object_info.json -/user/ \ No newline at end of file +/user/ +*.log \ No newline at end of file From 175fe02522751c9f5e9a8309b3aaa5d07a542296 Mon Sep 17 00:00:00 2001 From: Chenlei Hu Date: Wed, 26 Jun 2024 19:59:19 -0400 Subject: [PATCH 323/378] Ignore .vscode/ (#3879) --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 9473f43a..a9beebe7 100644 --- a/.gitignore +++ b/.gitignore @@ -9,6 +9,7 @@ __pycache__/ !custom_nodes/example_node.py.example extra_model_paths.yaml /.vs +.vscode/ .idea/ venv/ /web/extensions/* From 44947e7ad489eaadb38007ca7aabd71aef7fa10e Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 26 Jun 2024 22:40:05 -0400 Subject: [PATCH 324/378] Add DEIS order 3 sampler. Order 4 seems to give bad results. --- comfy/k_diffusion/deis.py | 122 ++++++++++++++++++++++++++++++++++ comfy/k_diffusion/sampling.py | 50 ++++++++++++++ comfy/samplers.py | 2 +- 3 files changed, 173 insertions(+), 1 deletion(-) create mode 100644 comfy/k_diffusion/deis.py diff --git a/comfy/k_diffusion/deis.py b/comfy/k_diffusion/deis.py new file mode 100644 index 00000000..0be2036c --- /dev/null +++ b/comfy/k_diffusion/deis.py @@ -0,0 +1,122 @@ +#Taken from: https://github.com/zju-pi/diff-sampler/blob/main/gits-main/solver_utils.py +#under Apache 2 license +import torch +import numpy as np + +# A pytorch reimplementation of DEIS (https://github.com/qsh-zh/deis). +############################# +### Utils for DEIS solver ### +############################# +#---------------------------------------------------------------------------- +# Transfer from the input time (sigma) used in EDM to that (t) used in DEIS. + +def edm2t(edm_steps, epsilon_s=1e-3, sigma_min=0.002, sigma_max=80): + vp_sigma = lambda beta_d, beta_min: lambda t: (np.e ** (0.5 * beta_d * (t ** 2) + beta_min * t) - 1) ** 0.5 + vp_sigma_inv = lambda beta_d, beta_min: lambda sigma: ((beta_min ** 2 + 2 * beta_d * (sigma ** 2 + 1).log()).sqrt() - beta_min) / beta_d + vp_beta_d = 2 * (np.log(torch.tensor(sigma_min).cpu() ** 2 + 1) / epsilon_s - np.log(torch.tensor(sigma_max).cpu() ** 2 + 1)) / (epsilon_s - 1) + vp_beta_min = np.log(torch.tensor(sigma_max).cpu() ** 2 + 1) - 0.5 * vp_beta_d + t_steps = vp_sigma_inv(vp_beta_d.clone().detach().cpu(), vp_beta_min.clone().detach().cpu())(edm_steps.clone().detach().cpu()) + return t_steps, vp_beta_min, vp_beta_d + vp_beta_min + +#---------------------------------------------------------------------------- + +def cal_poly(prev_t, j, taus): + poly = 1 + for k in range(prev_t.shape[0]): + if k == j: + continue + poly *= (taus - prev_t[k]) / (prev_t[j] - prev_t[k]) + return poly + +#---------------------------------------------------------------------------- +# Transfer from t to alpha_t. + +def t2alpha_fn(beta_0, beta_1, t): + return torch.exp(-0.5 * t ** 2 * (beta_1 - beta_0) - t * beta_0) + +#---------------------------------------------------------------------------- + +def cal_intergrand(beta_0, beta_1, taus): + with torch.inference_mode(mode=False): + taus = taus.clone() + beta_0 = beta_0.clone() + beta_1 = beta_1.clone() + with torch.enable_grad(): + taus.requires_grad_(True) + alpha = t2alpha_fn(beta_0, beta_1, taus) + log_alpha = alpha.log() + log_alpha.sum().backward() + d_log_alpha_dtau = taus.grad + integrand = -0.5 * d_log_alpha_dtau / torch.sqrt(alpha * (1 - alpha)) + return integrand + +#---------------------------------------------------------------------------- + +def get_deis_coeff_list(t_steps, max_order, N=10000, deis_mode='tab'): + """ + Get the coefficient list for DEIS sampling. + + Args: + t_steps: A pytorch tensor. The time steps for sampling. + max_order: A `int`. Maximum order of the solver. 1 <= max_order <= 4 + N: A `int`. Use how many points to perform the numerical integration when deis_mode=='tab'. + deis_mode: A `str`. Select between 'tab' and 'rhoab'. Type of DEIS. + Returns: + A pytorch tensor. A batch of generated samples or sampling trajectories if return_inters=True. + """ + if deis_mode == 'tab': + t_steps, beta_0, beta_1 = edm2t(t_steps) + C = [] + for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])): + order = min(i+1, max_order) + if order == 1: + C.append([]) + else: + taus = torch.linspace(t_cur, t_next, N) # split the interval for integral appximation + dtau = (t_next - t_cur) / N + prev_t = t_steps[[i - k for k in range(order)]] + coeff_temp = [] + integrand = cal_intergrand(beta_0, beta_1, taus) + for j in range(order): + poly = cal_poly(prev_t, j, taus) + coeff_temp.append(torch.sum(integrand * poly) * dtau) + C.append(coeff_temp) + + elif deis_mode == 'rhoab': + # Analytical solution, second order + def get_def_intergral_2(a, b, start, end, c): + coeff = (end**3 - start**3) / 3 - (end**2 - start**2) * (a + b) / 2 + (end - start) * a * b + return coeff / ((c - a) * (c - b)) + + # Analytical solution, third order + def get_def_intergral_3(a, b, c, start, end, d): + coeff = (end**4 - start**4) / 4 - (end**3 - start**3) * (a + b + c) / 3 \ + + (end**2 - start**2) * (a*b + a*c + b*c) / 2 - (end - start) * a * b * c + return coeff / ((d - a) * (d - b) * (d - c)) + + C = [] + for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])): + order = min(i, max_order) + if order == 0: + C.append([]) + else: + prev_t = t_steps[[i - k for k in range(order+1)]] + if order == 1: + coeff_cur = ((t_next - prev_t[1])**2 - (t_cur - prev_t[1])**2) / (2 * (t_cur - prev_t[1])) + coeff_prev1 = (t_next - t_cur)**2 / (2 * (prev_t[1] - t_cur)) + coeff_temp = [coeff_cur, coeff_prev1] + elif order == 2: + coeff_cur = get_def_intergral_2(prev_t[1], prev_t[2], t_cur, t_next, t_cur) + coeff_prev1 = get_def_intergral_2(t_cur, prev_t[2], t_cur, t_next, prev_t[1]) + coeff_prev2 = get_def_intergral_2(t_cur, prev_t[1], t_cur, t_next, prev_t[2]) + coeff_temp = [coeff_cur, coeff_prev1, coeff_prev2] + elif order == 3: + coeff_cur = get_def_intergral_3(prev_t[1], prev_t[2], prev_t[3], t_cur, t_next, t_cur) + coeff_prev1 = get_def_intergral_3(t_cur, prev_t[2], prev_t[3], t_cur, t_next, prev_t[1]) + coeff_prev2 = get_def_intergral_3(t_cur, prev_t[1], prev_t[3], t_cur, t_next, prev_t[2]) + coeff_prev3 = get_def_intergral_3(t_cur, prev_t[1], prev_t[2], t_cur, t_next, prev_t[3]) + coeff_temp = [coeff_cur, coeff_prev1, coeff_prev2, coeff_prev3] + C.append(coeff_temp) + print(C) + return C + diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py index 9b58cb1d..f8091bb3 100644 --- a/comfy/k_diffusion/sampling.py +++ b/comfy/k_diffusion/sampling.py @@ -7,6 +7,7 @@ import torchsde from tqdm.auto import trange, tqdm from . import utils +from . import deis import comfy.model_patcher def append_zero(x): @@ -946,6 +947,55 @@ def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=Non return x_next +#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py +#under Apache 2 license +@torch.no_grad() +def sample_deis(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=3, deis_mode='tab'): + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + + x_next = x + t_steps = sigmas + + coeff_list = deis.get_deis_coeff_list(t_steps, max_order, deis_mode=deis_mode) + + buffer_model = [] + for i in trange(len(sigmas) - 1, disable=disable): + t_cur = sigmas[i] + t_next = sigmas[i + 1] + + x_cur = x_next + + denoised = model(x_cur, t_cur * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + + d_cur = (x_cur - denoised) / t_cur + + order = min(max_order, i+1) + if t_next <= 0: + order = 1 + + if order == 1: # First Euler step. + x_next = x_cur + (t_next - t_cur) * d_cur + elif order == 2: # Use one history point. + coeff_cur, coeff_prev1 = coeff_list[i] + x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + elif order == 3: # Use two history points. + coeff_cur, coeff_prev1, coeff_prev2 = coeff_list[i] + x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + coeff_prev2 * buffer_model[-2] + elif order == 4: # Use three history points. + coeff_cur, coeff_prev1, coeff_prev2, coeff_prev3 = coeff_list[i] + x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + coeff_prev2 * buffer_model[-2] + coeff_prev3 * buffer_model[-3] + + if len(buffer_model) == max_order - 1: + for k in range(max_order - 2): + buffer_model[k] = buffer_model[k+1] + buffer_model[-1] = d_cur.detach() + else: + buffer_model.append(d_cur.detach()) + + return x_next @torch.no_grad() def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=None): diff --git a/comfy/samplers.py b/comfy/samplers.py index c3a80dde..7f7114db 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -540,7 +540,7 @@ class Sampler: KSAMPLER_NAMES = ["euler", "euler_pp", "euler_ancestral", "euler_ancestral_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral", "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu", "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm", - "ipndm", "ipndm_v"] + "ipndm", "ipndm_v", "deis"] class KSAMPLER(Sampler): def __init__(self, sampler_function, extra_options={}, inpaint_options={}): From 3b423afccab877b57fc24141690426ca1660e4bb Mon Sep 17 00:00:00 2001 From: Chenlei Hu Date: Thu, 27 Jun 2024 00:22:55 -0400 Subject: [PATCH 325/378] Add audio widget (#3863) * Add audio widget * Fix audio bugs * Add CSS * Populate audio widget when load history --- comfy_extras/nodes_audio.py | 11 +- web/extensions/core/uploadAudio.js | 176 +++++++++++++++++++++++++++++ web/scripts/app.js | 11 +- web/style.css | 4 + 4 files changed, 199 insertions(+), 3 deletions(-) create mode 100644 web/extensions/core/uploadAudio.js diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py index 57d0a20a..8efa7c0a 100644 --- a/comfy_extras/nodes_audio.py +++ b/comfy_extras/nodes_audio.py @@ -93,11 +93,18 @@ class SaveAudio: return { "ui": { "audio": results } } class LoadAudio: + SUPPORTED_FORMATS = ('.wav', '.mp3', '.ogg', '.flac', '.aiff', '.aif') + @classmethod def INPUT_TYPES(s): input_dir = folder_paths.get_input_directory() - files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] - return {"required": {"audio": [sorted(files), ]}, } + files = [ + f for f in os.listdir(input_dir) + if (os.path.isfile(os.path.join(input_dir, f)) + and f.endswith(LoadAudio.SUPPORTED_FORMATS) + ) + ] + return {"required": {"audio": (sorted(files), {"audio_upload": True})}} CATEGORY = "_for_testing/audio" diff --git a/web/extensions/core/uploadAudio.js b/web/extensions/core/uploadAudio.js new file mode 100644 index 00000000..d3dfef4a --- /dev/null +++ b/web/extensions/core/uploadAudio.js @@ -0,0 +1,176 @@ +import { app } from "../../scripts/app.js" +import { api } from "../../scripts/api.js" + +function splitFilePath(path) { + const folder_separator = path.lastIndexOf("/") + if (folder_separator === -1) { + return ["", path] + } + return [ + path.substring(0, folder_separator), + path.substring(folder_separator + 1) + ] +} + +function getResourceURL(subfolder, filename, type = "input") { + const params = [ + "filename=" + encodeURIComponent(filename), + "type=" + type, + "subfolder=" + subfolder, + app.getPreviewFormatParam().substring(1), + app.getRandParam().substring(1) + ].join("&") + + return `/view?${params}` +} + +async function uploadFile( + audioWidget, + audioUIWidget, + file, + updateNode, + pasted = false +) { + try { + // Wrap file in formdata so it includes filename + const body = new FormData() + body.append("image", file) + if (pasted) body.append("subfolder", "pasted") + const resp = await api.fetchApi("/upload/image", { + method: "POST", + body + }) + + if (resp.status === 200) { + const data = await resp.json() + // Add the file to the dropdown list and update the widget value + let path = data.name + if (data.subfolder) path = data.subfolder + "/" + path + + if (!audioWidget.options.values.includes(path)) { + audioWidget.options.values.push(path) + } + + if (updateNode) { + audioUIWidget.element.src = api.apiURL( + getResourceURL(...splitFilePath(path)) + ) + audioWidget.value = path + } + } else { + alert(resp.status + " - " + resp.statusText) + } + } catch (error) { + alert(error) + } +} + +// AudioWidget MUST be registered first, as AUDIOUPLOAD depends on AUDIO_UI to be +// present. +app.registerExtension({ + name: "Comfy.AudioWidget", + async beforeRegisterNodeDef(nodeType, nodeData) { + if (["LoadAudio", "SaveAudio"].includes(nodeType.comfyClass)) { + nodeData.input.required.audioUI = ["AUDIO_UI"] + } + }, + getCustomWidgets() { + return { + AUDIO_UI(node, inputName) { + const audio = document.createElement("audio") + audio.controls = true + audio.classList.add("comfy-audio") + audio.setAttribute("name", "media") + + const audioUIWidget = node.addDOMWidget( + inputName, + /* name=*/ "audioUI", + audio + ) + // @ts-ignore + // TODO: Sort out the DOMWidget type. + audioUIWidget.serialize = false + + const isOutputNode = node.constructor.nodeData.output_node + if (isOutputNode) { + // Hide the audio widget when there is no audio initially. + audioUIWidget.element.classList.add("empty-audio-widget") + // Populate the audio widget UI on node execution. + const onExecuted = node.onExecuted + node.onExecuted = function(message) { + onExecuted?.apply(this, arguments) + const audios = message.audio + if (!audios) return + const audio = audios[0] + audioUIWidget.element.src = api.apiURL( + getResourceURL(audio.subfolder, audio.filename, "output") + ) + audioUIWidget.element.classList.remove("empty-audio-widget") + } + } + return { widget: audioUIWidget } + } + } + }, + onNodeOutputsUpdated(nodeOutputs) { + for (const [nodeId, output] of Object.entries(nodeOutputs)) { + const node = app.graph.getNodeById(Number.parseInt(nodeId)); + if ("audio" in output) { + const audioUIWidget = node.widgets.find((w) => w.name === "audioUI"); + const audio = output.audio[0]; + audioUIWidget.element.src = api.apiURL(getResourceURL(audio.subfolder, audio.filename, "output")); + audioUIWidget.element.classList.remove("empty-audio-widget"); + } + } + }, +}) + +app.registerExtension({ + name: "Comfy.UploadAudio", + async beforeRegisterNodeDef(nodeType, nodeData) { + if (nodeData?.input?.required?.audio?.[1]?.audio_upload === true) { + nodeData.input.required.upload = ["AUDIOUPLOAD"] + } + }, + getCustomWidgets() { + return { + AUDIOUPLOAD(node, inputName) { + // The widget that allows user to select file. + const audioWidget = node.widgets.find(w => w.name === "audio") + const audioUIWidget = node.widgets.find(w => w.name === "audioUI") + + const onAudioWidgetUpdate = () => { + audioUIWidget.element.src = api.apiURL( + getResourceURL(...splitFilePath(audioWidget.value)) + ) + } + // Initially load default audio file to audioUIWidget. + onAudioWidgetUpdate() + audioWidget.callback = onAudioWidgetUpdate + + const fileInput = document.createElement("input") + fileInput.type = "file" + fileInput.accept = "audio/*" + fileInput.style.display = "none" + fileInput.onchange = () => { + if (fileInput.files.length) { + uploadFile(audioWidget, audioUIWidget, fileInput.files[0], true) + } + } + // The widget to pop up the upload dialog. + const uploadWidget = node.addWidget( + "button", + inputName, + /* value=*/ "", + () => { + fileInput.click() + } + ) + uploadWidget.label = "choose file to upload" + uploadWidget.serialize = false + + return { widget: uploadWidget } + } + } + } +}) diff --git a/web/scripts/app.js b/web/scripts/app.js index a354ff80..5fc9b9c6 100644 --- a/web/scripts/app.js +++ b/web/scripts/app.js @@ -71,7 +71,7 @@ export class ComfyApp { * Stores the execution output data for each node * @type {Record} */ - this.nodeOutputs = {}; + this._nodeOutputs = {}; /** * Stores the preview image data for each node @@ -86,6 +86,15 @@ export class ComfyApp { this.shiftDown = false; } + get nodeOutputs() { + return this._nodeOutputs; + } + + set nodeOutputs(value) { + this._nodeOutputs = value; + this.