From d170292594770377d9e0442078ef43668e2331b6 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 27 Dec 2024 18:02:21 -0500 Subject: [PATCH] Remove some trailing white space. --- comfy/controlnet.py | 2 +- comfy/extra_samplers/uni_pc.py | 10 +++++----- comfy/hooks.py | 2 +- comfy/k_diffusion/sampling.py | 2 +- comfy/ldm/modules/sub_quadratic_attention.py | 2 +- comfy/ldm/pixart/pixartms.py | 2 +- comfy/model_detection.py | 4 ++-- comfy/sample.py | 2 +- comfy/sampler_helpers.py | 2 +- comfy/utils.py | 10 +++++----- comfy_extras/nodes_mask.py | 4 ++-- comfy_extras/nodes_perpneg.py | 2 +- comfy_extras/nodes_rebatch.py | 4 ++-- comfy_extras/nodes_tomesd.py | 7 +++---- nodes.py | 20 ++++++++++---------- 15 files changed, 37 insertions(+), 38 deletions(-) diff --git a/comfy/controlnet.py b/comfy/controlnet.py index 7f598837..ee29251b 100644 --- a/comfy/controlnet.py +++ b/comfy/controlnet.py @@ -120,7 +120,7 @@ class ControlBase: if self.previous_controlnet is not None: out += self.previous_controlnet.get_models() return out - + def get_extra_hooks(self): out = [] if self.extra_hooks is not None: diff --git a/comfy/extra_samplers/uni_pc.py b/comfy/extra_samplers/uni_pc.py index b61baaa8..77d20bbf 100644 --- a/comfy/extra_samplers/uni_pc.py +++ b/comfy/extra_samplers/uni_pc.py @@ -80,7 +80,7 @@ class NoiseScheduleVP: 'linear' or 'cosine' for continuous-time DPMs. Returns: A wrapper object of the forward SDE (VP type). - + =============================================================== Example: @@ -208,7 +208,7 @@ def model_wrapper( arXiv preprint arXiv:2202.00512 (2022). [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models." arXiv preprint arXiv:2210.02303 (2022). - + 4. "score": marginal score function. (Trained by denoising score matching). Note that the score function and the noise prediction model follows a simple relationship: ``` @@ -245,7 +245,7 @@ def model_wrapper( [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance." arXiv preprint arXiv:2207.12598 (2022). - + The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999) or continuous-time labels (i.e. epsilon to T). @@ -621,7 +621,7 @@ class UniPC: B_h = torch.expm1(hh) else: raise NotImplementedError() - + for i in range(1, order + 1): R.append(torch.pow(rks, i - 1)) b.append(h_phi_k * factorial_i / B_h) @@ -870,4 +870,4 @@ def sample_unipc(model, noise, sigmas, extra_args=None, callback=None, disable=F 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 + return sample_unipc(model, noise, sigmas, extra_args, callback, disable, variant='bh2') diff --git a/comfy/hooks.py b/comfy/hooks.py index b6f0ac21..7f9eee30 100644 --- a/comfy/hooks.py +++ b/comfy/hooks.py @@ -101,7 +101,7 @@ class WeightHook(Hook): self.need_weight_init = True self._strength_model = strength_model self._strength_clip = strength_clip - + @property def strength_model(self): return self._strength_model * self.strength diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py index f08370f8..0f7cc4ca 100644 --- a/comfy/k_diffusion/sampling.py +++ b/comfy/k_diffusion/sampling.py @@ -1230,7 +1230,7 @@ def sample_dpmpp_2m_cfg_pp(model, x, sigmas, extra_args=None, callback=None, dis nonlocal uncond_denoised uncond_denoised = 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) diff --git a/comfy/ldm/modules/sub_quadratic_attention.py b/comfy/ldm/modules/sub_quadratic_attention.py index 7c5f1d9f..fca8d117 100644 --- a/comfy/ldm/modules/sub_quadratic_attention.py +++ b/comfy/ldm/modules/sub_quadratic_attention.py @@ -261,7 +261,7 @@ def efficient_dot_product_attention( value=value, mask=mask, ) - + # TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance, # and pass slices to be mutated, instead of torch.cat()ing the returned slices res = torch.cat([ diff --git a/comfy/ldm/pixart/pixartms.py b/comfy/ldm/pixart/pixartms.py index 50dc58c2..7d4eebdc 100644 --- a/comfy/ldm/pixart/pixartms.py +++ b/comfy/ldm/pixart/pixartms.py @@ -223,7 +223,7 @@ class