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