#invokeExtensions("onNodeOutputsUpdated", value); + } + getPreviewFormatParam() { let preview_format = this.ui.settings.getSettingValue("Comfy.PreviewFormat"); if(preview_format) diff --git a/web/style.css b/web/style.css index 8091a489..e983b652 100644 --- a/web/style.css +++ b/web/style.css @@ -632,3 +632,7 @@ dialog::backdrop { border-top: none; } } + +audio.comfy-audio.empty-audio-widget { + display: none; +} From 4650e7d6e9e846160906b94d575705151d59fdd1 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 27 Jun 2024 01:28:59 -0400 Subject: [PATCH 326/378] Save and load workflow from the flac files output by SaveAudio. --- comfy_extras/nodes_audio.py | 79 ++++++++++++++++++++++++++++++++++++- web/scripts/app.js | 15 ++++++- web/scripts/pnginfo.js | 72 +++++++++++++++++++++++++++++++++ web/scripts/ui.js | 2 +- 4 files changed, 164 insertions(+), 4 deletions(-) diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py index 8efa7c0a..34bcfa96 100644 --- a/comfy_extras/nodes_audio.py +++ b/comfy_extras/nodes_audio.py @@ -3,6 +3,10 @@ import torch import comfy.model_management import folder_paths import os +import io +import json +import struct +from comfy.cli_args import args class EmptyLatentAudio: def __init__(self): @@ -53,6 +57,61 @@ class VAEDecodeAudio: audio = vae.decode(samples["samples"]).movedim(-1, 1) return ({"waveform": audio, "sample_rate": 44100}, ) + +def create_vorbis_comment_block(comment_dict, last_block): + vendor_string = b'ComfyUI' + vendor_length = len(vendor_string) + + comments = [] + for key, value in comment_dict.items(): + comment = f"{key}={value}".encode('utf-8') + comments.append(struct.pack('I', len(comment_data))[1:] + comment_data + + return comment_block + +def insert_or_replace_vorbis_comment(flac_io, comment_dict): + if len(comment_dict) == 0: + return flac_io + + flac_io.seek(4) + + blocks = [] + last_block = False + + while not last_block: + header = flac_io.read(4) + last_block = (header[0] & 0x80) != 0 + block_type = header[0] & 0x7F + block_length = struct.unpack('>I', b'\x00' + header[1:])[0] + block_data = flac_io.read(block_length) + + if block_type == 4 or block_type == 1: + pass + else: + header = bytes([(header[0] & (~0x80))]) + header[1:] + blocks.append(header + block_data) + + blocks.append(create_vorbis_comment_block(comment_dict, last_block=True)) + + new_flac_io = io.BytesIO() + new_flac_io.write(b'fLaC') + for block in blocks: + new_flac_io.write(block) + + new_flac_io.write(flac_io.read()) + return new_flac_io + + class SaveAudio: def __init__(self): self.output_dir = folder_paths.get_output_directory() @@ -78,11 +137,27 @@ class SaveAudio: filename_prefix += self.prefix_append full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) results = list() + + metadata = {} + if not args.disable_metadata: + if prompt is not None: + metadata["prompt"] = json.dumps(prompt) + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata[x] = json.dumps(extra_pnginfo[x]) + for (batch_number, waveform) in enumerate(audio["waveform"]): - #TODO: metadata filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) file = f"{filename_with_batch_num}_{counter:05}_.flac" - torchaudio.save(os.path.join(full_output_folder, file), waveform, audio["sample_rate"], format="FLAC") + + buff = io.BytesIO() + torchaudio.save(buff, waveform, audio["sample_rate"], format="FLAC") + + buff = insert_or_replace_vorbis_comment(buff, metadata) + + with open(os.path.join(full_output_folder, file), 'wb') as f: + f.write(buff.getbuffer()) + results.append({ "filename": file, "subfolder": subfolder, diff --git a/web/scripts/app.js b/web/scripts/app.js index 5fc9b9c6..43df6106 100644 --- a/web/scripts/app.js +++ b/web/scripts/app.js @@ -3,7 +3,7 @@ import { ComfyWidgets, initWidgets } from "./widgets.js"; import { ComfyUI, $el } from "./ui.js"; import { api } from "./api.js"; import { defaultGraph } from "./defaultGraph.js"; -import { getPngMetadata, getWebpMetadata, importA1111, getLatentMetadata } from "./pnginfo.js"; +import { getPngMetadata, getWebpMetadata, getFlacMetadata, importA1111, getLatentMetadata } from "./pnginfo.js"; import { addDomClippingSetting } from "./domWidget.js"; import { createImageHost, calculateImageGrid } from "./ui/imagePreview.js"; import { ComfyAppMenu } from "./ui/menu/index.js"; @@ -2277,6 +2277,19 @@ export class ComfyApp { const workflow = pngInfo?.workflow || pngInfo?.Workflow; const prompt = pngInfo?.prompt || pngInfo?.Prompt; + if (workflow) { + this.loadGraphData(JSON.parse(workflow), true, true, fileName); + } else if (prompt) { + this.loadApiJson(JSON.parse(prompt), fileName); + } else { + this.showErrorOnFileLoad(file); + } + } else if (file.type === "audio/flac") { + const pngInfo = await getFlacMetadata(file); + // Support loading workflows from that webp custom node. + const workflow = pngInfo?.workflow; + const prompt = pngInfo?.prompt; + if (workflow) { this.loadGraphData(JSON.parse(workflow), true, true, fileName); } else if (prompt) { diff --git a/web/scripts/pnginfo.js b/web/scripts/pnginfo.js index 7132fb60..2c03cf74 100644 --- a/web/scripts/pnginfo.js +++ b/web/scripts/pnginfo.js @@ -163,6 +163,78 @@ export function getLatentMetadata(file) { }); } + +function getString(dataView, offset, length) { + let string = ''; + for (let i = 0; i < length; i++) { + string += String.fromCharCode(dataView.getUint8(offset + i)); + } + return string; +} + +// Function to parse the Vorbis Comment block +function parseVorbisComment(dataView) { + let offset = 0; + const vendorLength = dataView.getUint32(offset, true); + offset += 4; + const vendorString = getString(dataView, offset, vendorLength); + offset += vendorLength; + + const userCommentListLength = dataView.getUint32(offset, true); + offset += 4; + const comments = {}; + for (let i = 0; i < userCommentListLength; i++) { + const commentLength = dataView.getUint32(offset, true); + offset += 4; + const comment = getString(dataView, offset, commentLength); + offset += commentLength; + + const [key, value] = comment.split('='); + + comments[key] = value; + } + + return comments; +} + +// Function to read a FLAC file and parse Vorbis comments +export function getFlacMetadata(file) { + return new Promise((r) => { + const reader = new FileReader(); + reader.onload = function(event) { + const arrayBuffer = event.target.result; + const dataView = new DataView(arrayBuffer); + + // Verify the FLAC signature + const signature = String.fromCharCode(...new Uint8Array(arrayBuffer, 0, 4)); + if (signature !== 'fLaC') { + console.error('Not a valid FLAC file'); + return; + } + + // Parse metadata blocks + let offset = 4; + let vorbisComment = null; + while (offset < dataView.byteLength) { + const isLastBlock = dataView.getUint8(offset) & 0x80; + const blockType = dataView.getUint8(offset) & 0x7F; + const blockSize = dataView.getUint32(offset, false) & 0xFFFFFF; + offset += 4; + + if (blockType === 4) { // Vorbis Comment block type + vorbisComment = parseVorbisComment(new DataView(arrayBuffer, offset, blockSize)); + } + + offset += blockSize; + if (isLastBlock) break; + } + + r(vorbisComment); + }; + reader.readAsArrayBuffer(file); + }); +} + export async function importA1111(graph, parameters) { const p = parameters.lastIndexOf("\nSteps:"); if (p > -1) { diff --git a/web/scripts/ui.js b/web/scripts/ui.js index bb094d8b..2c47412c 100644 --- a/web/scripts/ui.js +++ b/web/scripts/ui.js @@ -373,7 +373,7 @@ export class ComfyUI { const fileInput = $el("input", { id: "comfy-file-input", type: "file", - accept: ".json,image/png,.latent,.safetensors,image/webp", + accept: ".json,image/png,.latent,.safetensors,image/webp,audio/flac", style: {display: "none"}, parent: document.body, onchange: () => { From 4f9d2b057c97be22aa46b91ed8954a3cee424fb4 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 27 Jun 2024 02:54:15 -0400 Subject: [PATCH 327/378] Remove print. --- comfy/k_diffusion/deis.py | 1 - 1 file changed, 1 deletion(-) diff --git a/comfy/k_diffusion/deis.py b/comfy/k_diffusion/deis.py index 0be2036c..60741065 100644 --- a/comfy/k_diffusion/deis.py +++ b/comfy/k_diffusion/deis.py @@ -117,6 +117,5 @@ def get_deis_coeff_list(t_steps, max_order, N=10000, deis_mode='tab'): coeff_prev3 = get_def_intergral_3(t_cur, prev_t[1], prev_t[2], t_cur, t_next, prev_t[3]) coeff_temp = [coeff_cur, coeff_prev1, coeff_prev2, coeff_prev3] C.append(coeff_temp) - print(C) return C From 5ff3d4eb3a91d1e66d35a919dd38c02b4652e96a Mon Sep 17 00:00:00 2001 From: Chenlei Hu Date: Thu, 27 Jun 2024 09:13:52 -0400 Subject: [PATCH 328/378] Fix audio upload when no audio in input dir (#3891) --- web/extensions/core/uploadAudio.js | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/web/extensions/core/uploadAudio.js b/web/extensions/core/uploadAudio.js index d3dfef4a..12a2a196 100644 --- a/web/extensions/core/uploadAudio.js +++ b/web/extensions/core/uploadAudio.js @@ -145,7 +145,9 @@ app.registerExtension({ ) } // Initially load default audio file to audioUIWidget. - onAudioWidgetUpdate() + if (audioWidget.value) { + onAudioWidgetUpdate() + } audioWidget.callback = onAudioWidgetUpdate const fileInput = document.createElement("input") From 8ceb5a02a3182e6551690d9e56934bdca1cebcca Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 27 Jun 2024 11:06:52 -0400 Subject: [PATCH 329/378] Support saving stable audio checkpoint that can be loaded back. --- comfy/model_base.py | 9 +++++++++ comfy/sd.py | 2 +- comfy/supported_models.py | 5 ++++- 3 files changed, 14 insertions(+), 2 deletions(-) diff --git a/comfy/model_base.py b/comfy/model_base.py index f45b375d..80f6667e 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -627,3 +627,12 @@ class StableAudio1(BaseModel): cross_attn = torch.cat([cross_attn.to(device), seconds_start_embed.repeat((cross_attn.shape[0], 1, 1)), seconds_total_embed.repeat((cross_attn.shape[0], 1, 1))], dim=1) out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) return out + + def state_dict_for_saving(self, clip_state_dict=None, vae_state_dict=None, clip_vision_state_dict=None): + sd = super().state_dict_for_saving(clip_state_dict=clip_state_dict, vae_state_dict=vae_state_dict, clip_vision_state_dict=clip_vision_state_dict) + d = {"conditioner.conditioners.seconds_start.": self.seconds_start_embedder.state_dict(), "conditioner.conditioners.seconds_total.": self.seconds_total_embedder.state_dict()} + for k in d: + s = d[k] + for l in s: + sd["{}{}".format(k, l)] = s[l] + return sd diff --git a/comfy/sd.py b/comfy/sd.py index ea6e9b66..dda0887b 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -236,7 +236,7 @@ class VAE: self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig}, decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig}) - elif "decoder.layers.0.weight_v" in sd: + elif "decoder.layers.1.layers.0.beta" in sd: self.first_stage_model = AudioOobleckVAE() self.memory_used_encode = lambda shape, dtype: (1000 * shape[2]) * model_management.dtype_size(dtype) self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * 2048) * model_management.dtype_size(dtype) diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 761498db..21fdb7ec 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -543,13 +543,16 @@ class StableAudio(supported_models_base.BASE): seconds_total_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_total.": ""}, filter_keys=True) return model_base.StableAudio1(self, seconds_start_embedder_weights=seconds_start_sd, seconds_total_embedder_weights=seconds_total_sd, device=device) - def process_unet_state_dict(self, state_dict): for k in list(state_dict.keys()): if k.endswith(".cross_attend_norm.beta") or k.endswith(".ff_norm.beta") or k.endswith(".pre_norm.beta"): #These weights are all zero state_dict.pop(k) return state_dict + def process_unet_state_dict_for_saving(self, state_dict): + replace_prefix = {"": "model.model."} + return utils.state_dict_prefix_replace(state_dict, replace_prefix) + def clip_target(self, state_dict={}): return supported_models_base.ClipTarget(sa_t5.SAT5Tokenizer, sa_t5.SAT5Model) From 449bf52923896236ed645da2ff05aadbf2b0d536 Mon Sep 17 00:00:00 2001 From: Robin Huang Date: Thu, 27 Jun 2024 13:08:26 -0700 Subject: [PATCH 330/378] Add integration test for Linux with Nvidia GPU. #3884 (#3895) * Add linux integration test. * Fix directory path. * Add paths ignore. * Fix conda env directory path. --- .../conda-environments/linux-environment.yml | 28 +++++++++++ .github/workflows/linux-integration-test.yml | 50 +++++++++++++++++++ 2 files changed, 78 insertions(+) create mode 100644 .github/conda-environments/linux-environment.yml create mode 100644 .github/workflows/linux-integration-test.yml diff --git a/.github/conda-environments/linux-environment.yml b/.github/conda-environments/linux-environment.yml new file mode 100644 index 00000000..c9ebd640 --- /dev/null +++ b/.github/conda-environments/linux-environment.yml @@ -0,0 +1,28 @@ +name: comfyui +channels: + - pytorch + - nvidia + - defaults +dependencies: + - python>=3.9 + - pip + - pytorch + - torchvision + - torchaudio + - pytorch-cuda=12.1 + - pip: + # comfyui requirements + - einops + - transformers>=4.25.1 + - safetensors>=0.4.2 + - aiohttp + - pyyaml + - Pillow + - scipy + - tqdm + - psutil + # comfy-action requirements + - requests + - google-cloud-storage + - comfy-cli + - charset-normalizer \ No newline at end of file diff --git a/.github/workflows/linux-integration-test.yml b/.github/workflows/linux-integration-test.yml new file mode 100644 index 00000000..eed84a77 --- /dev/null +++ b/.github/workflows/linux-integration-test.yml @@ -0,0 +1,50 @@ +name: (Linux) ComfyUI Integration Tests +on: + push: + branches: + - master + paths-ignore: + - 'app/**' + - 'input/**' + - 'output/**' + - 'model/**' + - 'notebook/**' + - 'script_example/**' + - 'tests/**' + - 'tests-ui/**' + - '.github/**' + - '.ci/**' + - 'web/**' + workflow_dispatch: + pull_request: + branches: + - master + paths-ignore: + - 'app/**' + - 'input/**' + - 'output/**' + - 'model/**' + - 'notebook/**' + - 'script_example/**' + - 'tests/**' + - 'tests-ui/**' + - '.github/**' + - '.ci/**' + - 'web/**' + +jobs: + test-workflows: + runs-on: [self-hosted, Linux, t4] + steps: + - name: Test ComfyUI Workflows + uses: comfy-org/comfy-action@main + with: + os: linux + cuda_version: 12.1 + models-json: '{"v1-5-pruned-emaonly.ckpt": {"url": "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt", "directory": "checkpoints"}}' + workflow_filenames: "default.json" + gcs_bucket_name: 'comfy-ci-results' + google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }} + output_prefix: 'ComfyUI' + conda_env_file: '.github/conda-environments/linux-environment.yml' + timeout: 50 From 97b409cd486b1a3b1114ae204802c81f3727d1a2 Mon Sep 17 00:00:00 2001 From: Robin Huang Date: Thu, 27 Jun 2024 13:10:16 -0700 Subject: [PATCH 331/378] Add macOs integration test for default workflow. (#3898) --- .../conda-environments/mac-environment.yml | 26 ++++++++++ .github/workflows/mac-integration-test.yml | 50 +++++++++++++++++++ 2 files changed, 76 insertions(+) create mode 100644 .github/conda-environments/mac-environment.yml create mode 100644 .github/workflows/mac-integration-test.yml diff --git a/.github/conda-environments/mac-environment.yml b/.github/conda-environments/mac-environment.yml new file mode 100644 index 00000000..33832b85 --- /dev/null +++ b/.github/conda-environments/mac-environment.yml @@ -0,0 +1,26 @@ +name: comfyui +channels: + - pytorch-nightly + - defaults +dependencies: + - python>=3.9 + - pytorch-nightly::pytorch + - torchvision + - torchaudio + - pip: + - pip + # comfyui requirements + - einops + - transformers>=4.25.1 + - safetensors>=0.4.2 + - aiohttp + - pyyaml + - Pillow + - scipy + - tqdm + - psutil + # comfy-action requirements + - requests + - google-cloud-storage + - comfy-cli + - charset-normalizer diff --git a/.github/workflows/mac-integration-test.yml b/.github/workflows/mac-integration-test.yml new file mode 100644 index 00000000..6a0482b1 --- /dev/null +++ b/.github/workflows/mac-integration-test.yml @@ -0,0 +1,50 @@ +name: (macOS) ComfyUI Integration Tests +on: + push: + branches: + - master + paths-ignore: + - 'app/**' + - 'input/**' + - 'output/**' + - 'model/**' + - 'notebook/**' + - 'script_example/**' + - 'tests/**' + - 'tests-ui/**' + - '.github/**' + - '.ci/**' + - 'web/**' + workflow_dispatch: + pull_request: + branches: + - master + paths-ignore: + - 'app/**' + - 'input/**' + - 'output/**' + - 'model/**' + - 'notebook/**' + - 'script_example/**' + - 'tests/**' + - 'tests-ui/**' + - '.github/**' + - '.ci/**' + - 'web/**' + +jobs: + test-workflows: + runs-on: [self-hosted, m2] + steps: + - name: Test ComfyUI Workflows + uses: comfy-org/comfy-action@main + with: + os: mac + models-json: '{"v1-5-pruned-emaonly.ckpt": {"url": "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt", "directory": "checkpoints"}}' + workflow_filenames: "default.json" + gcs_bucket_name: 'comfy-ci-results' + google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }} + output_prefix: 'ComfyUI' + conda_env_file: '.github/conda-environments/mac-environment.yml' + timeout: 50 + From 66aaa14001be9f7cf2b52f84c0dff588e36aabbf Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 25 Jun 2024 17:02:05 -0400 Subject: [PATCH 332/378] Controlnet refactor. --- comfy/cldm/cldm.py | 9 +++++---- comfy/controlnet.py | 35 ++++++++++----------------------- comfy/ldm/cascade/controlnet.py | 2 +- comfy/t2i_adapter/adapter.py | 10 ++++++++-- 4 files changed, 24 insertions(+), 32 deletions(-) diff --git a/comfy/cldm/cldm.py b/comfy/cldm/cldm.py index 28076dd9..f8a16159 100644 --- a/comfy/cldm/cldm.py +++ b/comfy/cldm/cldm.py @@ -289,7 +289,8 @@ class ControlNet(nn.Module): guided_hint = self.input_hint_block(hint, emb, context) - outs = [] + out_output = [] + out_middle = [] hs = [] if self.num_classes is not None: @@ -304,10 +305,10 @@ class ControlNet(nn.Module): guided_hint = None else: h = module(h, emb, context) - outs.append(zero_conv(h, emb, context)) + out_output.append(zero_conv(h, emb, context)) h = self.middle_block(h, emb, context) - outs.append(self.middle_block_out(h, emb, context)) + out_middle.append(self.middle_block_out(h, emb, context)) - return outs + return {"middle": out_middle, "output": out_output} diff --git a/comfy/controlnet.py b/comfy/controlnet.py index 8cf4a61a..f50df683 100644 --- a/comfy/controlnet.py +++ b/comfy/controlnet.py @@ -89,27 +89,12 @@ class ControlBase: return self.previous_controlnet.inference_memory_requirements(dtype) return 0 - def control_merge(self, control_input, control_output, control_prev, output_dtype): + def control_merge(self, control, control_prev, output_dtype): out = {'input':[], 'middle':[], 'output': []} - if control_input is not None: - for i in range(len(control_input)): - key = 'input' - x = control_input[i] - if x is not None: - x *= self.strength - if x.dtype != output_dtype: - x = x.to(output_dtype) - out[key].insert(0, x) - - if control_output is not None: + for key in control: + control_output = control[key] for i in range(len(control_output)): - if i == (len(control_output) - 1): - key = 'middle' - index = 0 - else: - key = 'output' - index = i x = control_output[i] if x is not None: if self.global_average_pooling: @@ -120,6 +105,7 @@ class ControlBase: x = x.to(output_dtype) out[key].append(x) + if control_prev is not None: for x in ['input', 'middle', 'output']: o = out[x] @@ -182,7 +168,7 @@ class ControlNet(ControlBase): x_noisy = self.model_sampling_current.calculate_input(t, x_noisy) control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.float(), context=context.to(dtype), y=y) - return self.control_merge(None, control, control_prev, output_dtype) + return self.control_merge(control, control_prev, output_dtype) def copy(self): c = ControlNet(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype) @@ -490,12 +476,11 @@ class T2IAdapter(ControlBase): self.control_input = self.t2i_model(self.cond_hint.to(x_noisy.dtype)) self.t2i_model.cpu() - control_input = list(map(lambda a: None if a is None else a.clone(), self.control_input)) - mid = None - if self.t2i_model.xl == True: - mid = control_input[-1:] - control_input = control_input[:-1] - return self.control_merge(control_input, mid, control_prev, x_noisy.dtype) + control_input = {} + for k in self.control_input: + control_input[k] = list(map(lambda a: None if a is None else a.clone(), self.control_input[k])) + + return self.control_merge(control_input, control_prev, x_noisy.dtype) def copy(self): c = T2IAdapter(self.t2i_model, self.channels_in, self.compression_ratio, self.upscale_algorithm) diff --git a/comfy/ldm/cascade/controlnet.py b/comfy/ldm/cascade/controlnet.py index 5dac5939..7a52c3c2 100644 --- a/comfy/ldm/cascade/controlnet.py +++ b/comfy/ldm/cascade/controlnet.py @@ -90,4 +90,4 @@ class ControlNet(nn.Module): proj_outputs = [None for _ in range(max(self.proj_blocks) + 1)] for i, idx in enumerate(self.proj_blocks): proj_outputs[idx] = self.projections[i](x) - return proj_outputs + return {"input": proj_outputs[::-1]} diff --git a/comfy/t2i_adapter/adapter.py b/comfy/t2i_adapter/adapter.py index e9a606b1..10ea18e3 100644 --- a/comfy/t2i_adapter/adapter.py +++ b/comfy/t2i_adapter/adapter.py @@ -153,7 +153,13 @@ class Adapter(nn.Module): features.append(None) features.append(x) - return features + features = features[::-1] + + if self.xl: + return {"input": features[1:], "middle": features[:1]} + else: + return {"input": features} + class LayerNorm(nn.LayerNorm): @@ -290,4 +296,4 @@ class Adapter_light(nn.Module): features.append(None) features.append(x) - return features + return {"input": features[::-1]} From f8f7568d031a61f5d21ca0ed56ff56d7d83c9115 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 25 Jun 2024 23:40:44 -0400 Subject: [PATCH 333/378] Basic SD3 controlnet implementation. Still missing the node to properly use it. --- comfy/cldm/mmdit.py | 91 +++++++++++++++++++++ comfy/controlnet.py | 45 +++++++++- comfy/ldm/modules/diffusionmodules/mmdit.py | 30 +++++-- comfy/model_detection.py | 11 ++- comfy/utils.py | 3 +- 5 files changed, 165 insertions(+), 15 deletions(-) create mode 100644 comfy/cldm/mmdit.py diff --git a/comfy/cldm/mmdit.py b/comfy/cldm/mmdit.py new file mode 100644 index 00000000..6e72474c --- /dev/null +++ b/comfy/cldm/mmdit.py @@ -0,0 +1,91 @@ +import torch +from typing import Dict, Optional +import comfy.ldm.modules.diffusionmodules.mmdit +import comfy.latent_formats + +class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT): + def __init__( + self, + num_blocks = None, + dtype = None, + device = None, + operations = None, + **kwargs, + ): + super().__init__(dtype=dtype, device=device, operations=operations, final_layer=False, num_blocks=num_blocks, **kwargs) + # controlnet_blocks + self.controlnet_blocks = torch.nn.ModuleList([]) + for _ in range(len(self.joint_blocks)): + self.controlnet_blocks.append(operations.Linear(self.hidden_size, self.hidden_size, device=device, dtype=dtype)) + + self.pos_embed_input = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed( + None, + self.patch_size, + self.in_channels, + self.hidden_size, + bias=True, + strict_img_size=False, + dtype=dtype, + device=device, + operations=operations + ) + + self.latent_format = comfy.latent_formats.SD3() + + def forward( + self, + x: torch.Tensor, + timesteps: torch.Tensor, + y: Optional[torch.Tensor] = None, + context: Optional[torch.Tensor] = None, + hint = None, + ) -> torch.Tensor: + + #weird sd3 controlnet specific stuff + hint = hint * self.latent_format.scale_factor # self.latent_format.process_in(hint) + y = torch.zeros_like(y) + + + if self.context_processor is not None: + context = self.context_processor(context) + + hw = x.shape[-2:] + x = self.x_embedder(x) + self.cropped_pos_embed(hw, device=x.device).to(dtype=x.dtype, device=x.device) + x += self.pos_embed_input(hint) + + c = self.t_embedder(timesteps, dtype=x.dtype) + if y is not None and self.y_embedder is not None: + y = self.y_embedder(y) + c = c + y + + if context is not None: + context = self.context_embedder(context) + + if self.register_length > 0: + context = torch.cat( + ( + repeat(self.register, "1 ... -> b ...", b=x.shape[0]), + default(context, torch.Tensor([]).type_as(x)), + ), + 1, + ) + + output = [] + + blocks = len(self.joint_blocks) + for i in range(blocks): + context, x = self.joint_blocks[i]( + context, + x, + c=c, + use_checkpoint=self.use_checkpoint, + ) + + out = self.controlnet_blocks[i](x) + count = self.depth // blocks + if i == blocks - 1: + count -= 1 + for j in range(count): + output.append(out) + + return {"output": output} diff --git a/comfy/controlnet.py b/comfy/controlnet.py index f50df683..9202c319 100644 --- a/comfy/controlnet.py +++ b/comfy/controlnet.py @@ -11,6 +11,7 @@ import comfy.ops import comfy.cldm.cldm import comfy.t2i_adapter.adapter import comfy.ldm.cascade.controlnet +import comfy.cldm.mmdit def broadcast_image_to(tensor, target_batch_size, batched_number): @@ -94,13 +95,17 @@ class ControlBase: for key in control: control_output = control[key] + applied_to = set() for i in range(len(control_output)): x = control_output[i] if x is not None: if self.global_average_pooling: x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3]) - x *= self.strength + if x not in applied_to: #memory saving strategy, allow shared tensors and only apply strength to shared tensors once + applied_to.add(x) + x *= self.strength + if x.dtype != output_dtype: x = x.to(output_dtype) @@ -120,17 +125,18 @@ class ControlBase: if o[i].shape[0] < prev_val.shape[0]: o[i] = prev_val + o[i] else: - o[i] += prev_val + o[i] = prev_val + o[i] #TODO: change back to inplace add if shared tensors stop being an issue return out class ControlNet(ControlBase): - def __init__(self, control_model=None, global_average_pooling=False, device=None, load_device=None, manual_cast_dtype=None): + def __init__(self, control_model=None, global_average_pooling=False, compression_ratio=8, device=None, load_device=None, manual_cast_dtype=None): super().__init__(device) self.control_model = control_model self.load_device = load_device if control_model is not None: self.control_model_wrapped = comfy.model_patcher.ModelPatcher(self.control_model, load_device=load_device, offload_device=comfy.model_management.unet_offload_device()) + self.compression_ratio = compression_ratio self.global_average_pooling = global_average_pooling self.model_sampling_current = None self.manual_cast_dtype = manual_cast_dtype @@ -308,6 +314,37 @@ class ControlLora(ControlNet): def inference_memory_requirements(self, dtype): return comfy.utils.calculate_parameters(self.control_weights) * comfy.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype) +def load_controlnet_mmdit(sd): + new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "") + model_config = comfy.model_detection.model_config_from_unet(new_sd, "", True) + num_blocks = comfy.model_detection.count_blocks(new_sd, 'joint_blocks.{}.') + for k in sd: + new_sd[k] = sd[k] + + supported_inference_dtypes = model_config.supported_inference_dtypes + + controlnet_config = model_config.unet_config + unet_dtype = comfy.model_management.unet_dtype(supported_dtypes=supported_inference_dtypes) + load_device = comfy.model_management.get_torch_device() + manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device) + if manual_cast_dtype is not None: + operations = comfy.ops.manual_cast + else: + operations = comfy.ops.disable_weight_init + + control_model = comfy.cldm.mmdit.ControlNet(num_blocks=num_blocks, operations=operations, device=load_device, dtype=unet_dtype, **controlnet_config) + missing, unexpected = control_model.load_state_dict(new_sd, strict=False) + + if len(missing) > 0: + logging.warning("missing controlnet keys: {}".format(missing)) + + if len(unexpected) > 0: + logging.debug("unexpected controlnet keys: {}".format(unexpected)) + + control = ControlNet(control_model, compression_ratio=1, load_device=load_device, manual_cast_dtype=manual_cast_dtype) + return control + + def load_controlnet(ckpt_path, model=None): controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True) if "lora_controlnet" in controlnet_data: @@ -360,6 +397,8 @@ def load_controlnet(ckpt_path, model=None): if len(leftover_keys) > 0: logging.warning("leftover keys: {}".format(leftover_keys)) controlnet_data = new_sd + elif "controlnet_blocks.0.weight" in controlnet_data: #SD3 diffusers format + return load_controlnet_mmdit(controlnet_data) pth_key = 'control_model.zero_convs.0.0.weight' pth = False diff --git a/comfy/ldm/modules/diffusionmodules/mmdit.py b/comfy/ldm/modules/diffusionmodules/mmdit.py index 20d3a321..92745153 100644 --- a/comfy/ldm/modules/diffusionmodules/mmdit.py +++ b/comfy/ldm/modules/diffusionmodules/mmdit.py @@ -745,6 +745,8 @@ class MMDiT(nn.Module): qkv_bias: bool = True, context_processor_layers = None, context_size = 4096, + num_blocks = None, + final_layer = True, dtype = None, #TODO device = None, operations = None, @@ -766,7 +768,10 @@ class MMDiT(nn.Module): # apply magic --> this defines a head_size of 64 self.hidden_size = 64 * depth num_heads = depth + if num_blocks is None: + num_blocks = depth + self.depth = depth self.num_heads = num_heads self.x_embedder = PatchEmbed( @@ -821,7 +826,7 @@ class MMDiT(nn.Module): mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, attn_mode=attn_mode, - pre_only=i == depth - 1, + pre_only=(i == num_blocks - 1) and final_layer, rmsnorm=rmsnorm, scale_mod_only=scale_mod_only, swiglu=swiglu, @@ -830,11 +835,12 @@ class MMDiT(nn.Module): device=device, operations=operations ) - for i in range(depth) + for i in range(num_blocks) ] ) - self.final_layer = FinalLayer(self.hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations) + if final_layer: + self.final_layer = FinalLayer(self.hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations) if compile_core: assert False @@ -893,6 +899,7 @@ class MMDiT(nn.Module): x: torch.Tensor, c_mod: torch.Tensor, context: Optional[torch.Tensor] = None, + control = None, ) -> torch.Tensor: if self.register_length > 0: context = torch.cat( @@ -905,13 +912,20 @@ class MMDiT(nn.Module): # context is B, L', D # x is B, L, D - for block in self.joint_blocks: - context, x = block( + blocks = len(self.joint_blocks) + for i in range(blocks): + context, x = self.joint_blocks[i]( context, x, c=c_mod, use_checkpoint=self.use_checkpoint, ) + if control is not None: + control_o = control.get("output") + if i < len(control_o): + add = control_o[i] + if add is not None: + x += add x = self.final_layer(x, c_mod) # (N, T, patch_size ** 2 * out_channels) return x @@ -922,6 +936,7 @@ class MMDiT(nn.Module): t: torch.Tensor, y: Optional[torch.Tensor] = None, context: Optional[torch.Tensor] = None, + control = None, ) -> torch.Tensor: """ Forward pass of DiT. @@ -943,7 +958,7 @@ class MMDiT(nn.Module): if context is not None: context = self.context_embedder(context) - x = self.forward_core_with_concat(x, c, context) + x = self.forward_core_with_concat(x, c, context, control) x = self.unpatchify(x, hw=hw) # (N, out_channels, H, W) return x[:,:,:hw[-2],:hw[-1]] @@ -956,7 +971,8 @@ class OpenAISignatureMMDITWrapper(MMDiT): timesteps: torch.Tensor, context: Optional[torch.Tensor] = None, y: Optional[torch.Tensor] = None, + control = None, **kwargs, ) -> torch.Tensor: - return super().forward(x, timesteps, context=context, y=y) + return super().forward(x, timesteps, context=context, y=y, control=control) diff --git a/comfy/model_detection.py b/comfy/model_detection.py index e09dd381..0b678480 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -41,7 +41,9 @@ def detect_unet_config(state_dict, key_prefix): unet_config["in_channels"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[1] patch_size = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[2] unet_config["patch_size"] = patch_size - unet_config["out_channels"] = state_dict['{}final_layer.linear.weight'.format(key_prefix)].shape[0] // (patch_size * patch_size) + final_layer = '{}final_layer.linear.weight'.format(key_prefix) + if final_layer in state_dict: + unet_config["out_channels"] = state_dict[final_layer].shape[0] // (patch_size * patch_size) unet_config["depth"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0] // 64 unet_config["input_size"] = None @@ -435,10 +437,11 @@ def model_config_from_diffusers_unet(state_dict): return None def convert_diffusers_mmdit(state_dict, output_prefix=""): - depth = count_blocks(state_dict, 'transformer_blocks.{}.') - if depth > 0: + num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.') + if num_blocks > 0: + depth = state_dict["pos_embed.proj.weight"].shape[0] // 64 out_sd = {} - sd_map = comfy.utils.mmdit_to_diffusers({"depth": depth}, output_prefix=output_prefix) + sd_map = comfy.utils.mmdit_to_diffusers({"depth": depth, "num_blocks": num_blocks}, output_prefix=output_prefix) for k in sd_map: weight = state_dict.get(k, None) if weight is not None: diff --git a/comfy/utils.py b/comfy/utils.py index ed6c58a6..48618e07 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -298,7 +298,8 @@ def mmdit_to_diffusers(mmdit_config, output_prefix=""): key_map = {} depth = mmdit_config.get("depth", 0) - for i in range(depth): + num_blocks = mmdit_config.get("num_blocks", depth) + for i in range(num_blocks): block_from = "transformer_blocks.{}".format(i) block_to = "{}joint_blocks.{}".format(output_prefix, i) From 264caca20e1e051f3bcbec59d7209513ea99d4ed Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 26 Jun 2024 16:14:47 -0400 Subject: [PATCH 334/378] ControlNetApplySD3 node can now be used to use SD3 controlnets. --- comfy/cldm/mmdit.py | 5 ----- comfy/controlnet.py | 28 ++++++++++++++++++++++++---- comfy_extras/nodes_sd3.py | 15 +++++++++++++++ nodes.py | 4 ++-- 4 files changed, 41 insertions(+), 11 deletions(-) diff --git a/comfy/cldm/mmdit.py b/comfy/cldm/mmdit.py index 6e72474c..ee0282bc 100644 --- a/comfy/cldm/mmdit.py +++ b/comfy/cldm/mmdit.py @@ -1,7 +1,6 @@ import torch from typing import Dict, Optional import comfy.ldm.modules.diffusionmodules.mmdit -import comfy.latent_formats class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT): def __init__( @@ -30,8 +29,6 @@ class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT): operations=operations ) - self.latent_format = comfy.latent_formats.SD3() - def forward( self, x: torch.Tensor, @@ -42,10 +39,8 @@ class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT): ) -> torch.Tensor: #weird sd3 controlnet specific stuff - hint = hint * self.latent_format.scale_factor # self.latent_format.process_in(hint) y = torch.zeros_like(y) - if self.context_processor is not None: context = self.context_processor(context) diff --git a/comfy/controlnet.py b/comfy/controlnet.py index 9202c319..d0039513 100644 --- a/comfy/controlnet.py +++ b/comfy/controlnet.py @@ -7,6 +7,7 @@ import comfy.model_management import comfy.model_detection import comfy.model_patcher import comfy.ops +import comfy.latent_formats import comfy.cldm.cldm import comfy.t2i_adapter.adapter @@ -38,6 +39,8 @@ class ControlBase: self.cond_hint = None self.strength = 1.0 self.timestep_percent_range = (0.0, 1.0) + self.latent_format = None + self.vae = None self.global_average_pooling = False self.timestep_range = None self.compression_ratio = 8 @@ -48,10 +51,12 @@ class ControlBase: self.device = device self.previous_controlnet = None - def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0)): + def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0), vae=None): self.cond_hint_original = cond_hint self.strength = strength self.timestep_percent_range = timestep_percent_range + if self.latent_format is not None: + self.vae = vae return self def pre_run(self, model, percent_to_timestep_function): @@ -84,6 +89,8 @@ class ControlBase: c.global_average_pooling = self.global_average_pooling c.compression_ratio = self.compression_ratio c.upscale_algorithm = self.upscale_algorithm + c.latent_format = self.latent_format + c.vae = self.vae def inference_memory_requirements(self, dtype): if self.previous_controlnet is not None: @@ -129,7 +136,7 @@ class ControlBase: return out class ControlNet(ControlBase): - def __init__(self, control_model=None, global_average_pooling=False, compression_ratio=8, device=None, load_device=None, manual_cast_dtype=None): + def __init__(self, control_model=None, global_average_pooling=False, compression_ratio=8, latent_format=None, device=None, load_device=None, manual_cast_dtype=None): super().