[pull] master from comfyanonymous:master

comfy/ldm/wan/model.py

@@ -146,6 +146,15 @@ def forward(self, x, context, context_img_len):
 }
 
 
+def repeat_e(e, x):
+    repeats = 1
+    if e.shape[1] > 1:
+        repeats = x.shape[1] // e.shape[1]
+    if repeats == 1:
+        return e
+    return torch.repeat_interleave(e, repeats, dim=1)
+
+
 class WanAttentionBlock(nn.Module):
 
     def __init__(self,
@@ -201,6 +210,7 @@ def forward(
             freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
         """
         # assert e.dtype == torch.float32
+
         if e.ndim < 4:
             e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e).chunk(6, dim=1)
         else:
@@ -209,15 +219,15 @@ def forward(
 
         # self-attention
         y = self.self_attn(
-            self.norm1(x) * (1 + e[1]) + e[0],
+            self.norm1(x) * (1 + repeat_e(e[1], x)) + repeat_e(e[0], x),
             freqs)
 
-        x = x + y * e[2]
+        x = x + y * repeat_e(e[2], x)
 
         # cross-attention & ffn
         x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len)
-        y = self.ffn(self.norm2(x) * (1 + e[4]) + e[3])
-        x = x + y * e[5]
+        y = self.ffn(self.norm2(x) * (1 + repeat_e(e[4], x)) + repeat_e(e[3], x))
+        x = x + y * repeat_e(e[5], x)
         return x
 
 
@@ -331,7 +341,8 @@ def forward(self, x, e):
             e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e.unsqueeze(1)).chunk(2, dim=1)
         else:
             e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device).unsqueeze(0) + e.unsqueeze(2)).unbind(2)
-        x = (self.head(self.norm(x) * (1 + e[1]) + e[0]))
+
+        x = (self.head(self.norm(x) * (1 + repeat_e(e[1], x)) + repeat_e(e[0], x)))
         return x
 
 

comfy/model_base.py

@@ -1202,7 +1202,7 @@ def extra_conds(self, **kwargs):
     def process_timestep(self, timestep, x, denoise_mask=None, **kwargs):
         if denoise_mask is None:
             return timestep
-        temp_ts = (torch.mean(denoise_mask[:, :, :, ::2, ::2], dim=1, keepdim=True) * timestep.view([timestep.shape[0]] + [1] * (denoise_mask.ndim - 1))).reshape(timestep.shape[0], -1)
+        temp_ts = (torch.mean(denoise_mask[:, :, :, :, :], dim=(1, 3, 4), keepdim=True) * timestep.view([timestep.shape[0]] + [1] * (denoise_mask.ndim - 1))).reshape(timestep.shape[0], -1)
         return temp_ts
 
     def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):

comfy_api/generate_api_stubs.py

@@ -0,0 +1,86 @@
+#!/usr/bin/env python3
+"""
+Script to generate .pyi stub files for the synchronous API wrappers.
+This allows generating stubs without running the full ComfyUI application.
+"""
+
+import os
+import sys
+import logging
+import importlib
+
+# Add ComfyUI to path so we can import modules
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from comfy_api.internal.async_to_sync import AsyncToSyncConverter
+from comfy_api.version_list import supported_versions
+
+
+def generate_stubs_for_module(module_name: str) -> None:
+    """Generate stub files for a specific module that exports ComfyAPI and ComfyAPISync."""
+    try:
+        # Import the module
+        module = importlib.import_module(module_name)
+
+        # Check if module has ComfyAPISync (the sync wrapper)
+        if hasattr(module, "ComfyAPISync"):
+            # Module already has a sync class
+            api_class = getattr(module, "ComfyAPI", None)
+            sync_class = getattr(module, "ComfyAPISync")
+
+            if api_class:
+                # Generate the stub file
+                AsyncToSyncConverter.generate_stub_file(api_class, sync_class)
+                logging.info(f"Generated stub file for {module_name}")
+            else:
+                logging.warning(
+                    f"Module {module_name} has ComfyAPISync but no ComfyAPI"
+                )
+
+        elif hasattr(module, "ComfyAPI"):
+            # Module only has async API, need to create sync wrapper first
+            from comfy_api.internal.async_to_sync import create_sync_class
+
+            api_class = getattr(module, "ComfyAPI")
+            sync_class = create_sync_class(api_class)
+
+            # Generate the stub file
+            AsyncToSyncConverter.generate_stub_file(api_class, sync_class)
+            logging.info(f"Generated stub file for {module_name}")
+        else:
+            logging.warning(
+                f"Module {module_name} does not export ComfyAPI or ComfyAPISync"
+            )
+
+    except Exception as e:
+        logging.error(f"Failed to generate stub for {module_name}: {e}")
+        import traceback
+
+        traceback.print_exc()
+
+
+def main():
+    """Main function to generate all API stub files."""
+    logging.basicConfig(level=logging.INFO)
+
+    logging.info("Starting stub generation...")
+
+    # Dynamically get module names from supported_versions
+    api_modules = []
+    for api_class in supported_versions:
+        # Extract module name from the class
+        module_name = api_class.__module__
+        if module_name not in api_modules:
+            api_modules.append(module_name)
+
+    logging.info(f"Found {len(api_modules)} API modules: {api_modules}")
+
+    # Generate stubs for each module
+    for module_name in api_modules:
+        generate_stubs_for_module(module_name)
+
+    logging.info("Stub generation complete!")
+
+
+if __name__ == "__main__":
+    main()

