Gili Ben Shahar
e442b15b3f
- Migrated 21 node files to V3 architecture - All nodes now inherit from io.ComfyNode - Replaced INPUT_TYPES() with define_schema() returning io.Schema - Standardized all execution methods to execute() classmethods - Updated returns to use io.NodeOutput - Renamed VALIDATE_INPUTS -> validate_inputs, IS_CHANGED -> fingerprint_inputs - Updated __init__.py to use ComfyExtension with comfy_entrypoint() - Removed __init__ methods (V3 nodes are stateless) - Added OpenSpec proposal documenting the migration BREAKING: Requires ComfyUI with V3 schema support (comfy_api package)
71 lines
2.2 KiB
Python
71 lines
2.2 KiB
Python
import os
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from PIL import Image
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import torch
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import numpy as np
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from comfy_api.latest import io
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class FolderLoader(io.ComfyNode):
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@classmethod
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def define_schema(cls) -> io.Schema:
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"""Define the schema for the folder loader node.
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Returns:
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io.Schema: Node schema with inputs and outputs
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"""
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return io.Schema(
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node_id="FolderLoader",
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display_name="Folder Loader - klinter",
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category="klinter",
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description="Load images from a folder",
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inputs=[
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io.String.Input("folder_path", default=""),
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io.Int.Input("image_load_cap", default=0, min=0, step=1, optional=True),
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io.Int.Input("start_index", default=0, min=0, step=1, optional=True),
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],
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outputs=[
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io.Image.Output(display_name="images")
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]
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)
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@classmethod
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def execute(cls, folder_path: str, image_load_cap: int = 0, start_index: int = 0) -> io.NodeOutput:
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if not os.path.isdir(folder_path):
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raise FileNotFoundError(f"Folder '{folder_path}' cannot be found.")
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valid_extensions = {'.jpg', '.jpeg', '.png', '.webp'}
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image_files = sorted([
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f for f in os.listdir(folder_path)
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if os.path.splitext(f.lower())[1] in valid_extensions
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])
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image_files = image_files[start_index:]
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if image_load_cap > 0:
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image_files = image_files[:image_load_cap]
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if not image_files:
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raise ValueError("No valid images found in folder")
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images = []
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for image_path in image_files:
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full_path = os.path.join(folder_path, image_path)
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img = Image.open(full_path)
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if img.mode != 'RGB':
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img = img.convert('RGB')
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img_tensor = torch.from_numpy(np.array(img).astype(np.float32) / 255.0)
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images.append(img_tensor)
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return io.NodeOutput(torch.stack(images))
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# Register the node
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NODE_CLASS_MAPPINGS = {
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"FolderLoader": FolderLoader
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"FolderLoader": "Folder Loader - klinter"
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}
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# Export the class
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__all__ = ['FolderLoader'] |