.

app/model_manager.py

@@ -44,7 +44,7 @@ class ModelFileManager:
         @routes.get("/experiment/models/{folder}")
         async def get_all_models(request):
             folder = request.match_info.get("folder", None)
-            if not folder in folder_paths.folder_names_and_paths:
+            if folder not in folder_paths.folder_names_and_paths:
                 return web.Response(status=404)
             files = self.get_model_file_list(folder)
             return web.json_response(files)
@@ -55,7 +55,7 @@ class ModelFileManager:
             path_index = int(request.match_info.get("path_index", None))
             filename = request.match_info.get("filename", None)
 
-            if not folder_name in folder_paths.folder_names_and_paths:
+            if folder_name not in folder_paths.folder_names_and_paths:
                 return web.Response(status=404)
 
             folders = folder_paths.folder_names_and_paths[folder_name]

comfy/clip_model.py

@@ -2,6 +2,25 @@ import torch
 from comfy.ldm.modules.attention import optimized_attention_for_device
 import comfy.ops
 
+def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711], crop=True):
+    image = image[:, :, :, :3] if image.shape[3] > 3 else image
+    mean = torch.tensor(mean, device=image.device, dtype=image.dtype)
+    std = torch.tensor(std, device=image.device, dtype=image.dtype)
+    image = image.movedim(-1, 1)
+    if not (image.shape[2] == size and image.shape[3] == size):
+        if crop:
+            scale = (size / min(image.shape[2], image.shape[3]))
+            scale_size = (round(scale * image.shape[2]), round(scale * image.shape[3]))
+        else:
+            scale_size = (size, size)
+
+        image = torch.nn.functional.interpolate(image, size=scale_size, mode="bicubic", antialias=True)
+        h = (image.shape[2] - size)//2
+        w = (image.shape[3] - size)//2
+        image = image[:,:,h:h+size,w:w+size]
+    image = torch.clip((255. * image), 0, 255).round() / 255.0
+    return (image - mean.view([3,1,1])) / std.view([3,1,1])
+
 class CLIPAttention(torch.nn.Module):
     def __init__(self, embed_dim, heads, dtype, device, operations):
         super().__init__()

comfy/clip_vision.py

@@ -1,6 +1,5 @@
 from .utils import load_torch_file, transformers_convert, state_dict_prefix_replace
 import os
-import torch
 import json
 import logging
 
@@ -17,24 +16,7 @@ class Output:
     def __setitem__(self, key, item):
         setattr(self, key, item)
 
-def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711], crop=True):
-    image = image[:, :, :, :3] if image.shape[3] > 3 else image
-    mean = torch.tensor(mean, device=image.device, dtype=image.dtype)
-    std = torch.tensor(std, device=image.device, dtype=image.dtype)
-    image = image.movedim(-1, 1)
-    if not (image.shape[2] == size and image.shape[3] == size):
-        if crop:
-            scale = (size / min(image.shape[2], image.shape[3]))
-            scale_size = (round(scale * image.shape[2]), round(scale * image.shape[3]))
-        else:
-            scale_size = (size, size)
-
-        image = torch.nn.functional.interpolate(image, size=scale_size, mode="bicubic", antialias=True)
-        h = (image.shape[2] - size)//2
-        w = (image.shape[3] - size)//2
-        image = image[:,:,h:h+size,w:w+size]
-    image = torch.clip((255. * image), 0, 255).round() / 255.0
-    return (image - mean.view([3,1,1])) / std.view([3,1,1])
+clip_preprocess = comfy.clip_model.clip_preprocess  # Prevent some stuff from breaking, TODO: remove eventually
 
