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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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class Uniformer_SemSegPreprocessor: |
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@classmethod |
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def INPUT_TYPES(s): |
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return define_preprocessor_inputs(resolution=INPUT.RESOLUTION()) |
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RETURN_TYPES = ("IMAGE",) |
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FUNCTION = "semantic_segmentate" |
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CATEGORY = "ControlNet Preprocessors/Semantic Segmentation" |
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def semantic_segmentate(self, image, resolution=512): |
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from custom_controlnet_aux.uniformer import UniformerSegmentor |
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model = UniformerSegmentor.from_pretrained().to(model_management.get_torch_device()) |
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out = common_annotator_call(model, image, resolution=resolution) |
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del model |
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return (out, ) |
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NODE_CLASS_MAPPINGS = { |
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"UniFormer-SemSegPreprocessor": Uniformer_SemSegPreprocessor, |
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"SemSegPreprocessor": Uniformer_SemSegPreprocessor, |
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} |
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NODE_DISPLAY_NAME_MAPPINGS = { |
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"UniFormer-SemSegPreprocessor": "UniFormer Segmentor", |
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"SemSegPreprocessor": "Semantic Segmentor (legacy, alias for UniFormer)", |
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} |