comfy_api/input/__init__.py

@@ -1,8 +1,16 @@
-from .basic_types import ImageInput, AudioInput
-from .video_types import VideoInput
+# This file only exists for backwards compatibility.
+from comfy_api.latest._input import (
+    ImageInput,
+    AudioInput,
+    MaskInput,
+    LatentInput,
+    VideoInput,
+)
 
 __all__ = [
     "ImageInput",
     "AudioInput",
+    "MaskInput",
+    "LatentInput",
     "VideoInput",
 ]

comfy_api/input/basic_types.py

@@ -1,20 +1,14 @@
-import torch
-from typing import TypedDict
-
-ImageInput = torch.Tensor
-"""
-An image in format [B, H, W, C] where B is the batch size, C is the number of channels,
-"""
-
-class AudioInput(TypedDict):
-    """
-    TypedDict representing audio input.
-    """
-
-    waveform: torch.Tensor
-    """
-    Tensor in the format [B, C, T] where B is the batch size, C is the number of channels,
-    """
-
-    sample_rate: int
-
+# This file only exists for backwards compatibility.
+from comfy_api.latest._input.basic_types import (
+    ImageInput,
+    AudioInput,
+    MaskInput,
+    LatentInput,
+)
+
+__all__ = [
+    "ImageInput",
+    "AudioInput",
+    "MaskInput",
+    "LatentInput",
+]

comfy_api/input/video_types.py

@@ -1,85 +1,6 @@
-from __future__ import annotations
-from abc import ABC, abstractmethod
-from typing import Optional, Union
-import io
-import av
-from comfy_api.util import VideoContainer, VideoCodec, VideoComponents
+# This file only exists for backwards compatibility.
+from comfy_api.latest._input.video_types import VideoInput
 
-class VideoInput(ABC):
-    """
-    Abstract base class for video input types.
-    """
-
-    @abstractmethod
-    def get_components(self) -> VideoComponents:
-        """
-        Abstract method to get the video components (images, audio, and frame rate).
-
-        Returns:
-            VideoComponents containing images, audio, and frame rate
-        """
-        pass
-
-    @abstractmethod
-    def save_to(
-        self,
-        path: str,
-        format: VideoContainer = VideoContainer.AUTO,
-        codec: VideoCodec = VideoCodec.AUTO,
-        metadata: Optional[dict] = None
-    ):
-        """
-        Abstract method to save the video input to a file.
-        """
-        pass
-
-    def get_stream_source(self) -> Union[str, io.BytesIO]:
-        """
-        Get a streamable source for the video. This allows processing without
-        loading the entire video into memory.
-
-        Returns:
-            Either a file path (str) or a BytesIO object that can be opened with av.
-
-        Default implementation creates a BytesIO buffer, but subclasses should
-        override this for better performance when possible.
-        """
-        buffer = io.BytesIO()
-        self.save_to(buffer)
-        buffer.seek(0)
-        return buffer
-
-    # Provide a default implementation, but subclasses can provide optimized versions
-    # if possible.
-    def get_dimensions(self) -> tuple[int, int]:
-        """
-        Returns the dimensions of the video input.
-
-        Returns:
-            Tuple of (width, height)
-        """
-        components = self.get_components()
-        return components.images.shape[2], components.images.shape[1]
-
-    def get_duration(self) -> float:
-        """
-        Returns the duration of the video in seconds.
-
-        Returns:
-            Duration in seconds
-        """
-        components = self.get_components()
-        frame_count = components.images.shape[0]
-        return float(frame_count / components.frame_rate)
-
-    def get_container_format(self) -> str:
-        """
-        Returns the container format of the video (e.g., 'mp4', 'mov', 'avi').
-
-        Returns:
-            Container format as string
-        """
-        # Default implementation - subclasses should override for better performance
-        source = self.get_stream_source()
-        with av.open(source, mode="r") as container:
-            return container.format.name
+__all__ = [
+    "VideoInput",
+]

comfy_api/input_impl/__init__.py

@@ -1,7 +1,7 @@
-from .video_types import VideoFromFile, VideoFromComponents
+# This file only exists for backwards compatibility.
+from comfy_api.latest._input_impl import VideoFromFile, VideoFromComponents
 
 __all__ = [
-    # Implementations
     "VideoFromFile",
     "VideoFromComponents",
 ]