 IMAGE_ENCODERS = {
     "clip_vision_model": comfy.clip_model.CLIPVisionModelProjection,
@@ -73,7 +55,7 @@ class ClipVisionModel():
 
     def encode_image(self, image, crop=True):
         comfy.model_management.load_model_gpu(self.patcher)
-        pixel_values = clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float()
+        pixel_values = comfy.clip_model.clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float()
         out = self.model(pixel_values=pixel_values, intermediate_output='all' if self.return_all_hidden_states else -2)
 
         outputs = Output()

comfy/hooks.py

@@ -527,7 +527,8 @@ class HookKeyframeGroup:
                         if self._current_keyframe.get_effective_guarantee_steps(max_sigma) > 0:
                             break
                     # if eval_c is outside the percent range, stop looking further
-                    else: break
+                    else:
+                        break
         # update steps current context is used
         self._current_used_steps += 1
         # update current timestep this was performed on

comfy/ldm/chroma_radiance/model.py

@@ -270,7 +270,7 @@ class ChromaRadiance(Chroma):
         bad_keys = tuple(
             k
             for k, v in overrides.items()
-            if type(v) != type(getattr(params, k)) and (v is not None or k not in nullable_keys)
+            if not isinstance(v, type(getattr(params, k))) and (v is not None or k not in nullable_keys)
         )
         if bad_keys:
             e = f"Invalid value(s) in transformer_options chroma_radiance_options: {', '.join(bad_keys)}"

comfy/ldm/hunyuan_video/upsampler.py

@@ -3,7 +3,8 @@ import torch.nn as nn
 import torch.nn.functional as F
 from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, VideoConv3d
 from comfy.ldm.hunyuan_video.vae_refiner import RMS_norm
-import model_management, model_patcher
+import model_management
+import model_patcher
 
 class SRResidualCausalBlock3D(nn.Module):
     def __init__(self, channels: int):

comfy/ldm/modules/attention.py

@@ -98,7 +98,7 @@ def var_attn_arg(kwargs):
     cu_seqlens_k = kwargs.get("cu_seqlens_k", cu_seqlens_q)
     max_seqlen_q = kwargs.get("max_seqlen_q", None)
     max_seqlen_k = kwargs.get("max_seqlen_k", max_seqlen_q)
-    assert cu_seqlens_q != None, "cu_seqlens_q shouldn't be None when var_length is True"
+    assert cu_seqlens_q is not None, "cu_seqlens_q shouldn't be None when var_length is True"
     return cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k
 # feedforward
 class GEGLU(nn.Module):
@@ -423,7 +423,8 @@ except:
     pass
 
 @wrap_attn
-def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, var_length=False, **kwargs):
+def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    var_length = kwargs.get("var_length", False)
     b = q.shape[0]
     dim_head = q.shape[-1]
     # check to make sure xformers isn't broken
@@ -448,9 +449,12 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_resh
             k = k.view(1, total_tokens, heads, dim_head)
             v = v.view(1, total_tokens, heads, dim_head)
         else:
-            if q.ndim == 3: q = q.unsqueeze(0)
-            if k.ndim == 3: k = k.unsqueeze(0)
-            if v.ndim == 3: v = v.unsqueeze(0)
+            if q.ndim == 3:
+                q = q.unsqueeze(0)
+            if k.ndim == 3:
+                k = k.unsqueeze(0)
+            if v.ndim == 3:
+                v = v.unsqueeze(0)
             dim_head = q.shape[-1]
 
         target_output_shape = (q.shape[1], -1)
@@ -513,7 +517,8 @@ else:
     SDP_BATCH_LIMIT = 2**31
 
 @wrap_attn
-def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, var_length=False, **kwargs):
+def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    var_length = kwargs.get("var_length", False)
     if var_length:
         cu_seqlens_q, cu_seqlens_k, _, _ = var_attn_arg(kwargs)
         if not skip_reshape:
@@ -524,7 +529,8 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
             k = k.view(k.shape[0], heads, head_dim)
             v = v.view(v.shape[0], heads, head_dim)
 
-        b = q.size(0); dim_head = q.shape[-1]
+        b = q.size(0)
+        dim_head = q.shape[-1]
         q = torch.nested.nested_tensor_from_jagged(q, offsets=cu_seqlens_q.long())
         k = torch.nested.nested_tensor_from_jagged(k, offsets=cu_seqlens_k.long())
         v = torch.nested.nested_tensor_from_jagged(v, offsets=cu_seqlens_k.long())
@@ -577,7 +583,8 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
     return out
 
