56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
import torch
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import gc
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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.paint_by_example import PaintByExample
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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, "paint_by_example": PaintByExample}
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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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if (torch.cuda.memory_allocated() > 0):
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# Clear current loaded model from memory
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torch.cuda.empty_cache()
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del self.model
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gc.collect()
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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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