__init__(device) self.control_model = control_model self.load_device = load_device @@ -140,6 +147,7 @@ class ControlNet(ControlBase): self.global_average_pooling = global_average_pooling self.model_sampling_current = None self.manual_cast_dtype = manual_cast_dtype + self.latent_format = latent_format def get_control(self, x_noisy, t, cond, batched_number): control_prev = None @@ -162,7 +170,17 @@ class ControlNet(ControlBase): if self.cond_hint is not None: del self.cond_hint self.cond_hint = None - self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * self.compression_ratio, x_noisy.shape[2] * self.compression_ratio, self.upscale_algorithm, "center").to(dtype).to(self.device) + compression_ratio = self.compression_ratio + if self.vae is not None: + compression_ratio *= self.vae.downscale_ratio + self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * compression_ratio, x_noisy.shape[2] * compression_ratio, self.upscale_algorithm, "center") + if self.vae is not None: + loaded_models = comfy.model_management.loaded_models(only_currently_used=True) + self.cond_hint = self.vae.encode(self.cond_hint.movedim(1, -1)) + comfy.model_management.load_models_gpu(loaded_models) + if self.latent_format is not None: + self.cond_hint = self.latent_format.process_in(self.cond_hint) + self.cond_hint = self.cond_hint.to(device=self.device, dtype=dtype) if x_noisy.shape[0] != self.cond_hint.shape[0]: self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) @@ -341,7 +359,9 @@ def load_controlnet_mmdit(sd): if len(unexpected) > 0: logging.debug("unexpected controlnet keys: {}".format(unexpected)) - control = ControlNet(control_model, compression_ratio=1, load_device=load_device, manual_cast_dtype=manual_cast_dtype) + latent_format = comfy.latent_formats.SD3() + latent_format.shift_factor = 0 #SD3 controlnet weirdness + control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype) return control diff --git a/comfy_extras/nodes_sd3.py b/comfy_extras/nodes_sd3.py index d0303aec..548b1ad6 100644 --- a/comfy_extras/nodes_sd3.py +++ b/comfy_extras/nodes_sd3.py @@ -80,8 +80,23 @@ class CLIPTextEncodeSD3: return ([[cond, {"pooled_output": pooled}]], ) +class ControlNetApplySD3(nodes.ControlNetApplyAdvanced): + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "control_net": ("CONTROL_NET", ), + "vae": ("VAE", ), + "image": ("IMAGE", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) + }} + CATEGORY = "_for_testing/sd3" + NODE_CLASS_MAPPINGS = { "TripleCLIPLoader": TripleCLIPLoader, "EmptySD3LatentImage": EmptySD3LatentImage, "CLIPTextEncodeSD3": CLIPTextEncodeSD3, + "ControlNetApplySD3": ControlNetApplySD3, } diff --git a/nodes.py b/nodes.py index 99645b81..04775d29 100644 --- a/nodes.py +++ b/nodes.py @@ -783,7 +783,7 @@ class ControlNetApplyAdvanced: CATEGORY = "conditioning" - def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent): + def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent, vae=None): if strength == 0: return (positive, negative) @@ -800,7 +800,7 @@ class ControlNetApplyAdvanced: if prev_cnet in cnets: c_net = cnets[prev_cnet] else: - c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent)) + c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent), vae) c_net.set_previous_controlnet(prev_cnet) cnets[prev_cnet] = c_net From 0d9009c96ea5a1922a1c187deef27c7f133ee946 Mon Sep 17 00:00:00 2001 From: pythongosssss <125205205+pythongosssss@users.noreply.github.com> Date: Fri, 28 Jun 2024 06:07:19 +0100 Subject: [PATCH 335/378] New menu/workflows fixes (#3900) * Fix auto queue * Detect added nodes via search * Fix loading workflows * Add button click style --- web/scripts/changeTracker.js | 12 +++++++++++- web/scripts/ui/menu/menu.css | 4 ++++ web/scripts/ui/menu/queueButton.js | 4 ++-- web/scripts/workflows.js | 4 ++-- 4 files changed, 19 insertions(+), 5 deletions(-) diff --git a/web/scripts/changeTracker.js b/web/scripts/changeTracker.js index 59901d5f..041c8312 100644 --- a/web/scripts/changeTracker.js +++ b/web/scripts/changeTracker.js @@ -3,7 +3,6 @@ import { api } from "./api.js"; import { clone } from "./utils.js"; - export class ChangeTracker { static MAX_HISTORY = 50; #app; @@ -170,6 +169,17 @@ export class ChangeTracker { return v; }; + // Detects nodes being added via the node search dialog + const onNodeAdded = LiteGraph.LGraph.prototype.onNodeAdded; + LiteGraph.LGraph.prototype.onNodeAdded = function () { + const v = onNodeAdded?.apply(this, arguments); + const ct = changeTracker(); + if (!ct.isOurLoad) { + ct.checkState(); + } + return v; + }; + // Store node outputs api.addEventListener("executed", ({ detail }) => { const prompt = app.workflowManager.queuedPrompts[detail.prompt_id]; diff --git a/web/scripts/ui/menu/menu.css b/web/scripts/ui/menu/menu.css index 20eeab2c..afaed3fb 100644 --- a/web/scripts/ui/menu/menu.css +++ b/web/scripts/ui/menu/menu.css @@ -19,8 +19,12 @@ padding: 4px 8px; box-sizing: border-box; margin: 0; + transition: box-shadow 0.1s; } +.comfyui-button:active { + box-shadow: inset 1px 1px 10px rgba(0, 0, 0, 0.5); +} .comfyui-button:disabled { opacity: 0.5; cursor: not-allowed; diff --git a/web/scripts/ui/menu/queueButton.js b/web/scripts/ui/menu/queueButton.js index 3c29ab09..608f4cc9 100644 --- a/web/scripts/ui/menu/queueButton.js +++ b/web/scripts/ui/menu/queueButton.js @@ -13,8 +13,8 @@ export class ComfyQueueButton { queuePrompt = async (e) => { this.#internalQueueSize += this.queueOptions.batchCount; - // Hold shift to queue front - await this.app.queuePrompt(-e.shiftKey, this.queueOptions.batchCount); + // Hold shift to queue front, event is undefined when auto-queue is enabled + await this.app.queuePrompt(e?.shiftKey ? -1 : 0, this.queueOptions.batchCount); }; constructor(app) { diff --git a/web/scripts/workflows.js b/web/scripts/workflows.js index 16bbc997..d38b6f5f 100644 --- a/web/scripts/workflows.js +++ b/web/scripts/workflows.js @@ -301,11 +301,11 @@ export class ComfyWorkflow { load = async () => { if (this.isOpen) { - await this.manager.app.loadGraphData(this.changeTracker.activeState, true, this); + await this.manager.app.loadGraphData(this.changeTracker.activeState, true, true, this); } else { const data = await this.getWorkflowData(); if (!data) return; - await this.manager.app.loadGraphData(data, true, this); + await this.manager.app.loadGraphData(data, true, true, this); } }; From 7ecb2ec1699dc988f93188c04733eeb8a41e8196 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 28 Jun 2024 02:55:36 -0400 Subject: [PATCH 336/378] Audio second setting in EmptyLatentAudio. --- comfy_extras/nodes_audio.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py index 34bcfa96..6481ed3b 100644 --- a/comfy_extras/nodes_audio.py +++ b/comfy_extras/nodes_audio.py @@ -14,15 +14,16 @@ class EmptyLatentAudio: @classmethod def INPUT_TYPES(s): - return {"required": {}} + return {"required": {"seconds": ("FLOAT", {"default": 47.6, "min": 1.0, "max": 1000.0, "step": 0.1})}} RETURN_TYPES = ("LATENT",) FUNCTION = "generate" CATEGORY = "_for_testing/audio" - def generate(self): + def generate(self, seconds): batch_size = 1 - latent = torch.zeros([batch_size, 64, 1024], device=self.device) + length = round((seconds * 44100 / 2048) / 2) * 2 + latent = torch.zeros([batch_size, 64, length], device=self.device) return ({"samples":latent, "type": "audio"}, ) class VAEEncodeAudio: From 02cac1d48783725d5994d37ed03e9f3dc32ec504 Mon Sep 17 00:00:00 2001 From: Robin Huang Date: Fri, 28 Jun 2024 13:09:39 -0700 Subject: [PATCH 337/378] Revert "Add macOs integration test for default workflow. (#3898)" (#3904) This reverts commit 97b409cd486b1a3b1114ae204802c81f3727d1a2. --- .../conda-environments/mac-environment.yml | 26 ---------- .github/workflows/mac-integration-test.yml | 50 ------------------- 2 files changed, 76 deletions(-) delete mode 100644 .github/conda-environments/mac-environment.yml delete mode 100644 .github/workflows/mac-integration-test.yml diff --git a/.github/conda-environments/mac-environment.yml b/.github/conda-environments/mac-environment.yml deleted file mode 100644 index 33832b85..00000000 --- a/.github/conda-environments/mac-environment.yml +++ /dev/null @@ -1,26 +0,0 @@ -name: comfyui -channels: - - pytorch-nightly - - defaults -dependencies: - - python>=3.9 - - pytorch-nightly::pytorch - - torchvision - - torchaudio - - pip: - - pip - # comfyui requirements - - einops - - transformers>=4.25.1 - - safetensors>=0.4.2 - - aiohttp - - pyyaml - - Pillow - - scipy - - tqdm - - psutil - # comfy-action requirements - - requests - - google-cloud-storage - - comfy-cli - - charset-normalizer diff --git a/.github/workflows/mac-integration-test.yml b/.github/workflows/mac-integration-test.yml deleted file mode 100644 index 6a0482b1..00000000 --- a/.github/workflows/mac-integration-test.yml +++ /dev/null @@ -1,50 +0,0 @@ -name: (macOS) ComfyUI Integration Tests -on: - push: - branches: - - master - paths-ignore: - - 'app/**' - - 'input/**' - - 'output/**' - - 'model/**' - - 'notebook/**' - - 'script_example/**' - - 'tests/**' - - 'tests-ui/**' - - '.github/**' - - '.ci/**' - - 'web/**' - workflow_dispatch: - pull_request: - branches: - - master - paths-ignore: - - 'app/**' - - 'input/**' - - 'output/**' - - 'model/**' - - 'notebook/**' - - 'script_example/**' - - 'tests/**' - - 'tests-ui/**' - - '.github/**' - - '.ci/**' - - 'web/**' - -jobs: - test-workflows: - runs-on: [self-hosted, m2] - steps: - - name: Test ComfyUI Workflows - uses: comfy-org/comfy-action@main - with: - os: mac - models-json: '{"v1-5-pruned-emaonly.ckpt": {"url": "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt", "directory": "checkpoints"}}' - workflow_filenames: "default.json" - gcs_bucket_name: 'comfy-ci-results' - google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }} - output_prefix: 'ComfyUI' - conda_env_file: '.github/conda-environments/mac-environment.yml' - timeout: 50 - From c39cf7fff09c728d573835a48106e2133c446e11 Mon Sep 17 00:00:00 2001 From: Robin Huang Date: Fri, 28 Jun 2024 13:09:55 -0700 Subject: [PATCH 338/378] Revert "Add integration test for Linux with Nvidia GPU. #3884 (#3895)" (#3905) This reverts commit 449bf52923896236ed645da2ff05aadbf2b0d536. --- .../conda-environments/linux-environment.yml | 28 ----------- .github/workflows/linux-integration-test.yml | 50 ------------------- 2 files changed, 78 deletions(-) delete mode 100644 .github/conda-environments/linux-environment.yml delete mode 100644 .github/workflows/linux-integration-test.yml diff --git a/.github/conda-environments/linux-environment.yml b/.github/conda-environments/linux-environment.yml deleted file mode 100644 index c9ebd640..00000000 --- a/.github/conda-environments/linux-environment.yml +++ /dev/null @@ -1,28 +0,0 @@ -name: comfyui -channels: - - pytorch - - nvidia - - defaults -dependencies: - - python>=3.9 - - pip - - pytorch - - torchvision - - torchaudio - - pytorch-cuda=12.1 - - pip: - # comfyui requirements - - einops - - transformers>=4.25.1 - - safetensors>=0.4.2 - - aiohttp - - pyyaml - - Pillow - - scipy - - tqdm - - psutil - # comfy-action requirements - - requests - - google-cloud-storage - - comfy-cli - - charset-normalizer \ No newline at end of file diff --git a/.github/workflows/linux-integration-test.yml b/.github/workflows/linux-integration-test.yml deleted file mode 100644 index eed84a77..00000000 --- a/.github/workflows/linux-integration-test.yml +++ /dev/null @@ -1,50 +0,0 @@ -name: (Linux) ComfyUI Integration Tests -on: - push: - branches: - - master - paths-ignore: - - 'app/**' - - 'input/**' - - 'output/**' - - 'model/**' - - 'notebook/**' - - 'script_example/**' - - 'tests/**' - - 'tests-ui/**' - - '.github/**' - - '.ci/**' - - 'web/**' - workflow_dispatch: - pull_request: - branches: - - master - paths-ignore: - - 'app/**' - - 'input/**' - - 'output/**' - - 'model/**' - - 'notebook/**' - - 'script_example/**' - - 'tests/**' - - 'tests-ui/**' - - '.github/**' - - '.ci/**' - - 'web/**' - -jobs: - test-workflows: - runs-on: [self-hosted, Linux, t4] - steps: - - name: Test ComfyUI Workflows - uses: comfy-org/comfy-action@main - with: - os: linux - cuda_version: 12.1 - models-json: '{"v1-5-pruned-emaonly.ckpt": {"url": "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt", "directory": "checkpoints"}}' - workflow_filenames: "default.json" - gcs_bucket_name: 'comfy-ci-results' - google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }} - output_prefix: 'ComfyUI' - conda_env_file: '.github/conda-environments/linux-environment.yml' - timeout: 50 From fbb7a1f1b663b170d49718590aa1cb4093586f20 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 29 Jun 2024 01:33:22 -0400 Subject: [PATCH 339/378] PreviewAudio node. --- comfy_extras/nodes_audio.py | 16 +++++++++++++++- web/extensions/core/uploadAudio.js | 6 +++--- 2 files changed, 18 insertions(+), 4 deletions(-) diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py index 6481ed3b..9950d58e 100644 --- a/comfy_extras/nodes_audio.py +++ b/comfy_extras/nodes_audio.py @@ -6,6 +6,7 @@ import os import io import json import struct +import random from comfy.cli_args import args class EmptyLatentAudio: @@ -118,7 +119,6 @@ class SaveAudio: self.output_dir = folder_paths.get_output_directory() self.type = "output" self.prefix_append = "" - self.compress_level = 4 @classmethod def INPUT_TYPES(s): @@ -168,6 +168,19 @@ class SaveAudio: return { "ui": { "audio": results } } +class PreviewAudio(SaveAudio): + def __init__(self): + self.output_dir = folder_paths.get_temp_directory() + self.type = "temp" + self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5)) + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"audio": ("AUDIO", ), }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + class LoadAudio: SUPPORTED_FORMATS = ('.wav', '.mp3', '.ogg', '.flac', '.aiff', '.aif') @@ -214,4 +227,5 @@ NODE_CLASS_MAPPINGS = { "VAEDecodeAudio": VAEDecodeAudio, "SaveAudio": SaveAudio, "LoadAudio": LoadAudio, + "PreviewAudio": PreviewAudio, } diff --git a/web/extensions/core/uploadAudio.js b/web/extensions/core/uploadAudio.js index 12a2a196..0ac9cb80 100644 --- a/web/extensions/core/uploadAudio.js +++ b/web/extensions/core/uploadAudio.js @@ -70,7 +70,7 @@ async function uploadFile( app.registerExtension({ name: "Comfy.AudioWidget", async beforeRegisterNodeDef(nodeType, nodeData) { - if (["LoadAudio", "SaveAudio"].includes(nodeType.comfyClass)) { + if (["LoadAudio", "SaveAudio", "PreviewAudio"].includes(nodeType.comfyClass)) { nodeData.input.required.audioUI = ["AUDIO_UI"] } }, @@ -103,7 +103,7 @@ app.registerExtension({ if (!audios) return const audio = audios[0] audioUIWidget.element.src = api.apiURL( - getResourceURL(audio.subfolder, audio.filename, "output") + getResourceURL(audio.subfolder, audio.filename, audio.type) ) audioUIWidget.element.classList.remove("empty-audio-widget") } @@ -118,7 +118,7 @@ app.registerExtension({ if ("audio" in output) { const audioUIWidget = node.widgets.find((w) => w.name === "audioUI"); const audio = output.audio[0]; - audioUIWidget.element.src = api.apiURL(getResourceURL(audio.subfolder, audio.filename, "output")); + audioUIWidget.element.src = api.apiURL(getResourceURL(audio.subfolder, audio.filename, audio.type)); audioUIWidget.element.classList.remove("empty-audio-widget"); } } From 05e831697a03984032863b2aa5d6443e422713e6 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 29 Jun 2024 11:59:48 -0400 Subject: [PATCH 340/378] Switch to the real cfg++ method in the samplers. The old _pp ones will be updated automatically to the regular ones with 2x the cfg. My fault for not checking what the "_pp" samplers actually did. --- comfy/k_diffusion/sampling.py | 12 ++++++------ comfy/samplers.py | 2 +- web/scripts/app.js | 8 ++++++++ 3 files changed, 15 insertions(+), 7 deletions(-) diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py index f8091bb3..763d8cc7 100644 --- a/comfy/k_diffusion/sampling.py +++ b/comfy/k_diffusion/sampling.py @@ -998,7 +998,7 @@ def sample_deis(model, x, sigmas, extra_args=None, callback=None, disable=None, return x_next @torch.no_grad() -def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=None): +def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None): extra_args = {} if extra_args is None else extra_args temp = [0] @@ -1013,16 +1013,16 @@ def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=No for i in trange(len(sigmas) - 1, disable=disable): sigma_hat = sigmas[i] denoised = model(x, sigma_hat * s_in, **extra_args) - d = to_d(x - denoised + temp[0], sigma_hat, denoised) + d = to_d(x, sigma_hat, temp[0]) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) dt = sigmas[i + 1] - sigma_hat # Euler method - x = x + d * dt + x = denoised + d * sigmas[i + 1] return x @torch.no_grad() -def sample_euler_ancestral_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): +def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): """Ancestral sampling with Euler method steps.""" extra_args = {} if extra_args is None else extra_args noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler @@ -1041,10 +1041,10 @@ def sample_euler_ancestral_pp(model, x, sigmas, extra_args=None, callback=None, sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - d = to_d(x - denoised + temp[0], sigmas[i], denoised) + d = to_d(x, sigmas[i], temp[0]) # Euler method dt = sigma_down - sigmas[i] - x = x + d * dt + x = denoised + d * sigma_down if sigmas[i + 1] > 0: x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up return x diff --git a/comfy/samplers.py b/comfy/samplers.py index 7f7114db..c0aa1291 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -537,7 +537,7 @@ class Sampler: sigma = float(sigmas[0]) return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma -KSAMPLER_NAMES = ["euler", "euler_pp", "euler_ancestral", "euler_ancestral_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral", +KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral", "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu", "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm", "ipndm", "ipndm_v", "deis"] diff --git a/web/scripts/app.js b/web/scripts/app.js index 43df6106..3a91aa23 100644 --- a/web/scripts/app.js +++ b/web/scripts/app.js @@ -1966,6 +1966,14 @@ export class ComfyApp { if (widget.value.startsWith("sample_")) { widget.value = widget.value.slice(7); } + if (widget.value === "euler_pp" || widget.value === "euler_ancestral_pp") { + widget.value = widget.value.slice(0, -3); + for (let w of node.widgets) { + if (w.name == "cfg") { + w.value *= 2.0; + } + } + } } } if (node.type == "KSampler" || node.type == "KSamplerAdvanced" || node.type == "PrimitiveNode") { From dbb7dd3b5eb6672e1141a5fc7006d3bee08b0d21 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 30 Jun 2024 00:15:49 -0400 Subject: [PATCH 341/378] Add to readme that Stable Audio is supported. --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index a40dd07d..2ce7c3e6 100644 --- a/README.md +++ b/README.md @@ -11,16 +11,16 @@ This ui will let you design and execute advanced stable diffusion pipelines usin ## Features - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything. -- Fully supports SD1.x, SD2.x, [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/), [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/) and [SD3](https://comfyanonymous.github.io/ComfyUI_examples/sd3/) +- Fully supports SD1.x, SD2.x, [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/), [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/), [SD3](https://comfyanonymous.github.io/ComfyUI_examples/sd3/) and [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/) - Asynchronous Queue system - Many optimizations: Only re-executes the parts of the workflow that changes between executions. -- Command line option: ```--lowvram``` to make it work on GPUs with less than 3GB vram (enabled automatically on GPUs with low vram) +- Smart memory management: can automatically run models on GPUs with as low as 1GB vram. - Works even if you don't have a GPU with: ```--cpu``` (slow) - Can load ckpt, safetensors and diffusers models/checkpoints. Standalone VAEs and CLIP models. - Embeddings/Textual inversion - [Loras (regular, locon and loha)](https://comfyanonymous.github.io/ComfyUI_examples/lora/) - [Hypernetworks](https://comfyanonymous.github.io/ComfyUI_examples/hypernetworks/) -- Loading full workflows (with seeds) from generated PNG files. +- Loading full workflows (with seeds) from generated PNG, WebP and FLAC files. - Saving/Loading workflows as Json files. - Nodes interface can be used to create complex workflows like one for [Hires fix](https://comfyanonymous.github.io/ComfyUI_examples/2_pass_txt2img/) or much more advanced ones. - [Area Composition](https://comfyanonymous.github.io/ComfyUI_examples/area_composition/) From 521421f53ee1ba74304dfaa138b0f851093e1595 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 30 Jun 2024 15:51:54 -0400 Subject: [PATCH 342/378] Fix workflow not importing from flac files on some systems. --- web/scripts/app.js | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/web/scripts/app.js b/web/scripts/app.js index 3a91aa23..776fc197 100644 --- a/web/scripts/app.js +++ b/web/scripts/app.js @@ -2292,7 +2292,7 @@ export class ComfyApp { } else { this.showErrorOnFileLoad(file); } - } else if (file.type === "audio/flac") { + } else if (file.type === "audio/flac" || file.type === "audio/x-flac") { const pngInfo = await getFlacMetadata(file); // Support loading workflows from that webp custom node. const workflow = pngInfo?.workflow; From 7c5fa7f4a2046c1f4bb77ed5e480918ffb8a10aa Mon Sep 17 00:00:00 2001 From: Chenlei Hu Date: Mon, 1 Jul 2024 12:10:44 -0400 Subject: [PATCH 343/378] Fix loadGraphData func call (#3918) --- web/scripts/app.js | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/web/scripts/app.js b/web/scripts/app.js index 776fc197..b8889a4c 100644 --- a/web/scripts/app.js +++ b/web/scripts/app.js @@ -2314,14 +2314,14 @@ export class ComfyApp { } else if(this.isApiJson(jsonContent)) { this.loadApiJson(jsonContent, fileName); } else { - await this.loadGraphData(jsonContent, true, fileName); + await this.loadGraphData(jsonContent, true, true, fileName); } }; reader.readAsText(file); } else if (file.name?.endsWith(".latent") || file.name?.endsWith(".safetensors")) { const info = await getLatentMetadata(file); if (info.workflow) { - await this.loadGraphData(JSON.parse(info.workflow), true, fileName); + await this.loadGraphData(JSON.parse(info.workflow), true, true, fileName); } else if (info.prompt) { this.loadApiJson(JSON.parse(info.prompt)); } else { From e53b1592ba8426e2c01cd9278bc19c2e4264065b Mon Sep 17 00:00:00 2001 From: ruucm Date: Mon, 1 Jul 2024 10:45:34 -0700 Subject: [PATCH 344/378] enable cmd shortcuts for mac (mute & bypass) (#3792) --- web/scripts/app.js | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/web/scripts/app.js b/web/scripts/app.js index b8889a4c..67e488bf 100644 --- a/web/scripts/app.js +++ b/web/scripts/app.js @@ -1084,7 +1084,7 @@ export class ComfyApp { if (e.type == "keydown" && !e.repeat) { // Ctrl + M mute/unmute - if (e.key === 'm' && e.ctrlKey) { + if (e.key === 'm' && (e.metaKey || e.ctrlKey)) { if (this.selected_nodes) { for (var i in this.selected_nodes) { if (this.selected_nodes[i].mode === 2) { // never @@ -1098,7 +1098,7 @@ export class ComfyApp { } // Ctrl + B bypass - if (e.key === 'b' && e.ctrlKey) { + if (e.key === 'b' && (e.metaKey || e.ctrlKey)) { if (this.selected_nodes) { for (var i in this.selected_nodes) { if (this.selected_nodes[i].mode === 4) { // never From 601b4b63e1c0af2deef8630449497b9e84aa00f8 Mon Sep 17 00:00:00 2001 From: Robin Huang Date: Mon, 1 Jul 2024 10:51:00 -0700 Subject: [PATCH 345/378] Add CONTRIBUTING.md (#3910) * Create CONTRIBUTING.md * Add feature-request channel link. * Remove discord links for channels. --- CONTRIBUTING.md | 41 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 41 insertions(+) create mode 100644 CONTRIBUTING.md diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 00000000..048f127e --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,41 @@ +# Contributing to ComfyUI + +Welcome, and thank you for your interest in contributing to ComfyUI! + +There are several ways in which you can contribute, beyond writing code. The goal of this document is to provide a high-level overview of how you can get involved. + +## Asking Questions + +Have a question? Instead of opening an issue, please ask on [Discord](https://comfy.org/discord) or [Matrix](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) channels. Our team and the community will help you. + +## Providing Feedback + +Your comments and feedback are welcome, and the development team is available via a handful of different channels. + +See the `#bug-report`, `#feature-request` and `#feedback` channels on Discord. + +## Reporting Issues + +Have you identified a reproducible problem in ComfyUI? Do you have a feature request? We want to hear about it! Here's how you can report your issue as effectively as possible. + + +### Look For an Existing Issue + +Before you create a new issue, please do a search in [open issues](https://github.com/comfyanonymous/ComfyUI/issues) to see if the issue or feature request has already been filed. + +If you find your issue already exists, make relevant comments and add your [reaction](https://github.com/blog/2119-add-reactions-to-pull-requests-issues-and-comments). Use a reaction in place of a "+1" comment: + +* 👍 - upvote +* 👎 - downvote + +If you cannot find an existing issue that describes your bug or feature, create a new issue. We have an issue template in place to organize new issues. + + +### Creating Pull Requests + +* Please refer to the article on [creating pull requests](https://github.com/comfyanonymous/ComfyUI/wiki/How-to-Contribute-Code) and contributing to this project. + + +## Thank You + +Your contributions to open source, large or small, make great projects like this possible. Thank you for taking the time to contribute. From 0cd4a6a5e5101172a1496c337e67f15ad266fb98 Mon Sep 17 00:00:00 2001 From: YAN Wenkun Date: Tue, 2 Jul 2024 05:15:49 +0800 Subject: [PATCH 346/378] Fine-tuning GitHub Actions (#3169) * Bumping GitHub Actions versions * Using LZMA2 for 7zip compression in Windows packaging --- .github/workflows/windows_release_dependencies.yml | 6 +++--- .github/workflows/windows_release_nightly_pytorch.yml | 4 ++-- .github/workflows/windows_release_package.yml | 6 +++--- 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/.github/workflows/windows_release_dependencies.yml b/.github/workflows/windows_release_dependencies.yml index ffd3e221..5aa57e7d 100644 --- a/.github/workflows/windows_release_dependencies.yml +++ b/.github/workflows/windows_release_dependencies.yml @@ -33,8 +33,8 @@ jobs: build_dependencies: runs-on: windows-latest steps: - - uses: actions/checkout@v3 - - uses: actions/setup-python@v4 + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 with: python-version: 3.${{ inputs.python_minor }}.${{ inputs.python_patch }} @@ -58,7 +58,7 @@ jobs: mv temp_wheel_dir cu${{ inputs.cu }}_python_deps tar cf cu${{ inputs.cu }}_python_deps.tar cu${{ inputs.cu }}_python_deps - - uses: actions/cache/save@v3 + - uses: actions/cache/save@v4 with: path: | cu${{ inputs.cu }}_python_deps.tar diff --git a/.github/workflows/windows_release_nightly_pytorch.yml b/.github/workflows/windows_release_nightly_pytorch.yml index fa24a985..d27c56b3 100644 --- a/.github/workflows/windows_release_nightly_pytorch.yml +++ b/.github/workflows/windows_release_nightly_pytorch.yml @@ -32,11 +32,11 @@ jobs: pull-requests: "read" runs-on: windows-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 with: fetch-depth: 0 persist-credentials: false - - uses: actions/setup-python@v4 + - uses: actions/setup-python@v5 with: python-version: 3.${{ inputs.python_minor }}.${{ inputs.python_patch }} - shell: bash diff --git a/.github/workflows/windows_release_package.yml b/.github/workflows/windows_release_package.yml index 4e3cdabd..020741c4 100644 --- a/.github/workflows/windows_release_package.yml +++ b/.github/workflows/windows_release_package.yml @@ -32,7 +32,7 @@ jobs: pull-requests: "read" runs-on: windows-latest steps: - - uses: actions/cache/restore@v3 + - uses: actions/cache/restore@v4 id: cache with: path: | @@ -48,7 +48,7 @@ jobs: pwd ls - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 with: fetch-depth: 0 persist-credentials: false @@ -82,7 +82,7 @@ jobs: cd .. - "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable + "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable mv ComfyUI_windows_portable.7z ComfyUI/new_ComfyUI_windows_portable_nvidia_cu${{ inputs.cu }}_or_cpu.7z cd ComfyUI_windows_portable From 5dccfefe8d4ca9b33dfe6dfabcc6c666853c73b9 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 1 Jul 2024 17:17:25 -0400 Subject: [PATCH 347/378] Switch nightly pytorch standalone package to lzma2. --- .github/workflows/windows_release_nightly_pytorch.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/windows_release_nightly_pytorch.yml b/.github/workflows/windows_release_nightly_pytorch.yml index d27c56b3..e68011b6 100644 --- a/.github/workflows/windows_release_nightly_pytorch.yml +++ b/.github/workflows/windows_release_nightly_pytorch.yml @@ -73,7 +73,7 @@ jobs: pause" > ./update/update_comfyui_and_python_dependencies.bat cd .. - "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI_windows_portable_nightly_pytorch + "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI_windows_portable_nightly_pytorch mv ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI/ComfyUI_windows_portable_nvidia_or_cpu_nightly_pytorch.7z cd ComfyUI_windows_portable_nightly_pytorch From 755c48d78ea2381b3554de729e36acfb381ebc67 Mon Sep 17 00:00:00 2001 From: Hayden Reeve <39004735+HaydenReeve@users.noreply.github.com> Date: Tue, 2 Jul 2024 05:21:12 +0800 Subject: [PATCH 348/378] Fix several typos in example_node.py.example (#3204) This change includes corrections for several spelling errors in the documentation of example_node.py.example file. These were previously raised by #3157, but they missed a few. --- custom_nodes/example_node.py.example | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/custom_nodes/example_node.py.example b/custom_nodes/example_node.py.example index f0663259..57c9cbed 100644 --- a/custom_nodes/example_node.py.example +++ b/custom_nodes/example_node.py.example @@ -12,9 +12,9 @@ class Example: Attributes ---------- RETURN_TYPES (`tuple`): - The type of each element in the output tulple. + The type of each element in the output tuple. RETURN_NAMES (`tuple`): - Optional: The name of each output in the output tulple. + Optional: The name of each output in the output tuple. FUNCTION (`str`): The name of the entry-point method. For example, if `FUNCTION = "execute"` then it will run Example().execute() OUTPUT_NODE ([`bool`]): @@ -44,7 +44,7 @@ class Example: * Key field_name (`string`): Name of a entry-point method's argument * Value field_config (`tuple`): + First value is a string indicate the type of field or a list for selection. - + Secound value is a config for type "INT", "STRING" or "FLOAT". + + Second value is a config for type "INT", "STRING" or "FLOAT". """ return { "required": { @@ -61,7 +61,7 @@ class Example: "min": 0.0, "max": 10.0, "step": 0.01, - "round": 0.001, #The value represeting the precision to round to, will be set to the step value by default. Can be set to False to disable rounding. + "round": 0.001, #The value representing the precision to round to, will be set to the step value by default. Can be set to False to disable rounding. "display": "number"}), "print_to_screen": (["enable", "disable"],), "string_field": ("STRING", { From b82d67d5bfa9a1f8c6d0167bf871cbd151c1005f Mon Sep 17 00:00:00 2001 From: Peter Crabtree Date: Mon, 1 Jul 2024 17:42:17 -0400 Subject: [PATCH 349/378] Add SamplerEulerAncestralCFG++ custom sampler node (#3901) (for eta and s_noise) --- comfy_extras/nodes_custom_sampler.py | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index 69f1b941..64a8c063 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -293,6 +293,25 @@ class SamplerEulerAncestral: sampler = comfy.samplers.ksampler("euler_ancestral", {"eta": eta, "s_noise": s_noise}) return (sampler, ) +class SamplerEulerAncestralCFGPP: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step":0.01, "round": False}), + "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step":0.01, "round": False}), + }} + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, eta, s_noise): + sampler = comfy.samplers.ksampler( + "euler_ancestral_cfg_pp", + {"eta": eta, "s_noise": s_noise}) + return (sampler, ) + class SamplerLMS: @classmethod def INPUT_TYPES(s): @@ -622,6 +641,7 @@ NODE_CLASS_MAPPINGS = { "SDTurboScheduler": SDTurboScheduler, "KSamplerSelect": KSamplerSelect, "SamplerEulerAncestral": SamplerEulerAncestral, + "SamplerEulerAncestralCFGPP": SamplerEulerAncestralCFGPP, "SamplerLMS": SamplerLMS, "SamplerDPMPP_3M_SDE": SamplerDPMPP_3M_SDE, "SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE, @@ -639,3 +659,7 @@ NODE_CLASS_MAPPINGS = { "AddNoise": AddNoise, "SamplerCustomAdvanced": SamplerCustomAdvanced, } + +NODE_DISPLAY_NAME_MAPPINGS = { + "SamplerEulerAncestralCFGPP": "SamplerEulerAncestralCFG++", +} \ No newline at end of file From 9dd549e253f0e2c5e6360f9349b0872af18d8962 Mon Sep 17 00:00:00 2001 From: Chenlei Hu Date: Mon, 1 Jul 2024 17:54:03 -0400 Subject: [PATCH 350/378] Add `--no-custom-node` cmd flag (#3903) * Add --no-custom-node cmd flag * nit --- comfy/cli_args.py | 1 + folder_paths.py | 8 +++++--- main.py | 13 +++++++++++-- nodes.py | 24 ++++++++++++++++++++---- 4 files changed, 37 insertions(+), 9 deletions(-) diff --git a/comfy/cli_args.py b/comfy/cli_args.py index fb0d37ce..b72bf399 100644 --- a/comfy/cli_args.py +++ b/comfy/cli_args.py @@ -118,6 +118,7 @@ parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test parser.add_argument("--windows-standalone-build", action="store_true", help="Windows standalone build: Enable convenient things that most people using the standalone windows build will probably enjoy (like auto opening the page on startup).") parser.add_argument("--disable-metadata", action="store_true", help="Disable saving prompt metadata in files.") +parser.add_argument("--disable-all-custom-nodes", action="store_true", help="Disable loading all custom nodes.") parser.add_argument("--multi-user", action="store_true", help="Enables per-user storage.") diff --git a/folder_paths.py b/folder_paths.py index 234b7340..2cf45f12 100644 --- a/folder_paths.py +++ b/folder_paths.py @@ -2,9 +2,11 @@ import os import time import logging -supported_pt_extensions = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl']) +supported_pt_extensions: set[str] = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl']) -folder_names_and_paths = {} +SupportedFileExtensionsType = set[str] +ScanPathType = list[str] +folder_names_and_paths: dict[str, tuple[ScanPathType, SupportedFileExtensionsType]] = {} base_path = os.path.dirname(os.path.realpath(__file__)) models_dir = os.path.join(base_path, "models") @@ -26,7 +28,7 @@ folder_names_and_paths["gligen"] = ([os.path.join(models_dir, "gligen")], suppor folder_names_and_paths["upscale_models"] = ([os.path.join(models_dir, "upscale_models")], supported_pt_extensions) -folder_names_and_paths["custom_nodes"] = ([os.path.join(base_path, "custom_nodes")], []) +folder_names_and_paths["custom_nodes"] = ([os.path.join(base_path, "custom_nodes")], set()) folder_names_and_paths["hypernetworks"] = ([os.path.join(models_dir, "hypernetworks")], supported_pt_extensions) diff --git a/main.py b/main.py index a374f2b1..2957dd2f 