 @wrap_attn
-def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, var_length = False, **kwargs):
+def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    var_length = kwargs.get("var_length", False)
     exception_fallback = False
     if var_length:
         if not skip_reshape:
@@ -750,7 +757,8 @@ except AttributeError as error:
         assert False, f"Could not define flash_attn_wrapper: {FLASH_ATTN_ERROR}"
 
 @wrap_attn
-def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, var_length=False, **kwargs):
+def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    var_length = kwargs.get("var_length", False)
     if var_length:
         cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k = var_attn_arg(kwargs)
         return flash_attn_varlen_func(

comfy/ldm/modules/diffusionmodules/model.py

@@ -397,7 +397,8 @@ class Model(nn.Module):
                  attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
                  resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
         super().__init__()
-        if use_linear_attn: attn_type = "linear"
+        if use_linear_attn:
+            attn_type = "linear"
         self.ch = ch
         self.temb_ch = self.ch*4
         self.num_resolutions = len(ch_mult)
@@ -551,7 +552,8 @@ class Encoder(nn.Module):
                  conv3d=False, time_compress=None,
                  **ignore_kwargs):
         super().__init__()
-        if use_linear_attn: attn_type = "linear"
+        if use_linear_attn:
+            attn_type = "linear"
         self.ch = ch
         self.temb_ch = 0
         self.num_resolutions = len(ch_mult)

comfy/ldm/modules/ema.py

@@ -45,7 +45,7 @@ class LitEma(nn.Module):
                     shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
                     shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
                 else:
-                    assert not key in self.m_name2s_name
+                    assert key not in self.m_name2s_name
 
     def copy_to(self, model):
         m_param = dict(model.named_parameters())
@@ -54,7 +54,7 @@ class LitEma(nn.Module):
             if m_param[key].requires_grad:
                 m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
             else:
-                assert not key in self.m_name2s_name
+                assert key not in self.m_name2s_name
 
     def store(self, parameters):
         """

comfy/ldm/seedvr/model.py

@@ -13,6 +13,7 @@ from comfy.rmsnorm import RMSNorm
 from torch.nn.modules.utils import _triple
 from torch import nn
 import math
+from comfy.ldm.flux.math import apply_rope1
 
 class Cache:
     def __init__(self, disable=False, prefix="", cache=None):
@@ -443,7 +444,6 @@ def apply_rotary_emb(
     freqs_seq_dim = None
 ):
     dtype = t.dtype
-
     if not exists(freqs_seq_dim):
         if freqs.ndim == 2 or t.ndim == 3:
             freqs_seq_dim = 0
@@ -452,20 +452,23 @@ def apply_rotary_emb(
         seq_len = t.shape[seq_dim]
         freqs = slice_at_dim(freqs, slice(-seq_len, None), dim = freqs_seq_dim)
 
-    rot_dim = freqs.shape[-1]
-    end_index = start_index + rot_dim
-
-    assert rot_dim <= t.shape[-1], f'feature dimension {t.shape[-1]} is not of sufficient size to rotate in all the positions {rot_dim}'
+    rot_feats = freqs.shape[-1]
+    end_index = start_index + rot_feats
 
     t_left = t[..., :start_index]
     t_middle = t[..., start_index:end_index]
     t_right = t[..., end_index:]
 
-    freqs = freqs.to(t_middle.device)
-    t_transformed = (t_middle * freqs.cos() * scale) + (rotate_half(t_middle) * freqs.sin() * scale)
+    angles = freqs.to(t_middle.device)[..., ::2]
+    cos = torch.cos(angles) * scale
+    sin = torch.sin(angles) * scale
 
-    out = torch.cat((t_left, t_transformed, t_right), dim=-1)
+    col0 = torch.stack([cos, sin], dim=-1)
+    col1 = torch.stack([-sin, cos], dim=-1)
+    freqs_mat = torch.stack([col0, col1], dim=-1)
 
