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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>
32 lines
1.2 KiB
Python
32 lines
1.2 KiB
Python
from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT
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import comfy.model_management as model_management
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class LERES_Depth_Map_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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rm_nearest=INPUT.FLOAT(max=100.0),
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rm_background=INPUT.FLOAT(max=100.0),
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boost=INPUT.COMBO(["disable", "enable"]),
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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/Normal and Depth Estimators"
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def execute(self, image, rm_nearest=0, rm_background=0, resolution=512, boost="disable", **kwargs):
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from custom_controlnet_aux.leres import LeresDetector
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model = LeresDetector.from_pretrained().to(model_management.get_torch_device())
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out = common_annotator_call(model, image, resolution=resolution, thr_a=rm_nearest, thr_b=rm_background, boost=boost == "enable")
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del model
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return (out, )
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NODE_CLASS_MAPPINGS = {
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"LeReS-DepthMapPreprocessor": LERES_Depth_Map_Preprocessor
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LeReS-DepthMapPreprocessor": "LeReS Depth Map (enable boost for leres++)"
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} |