IOPaint/lama_cleaner/model_manager.py

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import torch
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
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 = {
"lama": LaMa,
"ldm": LDM,
"zits": ZITS,
"mat": MAT,
"fcf": FcF,
"sd1.5": SD15,
"cv2": OpenCV2,
"manga": Manga,
"sd2": SD2,
"paint_by_example": PaintByExample,
"instruct_pix2pix": InstructPix2Pix,
}
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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
self.device = device
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self.kwargs = kwargs
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:
raise NotImplementedError(f"Not supported model: {name}")
return model
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def is_downloaded(self, name: str) -> bool:
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if name in models:
return models[name].is_downloaded()
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else:
raise NotImplementedError(f"Not supported model: {name}")
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def __call__(self, image, mask, config: Config):
return self.model(image, mask, config)
def switch(self, new_name: str):
if new_name == self.name:
return
try:
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if torch.cuda.memory_allocated() > 0:
# Clear current loaded model from memory
torch.cuda.empty_cache()
del self.model
gc.collect()
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self.model = self.init_model(
new_name, switch_mps_device(new_name, self.device), **self.kwargs
)
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self.name = new_name
except NotImplementedError as e:
raise e