+    t_middle_out = apply_rope1(t_middle, freqs_mat)
+    out = torch.cat((t_left, t_middle_out, t_right), dim=-1)
     return out.type(dtype)
 
 class NaMMRotaryEmbedding3d(MMRotaryEmbeddingBase):

comfy/ldm/seedvr/vae.py

@@ -16,6 +16,7 @@ import math
 from enum import Enum
 from comfy.ops import NVIDIA_MEMORY_CONV_BUG_WORKAROUND
 
+import logging
 import comfy.ops
 ops = comfy.ops.disable_weight_init
 
@@ -446,6 +447,7 @@ class InflatedCausalConv3d(ops.Conv3d):
         self.memory_device = memory_device
         self.padding = (0, *self.padding[1:])
         self.memory_limit = float("inf")
+        self.logged_once = False
 
     def set_memory_limit(self, value: float):
         self.memory_limit = value
@@ -469,8 +471,16 @@ class InflatedCausalConv3d(ops.Conv3d):
                 return out
             except RuntimeError:
                 pass
-
-        return super()._conv_forward(input, weight, bias, *args, **kwargs)
+            except NotImplementedError:
+                pass
+        try:
+            return super()._conv_forward(input, weight, bias, *args, **kwargs)
+        except NotImplementedError:
+            # for: Could not run 'aten::cudnn_convolution' with arguments from the 'CPU' backend
+            if not self.logged_once:
+                logging.warning("VAE is on CPU for decoding. This is most likely due to not enough memory")
+                self.logged_once = True
+            return F.conv3d(input, weight, bias, *args, **kwargs)
 
     def memory_limit_conv(
         self,

comfy/ldm/util.py

@@ -71,7 +71,7 @@ def count_params(model, verbose=False):
 
 
 def instantiate_from_config(config):
-    if not "target" in config:
+    if "target" not in config:
         if config == '__is_first_stage__':
             return None
         elif config == "__is_unconditional__":

comfy/model_management.py

@@ -1542,6 +1542,10 @@ def soft_empty_cache(force=False):
 def unload_all_models():
     free_memory(1e30, get_torch_device())
 
+def debug_memory_summary():
+    if is_amd() or is_nvidia():
+        return torch.cuda.memory.memory_summary()
+    return ""
 
 #TODO: might be cleaner to put this somewhere else
 import threading

comfy/sd.py

@@ -382,8 +382,8 @@ class VAE:
                 self.latent_channels = 16
             elif "decoder.up_blocks.2.upsamplers.0.upscale_conv.weight" in sd: # seedvr2
                 self.first_stage_model = comfy.ldm.seedvr.vae.VideoAutoencoderKLWrapper()
-                self.memory_used_decode = lambda shape, dtype: (10 * shape[1] * shape[2] * shape[3] * (4 * 8 * 8)) * model_management.dtype_size(dtype)
-                self.memory_used_encode = lambda shape, dtype: (10 * max(shape[1], 5) * shape[2] * shape[3]) * model_management.dtype_size(dtype)
+                self.memory_used_decode = lambda shape, dtype: (shape[1] * shape[2] * shape[3] * (4 * 8 * 8)) * model_management.dtype_size(dtype)
+                self.memory_used_encode = lambda shape, dtype: (max(shape[1], 5) * shape[2] * shape[3]) * model_management.dtype_size(dtype)
                 self.working_dtypes = [torch.bfloat16, torch.float32]
                 self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)
                 self.downscale_index_formula = (4, 8, 8)

comfy/taesd/taehv.py

@@ -154,7 +154,8 @@ class TAEHV(nn.Module):
             self._show_progress_bar = value
 
