| import os |
| |
| os.environ['NPU_DEVICE_COUNT'] = '0' |
| os.environ['MMCV_WITH_OPS'] = '0' |
|
|
| 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)", |
| } |