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)
90 lines
3.0 KiB
Python
90 lines
3.0 KiB
Python
"""Node for loading images with filename output in ComfyUI."""
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import os
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import torch
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import numpy as np
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from PIL import Image, ImageOps
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import folder_paths
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from comfy_api.latest import io, ui
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class LoadImagePlusKlinter(io.ComfyNode):
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"""Node that loads an image and returns the image, mask, and filename."""
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@classmethod
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def define_schema(cls) -> io.Schema:
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"""Define the schema for the image loading 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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input_dir = folder_paths.get_input_directory()
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files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
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return io.Schema(
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node_id="LoadImagePlus",
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display_name="Load Image Plus - klinter",
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category="klinter",
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description="Load an image and return it along with its mask and filename",
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inputs=[
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io.Combo.Input("image", options=sorted(files)),
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],
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outputs=[
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io.Image.Output(display_name="image"),
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io.Mask.Output(display_name="mask"),
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io.String.Output(display_name="filename")
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]
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)
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@classmethod
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def execute(cls, image: str) -> io.NodeOutput:
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"""Load an image and return it along with its mask and filename.
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Args:
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image: Name of the image file to load
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Returns:
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io.NodeOutput: Image tensor, mask, and filename without extension
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"""
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image_path = folder_paths.get_annotated_filepath(image)
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i = Image.open(image_path)
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i = ImageOps.exif_transpose(i)
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img = i.convert("RGB")
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img_array = np.array(img).astype(np.float32) / 255.0
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img_tensor = torch.from_numpy(img_array)[None,]
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# Get filename without extension
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filename = os.path.splitext(os.path.basename(image_path))[0]
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# Extract alpha channel if present, otherwise create zero mask
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if i.mode == "RGBA":
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mask = torch.from_numpy(np.array(i.split()[-1]).astype(np.float32))[None,] / 255.0
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else:
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mask = torch.zeros((1, img_tensor.shape[1], img_tensor.shape[2]), dtype=torch.float32)
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return io.NodeOutput(img_tensor, mask, filename, ui=ui.PreviewImage(img_tensor, cls=cls))
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@classmethod
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def fingerprint_inputs(cls, image: str) -> str:
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"""Return hash of file content for cache control.
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Args:
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image: Name of the image file
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Returns:
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str: Hash of the file for cache invalidation
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"""
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image_path = folder_paths.get_annotated_filepath(image)
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return str(os.path.getmtime(image_path))
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# Register the node
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NODE_CLASS_MAPPINGS = {
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"LoadImagePlus": LoadImagePlusKlinter
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LoadImagePlus": "Load Image Plus - klinter"
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}
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# Export the class
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__all__ = ['LoadImagePlusKlinter']
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