100644 --- a/main.py +++ b/main.py @@ -5,6 +5,8 @@ import os import importlib.util import folder_paths import time +from comfy.cli_args import args + def execute_prestartup_script(): def execute_script(script_path): @@ -18,6 +20,9 @@ def execute_prestartup_script(): print(f"Failed to execute startup-script: {script_path} / {e}") return False + if args.disable_all_custom_nodes: + return + node_paths = folder_paths.get_folder_paths("custom_nodes") for custom_node_path in node_paths: possible_modules = os.listdir(custom_node_path) @@ -76,7 +81,7 @@ import yaml import execution import server from server import BinaryEventTypes -from nodes import init_custom_nodes +from nodes import init_builtin_extra_nodes, init_external_custom_nodes import comfy.model_management def cuda_malloc_warning(): @@ -214,7 +219,11 @@ if __name__ == "__main__": for config_path in itertools.chain(*args.extra_model_paths_config): load_extra_path_config(config_path) - init_custom_nodes() + init_builtin_extra_nodes() + if not args.disable_all_custom_nodes: + init_external_custom_nodes() + else: + logging.info("Skipping loading of custom nodes") cuda_malloc_warning() diff --git a/nodes.py b/nodes.py index 04775d29..a0d2178f 100644 --- a/nodes.py +++ b/nodes.py @@ -1925,7 +1925,16 @@ def load_custom_node(module_path, ignore=set()): logging.warning(f"Cannot import {module_path} module for custom nodes: {e}") return False -def load_custom_nodes(): +def init_external_custom_nodes(): + """ + Initializes the external custom nodes. + + This function loads custom nodes from the specified folder paths and imports them into the application. + It measures the import times for each custom node and logs the results. + + Returns: + None + """ base_node_names = set(NODE_CLASS_MAPPINGS.keys()) node_paths = folder_paths.get_folder_paths("custom_nodes") node_import_times = [] @@ -1952,7 +1961,16 @@ def load_custom_nodes(): logging.info("{:6.1f} seconds{}: {}".format(n[0], import_message, n[1])) logging.info("") -def init_custom_nodes(): +def init_builtin_extra_nodes(): + """ + Initializes the built-in extra nodes in ComfyUI. + + This function loads the extra node files located in the "comfy_extras" directory and imports them into ComfyUI. + If any of the extra node files fail to import, a warning message is logged. + + Returns: + None + """ extras_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras") extras_files = [ "nodes_latent.py", @@ -1999,8 +2017,6 @@ def init_custom_nodes(): if not load_custom_node(os.path.join(extras_dir, node_file)): import_failed.append(node_file) - load_custom_nodes() - if len(import_failed) > 0: logging.warning("WARNING: some comfy_extras/ nodes did not import correctly. This may be because they are missing some dependencies.\n") for node in import_failed: From 1ef66b095586981633cd11906e6d2e555deab9f6 Mon Sep 17 00:00:00 2001 From: Bob Du Date: Tue, 2 Jul 2024 06:02:42 +0800 Subject: [PATCH 351/378] Add example for how to add custom API routes (#3597) --- custom_nodes/example_node.py.example | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/custom_nodes/example_node.py.example b/custom_nodes/example_node.py.example index 57c9cbed..72ca3688 100644 --- a/custom_nodes/example_node.py.example +++ b/custom_nodes/example_node.py.example @@ -106,6 +106,16 @@ class Example: # Set the web directory, any .js file in that directory will be loaded by the frontend as a frontend extension # WEB_DIRECTORY = "./somejs" + +# Add custom API routes, using router +from aiohttp import web +from server import PromptServer + +@PromptServer.instance.routes.get("/hello") +async def get_hello(request): + return web.json_response("hello") + + # A dictionary that contains all nodes you want to export with their names # NOTE: names should be globally unique NODE_CLASS_MAPPINGS = { From 52aaee251fcd87b7c554f4e9c1386b184236f81f Mon Sep 17 00:00:00 2001 From: shawnington <88048838+shawnington@users.noreply.github.com> Date: Tue, 2 Jul 2024 01:30:33 -0400 Subject: [PATCH 352/378] Fix to #3465. Prevent, resaving of duplicate images if overwrite not specified (#3472) * Fix to #3465. Prevent the, resaving of duplicate images if overwrite not specified This is a fix to #3465 Adds function compare_image_hash to do a sha256 hash comparison between an uploaded image and existing images with matching file names. This changes the behavior so that only images having the same filename that are actually different are saved to input, existing images are instead now opened instead of resaved with increment. Currently, exact duplicates with the same filename are resave saved with an incremented filename in the format: (n).ext with the code: ``` while os.path.exists(filepath): filename = f"{split[0]} ({i}){split[1]}" filepath = os.path.join(full_output_folder, filename) i += 1 ``` This commit changes this to: ``` while os.path.exists(filepath): if compare_image_hash(filepath, image): image_is_duplicate = True break filename = f"{split[0]} ({i}){split[1]}" filepath = os.path.join(full_output_folder, filename) i += 1 ``` a check for if image_is_duplicate = False is done before saving the file. Currently, if you load the same image of a cat named cat.jpg into the LoadImage node 3 times, you will get 3 new files in your input folder with incremented file names. With this change, you will now only have the single copy of cat.jpg, that will be re-opened instead of re-saved. However if you load 3 different images of cats named cat.jpg, you will get the expected behavior of having: cat.jpg cat (1).jpg cat (2).jpg This saves space and clutter. After checking my own input folder, I have 800+ images that are duplicates that were resaved with incremented file names amounting to more than 5GB of duplicated data. * fixed typo in expression --- server.py | 31 +++++++++++++++++++++++++------ 1 file changed, 25 insertions(+), 6 deletions(-) diff --git a/server.py b/server.py index 30ea90c6..5b98620a 100644 --- a/server.py +++ b/server.py @@ -12,6 +12,7 @@ import json import glob import struct import ssl +import hashlib from PIL import Image, ImageOps from PIL.PngImagePlugin import PngInfo from io import BytesIO @@ -153,10 +154,24 @@ class PromptServer(): type_dir = folder_paths.get_output_directory() return type_dir, dir_type - + + def compare_image_hash(filepath, image): + # function to compare hashes of two images to see if it already exists, fix to #3465 + if os.path.exists(filepath): + a = hashlib.sha256() + b = hashlib.sha256() + with open(filepath, "rb") as f: + a.update(f.read()) + b.update(image.file.read()) + image.file.seek(0) + f.close() + return a.hexdigest() == b.hexdigest() + return False + def image_upload(post, image_save_function=None): image = post.get("image") overwrite = post.get("overwrite") + image_is_duplicate = False image_upload_type = post.get("type") upload_dir, image_upload_type = get_dir_by_type(image_upload_type) @@ -183,15 +198,19 @@ class PromptServer(): else: i = 1 while os.path.exists(filepath): + if compare_image_hash(filepath, image): #compare hash to prevent saving of duplicates with same name, fix for #3465 + image_is_duplicate = True + break filename = f"{split[0]} ({i}){split[1]}" filepath = os.path.join(full_output_folder, filename) i += 1 - if image_save_function is not None: - image_save_function(image, post, filepath) - else: - with open(filepath, "wb") as f: - f.write(image.file.read()) + if not image_is_duplicate: + if image_save_function is not None: + image_save_function(image, post, filepath) + else: + with open(filepath, "wb") as f: + f.write(image.file.read()) return web.json_response({"name" : filename, "subfolder": subfolder, "type": image_upload_type}) else: From 2f03201690e0fb8a3ec551a125b20d89c9019a02 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 2 Jul 2024 01:32:23 -0400 Subject: [PATCH 353/378] Remove some empty lines. --- server.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/server.py b/server.py index 5b98620a..38b1bab8 100644 --- a/server.py +++ b/server.py @@ -111,7 +111,7 @@ class PromptServer(): # On reconnect if we are the currently executing client send the current node if self.client_id == sid and self.last_node_id is not None: await self.send("executing", { "node": self.last_node_id }, sid) - + async for msg in ws: if msg.type == aiohttp.WSMsgType.ERROR: logging.warning('ws connection closed with exception %s' % ws.exception()) @@ -132,9 +132,9 @@ class PromptServer(): async def get_extensions(request): files = glob.glob(os.path.join( glob.escape(self.web_root), 'extensions/**/*.js'), recursive=True) - + extensions = list(map(lambda f: "/" + os.path.relpath(f, self.web_root).replace("\\", "/"), files)) - + for name, dir in nodes.EXTENSION_WEB_DIRS.items(): files = glob.glob(os.path.join(glob.escape(dir), '**/*.js'), recursive=True) extensions.extend(list(map(lambda f: "/extensions/" + urllib.parse.quote( @@ -154,7 +154,7 @@ class PromptServer(): type_dir = folder_paths.get_output_directory() return type_dir, dir_type - + def compare_image_hash(filepath, image): # function to compare hashes of two images to see if it already exists, fix to #3465 if os.path.exists(filepath): @@ -167,7 +167,7 @@ class PromptServer(): f.close() return a.hexdigest() == b.hexdigest() return False - + def image_upload(post, image_save_function=None): image = post.get("image") overwrite = post.get("overwrite") @@ -205,7 +205,7 @@ class PromptServer(): filepath = os.path.join(full_output_folder, filename) i += 1 - if not image_is_duplicate: + if not image_is_duplicate: if image_save_function is not None: image_save_function(image, post, filepath) else: From 01991f72ce1bdce31ff4bd6fc2fa64454b1526ad Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 2 Jul 2024 12:21:08 -0400 Subject: [PATCH 354/378] Fix SamplerEulerCFGpp node. --- comfy_extras/nodes_advanced_samplers.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/comfy_extras/nodes_advanced_samplers.py b/comfy_extras/nodes_advanced_samplers.py index cee3a10c..820c250e 100644 --- a/comfy_extras/nodes_advanced_samplers.py +++ b/comfy_extras/nodes_advanced_samplers.py @@ -60,7 +60,7 @@ from comfy.k_diffusion.sampling import to_d import comfy.model_patcher @torch.no_grad() -def sample_euler_cfgpp(model, x, sigmas, extra_args=None, callback=None, disable=None): +def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=None): extra_args = {} if extra_args is None else extra_args temp = [0] @@ -75,11 +75,11 @@ def sample_euler_cfgpp(model, x, sigmas, extra_args=None, callback=None, disable for i in trange(len(sigmas) - 1, disable=disable): sigma_hat = sigmas[i] denoised = model(x, sigma_hat * s_in, **extra_args) - d = to_d(x, sigma_hat, temp[0]) + d = to_d(x - denoised + temp[0], sigmas[i], denoised) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) dt = sigmas[i + 1] - sigma_hat - x = denoised + sigmas[i + 1] * d + x = x + d * dt return x @@ -96,10 +96,10 @@ class SamplerEulerCFGpp: FUNCTION = "get_sampler" def get_sampler(self, version): - if version == "regular": - sampler = comfy.samplers.KSAMPLER(sample_euler_cfgpp) + if version == "alternative": + sampler = comfy.samplers.KSAMPLER(sample_euler_pp) else: - sampler = comfy.samplers.ksampler("euler_pp") + sampler = comfy.samplers.ksampler("euler_cfg_pp") return (sampler, ) NODE_CLASS_MAPPINGS = { From 3f46362d227fa0ca578a69aeb194951b9f87aea0 Mon Sep 17 00:00:00 2001 From: "Alex \"mcmonkey\" Goodwin" <4000772+mcmonkey4eva@users.noreply.github.com> Date: Tue, 2 Jul 2024 17:16:33 -0700 Subject: [PATCH 355/378] fix non-contiguous tensor saving (from channels-last) (#3932) --- comfy/sd.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/comfy/sd.py b/comfy/sd.py index dda0887b..b28bfe35 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -627,5 +627,7 @@ def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, m sd = model.model.state_dict_for_saving(clip_sd, vae.get_sd(), clip_vision_sd) for k in extra_keys: sd[k] = extra_keys[k] + for k in sd: + sd[k] = sd[k].contiguous() comfy.utils.save_torch_file(sd, output_path, metadata=metadata) From 537f35c7bc76e611b53102d3b5a2926e46ec7362 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 2 Jul 2024 20:21:51 -0400 Subject: [PATCH 356/378] Don't update dict if contiguous. --- comfy/sd.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/comfy/sd.py b/comfy/sd.py index b28bfe35..4ba00a62 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -627,7 +627,10 @@ def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, m sd = model.model.state_dict_for_saving(clip_sd, vae.get_sd(), clip_vision_sd) for k in extra_keys: sd[k] = extra_keys[k] + for k in sd: - sd[k] = sd[k].contiguous() + t = sd[k] + if not t.is_contiguous(): + sd[k] = t.contiguous() comfy.utils.save_torch_file(sd, output_path, metadata=metadata) From d7484ef30c97c077dde1c7ae07a16689bec86dd3 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 3 Jul 2024 11:34:32 -0400 Subject: [PATCH 357/378] Support loading checkpoints with the UNETLoader node. --- comfy/sd.py | 24 ++++++++++++++++-------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/comfy/sd.py b/comfy/sd.py index 4ba00a62..e343e1fa 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -563,24 +563,32 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o return (model_patcher, clip, vae, clipvision) -def load_unet_state_dict(sd): #load unet in diffusers format +def load_unet_state_dict(sd): #load unet in diffusers or regular format + + #Allow loading unets from checkpoint files + checkpoint = False + diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd) + temp_sd = comfy.utils.state_dict_prefix_replace(sd, {diffusion_model_prefix: ""}, filter_keys=True) + if len(temp_sd) > 0: + sd = temp_sd + checkpoint = True + parameters = comfy.utils.calculate_parameters(sd) unet_dtype = model_management.unet_dtype(model_params=parameters) load_device = model_management.get_torch_device() - if 'transformer_blocks.0.attn.add_q_proj.weight' in sd: #MMDIT SD3 + if checkpoint or "input_blocks.0.0.weight" in sd or 'clf.1.weight' in sd: #ldm or stable cascade + model_config = model_detection.model_config_from_unet(sd, "") + if model_config is None: + return None + new_sd = sd + elif 'transformer_blocks.0.attn.add_q_proj.weight' in sd: #MMDIT SD3 new_sd = model_detection.convert_diffusers_mmdit(sd, "") if new_sd is None: return None model_config = model_detection.model_config_from_unet(new_sd, "") if model_config is None: return None - elif "input_blocks.0.0.weight" in sd or 'clf.1.weight' in sd: #ldm or stable cascade - model_config = model_detection.model_config_from_unet(sd, "") - if model_config is None: - return None - new_sd = sd - else: #diffusers model_config = model_detection.model_config_from_diffusers_unet(sd) if model_config is None: From 086ac75228af3fea96be58ec45c2f6c17286e75d Mon Sep 17 00:00:00 2001 From: Chenlei Hu Date: Wed, 3 Jul 2024 19:31:46 -0400 Subject: [PATCH 358/378] 3.8 Compatible type annotation (#3938) --- folder_paths.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/folder_paths.py b/folder_paths.py index 2cf45f12..2baf8ce1 100644 --- a/folder_paths.py +++ b/folder_paths.py @@ -1,12 +1,13 @@ import os import time import logging +from typing import Set, List, Dict, Tuple -supported_pt_extensions: set[str] = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl']) +supported_pt_extensions: Set[str] = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl']) -SupportedFileExtensionsType = set[str] -ScanPathType = list[str] -folder_names_and_paths: dict[str, tuple[ScanPathType, SupportedFileExtensionsType]] = {} +SupportedFileExtensionsType = Set[str] +ScanPathType = List[str] +folder_names_and_paths: Dict[str, Tuple[ScanPathType, SupportedFileExtensionsType]] = {} base_path = os.path.dirname(os.path.realpath(__file__)) models_dir = os.path.join(base_path, "models") From 24b969d3dace581ed3fb547eedfbdf502ad0f7e9 Mon Sep 17 00:00:00 2001 From: bymyself Date: Wed, 3 Jul 2024 17:30:07 -0700 Subject: [PATCH 359/378] Skip state check hook on first load (#3915) --- web/scripts/changeTracker.js | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/web/scripts/changeTracker.js b/web/scripts/changeTracker.js index 041c8312..39bc4a81 100644 --- a/web/scripts/changeTracker.js +++ b/web/scripts/changeTracker.js @@ -173,9 +173,11 @@ export class ChangeTracker { const onNodeAdded = LiteGraph.LGraph.prototype.onNodeAdded; LiteGraph.LGraph.prototype.onNodeAdded = function () { const v = onNodeAdded?.apply(this, arguments); - const ct = changeTracker(); - if (!ct.isOurLoad) { - ct.checkState(); + if (!app?.configuringGraph) { + const ct = changeTracker(); + if (!ct.isOurLoad) { + ct.checkState(); + } } return v; }; From 739b76630e0298561c39d0d3341bc2856e55d995 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 4 Jul 2024 15:14:13 -0400 Subject: [PATCH 360/378] Remove useless code. --- comfy/cldm/mmdit.py | 9 --------- 1 file changed, 9 deletions(-) diff --git a/comfy/cldm/mmdit.py b/comfy/cldm/mmdit.py index ee0282bc..025c2fb5 100644 --- a/comfy/cldm/mmdit.py +++ b/comfy/cldm/mmdit.py @@ -56,15 +56,6 @@ class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT): if context is not None: context = self.context_embedder(context) - if self.register_length > 0: - context = torch.cat( - ( - repeat(self.register, "1 ... -> b ...", b=x.shape[0]), - default(context, torch.Tensor([]).type_as(x)), - ), - 1, - ) - output = [] blocks = len(self.joint_blocks) From 0e3dfd9e343d4aee7826fbc188e2af8ceb1bdde9 Mon Sep 17 00:00:00 2001 From: Chenlei Hu Date: Thu, 4 Jul 2024 20:49:07 -0400 Subject: [PATCH 361/378] Use relative path for custom/extra node module name (#3944) * Fix module name for comfy extra nodes * Use module name relative to root dir --- nodes.py | 30 ++++++++++++++++++++++++++---- 1 file changed, 26 insertions(+), 4 deletions(-) diff --git a/nodes.py b/nodes.py index a0d2178f..81efecf6 100644 --- a/nodes.py +++ b/nodes.py @@ -1887,11 +1887,33 @@ NODE_DISPLAY_NAME_MAPPINGS = { EXTENSION_WEB_DIRS = {} -def load_custom_node(module_path, ignore=set()): - module_name = os.path.basename(module_path) + +def get_module_name(module_path: str) -> str: + """ + Returns the module name based on the given module path. + Examples: + get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node.py") -> "custom_nodes.my_custom_node" + get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node") -> "custom_nodes.my_custom_node" + get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/") -> "custom_nodes.my_custom_node" + get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__.py") -> "custom_nodes.my_custom_node" + get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__") -> "custom_nodes.my_custom_node" + get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__/") -> "custom_nodes.my_custom_node" + get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node.disabled") -> "custom_nodes.my + + Args: + module_path (str): The path of the module. + + Returns: + str: The module name. + """ + relative_path = os.path.relpath(module_path, folder_paths.base_path) if os.path.isfile(module_path): - sp = os.path.splitext(module_path) - module_name = sp[0] + relative_path = os.path.splitext(relative_path)[0] + return relative_path.replace(os.sep, '.') + + +def load_custom_node(module_path, ignore=set()): + module_name = get_module_name(module_path) try: logging.debug("Trying to load custom node {}".format(module_path)) if os.path.isfile(module_path): From 720b17442d39b5991e1d3c336f9d16a0f75c929c Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 4 Jul 2024 21:09:05 -0400 Subject: [PATCH 362/378] Temporary revert. --- nodes.