mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-03-13 22:31:08 +00:00
Merge remote-tracking branch 'origin/master' into frontendrefactor
This commit is contained in:
pythongosssss
commit
1ee35fd909
@ -135,7 +135,7 @@ You can also set this command line setting to disable the upcasting to fp32 in s
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## Support and dev channel
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[Matrix room: #comfyui:matrix.org](https://app.element.io/#/room/%23comfyui%3Amatrix.org) (it's like discord but open source).
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[Matrix space: #comfyui_space:matrix.org](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) (it's like discord but open source).
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# QA
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@ -489,6 +489,8 @@ if XFORMERS_IS_AVAILBLE == False or "--disable-xformers" in sys.argv:
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if "--use-pytorch-cross-attention" in sys.argv:
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print("Using pytorch cross attention")
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torch.backends.cuda.enable_math_sdp(False)
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torch.backends.cuda.enable_flash_sdp(True)
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torch.backends.cuda.enable_mem_efficient_sdp(True)
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CrossAttention = CrossAttentionPytorch
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else:
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print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention")
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@ -497,6 +499,7 @@ else:
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print("Using xformers cross attention")
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CrossAttention = MemoryEfficientCrossAttention
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class BasicTransformerBlock(nn.Module):
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def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
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disable_self_attn=False):
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@ -7,6 +7,7 @@ from einops import rearrange
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from typing import Optional, Any
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from ldm.modules.attention import MemoryEfficientCrossAttention
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import model_management
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try:
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import xformers
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@ -199,12 +200,7 @@ class AttnBlock(nn.Module):
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r1 = torch.zeros_like(k, device=q.device)
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stats = torch.cuda.memory_stats(q.device)
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mem_active = stats['active_bytes.all.current']
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mem_reserved = stats['reserved_bytes.all.current']
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mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
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mem_free_torch = mem_reserved - mem_active
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mem_free_total = mem_free_cuda + mem_free_torch
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mem_free_total = model_management.get_free_memory(q.device)
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gb = 1024 ** 3
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tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
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13
comfy/sd.py
13
comfy/sd.py
@ -612,8 +612,17 @@ class T2IAdapter:
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def load_t2i_adapter(ckpt_path, model=None):
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t2i_data = load_torch_file(ckpt_path)
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cin = t2i_data['conv_in.weight'].shape[1]
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model_ad = adapter.Adapter(cin=cin, channels=[320, 640, 1280, 1280][:4], nums_rb=2, ksize=1, sk=True, use_conv=False)
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keys = t2i_data.keys()
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if "style_embedding" in keys:
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pass
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# TODO
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# model_ad = adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8)
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elif "body.0.in_conv.weight" in keys:
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cin = t2i_data['body.0.in_conv.weight'].shape[1]
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model_ad = adapter.Adapter_light(cin=cin, channels=[320, 640, 1280, 1280], nums_rb=4)
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else:
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cin = t2i_data['conv_in.weight'].shape[1]
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model_ad = adapter.Adapter(cin=cin, channels=[320, 640, 1280, 1280][:4], nums_rb=2, ksize=1, sk=True, use_conv=False)
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model_ad.load_state_dict(t2i_data)
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return T2IAdapter(model_ad, cin // 64)
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@ -1,9 +1,8 @@
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#taken from https://github.com/TencentARC/T2I-Adapter
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from ldm.modules.attention import SpatialTransformer, BasicTransformerBlock
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from collections import OrderedDict
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def conv_nd(dims, *args, **kwargs):
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"""
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@ -17,6 +16,7 @@ def conv_nd(dims, *args, **kwargs):
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return nn.Conv3d(*args, **kwargs)
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raise ValueError(f"unsupported dimensions: {dims}")
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def avg_pool_nd(dims, *args, **kwargs):
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"""
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Create a 1D, 2D, or 3D average pooling module.
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@ -29,6 +29,7 @@ def avg_pool_nd(dims, *args, **kwargs):
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return nn.AvgPool3d(*args, **kwargs)
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raise ValueError(f"unsupported dimensions: {dims}")
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class Downsample(nn.Module):
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"""
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A downsampling layer with an optional convolution.
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@ -38,7 +39,7 @@ class Downsample(nn.Module):
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downsampling occurs in the inner-two dimensions.