     def encode(self, x, **kwargs):
-        if self.patch_size > 1: x = F.pixel_unshuffle(x, self.patch_size)
+        if self.patch_size > 1:
+            x = F.pixel_unshuffle(x, self.patch_size)
         x = x.movedim(2, 1)  # [B, C, T, H, W] -> [B, T, C, H, W]
         if x.shape[1] % 4 != 0:
             # pad at end to multiple of 4
@@ -167,5 +168,6 @@ class TAEHV(nn.Module):
     def decode(self, x, **kwargs):
         x = self.process_in(x).movedim(2, 1)  # [B, C, T, H, W] -> [B, T, C, H, W]
         x = apply_model_with_memblocks(self.decoder, x, self.parallel, self.show_progress_bar)
-        if self.patch_size > 1: x = F.pixel_shuffle(x, self.patch_size)
+        if self.patch_size > 1:
+            x = F.pixel_shuffle(x, self.patch_size)
         return x[:, self.frames_to_trim:].movedim(2, 1)

comfy/text_encoders/llama.py

@@ -8,7 +8,6 @@ from comfy.ldm.modules.attention import optimized_attention_for_device
 import comfy.model_management
 import comfy.ldm.common_dit
 
-import comfy.model_management
 from . import qwen_vl
 
 @dataclass

comfy/utils.py

@@ -1230,6 +1230,8 @@ def convert_old_quants(state_dict, model_prefix="", metadata={}):
             out_sd = {}
             layers = {}
             for k in list(state_dict.keys()):
+                if k == scaled_fp8_key:
+                    continue
                 if not k.startswith(model_prefix):
                     out_sd[k] = state_dict[k]
                     continue

comfy_api_nodes/nodes_kling.py

@@ -807,6 +807,7 @@ class OmniProTextToVideoNode(IO.ComfyNode):
                 ),
                 IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1"]),
                 IO.Combo.Input("duration", options=[5, 10]),
+                IO.Combo.Input("resolution", options=["1080p", "720p"], optional=True),
             ],
             outputs=[
                 IO.Video.Output(),
@@ -826,6 +827,7 @@ class OmniProTextToVideoNode(IO.ComfyNode):
         prompt: str,
         aspect_ratio: str,
         duration: int,
+        resolution: str = "1080p",
     ) -> IO.NodeOutput:
         validate_string(prompt, min_length=1, max_length=2500)
         response = await sync_op(
@@ -837,6 +839,7 @@ class OmniProTextToVideoNode(IO.ComfyNode):
                 prompt=prompt,
                 aspect_ratio=aspect_ratio,
                 duration=str(duration),
+                mode="pro" if resolution == "1080p" else "std",
             ),
         )
         return await finish_omni_video_task(cls, response)
@@ -872,6 +875,7 @@ class OmniProFirstLastFrameNode(IO.ComfyNode):
                     optional=True,
                     tooltip="Up to 6 additional reference images.",
                 ),
+                IO.Combo.Input("resolution", options=["1080p", "720p"], optional=True),
             ],
             outputs=[
                 IO.Video.Output(),
@@ -893,6 +897,7 @@ class OmniProFirstLastFrameNode(IO.ComfyNode):
         first_frame: Input.Image,
         end_frame: Input.Image | None = None,
         reference_images: Input.Image | None = None,
+        resolution: str = "1080p",
     ) -> IO.NodeOutput:
         prompt = normalize_omni_prompt_references(prompt)
         validate_string(prompt, min_length=1, max_length=2500)
@@ -936,6 +941,7 @@ class OmniProFirstLastFrameNode(IO.ComfyNode):
                 prompt=prompt,
                 duration=str(duration),
                 image_list=image_list,
+                mode="pro" if resolution == "1080p" else "std",
             ),
         )
         return await finish_omni_video_task(cls, response)
@@ -964,6 +970,7 @@ class OmniProImageToVideoNode(IO.ComfyNode):
                     "reference_images",
                     tooltip="Up to 7 reference images.",
                 ),
+                IO.Combo.Input("resolution", options=["1080p", "720p"], optional=True),
             ],
             outputs=[
                 IO.Video.Output(),
@@ -984,6 +991,7 @@ class OmniProImageToVideoNode(IO.ComfyNode):
         aspect_ratio: str,
         duration: int,
         reference_images: Input.Image,
+        resolution: str = "1080p",
     ) -> IO.NodeOutput:
         prompt = normalize_omni_prompt_references(prompt)
         validate_string(prompt, min_length=1, max_length=2500)
@@ -1005,6 +1013,7 @@ class OmniProImageToVideoNode(IO.ComfyNode):
                 aspect_ratio=aspect_ratio,
                 duration=str(duration),
                 image_list=image_list,
+                mode="pro" if resolution == "1080p" else "std",
             ),
         )
         return await finish_omni_video_task(cls, response)
@@ -1036,6 +1045,7 @@ class OmniProVideoToVideoNode(IO.ComfyNode):
                     tooltip="Up to 4 additional reference images.",
                     optional=True,
                 ),
+                IO.Combo.Input("resolution", options=["1080p", "720p"], optional=True),
             ],
             outputs=[
                 IO.Video.Output(),
@@ -1058,6 +1068,7 @@ class OmniProVideoToVideoNode(IO.ComfyNode):
         reference_video: Input.Video,
         keep_original_sound: bool,
         reference_images: Input.Image | None = None,
+        resolution: str = "1080p",
     ) -> IO.NodeOutput:
         prompt = normalize_omni_prompt_references(prompt)
         validate_string(prompt, min_length=1, max_length=2500)
@@ -1090,6 +1101,7 @@ class OmniProVideoToVideoNode(IO.ComfyNode):
                 duration=str(duration),
                 image_list=image_list if image_list else None,
                 video_list=video_list,
+                mode="pro" if resolution == "1080p" else "std",
             ),
         )
         return await finish_omni_video_task(cls, response)
@@ -1119,6 +1131,7 @@ class OmniProEditVideoNode(IO.ComfyNode):
                     tooltip="Up to 4 additional reference images.",
                     optional=True,
                 ),
+                IO.Combo.Input("resolution", options=["1080p", "720p"], optional=True),
             ],
             outputs=[
                 IO.Video.Output(),
@@ -1139,6 +1152,7 @@ class OmniProEditVideoNode(IO.ComfyNode):
         video: Input.Video,
         keep_original_sound: bool,
         reference_images: Input.Image | None = None,
+        resolution: str = "1080p",
     ) -> IO.NodeOutput:
         prompt = normalize_omni_prompt_references(prompt)
         validate_string(prompt, min_length=1, max_length=2500)
@@ -1171,6 +1185,7 @@ class OmniProEditVideoNode(IO.ComfyNode):
                 duration=None,
                 image_list=image_list if image_list else None,
                 video_list=video_list,
+                mode="pro" if resolution == "1080p" else "std",
             ),
         )
         return await finish_omni_video_task(cls, response)

