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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}")
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