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"""
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def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
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def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
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super().__init__()
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self.channels = channels
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self.out_channels = out_channels or channels
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@ -61,8 +62,8 @@ class Downsample(nn.Module):
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class ResnetBlock(nn.Module):
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def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True):
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super().__init__()
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ps = ksize//2
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if in_c != out_c or sk==False:
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ps = ksize // 2
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if in_c != out_c or sk == False:
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self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps)
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else:
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# print('n_in')
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@ -70,7 +71,7 @@ class ResnetBlock(nn.Module):
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self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1)
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self.act = nn.ReLU()
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self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps)
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if sk==False:
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if sk == False:
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self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps)
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else:
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self.skep = None
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@ -82,7 +83,7 @@ class ResnetBlock(nn.Module):
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def forward(self, x):
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if self.down == True:
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x = self.down_opt(x)
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if self.in_conv is not None: # edit
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if self.in_conv is not None: # edit
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x = self.in_conv(x)
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h = self.block1(x)
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@ -103,12 +104,14 @@ class Adapter(nn.Module):
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self.body = []
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for i in range(len(channels)):
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for j in range(nums_rb):
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if (i!=0) and (j==0):
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self.body.append(ResnetBlock(channels[i-1], channels[i], down=True, ksize=ksize, sk=sk, use_conv=use_conv))
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if (i != 0) and (j == 0):
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self.body.append(
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ResnetBlock(channels[i - 1], channels[i], down=True, ksize=ksize, sk=sk, use_conv=use_conv))
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else:
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self.body.append(ResnetBlock(channels[i], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv))
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self.body.append(
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ResnetBlock(channels[i], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv))
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self.body = nn.ModuleList(self.body)
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self.conv_in = nn.Conv2d(cin,channels[0], 3, 1, 1)
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self.conv_in = nn.Conv2d(cin, channels[0], 3, 1, 1)
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def forward(self, x):
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# unshuffle
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@ -118,8 +121,139 @@ class Adapter(nn.Module):
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x = self.conv_in(x)
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for i in range(len(self.channels)):
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for j in range(self.nums_rb):
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idx = i*self.nums_rb +j
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idx = i * self.nums_rb + j
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x = self.body[idx](x)
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features.append(x)
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return features
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class LayerNorm(nn.LayerNorm):
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"""Subclass torch's LayerNorm to handle fp16."""
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def forward(self, x: torch.Tensor):
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orig_type = x.dtype
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ret = super().forward(x.type(torch.float32))
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return ret.type(orig_type)
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class QuickGELU(nn.Module):
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def forward(self, x: torch.Tensor):
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return x * torch.sigmoid(1.702 * x)
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class ResidualAttentionBlock(nn.Module):
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def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
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super().__init__()
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self.attn = nn.MultiheadAttention(d_model, n_head)
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self.ln_1 = LayerNorm(d_model)
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self.mlp = nn.Sequential(
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OrderedDict([("c_fc", nn.Linear(d_model, d_model * 4)), ("gelu", QuickGELU()),
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("c_proj", nn.Linear(d_model * 4, d_model))]))
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self.ln_2 = LayerNorm(d_model)
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self.attn_mask = attn_mask
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def attention(self, x: torch.Tensor):
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self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
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return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
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def forward(self, x: torch.Tensor):
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x = x + self.attention(self.ln_1(x))
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x = x + self.mlp(self.ln_2(x))
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return x
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class StyleAdapter(nn.Module):
