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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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import numpy as np |
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class MIDAS_Normal_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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a=INPUT.FLOAT(default=np.pi * 2.0, min=0.0, max=np.pi * 5.0), |
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bg_threshold=INPUT.FLOAT(default=0.1), |
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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, a=np.pi * 2.0, bg_threshold=0.1, resolution=512, **kwargs): |
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from custom_controlnet_aux.midas import MidasDetector |
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model = MidasDetector.from_pretrained().to(model_management.get_torch_device()) |
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cb = lambda image, **kargs: model(image, **kargs)[1] |
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out = common_annotator_call(cb, image, resolution=resolution, a=a, bg_th=bg_threshold, depth_and_normal=True) |
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del model |
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return (out, ) |
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class MIDAS_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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a=INPUT.FLOAT(default=np.pi * 2.0, min=0.0, max=np.pi * 5.0), |
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bg_threshold=INPUT.FLOAT(default=0.1), |
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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, a=np.pi * 2.0, bg_threshold=0.1, resolution=512, **kwargs): |
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from custom_controlnet_aux.midas import MidasDetector |
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model = MidasDetector.from_pretrained().to(model_management.get_torch_device()) |
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out = common_annotator_call(model, image, resolution=resolution, a=a, bg_th=bg_threshold) |
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del model |
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return (out, ) |
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NODE_CLASS_MAPPINGS = { |
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"MiDaS-NormalMapPreprocessor": MIDAS_Normal_Map_Preprocessor, |
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"MiDaS-DepthMapPreprocessor": MIDAS_Depth_Map_Preprocessor |
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} |
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NODE_DISPLAY_NAME_MAPPINGS = { |
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"MiDaS-NormalMapPreprocessor": "MiDaS Normal Map", |
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"MiDaS-DepthMapPreprocessor": "MiDaS Depth Map" |
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} |