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nodes.py b/nodes.py index 81efecf6..03544be0 100644 --- a/nodes.py +++ b/nodes.py @@ -1913,7 +1913,7 @@ def get_module_name(module_path: str) -> str: def load_custom_node(module_path, ignore=set()): - module_name = get_module_name(module_path) + module_name = os.path.basename(module_path) try: logging.debug("Trying to load custom node {}".format(module_path)) if os.path.isfile(module_path): From bd2d3e27d786b89e9ef89d6eb083e94172126306 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 4 Jul 2024 21:43:23 -0400 Subject: [PATCH 363/378] Show comfy_extras warning at the end. Remove code. --- main.py | 8 ++------ nodes.py | 35 +++++++++++------------------------ 2 files changed, 13 insertions(+), 30 deletions(-) diff --git a/main.py b/main.py index 2957dd2f..9650703a 100644 --- a/main.py +++ b/main.py @@ -81,7 +81,7 @@ import yaml import execution import server from server import BinaryEventTypes -from nodes import init_builtin_extra_nodes, init_external_custom_nodes +import nodes import comfy.model_management def cuda_malloc_warning(): @@ -219,11 +219,7 @@ if __name__ == "__main__": for config_path in itertools.chain(*args.extra_model_paths_config): load_extra_path_config(config_path) - init_builtin_extra_nodes() - if not args.disable_all_custom_nodes: - init_external_custom_nodes() - else: - logging.info("Skipping loading of custom nodes") + nodes.init_extra_nodes(init_custom_nodes=not args.disable_all_custom_nodes) cuda_malloc_warning() diff --git a/nodes.py b/nodes.py index 03544be0..8f8abb49 100644 --- a/nodes.py +++ b/nodes.py @@ -1888,30 +1888,6 @@ NODE_DISPLAY_NAME_MAPPINGS = { EXTENSION_WEB_DIRS = {} -def get_module_name(module_path: str) -> str: - """ - Returns the module name based on the given module path. - Examples: - get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node.py") -> "custom_nodes.my_custom_node" - get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node") -> "custom_nodes.my_custom_node" - get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/") -> "custom_nodes.my_custom_node" - get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__.py") -> "custom_nodes.my_custom_node" - get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__") -> "custom_nodes.my_custom_node" - get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__/") -> "custom_nodes.my_custom_node" - get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node.disabled") -> "custom_nodes.my - - Args: - module_path (str): The path of the module. - - Returns: - str: The module name. - """ - relative_path = os.path.relpath(module_path, folder_paths.base_path) - if os.path.isfile(module_path): - relative_path = os.path.splitext(relative_path)[0] - return relative_path.replace(os.sep, '.') - - def load_custom_node(module_path, ignore=set()): module_name = os.path.basename(module_path) try: @@ -2039,6 +2015,17 @@ def init_builtin_extra_nodes(): if not load_custom_node(os.path.join(extras_dir, node_file)): import_failed.append(node_file) + return import_failed + + +def init_extra_nodes(init_custom_nodes=True): + import_failed = init_external_custom_nodes() + + if init_custom_nodes: + init_external_custom_nodes() + else: + logging.info("Skipping loading of custom nodes") + if len(import_failed) > 0: logging.warning("WARNING: some comfy_extras/ nodes did not import correctly. This may be because they are missing some dependencies.\n") for node in import_failed: From cedbc94cc0551cfdeae01c48b922d546c816f898 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 4 Jul 2024 21:49:50 -0400 Subject: [PATCH 364/378] Forgot this in last commit. --- nodes.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/nodes.py b/nodes.py index 8f8abb49..9c541eba 100644 --- a/nodes.py +++ b/nodes.py @@ -1890,6 +1890,9 @@ EXTENSION_WEB_DIRS = {} def load_custom_node(module_path, ignore=set()): module_name = os.path.basename(module_path) + if os.path.isfile(module_path): + sp = os.path.splitext(module_path) + module_name = sp[0] try: logging.debug("Trying to load custom node {}".format(module_path)) if os.path.isfile(module_path): @@ -2019,7 +2022,7 @@ def init_builtin_extra_nodes(): def init_extra_nodes(init_custom_nodes=True): - import_failed = init_external_custom_nodes() + import_failed = init_builtin_extra_nodes() if init_custom_nodes: init_external_custom_nodes() From 1dc87df4c5f4925f8eb286236e71c9dd38941dd6 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 4 Jul 2024 22:03:37 -0400 Subject: [PATCH 365/378] Readme changes. --- README.md | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 2ce7c3e6..35e3238a 100644 --- a/README.md +++ b/README.md @@ -225,12 +225,11 @@ Use `--tls-keyfile key.pem --tls-certfile cert.pem` to enable TLS/SSL, the app w [Matrix space: #comfyui_space:matrix.org](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) (it's like discord but open source). +See also: [https://www.comfy.org/](https://www.comfy.org/) + # QA -### Why did you make this? +### Which GPU should I buy for this? -I wanted to learn how Stable Diffusion worked in detail. I also wanted something clean and powerful that would let me experiment with SD without restrictions. +[See this page for some recommendations](https://github.com/comfyanonymous/ComfyUI/wiki/Which-GPU-should-I-buy-for-ComfyUI) -### Who is this for? - -This is for anyone that wants to make complex workflows with SD or that wants to learn more how SD works. The interface follows closely how SD works and the code should be much more simple to understand than other SD UIs. From b4c2d03d476f97d9d91c0d7d0af7fde382084385 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 5 Jul 2024 12:10:22 -0400 Subject: [PATCH 366/378] Remove duplicate import. --- main.py | 1 - 1 file changed, 1 deletion(-) diff --git a/main.py b/main.py index 9650703a..196351a3 100644 --- a/main.py +++ b/main.py @@ -58,7 +58,6 @@ import shutil import threading import gc -from comfy.cli_args import args import logging if os.name == "nt": From ce649d61c05ab0e905155b4a010d4f0459a0db8d Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 5 Jul 2024 23:48:17 -0400 Subject: [PATCH 367/378] Allow zeroing out of embeds with unused attention mask. --- comfy/sd1_clip.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py index 78e556b5..3b812b44 100644 --- a/comfy/sd1_clip.py +++ b/comfy/sd1_clip.py @@ -169,7 +169,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): tokens = torch.LongTensor(tokens).to(device) attention_mask = None - if self.enable_attention_masks: + if self.enable_attention_masks or self.zero_out_masked: attention_mask = torch.zeros_like(tokens) end_token = self.special_tokens.get("end", -1) for x in range(attention_mask.shape[0]): @@ -178,7 +178,11 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): if tokens[x, y] == end_token: break - outputs = self.transformer(tokens, attention_mask, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state) + attention_mask_model = None + if self.enable_attention_masks: + attention_mask_model = attention_mask + + outputs = self.transformer(tokens, attention_mask_model, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state) self.transformer.set_input_embeddings(backup_embeds) if self.layer == "last": @@ -186,7 +190,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): else: z = outputs[1].float() - if self.zero_out_masked and attention_mask is not None: + if self.zero_out_masked: z *= attention_mask.unsqueeze(-1).float() pooled_output = None From 80c45909985aa37e6803ed59b6753fc289ee365f Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 6 Jul 2024 00:06:49 -0400 Subject: [PATCH 368/378] Allow specifying the padding token for the tokenizer. --- comfy/sd1_clip.py | 20 ++++++++++++-------- 1 file changed, 12 insertions(+), 8 deletions(-) diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py index 3b812b44..ed3dc229 100644 --- a/comfy/sd1_clip.py +++ b/comfy/sd1_clip.py @@ -364,7 +364,7 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No return embed_out class SDTokenizer: - def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, pad_to_max_length=True, min_length=None): + def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, pad_to_max_length=True, min_length=None, pad_token=None): if tokenizer_path is None: tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer") self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path) @@ -380,6 +380,14 @@ class SDTokenizer: self.tokens_start = 0 self.start_token = None self.end_token = empty[0] + + if pad_token is not None: + self.pad_token = pad_token + elif pad_with_end: + self.pad_token = self.end_token + else: + self.pad_token = 0 + self.pad_with_end = pad_with_end self.pad_to_max_length = pad_to_max_length @@ -412,10 +420,6 @@ class SDTokenizer: Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens. Returned list has the dimensions NxM where M is the input size of CLIP ''' - if self.pad_with_end: - pad_token = self.end_token - else: - pad_token = 0 text = escape_important(text) parsed_weights = token_weights(text, 1.0) @@ -467,7 +471,7 @@ class SDTokenizer: else: batch.append((self.end_token, 1.0, 0)) if self.pad_to_max_length: - batch.extend([(pad_token, 1.0, 0)] * (remaining_length)) + batch.extend([(self.pad_token, 1.0, 0)] * (remaining_length)) #start new batch batch = [] if self.start_token is not None: @@ -480,9 +484,9 @@ class SDTokenizer: #fill last batch batch.append((self.end_token, 1.0, 0)) if self.pad_to_max_length: - batch.extend([(pad_token, 1.0, 0)] * (self.max_length - len(batch))) + batch.extend([(self.pad_token, 1.0, 0)] * (self.max_length - len(batch))) if self.min_length is not None and len(batch) < self.min_length: - batch.extend([(pad_token, 1.0, 0)] * (self.min_length - len(batch))) + batch.extend([(self.pad_token, 1.0, 0)] * (self.min_length - len(batch))) if not return_word_ids: batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens] From b8e58a939463be85877c1244ee00763368723c07 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 6 Jul 2024 00:33:25 -0400 Subject: [PATCH 369/378] Cleanup T5 code a bit. --- comfy/t5.py | 35 +++++++++++++++++++++-------------- comfy/t5_config_base.json | 1 + comfy/t5_config_xxl.json | 1 + 3 files changed, 23 insertions(+), 14 deletions(-) diff --git a/comfy/t5.py b/comfy/t5.py index 06dfe476..d00b560a 100644 --- a/comfy/t5.py +++ b/comfy/t5.py @@ -13,29 +13,36 @@ class T5LayerNorm(torch.nn.Module): x = x * torch.rsqrt(variance + self.variance_epsilon) return self.weight.to(device=x.device, dtype=x.dtype) * x +activations = { + "gelu_pytorch_tanh": lambda a: torch.nn.functional.gelu(a, approximate="tanh"), + "relu": torch.nn.functional.relu, +} + class T5DenseActDense(torch.nn.Module): - def __init__(self, model_dim, ff_dim, dtype, device, operations): + def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations): super().__init__() self.wi = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device) self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device) # self.dropout = nn.Dropout(config.dropout_rate) + self.act = activations[ff_activation] def forward(self, x): - x = torch.nn.functional.relu(self.wi(x)) + x = self.act(self.wi(x)) # x = self.dropout(x) x = self.wo(x) return x class T5DenseGatedActDense(torch.nn.Module): - def __init__(self, model_dim, ff_dim, dtype, device, operations): + def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations): super().__init__() self.wi_0 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device) self.wi_1 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device) self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device) # self.dropout = nn.Dropout(config.dropout_rate) + self.act = activations[ff_activation] def forward(self, x): - hidden_gelu = torch.nn.functional.gelu(self.wi_0(x), approximate="tanh") + hidden_gelu = self.act(self.wi_0(x)) hidden_linear = self.wi_1(x) x = hidden_gelu * hidden_linear # x = self.dropout(x) @@ -43,12 +50,12 @@ class T5DenseGatedActDense(torch.nn.Module): return x class T5LayerFF(torch.nn.Module): - def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations): + def __init__(self, model_dim, ff_dim, ff_activation, gated_act, dtype, device, operations): super().__init__() - if ff_activation == "gelu_pytorch_tanh": - self.DenseReluDense = T5DenseGatedActDense(model_dim, ff_dim, dtype, device, operations) - elif ff_activation == "relu": - self.DenseReluDense = T5DenseActDense(model_dim, ff_dim, dtype, device, operations) + if gated_act: + self.DenseReluDense = T5DenseGatedActDense(model_dim, ff_dim, ff_activation, dtype, device, operations) + else: + self.DenseReluDense = T5DenseActDense(model_dim, ff_dim, ff_activation, dtype, device, operations) self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations) # self.dropout = nn.Dropout(config.dropout_rate) @@ -171,11 +178,11 @@ class T5LayerSelfAttention(torch.nn.Module): return x, past_bias class T5Block(torch.nn.Module): - def __init__(self, model_dim, inner_dim, ff_dim, ff_activation, num_heads, relative_attention_bias, dtype, device, operations): + def __init__(self, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias, dtype, device, operations): super().__init__() self.layer = torch.nn.ModuleList() self.layer.append(T5LayerSelfAttention(model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations)) - self.layer.append(T5LayerFF(model_dim, ff_dim, ff_activation, dtype, device, operations)) + self.layer.append(T5LayerFF(model_dim, ff_dim, ff_activation, gated_act, dtype, device, operations)) def forward(self, x, mask=None, past_bias=None, optimized_attention=None): x, past_bias = self.layer[0](x, mask, past_bias, optimized_attention) @@ -183,11 +190,11 @@ class T5Block(torch.nn.Module): return x, past_bias class T5Stack(torch.nn.Module): - def __init__(self, num_layers, model_dim, inner_dim, ff_dim, ff_activation, num_heads, dtype, device, operations): + def __init__(self, num_layers, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, dtype, device, operations): super().__init__() self.block = torch.nn.ModuleList( - [T5Block(model_dim, inner_dim, ff_dim, ff_activation, num_heads, relative_attention_bias=(i == 0), dtype=dtype, device=device, operations=operations) for i in range(num_layers)] + [T5Block(model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias=(i == 0), dtype=dtype, device=device, operations=operations) for i in range(num_layers)] ) self.final_layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations) # self.dropout = nn.Dropout(config.dropout_rate) @@ -216,7 +223,7 @@ class T5(torch.nn.Module): self.num_layers = config_dict["num_layers"] model_dim = config_dict["d_model"] - self.encoder = T5Stack(self.num_layers, model_dim, model_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["num_heads"], dtype, device, operations) + self.encoder = T5Stack(self.num_layers, model_dim, model_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["is_gated_act"], config_dict["num_heads"], dtype, device, operations) self.dtype = dtype self.shared = torch.nn.Embedding(config_dict["vocab_size"], model_dim, device=device) diff --git a/comfy/t5_config_base.json b/comfy/t5_config_base.json index facd85ef..71f68327 100644 --- a/comfy/t5_config_base.json +++ b/comfy/t5_config_base.json @@ -8,6 +8,7 @@ "dense_act_fn": "relu", "initializer_factor": 1.0, "is_encoder_decoder": true, + "is_gated_act": false, "layer_norm_epsilon": 1e-06, "model_type": "t5", "num_decoder_layers": 12, diff --git a/comfy/t5_config_xxl.json b/comfy/t5_config_xxl.json index bf4feadc..28283b51 100644 --- a/comfy/t5_config_xxl.json +++ b/comfy/t5_config_xxl.json @@ -8,6 +8,7 @@ "dense_act_fn": "gelu_pytorch_tanh", "initializer_factor": 1.0, "is_encoder_decoder": true, + "is_gated_act": true, "layer_norm_epsilon": 1e-06, "model_type": "t5", "num_decoder_layers": 24, From 404049114913f1a134656769a538779d7c927249 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 6 Jul 2024 00:53:33 -0400 Subject: [PATCH 370/378] Better T5xxl detection. --- comfy/sd.