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from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT
import comfy.model_management as model_management
class Uniformer_SemSegPreprocessor:
@classmethod
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)",
}