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import torch | |
import gc | |
from loguru import logger | |
from lama_cleaner.const import SD15_MODELS | |
from lama_cleaner.helper import switch_mps_device | |
from lama_cleaner.model.controlnet import ControlNet | |
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.paint_by_example import PaintByExample | |
from lama_cleaner.model.instruct_pix2pix import InstructPix2Pix | |
from lama_cleaner.model.sd import SD15, SD2, Anything4, RealisticVision14 | |
from lama_cleaner.model.utils import torch_gc | |
from lama_cleaner.model.zits import ZITS | |
from lama_cleaner.model.opencv2 import OpenCV2 | |
from lama_cleaner.schema import Config | |
models = { | |
"lama": LaMa, | |
"ldm": LDM, | |
"zits": ZITS, | |
"mat": MAT, | |
"fcf": FcF, | |
SD15.name: SD15, | |
Anything4.name: Anything4, | |
RealisticVision14.name: RealisticVision14, | |
"cv2": OpenCV2, | |
"manga": Manga, | |
"sd2": SD2, | |
"paint_by_example": PaintByExample, | |
"instruct_pix2pix": InstructPix2Pix, | |
} | |
class ModelManager: | |
def __init__(self, name: str, device: torch.device, **kwargs): | |
self.name = name | |
self.device = device | |
self.kwargs = kwargs | |
self.model = self.init_model(name, device, **kwargs) | |
def init_model(self, name: str, device, **kwargs): | |
if name in SD15_MODELS and kwargs.get("sd_controlnet", False): | |
return ControlNet(device, **{**kwargs, "name": name}) | |
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): | |
self.switch_controlnet_method(control_method=config.controlnet_method) | |
return self.model(image, mask, config) | |
def switch(self, new_name: str, **kwargs): | |
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, switch_mps_device(new_name, self.device), **self.kwargs | |
) | |
self.name = new_name | |
except NotImplementedError as e: | |
raise e | |
def switch_controlnet_method(self, control_method: str): | |
if not self.kwargs.get("sd_controlnet"): | |
return | |
if self.kwargs["sd_controlnet_method"] == control_method: | |
return | |
if not hasattr(self.model, "is_local_sd_model"): | |
return | |
if self.model.is_local_sd_model: | |
# is_native_control_inpaint 表示加载了普通 SD 模型 | |
if ( | |
self.model.is_native_control_inpaint | |
and control_method != "control_v11p_sd15_inpaint" | |
): | |
raise RuntimeError( | |
f"--sd-local-model-path load a normal SD model, " | |
f"to use {control_method} you should load an inpainting SD model" | |
) | |
elif ( | |
not self.model.is_native_control_inpaint | |
and control_method == "control_v11p_sd15_inpaint" | |
): | |
raise RuntimeError( | |
f"--sd-local-model-path load an inpainting SD model, " | |
f"to use {control_method} you should load a norml SD model" | |
) | |
del self.model | |
torch_gc() | |
old_method = self.kwargs["sd_controlnet_method"] | |
self.kwargs["sd_controlnet_method"] = control_method | |
self.model = self.init_model( | |
self.name, switch_mps_device(self.name, self.device), **self.kwargs | |
) | |
logger.info(f"Switch ControlNet method from {old_method} to {control_method}") | |