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import gc
import torch
from lama_cleaner.model.fcf import FcF
from lama_cleaner.model.lama import LaMa
from lama_cleaner.model.ldm import LDM
from lama_cleaner.model.manga import Manga
from lama_cleaner.model.mat import MAT
from lama_cleaner.model.opencv2 import OpenCV2
from lama_cleaner.model.paint_by_example import PaintByExample
from lama_cleaner.model.sd import SD15, SD2
from lama_cleaner.model.zits import ZITS
from lama_cleaner.schema import Config
models = {"lama": LaMa, "ldm": LDM, "zits": ZITS, "mat": MAT, "fcf": FcF, "sd1.5": SD15, "cv2": OpenCV2, "manga": Manga,
"sd2": SD2, "paint_by_example": PaintByExample}
class ModelManager:
def __init__(self, model_device, **kwargs):
self.name = "lama"
self.device = model_device
self.kwargs = kwargs
self.model = self.init_model(self.name, model_device, **kwargs)
def init_model(self, name: str, device, **kwargs):
if name in models:
model = models[name](device, **kwargs)
else:
raise NotImplementedError(f"Not supported model: {name}")
return model
def is_downloaded(self, name: str) -> bool:
if name in models:
return models[name].is_downloaded()
else:
raise NotImplementedError(f"Not supported model: {name}")
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:
if (torch.cuda.memory_allocated() > 0):
# Clear current loaded model from memory
torch.cuda.empty_cache()
del self.model
gc.collect()
self.model = self.init_model(new_name, self.device, **self.kwargs)
self.name = new_name
except NotImplementedError as e:
raise e
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