init
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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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#Dirty hack :))
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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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# Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_depth2image.py
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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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}
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