jaidaken
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Includes 30 custom nodes committed directly, 7 Civitai-exclusive loras stored via Git LFS, and a setup script that installs all dependencies and downloads HuggingFace-hosted models on vast.ai. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
41 lines
1.4 KiB
Python
41 lines
1.4 KiB
Python
from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT, run_script
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import comfy.model_management as model_management
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import sys
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def install_deps():
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try:
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import sklearn
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except:
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run_script([sys.executable, '-s', '-m', 'pip', 'install', 'scikit-learn'])
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class DiffusionEdge_Preprocessor:
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@classmethod
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def INPUT_TYPES(s):
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return define_preprocessor_inputs(
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environment=INPUT.COMBO(["indoor", "urban", "natrual"]),
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patch_batch_size=INPUT.INT(default=4, min=1, max=16),
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resolution=INPUT.RESOLUTION()
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)
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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CATEGORY = "ControlNet Preprocessors/Line Extractors"
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def execute(self, image, environment="indoor", patch_batch_size=4, resolution=512, **kwargs):
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install_deps()
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from custom_controlnet_aux.diffusion_edge import DiffusionEdgeDetector
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model = DiffusionEdgeDetector \
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.from_pretrained(filename = f"diffusion_edge_{environment}.pt") \
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.to(model_management.get_torch_device())
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out = common_annotator_call(model, image, resolution=resolution, patch_batch_size=patch_batch_size)
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del model
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return (out, )
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NODE_CLASS_MAPPINGS = {
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"DiffusionEdge_Preprocessor": DiffusionEdge_Preprocessor,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"DiffusionEdge_Preprocessor": "Diffusion Edge (batch size ↑ => speed ↑, VRAM ↑)",
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} |