71 lines
2.1 KiB
Python
71 lines
2.1 KiB
Python
import torch
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import gc
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from lama_cleaner.helper import switch_mps_device
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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.instruct_pix2pix import InstructPix2Pix
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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 = {
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"lama": LaMa,
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"ldm": LDM,
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"zits": ZITS,
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"mat": MAT,
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"fcf": FcF,
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"sd1.5": SD15,
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"cv2": OpenCV2,
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"manga": Manga,
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"sd2": SD2,
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"paint_by_example": PaintByExample,
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"instruct_pix2pix": InstructPix2Pix,
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}
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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(
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new_name, switch_mps_device(new_name, self.device), **self.kwargs
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)
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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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