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from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT
import comfy.model_management as model_management
class OneFormer_COCO_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.oneformer import OneformerSegmentor
model = OneformerSegmentor.from_pretrained(filename="150_16_swin_l_oneformer_coco_100ep.pth")
model = model.to(model_management.get_torch_device())
out = common_annotator_call(model, image, resolution=resolution)
del model
return (out,)
class OneFormer_ADE20K_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.oneformer import OneformerSegmentor
model = OneformerSegmentor.from_pretrained(filename="250_16_swin_l_oneformer_ade20k_160k.pth")
model = model.to(model_management.get_torch_device())
out = common_annotator_call(model, image, resolution=resolution)
del model
return (out,)
NODE_CLASS_MAPPINGS = {
"OneFormer-COCO-SemSegPreprocessor": OneFormer_COCO_SemSegPreprocessor,
"OneFormer-ADE20K-SemSegPreprocessor": OneFormer_ADE20K_SemSegPreprocessor
}
NODE_DISPLAY_NAME_MAPPINGS = {
"OneFormer-COCO-SemSegPreprocessor": "OneFormer COCO Segmentor",
"OneFormer-ADE20K-SemSegPreprocessor": "OneFormer ADE20K Segmentor"
} |