xiaden
7d82aa22e6
Sub-packages: - edge/ — Canny, Lineart, LineartAnime (3 nodes) - restore/ — SCUNet, NAFNet, Adversarial (3 nodes) - compare/ — CCIP, LPIPS (2 nodes) - validate/ — Classify (11 ops), Check (5 ops), Metric (2 ops) - tagging/ — WD14, DeepDanbooru, DeepGelbooru, MLDanbooru, Camie, PixAI (6 nodes) Root nodes: - ImgUtilsOCR — PaddleOCR with heat/box threshold - ImgUtilsDetect — 10 detection types + confidence knob - ImgUtilsPose — DWPose + auto_detect toggle - ImgUtilsSegment — isnetis + scale knob - ImgUtilsTags — tag string processing - ImgUtilsCDC — upscaling + tile_size - ImgUtilsCensor — NSFW censoring with method/confidence/body-part toggles - ImgUtilsAlign — resize to max dimension - ImgUtilsSqueeze — transparency-based auto-crop Every node exposes its native function parameters as knobs. Removed mega-nodes: transform (11 ops), compare (3 ops), validate (18 ops).
26 lines
1.1 KiB
Python
26 lines
1.1 KiB
Python
"""Node: ImgUtilsAdversarial — Adversarial noise removal."""
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from __future__ import annotations
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from comfy_api.latest import io
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from ..utils import comfy_to_pil, pil_to_comfy
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class ImgUtilsAdversarial(io.ComfyNode):
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@classmethod
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def define_schema(cls) -> io.Schema:
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return io.Schema(
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node_id="ImgUtilsAdversarial", display_name="Imgutils Restore (Adversarial)",
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category="imgutils/restore",
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description="Remove adversarial noise using bilateral + guided filtering. No ML model needed.",
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search_aliases=["restore", "adversarial", "denoise", "noise", "clean", "mist"],
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inputs=[
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io.Image.Input("image", tooltip="Input image with adversarial noise"),
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],
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outputs=[io.Image.Output(display_name="image")],
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)
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@classmethod
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def execute(cls, image) -> io.NodeOutput:
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from imgutils.restore import remove_adversarial_noise
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pil = comfy_to_pil(image.numpy() if hasattr(image, "numpy") else image)
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result = remove_adversarial_noise(pil)
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return io.NodeOutput(pil_to_comfy(result))
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