PixArtMS(nn.Module): if self.micro_conditioning: if c_size is None: c_size = torch.tensor([H*8, W*8], dtype=x.dtype, device=x.device).repeat(B, 1) - + if c_ar is None: c_ar = torch.tensor([H/W], dtype=x.dtype, device=x.device).repeat(B, 1) diff --git a/comfy/model_detection.py b/comfy/model_detection.py index c53bef5b..de00f773 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -229,7 +229,7 @@ def detect_unet_config(state_dict, key_prefix): if pe_key in state_dict_keys: dit_config["input_size"] = int(math.sqrt(state_dict[pe_key].shape[1])) * patch_size dit_config["pe_interpolation"] = dit_config["input_size"] // (512//8) # guess - + ar_key = "{}ar_embedder.mlp.0.weight".format(key_prefix) if ar_key in state_dict_keys: dit_config["image_model"] = "pixart_alpha" @@ -571,7 +571,7 @@ def unet_config_from_diffusers_unet(state_dict, dtype=None): '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} - + SD15_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 9, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8, diff --git a/comfy/sample.py b/comfy/sample.py index 98dcaca7..9974e065 100644 --- a/comfy/sample.py +++ b/comfy/sample.py @@ -13,7 +13,7 @@ def prepare_noise(latent_image, seed, noise_inds=None): generator = torch.manual_seed(seed) if noise_inds is None: return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu") - + unique_inds, inverse = np.unique(noise_inds, return_inverse=True) noises = [] for i in range(unique_inds[-1]+1): diff --git a/comfy/sampler_helpers.py b/comfy/sampler_helpers.py index 0691de63..ac973536 100644 --- a/comfy/sampler_helpers.py +++ b/comfy/sampler_helpers.py @@ -42,7 +42,7 @@ def get_hooks_from_cond(cond, hooks_dict: dict[comfy.hooks.EnumHookType, dict[co if cnet.previous_controlnet is None: return _list return get_extra_hooks_from_cnet(cnet.previous_controlnet, _list) - + hooks_list = [] cnets = set(cnets) for base_cnet in cnets: diff --git a/comfy/utils.py b/comfy/utils.py index 7de65933..de64b91d 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -455,7 +455,7 @@ def pixart_to_diffusers(mmdit_config, output_prefix=""): for k in PIXART_MAP_BASIC: key_map[k[1]] = "{}{}".format(output_prefix, k[0]) - + return key_map def auraflow_to_diffusers(mmdit_config, output_prefix=""): @@ -702,7 +702,7 @@ def get_attr(obj, attr): def bislerp(samples, width, height): def slerp(b1, b2, r): '''slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC''' - + c = b1.shape[-1] #norms @@ -730,13 +730,13 @@ def bislerp(samples, width, height): res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5] res[dot < 1e-5 - 1] = (b1 * (1.0-r) + b2 * r)[dot < 1e-5 - 1] return res - + def generate_bilinear_data(length_old, length_new, device): coords_1 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1)) coords_1 = torch.nn.functional.interpolate(coords_1, size=(1, length_new), mode="bilinear") ratios = coords_1 - coords_1.floor() coords_1 = coords_1.to(torch.int64) - + coords_2 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1)) + 1 coords_2[:,:,:,-1] -= 1 coords_2 = torch.nn.functional.interpolate(coords_2, size=(1, length_new), mode="bilinear") @@ -747,7 +747,7 @@ def bislerp(samples, width, height): samples = samples.float() n,c,h,w = samples.shape h_new, w_new = (height, width) - + #linear w ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new, samples.device) coords_1 = coords_1.expand((n, c, h, -1)) diff --git a/comfy_extras/nodes_mask.py b/comfy_extras/nodes_mask.py index 29589b4a..63fd13b9 100644 --- a/comfy_extras/nodes_mask.py +++ b/comfy_extras/nodes_mask.py @@ -305,7 +305,7 @@ class FeatherMask: output[:, -y, :] *= feather_rate return (output,) - + class GrowMask: @classmethod def INPUT_TYPES(cls): @@ -316,7 +316,7 @@ class GrowMask: "tapered_corners": ("BOOLEAN", {"default": True}), }, } - + CATEGORY = "mask" RETURN_TYPES = ("MASK",) diff --git a/comfy_extras/nodes_perpneg.py b/comfy_extras/nodes_perpneg.py index 762c4022..290bc4a5 100644 --- a/comfy_extras/nodes_perpneg.py +++ b/comfy_extras/nodes_perpneg.py @@ -64,7 +64,7 @@ class Guider_PerpNeg(comfy.samplers.CFGGuider): 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) diff --git a/comfy_extras/nodes_rebatch.py b/comfy_extras/nodes_rebatch.py index 3010fbd4..e29cb9ed 100644 --- a/comfy_extras/nodes_rebatch.py +++ b/comfy_extras/nodes_rebatch.py @@ -40,7 +40,7 @@ class LatentRebatch: return slices, indexable[num * batch_size:] else: return slices, None - + @staticmethod def slice_batch(batch, num, batch_size): result = [LatentRebatch.get_slices(x, num, batch_size) for x in batch] @@ -81,7 +81,7 @@ class LatentRebatch: if current_batch[0].shape[0] > batch_size: num = current_batch[0].shape[0] // batch_size sliced, remainder = self.slice_batch(current_batch, num, batch_size) - + for i in range(num): output_list.append({'samples': sliced[0][i], 'noise_mask': sliced[1][i], 'batch_index': sliced[2][i]}) diff --git a/comfy_extras/nodes_tomesd.py b/comfy_extras/nodes_tomesd.py index ce7b32c7..9f77c06f 100644 --- a/comfy_extras/nodes_tomesd.py +++ b/comfy_extras/nodes_tomesd.py @@ -40,9 +40,8 @@ def bipartite_soft_matching_random2d(metric: torch.Tensor, return do_nothing, do_nothing gather = mps_gather_workaround if metric.device.type == "mps" else torch.gather - + with torch.no_grad(): - hsy, wsx = h // sy, w // sx # For each sy by sx kernel, randomly assign one token to be dst and the rest src @@ -50,7 +49,7 @@ def bipartite_soft_matching_random2d(metric: torch.Tensor, rand_idx = torch.zeros(hsy, wsx, 1, device=metric.device, dtype=torch.int64) else: rand_idx = torch.randint(sy*sx, size=(hsy, wsx, 1), device=metric.device) - + # The image might not divide sx and sy, so we need to work on a view of the top left if the idx buffer instead idx_buffer_view = torch.zeros(hsy, wsx, sy*sx, device=metric.device, dtype=torch.int64) idx_buffer_view.scatter_(dim=2, index=rand_idx, src=-torch.ones_like(rand_idx, dtype=rand_idx.dtype)) @@ -99,7 +98,7 @@ def bipartite_soft_matching_random2d(metric: torch.Tensor, def merge(x: torch.Tensor, mode="mean") -> torch.Tensor: src, dst = split(x) n, t1, c = src.shape - + unm = gather(src, dim=-2, index=unm_idx.expand(n, t1 - r, c)) src = gather(src, dim=-2, index=src_idx.expand(n, r, c)) dst = dst.scatter_reduce(-2, dst_idx.expand(n, r, c), src, reduce=mode) diff --git a/nodes.py b/nodes.py index a135a612..89cecc48 100644 --- a/nodes.py +++ b/nodes.py @@ -65,7 +65,7 @@ class CLIPTextEncode(ComfyNodeABC): def encode(self, clip, text): tokens = clip.tokenize(text) return (clip.encode_from_tokens_scheduled(tokens), ) - + class ConditioningCombine: @classmethod @@ -641,7 +641,7 @@ class LoraLoader: "strength_clip": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the CLIP model. This value can be negative."}), } } - + RETURN_TYPES = ("MODEL", "CLIP") OUTPUT_TOOLTIPS = ("The modified diffusion model.", "The modified CLIP model.") FUNCTION = "load_lora" @@ -1211,7 +1211,7 @@ class LatentFromBatch: else: s["batch_index"] = samples["batch_index"][batch_index:batch_index + length] return (s,) - + class RepeatLatentBatch: @classmethod def INPUT_TYPES(s): @@ -1226,7 +1226,7 @@ class RepeatLatentBatch: def repeat(self, samples, amount): s = samples.copy() s_in = samples["samples"] - + s["samples"] = s_in.repeat((amount, 1,1,1)) if "noise_mask" in samples and samples["noise_mask"].shape[0] > 1: masks = samples["noise_mask"] @@ -1636,15 +1636,15 @@ class LoadImage: FUNCTION = "load_image" def load_image(self, image): image_path = folder_paths.get_annotated_filepath(image) - + img = node_helpers.pillow(Image.open, image_path) - + 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) @@ -1655,10 +1655,10 @@ class LoadImage: 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(): @@ -2234,5 +2234,5 @@ def init_extra_nodes(init_custom_nodes=True): else: logging.warning("Please do a: pip install -r requirements.txt") logging.warning("") - + return import_failed