comfy_api_nodes/nodes_tripo.py

@@ -155,7 +155,7 @@ class TripoTextToModelNode(IO.ComfyNode):
                 model_seed=model_seed,
                 texture_seed=texture_seed,
                 texture_quality=texture_quality,
-                face_limit=face_limit,
+                face_limit=face_limit if face_limit != -1 else None,
                 geometry_quality=geometry_quality,
                 auto_size=True,
                 quad=quad,
@@ -255,7 +255,7 @@ class TripoImageToModelNode(IO.ComfyNode):
                 texture_alignment=texture_alignment,
                 texture_seed=texture_seed,
                 texture_quality=texture_quality,
-                face_limit=face_limit,
+                face_limit=face_limit if face_limit != -1 else None,
                 auto_size=True,
                 quad=quad,
             ),
@@ -369,7 +369,7 @@ class TripoMultiviewToModelNode(IO.ComfyNode):
                 texture_quality=texture_quality,
                 geometry_quality=geometry_quality,
                 texture_alignment=texture_alignment,
-                face_limit=face_limit,
+                face_limit=face_limit if face_limit != -1 else None,
                 quad=quad,
             ),
         )

comfy_execution/graph.py

@@ -207,15 +207,15 @@ class ExecutionList(TopologicalSort):
         return self.output_cache.get(node_id) is not None
 
