jaidaken
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Includes 30 custom nodes committed directly, 7 Civitai-exclusive loras stored via Git LFS, and a setup script that installs all dependencies and downloads HuggingFace-hosted models on vast.ai. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
50 lines
1.8 KiB
Python
50 lines
1.8 KiB
Python
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 OneFormer_COCO_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.oneformer import OneformerSegmentor
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model = OneformerSegmentor.from_pretrained(filename="150_16_swin_l_oneformer_coco_100ep.pth")
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model = model.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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class OneFormer_ADE20K_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.oneformer import OneformerSegmentor
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model = OneformerSegmentor.from_pretrained(filename="250_16_swin_l_oneformer_ade20k_160k.pth")
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model = model.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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"OneFormer-COCO-SemSegPreprocessor": OneFormer_COCO_SemSegPreprocessor,
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"OneFormer-ADE20K-SemSegPreprocessor": OneFormer_ADE20K_SemSegPreprocessor
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"OneFormer-COCO-SemSegPreprocessor": "OneFormer COCO Segmentor",
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"OneFormer-ADE20K-SemSegPreprocessor": "OneFormer ADE20K Segmentor"
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} |