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def __init__(self, width=1024, context_dim=768, num_head=8, n_layes=3, num_token=4):
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super().__init__()
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scale = width ** -0.5
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self.transformer_layes = nn.Sequential(*[ResidualAttentionBlock(width, num_head) for _ in range(n_layes)])
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self.num_token = num_token
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self.style_embedding = nn.Parameter(torch.randn(1, num_token, width) * scale)
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self.ln_post = LayerNorm(width)
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self.ln_pre = LayerNorm(width)
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self.proj = nn.Parameter(scale * torch.randn(width, context_dim))
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def forward(self, x):
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# x shape [N, HW+1, C]
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style_embedding = self.style_embedding + torch.zeros(
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(x.shape[0], self.num_token, self.style_embedding.shape[-1]), device=x.device)
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x = torch.cat([x, style_embedding], dim=1)
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x = self.ln_pre(x)
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x = x.permute(1, 0, 2) # NLD -> LND
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x = self.transformer_layes(x)
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x = x.permute(1, 0, 2) # LND -> NLD
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x = self.ln_post(x[:, -self.num_token:, :])
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x = x @ self.proj
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return x
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class ResnetBlock_light(nn.Module):
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def __init__(self, in_c):
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super().__init__()
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self.block1 = nn.Conv2d(in_c, in_c, 3, 1, 1)
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self.act = nn.ReLU()
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self.block2 = nn.Conv2d(in_c, in_c, 3, 1, 1)
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def forward(self, x):
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h = self.block1(x)
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h = self.act(h)
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h = self.block2(h)
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return h + x
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class extractor(nn.Module):
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def __init__(self, in_c, inter_c, out_c, nums_rb, down=False):
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super().__init__()
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self.in_conv = nn.Conv2d(in_c, inter_c, 1, 1, 0)
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self.body = []
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for _ in range(nums_rb):
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self.body.append(ResnetBlock_light(inter_c))
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self.body = nn.Sequential(*self.body)
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self.out_conv = nn.Conv2d(inter_c, out_c, 1, 1, 0)
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self.down = down
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if self.down == True:
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self.down_opt = Downsample(in_c, use_conv=False)
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def forward(self, x):
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if self.down == True:
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x = self.down_opt(x)
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x = self.in_conv(x)
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x = self.body(x)
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x = self.out_conv(x)
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return x
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class Adapter_light(nn.Module):
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def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64):
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super(Adapter_light, self).__init__()
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self.unshuffle = nn.PixelUnshuffle(8)
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self.channels = channels
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self.nums_rb = nums_rb
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self.body = []
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for i in range(len(channels)):
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if i == 0:
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self.body.append(extractor(in_c=cin, inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=False))
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else:
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self.body.append(extractor(in_c=channels[i-1], inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=True))
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self.body = nn.ModuleList(self.body)
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def forward(self, x):
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# unshuffle
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x = self.unshuffle(x)
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# extract features
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features = []
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for i in range(len(self.channels)):
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x = self.body[i](x)
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features.append(x)
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return features
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@ -42,7 +42,7 @@
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{
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"cell_type": "markdown",
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"source": [
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"Download some models/checkpoints/vae (uncomment the wget commands for the ones you want)"
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"Download some models/checkpoints/vae or custom comfyui nodes (uncomment the commands for the ones you want)"
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],
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"metadata": {
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"id": "cccccccccc"
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@ -54,43 +54,52 @@
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"# Checkpoints\n",
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"\n",
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"# SD1.5\n",
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"!wget https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -P ./models/checkpoints/\n",
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"!wget -c https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -P ./models/checkpoints/\n",
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"\n",
