48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
import torch
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from lama_cleaner.model.fcf import FcF
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from lama_cleaner.model.lama import LaMa
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from lama_cleaner.model.ldm import LDM
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from lama_cleaner.model.manga import Manga
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from lama_cleaner.model.mat import MAT
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from lama_cleaner.model.sd import SD15, SD2
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from lama_cleaner.model.zits import ZITS
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from lama_cleaner.model.opencv2 import OpenCV2
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from lama_cleaner.schema import Config
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models = {"lama": LaMa, "ldm": LDM, "zits": ZITS, "mat": MAT, "fcf": FcF, "sd1.5": SD15, "cv2": OpenCV2, "manga": Manga,
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"sd2": SD2}
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class ModelManager:
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def __init__(self, name: str, device: torch.device, **kwargs):
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self.name = name
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self.device = device
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self.kwargs = kwargs
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self.model = self.init_model(name, device, **kwargs)
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def init_model(self, name: str, device, **kwargs):
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if name in models:
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model = models[name](device, **kwargs)
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else:
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raise NotImplementedError(f"Not supported model: {name}")
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return model
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def is_downloaded(self, name: str) -> bool:
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if name in models:
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return models[name].is_downloaded()
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else:
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raise NotImplementedError(f"Not supported model: {name}")
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def __call__(self, image, mask, config: Config):
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return self.model(image, mask, config)
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def switch(self, new_name: str):
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if new_name == self.name:
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return
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try:
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self.model = self.init_model(new_name, self.device, **self.kwargs)
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self.name = new_name
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except NotImplementedError as e:
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raise e
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