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from ..utils import common_annotator_call, create_node_input_types
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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 create_node_input_types(
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rm_nearest=("FLOAT", {"default": 0.0, "min": 0.0, "max": 100, "step": 0.1}),
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rm_background=("FLOAT", {"default": 0.0, "min": 0.0, "max": 100, "step": 0.1}),
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boost=(["enable", "disable"], {"default": "disable"})
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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, rm_background, resolution=512, **kwargs):
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from 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=kwargs["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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} |