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/comfy/sd.py b/comfy/sd.py index e343e1fa..b39230bd 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -432,9 +432,11 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI clip_target.clip = sd2_clip.SD2ClipModel clip_target.tokenizer = sd2_clip.SD2Tokenizer elif "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in clip_data[0]: - dtype_t5 = clip_data[0]["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"].dtype - clip_target.clip = sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, dtype_t5=dtype_t5) - clip_target.tokenizer = sd3_clip.SD3Tokenizer + weight = clip_data[0]["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"] + dtype_t5 = weight.dtype + if weight.shape[-1] == 4096: + clip_target.clip = sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, dtype_t5=dtype_t5) + clip_target.tokenizer = sd3_clip.SD3Tokenizer elif "encoder.block.0.layer.0.SelfAttention.k.weight" in clip_data[0]: clip_target.clip = sa_t5.SAT5Model clip_target.tokenizer = sa_t5.SAT5Tokenizer From ff63893d103d9c3168eaf9101fb3cebbc2abd8fa Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 6 Jul 2024 02:42:53 -0400 Subject: [PATCH 371/378] Support other types of T5 models. --- comfy/t5.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/comfy/t5.py b/comfy/t5.py index d00b560a..448c5aad 100644 --- a/comfy/t5.py +++ b/comfy/t5.py @@ -190,11 +190,11 @@ class T5Block(torch.nn.Module): return x, past_bias class T5Stack(torch.nn.Module): - def __init__(self, num_layers, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, dtype, device, operations): + def __init__(self, num_layers, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention, dtype, device, operations): super().__init__() self.block = torch.nn.ModuleList( - [T5Block(model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias=(i == 0), dtype=dtype, device=device, operations=operations) for i in range(num_layers)] + [T5Block(model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias=((not relative_attention) or (i == 0)), dtype=dtype, device=device, operations=operations) for i in range(num_layers)] ) self.final_layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations) # self.dropout = nn.Dropout(config.dropout_rate) @@ -223,7 +223,7 @@ class T5(torch.nn.Module): self.num_layers = config_dict["num_layers"] model_dim = config_dict["d_model"] - self.encoder = T5Stack(self.num_layers, model_dim, model_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["is_gated_act"], config_dict["num_heads"], dtype, device, operations) + self.encoder = T5Stack(self.num_layers, model_dim, model_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["is_gated_act"], config_dict["num_heads"], config_dict["model_type"] == "t5", dtype, device, operations) self.dtype = dtype self.shared = torch.nn.Embedding(config_dict["vocab_size"], model_dim, device=device) From 2dc84d14447782683862616eaf8c19c0c1feacf3 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 6 Jul 2024 04:06:03 -0400 Subject: [PATCH 372/378] Add a way to set the timestep multiplier in the flow sampling. --- comfy/model_sampling.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py index 6bd3a5d7..2d95a83d 100644 --- a/comfy/model_sampling.py +++ b/comfy/model_sampling.py @@ -190,11 +190,12 @@ class ModelSamplingDiscreteFlow(torch.nn.Module): else: sampling_settings = {} - self.set_parameters(shift=sampling_settings.get("shift", 1.0)) + self.set_parameters(shift=sampling_settings.get("shift", 1.0), multiplier=sampling_settings.get("multiplier", 1000)) - def set_parameters(self, shift=1.0, timesteps=1000): + def set_parameters(self, shift=1.0, timesteps=1000, multiplier=1000): self.shift = shift - ts = self.sigma(torch.arange(1, timesteps + 1, 1)) + self.multiplier = multiplier + ts = self.sigma((torch.arange(1, timesteps + 1, 1) / timesteps) * multiplier) self.register_buffer('sigmas', ts) @property @@ -206,10 +207,10 @@ class ModelSamplingDiscreteFlow(torch.nn.Module): return self.sigmas[-1] def timestep(self, sigma): - return sigma * 1000 + return sigma * self.multiplier def sigma(self, timestep): - return time_snr_shift(self.shift, timestep / 1000) + return time_snr_shift(self.shift, timestep / self.multiplier) def percent_to_sigma(self, percent): if percent <= 0.0: From 628f0b8ebc2c9a51205e5e5a9973f8db348a310f Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sun, 7 Jul 2024 09:22:32 -0400 Subject: [PATCH 373/378] Move audio nodes out of _for_testing. --- comfy_extras/nodes_audio.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py index 9950d58e..64d241e9 100644 --- a/comfy_extras/nodes_audio.py +++ b/comfy_extras/nodes_audio.py @@ -19,7 +19,7 @@ class EmptyLatentAudio: RETURN_TYPES = ("LATENT",) FUNCTION = "generate" - CATEGORY = "_for_testing/audio" + CATEGORY = "latent/audio" def generate(self, seconds): batch_size = 1 @@ -34,7 +34,7 @@ class VAEEncodeAudio: RETURN_TYPES = ("LATENT",) FUNCTION = "encode" - CATEGORY = "_for_testing/audio" + CATEGORY = "latent/audio" def encode(self, vae, audio): sample_rate = audio["sample_rate"] @@ -53,7 +53,7 @@ class VAEDecodeAudio: RETURN_TYPES = ("AUDIO",) FUNCTION = "decode" - CATEGORY = "_for_testing/audio" + CATEGORY = "latent/audio" def decode(self, vae, samples): audio = vae.decode(samples["samples"]).movedim(-1, 1) @@ -132,7 +132,7 @@ class SaveAudio: OUTPUT_NODE = True - CATEGORY = "_for_testing/audio" + CATEGORY = "audio" def save_audio(self, audio, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): filename_prefix += self.prefix_append @@ -195,7 +195,7 @@ class LoadAudio: ] return {"required": {"audio": (sorted(files), {"audio_upload": True})}} - CATEGORY = "_for_testing/audio" + CATEGORY = "audio" RETURN_TYPES = ("AUDIO", ) FUNCTION = "load" @@ -203,7 +203,6 @@ class LoadAudio: def load(self, audio): audio_path = folder_paths.get_annotated_filepath(audio) waveform, sample_rate = torchaudio.load(audio_path) - multiplier = 1.0 audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate} return (audio, ) From bb663bcd6c81a4395100c5f8527a221037d366f8 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 8 Jul 2024 08:48:38 -0400 Subject: [PATCH 374/378] Rename clip_t5base to t5base for stable audio text encoder. --- comfy/sa_t5.py | 2 +- comfy/sd1_clip.py | 12 +++++++++--- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/comfy/sa_t5.py b/comfy/sa_t5.py index 37be5287..acc302f6 100644 --- a/comfy/sa_t5.py +++ b/comfy/sa_t5.py @@ -19,4 +19,4 @@ class SAT5Tokenizer(sd1_clip.SD1Tokenizer): class SAT5Model(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None, **kwargs): - super().__init__(device=device, dtype=dtype, clip_name="t5base", clip_model=T5BaseModel, **kwargs) + super().__init__(device=device, dtype=dtype, name="t5base", clip_model=T5BaseModel, **kwargs) diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py index ed3dc229..0fe1f1d1 100644 --- a/comfy/sd1_clip.py +++ b/comfy/sd1_clip.py @@ -514,10 +514,16 @@ class SD1Tokenizer: class SD1ClipModel(torch.nn.Module): - def __init__(self, device="cpu", dtype=None, clip_name="l", clip_model=SDClipModel, **kwargs): + def __init__(self, device="cpu", dtype=None, clip_name="l", clip_model=SDClipModel, name=None, **kwargs): super().__init__() - self.clip_name = clip_name - self.clip = "clip_{}".format(self.clip_name) + + if name is not None: + self.clip_name = name + self.clip = "{}".format(self.clip_name) + else: + self.clip_name = clip_name + self.clip = "clip_{}".format(self.clip_name) + setattr(self, self.clip, clip_model(device=device, dtype=dtype, **kwargs)) self.dtypes = set() From faa57430b0ff882275b1afcf6610e8e9f8a5929b Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 8 Jul 2024 23:26:34 -0400 Subject: [PATCH 375/378] Controlnet union model basic implementation. This is only the model code itself, it currently defaults to an empty embedding [0] * 6 which seems to work better than treating it like a regular controlnet. TODO: Add nodes to select the image type. --- comfy/cldm/cldm.py | 113 +++++++++++++++++++++++++++++++++++++++++++- comfy/controlnet.py | 6 +++ 2 files changed, 118 insertions(+), 1 deletion(-) diff --git a/comfy/cldm/cldm.py b/comfy/cldm/cldm.py index f8a16159..3feb9bab 100644 --- a/comfy/cldm/cldm.py +++ b/comfy/cldm/cldm.py @@ -13,7 +13,47 @@ from ..ldm.modules.diffusionmodules.util import ( from ..ldm.modules.attention import SpatialTransformer from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample from ..ldm.util import exists +from ..ldm.cascade.common import OptimizedAttention +from collections import OrderedDict import comfy.ops +from comfy.ldm.modules.attention import optimized_attention + +class OptimizedAttention(nn.Module): + def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None): + super().__init__() + self.heads = nhead + self.c = c + + self.in_proj = operations.Linear(c, c * 3, bias=True, dtype=dtype, device=device) + self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device) + + def forward(self, x): + x = self.in_proj(x) + q, k, v = x.split(self.c, dim=2) + out = optimized_attention(q, k, v, self.heads) + return self.out_proj(out) + +class QuickGELU(nn.Module): + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + +class ResBlockUnionControlnet(nn.Module): + def __init__(self, dim, nhead, dtype=None, device=None, operations=None): + super().__init__() + self.attn = OptimizedAttention(dim, nhead, dtype=dtype, device=device, operations=operations) + self.ln_1 = operations.LayerNorm(dim, dtype=dtype, device=device) + self.mlp = nn.Sequential( + OrderedDict([("c_fc", operations.Linear(dim, dim * 4, dtype=dtype, device=device)), ("gelu", QuickGELU()), + ("c_proj", operations.Linear(dim * 4, dim, dtype=dtype, device=device))])) + self.ln_2 = operations.LayerNorm(dim, dtype=dtype, device=device) + + def attention(self, x: torch.Tensor): + return self.attn(x) + + def forward(self, x: torch.Tensor): + x = x + self.attention(self.ln_1(x)) + x = x + self.mlp(self.ln_2(x)) + return x class ControlledUnetModel(UNetModel): #implemented in the ldm unet @@ -53,6 +93,7 @@ class ControlNet(nn.Module): transformer_depth_middle=None, transformer_depth_output=None, attn_precision=None, + union_controlnet=False, device=None, operations=comfy.ops.disable_weight_init, **kwargs, @@ -280,6 +321,65 @@ class ControlNet(nn.Module): self.middle_block_out = self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device) self._feature_size += ch + if union_controlnet: + self.num_control_type = 6 + num_trans_channel = 320 + num_trans_head = 8 + num_trans_layer = 1 + num_proj_channel = 320 + # task_scale_factor = num_trans_channel ** 0.5 + self.task_embedding = nn.Parameter(torch.empty(self.num_control_type, num_trans_channel, dtype=self.dtype, device=device)) + + self.transformer_layes = nn.Sequential(*[ResBlockUnionControlnet(num_trans_channel, num_trans_head, dtype=self.dtype, device=device, operations=operations) for _ in range(num_trans_layer)]) + self.spatial_ch_projs = operations.Linear(num_trans_channel, num_proj_channel, dtype=self.dtype, device=device) + #----------------------------------------------------------------------------------------------------- + + control_add_embed_dim = 256 + class ControlAddEmbedding(nn.Module): + def __init__(self, in_dim, out_dim, num_control_type, dtype=None, device=None, operations=None): + super().__init__() + self.num_control_type = num_control_type + self.in_dim = in_dim + self.linear_1 = operations.Linear(in_dim * num_control_type, out_dim, dtype=dtype, device=device) + self.linear_2 = operations.Linear(out_dim, out_dim, dtype=dtype, device=device) + def forward(self, control_type, dtype, device): + c_type = torch.zeros((self.num_control_type,), device=device) + c_type[control_type] = 1.0 + c_type = timestep_embedding(c_type.flatten(), self.in_dim, repeat_only=False).to(dtype).reshape((-1, self.num_control_type * self.in_dim)) + return self.linear_2(torch.nn.functional.silu(self.linear_1(c_type))) + + self.control_add_embedding = ControlAddEmbedding(control_add_embed_dim, time_embed_dim, self.num_control_type, dtype=self.dtype, device=device, operations=operations) + else: + self.task_embedding = None + self.control_add_embedding = None + + def union_controlnet_merge(self, hint, control_type, emb, context): + # Equivalent to: https://github.com/xinsir6/ControlNetPlus/tree/main + inputs = [] + condition_list = [] + + for idx in range(min(1, len(control_type))): + controlnet_cond = self.input_hint_block(hint[idx], emb, context) + feat_seq = torch.mean(controlnet_cond, dim=(2, 3)) + if idx < len(control_type): + feat_seq += self.task_embedding[control_type[idx]] + + inputs.append(feat_seq.unsqueeze(1)) + condition_list.append(controlnet_cond) + + x = torch.cat(inputs, dim=1) + x = self.transformer_layes(x) + controlnet_cond_fuser = None + for idx in range(len(control_type)): + alpha = self.spatial_ch_projs(x[:, idx]) + alpha = alpha.unsqueeze(-1).unsqueeze(-1) + o = condition_list[idx] + alpha + if controlnet_cond_fuser is None: + controlnet_cond_fuser = o + else: + controlnet_cond_fuser += o + return controlnet_cond_fuser + def make_zero_conv(self, channels, operations=None, dtype=None, device=None): return TimestepEmbedSequential(operations.conv_nd(self.dims, channels, channels, 1, padding=0, dtype=dtype, device=device)) @@ -287,7 +387,18 @@ class ControlNet(nn.Module): t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) emb = self.time_embed(t_emb) - guided_hint = self.input_hint_block(hint, emb, context) + guided_hint = None + if self.control_add_embedding is not None: + control_type = kwargs.get("control_type", []) + + emb += self.control_add_embedding(control_type, emb.dtype, emb.device) + if len(control_type) > 0: + if len(hint.shape) < 5: + hint = hint.unsqueeze(dim=0) + guided_hint = self.union_controlnet_merge(hint, control_type, emb, context) + + if guided_hint is None: + guided_hint = self.input_hint_block(hint, emb, context) out_output = [] out_middle = [] diff --git a/comfy/controlnet.py b/comfy/controlnet.py index d0039513..84286f1f 100644 --- a/comfy/controlnet.py +++ b/comfy/controlnet.py @@ -413,6 +413,12 @@ def load_controlnet(ckpt_path, model=None): if k in controlnet_data: new_sd[diffusers_keys[k]] = controlnet_data.pop(k) + if "control_add_embedding.linear_1.bias" in controlnet_data: #Union Controlnet + controlnet_config["union_controlnet"] = True + for k in list(controlnet_data.keys()): + new_k = k.replace('.attn.in_proj_', '.attn.in_proj.') + new_sd[new_k] = controlnet_data.pop(k) + leftover_keys = controlnet_data.keys() if len(leftover_keys) > 0: logging.warning("leftover keys: {}".format(leftover_keys)) From ade7aa1b0ce7944f50f66af5d8742299ac49d07f Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 9 Jul 2024 11:05:05 -0400 Subject: [PATCH 376/378] Remove useless import. --- comfy/cldm/cldm.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/comfy/cldm/cldm.py b/comfy/cldm/cldm.py index 3feb9bab..d4d32b87 100644 --- a/comfy/cldm/cldm.py +++ b/comfy/cldm/cldm.py @@ -13,7 +13,6 @@ from ..ldm.modules.diffusionmodules.util import ( from ..ldm.modules.attention import SpatialTransformer from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample from ..ldm.util import exists -from ..ldm.cascade.common import OptimizedAttention from collections import OrderedDict import comfy.ops from comfy.ldm.modules.attention import optimized_attention @@ -388,7 +387,7 @@ class ControlNet(nn.Module): emb = self.time_embed(t_emb) guided_hint = None - if self.control_add_embedding is not None: + if self.control_add_embedding is not None: #Union Controlnet control_type = kwargs.get("control_type", []) emb += self.control_add_embedding(control_type, emb.dtype, emb.device) From d160073829b836fce97046ebb5f145fa1c677ae3 Mon Sep 17 00:00:00 2001 From: bymyself Date: Tue, 9 Jul 2024 08:23:26 -0700 Subject: [PATCH 377/378] Fix loadGraphData call during restore (#3976) --- web/scripts/app.js | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/web/scripts/app.js b/web/scripts/app.js index 67e488bf..8b4478a3 100644 --- a/web/scripts/app.js +++ b/web/scripts/app.js @@ -1599,7 +1599,7 @@ export class ComfyApp { if (json) { const workflow = JSON.parse(json); const workflowName = getStorageValue("Comfy.PreviousWorkflow"); - await this.loadGraphData(workflow, true, workflowName); + await this.loadGraphData(workflow, true, true, workflowName); return true; } }; From c3db344746163f9ab942591020043d47ca34e4f0 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 9 Jul 2024 11:52:31 -0400 Subject: [PATCH 378/378] Fix ConditioningZeroOut when there is no pooled output. --- nodes.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/nodes.py b/nodes.py index 9c541eba..a230f725 100644 --- a/nodes.py +++ b/nodes.py @@ -232,8 +232,9 @@ class ConditioningZeroOut: c = [] for t in conditioning: d = t[1].copy() - if "pooled_output" in d: - d["pooled_output"] = torch.zeros_like(d["pooled_output"]) + pooled_output = d.get("pooled_output", None) + if pooled_output is not None: + d["pooled_output"] = torch.zeros_like(pooled_output) n = [torch.zeros_like(t[0]), d] c.append(n) return (c, )