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"# SD2\n",
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"#!wget https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.safetensors -P ./models/checkpoints/\n",
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"#!wget https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.safetensors -P ./models/checkpoints/\n",
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"#!wget -c https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.safetensors -P ./models/checkpoints/\n",
|
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"#!wget -c https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.safetensors -P ./models/checkpoints/\n",
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"\n",
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"# Some SD1.5 anime style\n",
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"#!wget https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix2/AbyssOrangeMix2_hard.safetensors -P ./models/checkpoints/\n",
|
||||
"#!wget https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A1.safetensors -P ./models/checkpoints/\n",
|
||||
"#!wget https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A3.safetensors -P ./models/checkpoints/\n",
|
||||
"#!wget https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/anything-v3-fp16-pruned.safetensors -P ./models/checkpoints/\n",
|
||||
"#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix2/AbyssOrangeMix2_hard.safetensors -P ./models/checkpoints/\n",
|
||||
"#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A1.safetensors -P ./models/checkpoints/\n",
|
||||
"#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A3.safetensors -P ./models/checkpoints/\n",
|
||||
"#!wget -c https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/anything-v3-fp16-pruned.safetensors -P ./models/checkpoints/\n",
|
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"\n",
|
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"# Waifu Diffusion 1.5 (anime style SD2.x 768-v)\n",
|
||||
"#!wget https://huggingface.co/waifu-diffusion/wd-1-5-beta2/resolve/main/checkpoints/wd-1-5-beta2-fp16.safetensors -P ./models/checkpoints/\n",
|
||||
"#!wget -c https://huggingface.co/waifu-diffusion/wd-1-5-beta2/resolve/main/checkpoints/wd-1-5-beta2-fp16.safetensors -P ./models/checkpoints/\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# VAE\n",
|
||||
"!wget https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors -P ./models/vae/\n",
|
||||
"#!wget https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt -P ./models/vae/\n",
|
||||
"!wget -c https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors -P ./models/vae/\n",
|
||||
"#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt -P ./models/vae/\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Loras\n",
|
||||
"#!wget --content-disposition https://civitai.com/api/download/models/10350 -P ./models/loras/ #theovercomer8sContrastFix SD2.x 768-v\n",
|
||||
"#!wget --content-disposition https://civitai.com/api/download/models/10638 -P ./models/loras/ #theovercomer8sContrastFix SD1.x\n",
|
||||
"#!wget -c --content-disposition https://civitai.com/api/download/models/10350 -P ./models/loras/ #theovercomer8sContrastFix SD2.x 768-v\n",
|
||||
"#!wget -c --content-disposition https://civitai.com/api/download/models/10638 -P ./models/loras/ #theovercomer8sContrastFix SD1.x\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# T2I-Adapter\n",
|
||||
"#!wget https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_depth_sd14v1.pth -P ./models/t2i_adapter/\n",
|
||||
"#!wget https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_seg_sd14v1.pth -P ./models/t2i_adapter/\n",
|
||||
"#!wget https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_sketch_sd14v1.pth -P ./models/t2i_adapter/\n",
|
||||
"#!wget https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_keypose_sd14v1.pth -P ./models/t2i_adapter/\n",
|
||||
"#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_depth_sd14v1.pth -P ./models/t2i_adapter/\n",
|
||||
"#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_seg_sd14v1.pth -P ./models/t2i_adapter/\n",
|
||||
"#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_sketch_sd14v1.pth -P ./models/t2i_adapter/\n",
|
||||
"#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_keypose_sd14v1.pth -P ./models/t2i_adapter/\n",
|
||||
"#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_openpose_sd14v1.pth -P ./models/t2i_adapter/\n",
|
||||
"#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_color_sd14v1.pth -P ./models/t2i_adapter/\n",
|
||||
"#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_canny_sd14v1.pth -P ./models/t2i_adapter/\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# ControlNet\n",
|
||||
"#!wget https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_depth-fp16.safetensors -P ./models/controlnet/\n",
|
||||
"#!wget https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_scribble-fp16.safetensors -P ./models/controlnet/\n",
|
||||
"#!wget https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_openpose-fp16.safetensors -P ./models/controlnet/\n"
|
||||
"#!wget -c https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_depth-fp16.safetensors -P ./models/controlnet/\n",
|
||||
"#!wget -c https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_scribble-fp16.safetensors -P ./models/controlnet/\n",
|
||||
"#!wget -c https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_openpose-fp16.safetensors -P ./models/controlnet/\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Controlnet Preprocessor nodes by Fannovel16\n",
|
||||
"#!cd custom_nodes && git clone https://github.com/Fannovel16/comfy_controlnet_preprocessors; cd comfy_controlnet_preprocessors && python install.py\n",
|
||||
"\n"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "dddddddddd"
|
||||
@ -101,7 +110,7 @@
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"### Run ComfyUI with localtunnel\n",
|
||||
"### Run ComfyUI with localtunnel (Recommended Way)\n",
|
||||
"\n",
|
||||
"use the **fp16** model configs for more speed\n",
|
||||
"\n"
|
||||
@ -146,7 +155,7 @@
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"### Run ComfyUI with colab iframe (in case localtunnel doesn't work)\n",
|
||||
"### Run ComfyUI with colab iframe (use only in case the previous way with localtunnel doesn't work)\n",
|
||||
"use the **fp16** model configs for more speed\n",
|
||||
"\n",
|
||||
"You should see the ui appear in an iframe. If you get a 403 error, it's your firefox settings or an extension that's messing things up.\n",
|
||||
|
||||
@ -37,10 +37,9 @@ prompt_text = """
|
||||
}
|
||||
},
|
||||
"4": {
|
||||
"class_type": "CheckpointLoader",
|
||||
"class_type": "CheckpointLoaderSimple",
|
||||
"inputs": {
|
||||
"ckpt_name": "v1-5-pruned-emaonly.ckpt",
|
||||
"config_name": "v1-inference.yaml"
|
||||
"ckpt_name": "v1-5-pruned-emaonly.ckpt"
|
||||
}
|
||||
},
|
||||
"5": {
|
||||
|
||||
@ -86,9 +86,9 @@ export const defaultGraph = {
|
||||
},
|
||||
{
|
||||
id: 4,
|
||||
type: "CheckpointLoader",
|
||||
type: "CheckpointLoaderSimple",
|
||||
pos: [26, 474],
|
||||
size: { 0: 315, 1: 122 },
|
||||
size: { 0: 315, 1: 98 },
|
||||
flags: {},
|
||||
order: 0,
|
||||
mode: 0,
|
||||
@ -98,7 +98,7 @@ export const defaultGraph = {
|
||||
{ name: "VAE", type: "VAE", links: [8], slot_index: 2 },
|
||||
],
|
||||
properties: {},
|
||||
widgets_values: ["v1-inference.yaml", "v1-5-pruned-emaonly.ckpt"],
|
||||
widgets_values: ["v1-5-pruned-emaonly.ckpt"],
|
||||
},
|
||||
],
|
||||
links: [
|
||||
|
||||
Loading…
Reference in New Issue
Block a user