     def cache_link(self, from_node_id, to_node_id):
-        if not to_node_id in self.execution_cache:
+        if to_node_id not in self.execution_cache:
             self.execution_cache[to_node_id] = {}
         self.execution_cache[to_node_id][from_node_id] = self.output_cache.get(from_node_id)
-        if not from_node_id in self.execution_cache_listeners:
+        if from_node_id not in self.execution_cache_listeners:
             self.execution_cache_listeners[from_node_id] = set()
         self.execution_cache_listeners[from_node_id].add(to_node_id)
 
     def get_cache(self, from_node_id, to_node_id):
-        if not to_node_id in self.execution_cache:
+        if to_node_id not in self.execution_cache:
             return None
         value = self.execution_cache[to_node_id].get(from_node_id)
         if value is None:

comfy_extras/nodes_apg.py

@@ -55,7 +55,8 @@ class APG(io.ComfyNode):
         def pre_cfg_function(args):
             nonlocal running_avg, prev_sigma
 
-            if len(args["conds_out"]) == 1: return args["conds_out"]
+            if len(args["conds_out"]) == 1:
+                return args["conds_out"]
 
             cond = args["conds_out"][0]
             uncond = args["conds_out"][1]

comfy_extras/nodes_mahiro.py

@@ -10,7 +10,7 @@ class Mahiro(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="Mahiro",
-            display_name="Mahiro is so cute that she deserves a better guidance function!! (。・ω・。)",
+            display_name="Mahiro CFG",
             category="_for_testing",
             description="Modify the guidance to scale more on the 'direction' of the positive prompt rather than the difference between the negative prompt.",
             inputs=[

comfy_extras/nodes_seedvr.py

@@ -78,8 +78,10 @@ def tiled_vae(x, vae_model, tile_size=(512, 512), tile_overlap=(64, 64), tempora
             else:
                 out = vae_model.decode_(t_chunk)
 
-            if isinstance(out, (tuple, list)): out = out[0]
-            if out.ndim == 4: out = out.unsqueeze(2)
+            if isinstance(out, (tuple, list)):
+                out = out[0]
+            if out.ndim == 4:
+                out = out.unsqueeze(2)
 
             if pad_amount > 0:
                 if encode:
@@ -136,13 +138,17 @@ def tiled_vae(x, vae_model, tile_size=(512, 512), tile_overlap=(64, 64), tempora
 
             if cur_ov_h > 0:
                 r = get_ramp(cur_ov_h)
-                if y_idx > 0: w_h[:cur_ov_h] = r
-                if y_end < h: w_h[-cur_ov_h:] = 1.0 - r
+                if y_idx > 0:
+                    w_h[:cur_ov_h] = r
+                if y_end < h:
+                    w_h[-cur_ov_h:] = 1.0 - r
 
             if cur_ov_w > 0:
                 r = get_ramp(cur_ov_w)
-                if x_idx > 0: w_w[:cur_ov_w] = r
-                if x_end < w: w_w[-cur_ov_w:] = 1.0 - r
+                if x_idx > 0:
+                    w_w[:cur_ov_w] = r
+                if x_end < w:
+                    w_w[-cur_ov_w:] = 1.0 - r
 
             final_weight = w_h.view(1,1,1,-1,1) * w_w.view(1,1,1,1,-1)
 
@@ -335,7 +341,8 @@ class SeedVR2InputProcessing(io.ComfyNode):
 
         comfy.model_management.load_models_gpu([vae.patcher])
         vae_model = vae.first_stage_model
-        scale = 0.9152; shift = 0
+        scale = 0.9152
+        shift = 0
         if images.dim() != 5: # add the t dim
             images = images.unsqueeze(0)
         images = images.permute(0, 1, 4, 2, 3)

comfy_extras/nodes_wan.py

@@ -817,7 +817,7 @@ def get_sample_indices(original_fps,
     if required_duration > total_frames / original_fps:
         raise ValueError("required_duration must be less than video length")
 
-    if not fixed_start is None and fixed_start >= 0:
+    if fixed_start is not None and fixed_start >= 0:
         start_frame = fixed_start
     else:
         max_start = total_frames - required_origin_frames

execution.py

@@ -601,6 +601,7 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
 
         if isinstance(ex, comfy.model_management.OOM_EXCEPTION):
             tips = "This error means you ran out of memory on your GPU.\n\nTIPS: If the workflow worked before you might have accidentally set the batch_size to a large number."
+            logging.info("Memory summary: {}".format(comfy.model_management.debug_memory_summary()))
             logging.error("Got an OOM, unloading all loaded models.")
             comfy.model_management.unload_all_models()
 

nodes.py

@@ -1663,8 +1663,6 @@ class LoadImage:
         output_masks = []
         w, h = None, None
 
-        excluded_formats = ['MPO']
-
         for i in ImageSequence.Iterator(img):
             i = node_helpers.pillow(ImageOps.exif_transpose, i)
 
@@ -1692,7 +1690,10 @@ class LoadImage:
             output_images.append(image)
             output_masks.append(mask.unsqueeze(0))
 
-        if len(output_images) > 1 and img.format not in excluded_formats:
+            if img.format == "MPO":
+                break  # ignore all frames except the first one for MPO format
+
+        if len(output_images) > 1:
             output_image = torch.cat(output_images, dim=0)
             output_mask = torch.cat(output_masks, dim=0)
         else:
@@ -2242,8 +2243,10 @@ async def init_external_custom_nodes():
 
         for possible_module in possible_modules:
             module_path = os.path.join(custom_node_path, possible_module)
-            if os.path.isfile(module_path) and os.path.splitext(module_path)[1] != ".py": continue
-            if module_path.endswith(".disabled"): continue
+            if os.path.isfile(module_path) and os.path.splitext(module_path)[1] != ".py":
+                continue
+            if module_path.endswith(".disabled"):
+                continue
             if args.disable_all_custom_nodes and possible_module not in args.whitelist_custom_nodes:
                 logging.info(f"Skipping {possible_module} due to disable_all_custom_nodes and whitelist_custom_nodes")
                 continue

pyproject.toml

@@ -15,12 +15,16 @@ lint.select = [
   "N805", # invalid-first-argument-name-for-method
   "S307", # suspicious-eval-usage
   "S102", # exec
+  "E",
   "T",    # print-usage
   "W",
   # The "F" series in Ruff stands for "Pyflakes" rules, which catch various Python syntax errors and undefined names.
   # See all rules here: https://docs.astral.sh/ruff/rules/#pyflakes-f
   "F",
 ]
+
+lint.ignore = ["E501", "E722", "E731", "E712", "E402", "E741"]
+
 exclude = ["*.ipynb", "**/generated/*.pyi"]
 
 [tool.pylint]

requirements.txt

@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.35.9
-comfyui-workflow-templates==0.7.64
+comfyui-workflow-templates==0.7.65
 comfyui-embedded-docs==0.3.1
 torch
 torchsde

server.py

@@ -324,7 +324,7 @@ class PromptServer():
         @routes.get("/models/{folder}")
         async def get_models(request):
             folder = request.match_info.get("folder", None)
-            if not folder in folder_paths.folder_names_and_paths:
+            if folder not in folder_paths.folder_names_and_paths:
                 return web.Response(status=404)
             files = folder_paths.get_filename_list(folder)
             return web.json_response(files)
@@ -579,7 +579,7 @@ class PromptServer():
             folder_name = request.match_info.get("folder_name", None)
             if folder_name is None:
                 return web.Response(status=404)
-            if not "filename" in request.rel_url.query:
+            if "filename" not in request.rel_url.query:
                 return web.Response(status=404)
 
             filename = request.rel_url.query["filename"]
@@ -593,7 +593,7 @@ class PromptServer():
             if out is None:
                 return web.Response(status=404)
             dt = json.loads(out)
-            if not "__metadata__" in dt:
+            if "__metadata__" not in dt:
                 return web.Response(status=404)
             return web.json_response(dt["__metadata__"])