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from .registry import model_loader_configs
from .utils import (
load_state_dict,
init_weights_on_device,
hash_state_dict_keys,
split_state_dict_with_prefix,
)
def load_model_from_single_file(
state_dict, model_names, model_classes, model_resource, torch_dtype, device
):
loaded_model_names, loaded_models = ([], [])
for model_name, model_class in zip(model_names, model_classes):
print(f" model_name: {model_name} model_class: {model_class.__name__}")
state_dict_converter = model_class.state_dict_converter()
if model_resource == "civitai":
state_dict_results = state_dict_converter.from_civitai(state_dict)
elif model_resource == "diffusers":
state_dict_results = state_dict_converter.from_diffusers(state_dict)
if isinstance(state_dict_results, tuple):
model_state_dict, extra_kwargs = state_dict_results
print(
f" This model is initialized with extra kwargs: {extra_kwargs}"
)
else:
model_state_dict, extra_kwargs = (state_dict_results, {})
torch_dtype = (
torch.float32
if extra_kwargs.get("upcast_to_float32", False)
else torch_dtype
)
with init_weights_on_device():
model = model_class(**extra_kwargs)
if hasattr(model, "eval"):
model = model.eval()
model.load_state_dict(model_state_dict, assign=True)
model = model.to(dtype=torch_dtype, device=device)
loaded_model_names.append(model_name)
loaded_models.append(model)
return (loaded_model_names, loaded_models)
class ModelDetectorFromSingleFile:
def __init__(self, model_loader_configs=[]):
self.keys_hash_with_shape_dict = {}
self.keys_hash_dict = {}
for metadata in model_loader_configs:
self.add_model_metadata(*metadata)
def add_model_metadata(
self,
keys_hash,
keys_hash_with_shape,
model_names,
model_classes,
model_resource,
):
self.keys_hash_with_shape_dict[keys_hash_with_shape] = (
model_names,
model_classes,
model_resource,
)
if keys_hash is not None:
self.keys_hash_dict[keys_hash] = (
model_names,
model_classes,
model_resource,
)
def match(self, file_path="", state_dict={}):
if isinstance(file_path, str) and os.path.isdir(file_path):
return False
if len(state_dict) == 0:
state_dict = load_state_dict(file_path)
keys_hash_with_shape = hash_state_dict_keys(state_dict, with_shape=True)
if keys_hash_with_shape in self.keys_hash_with_shape_dict:
return True
keys_hash = hash_state_dict_keys(state_dict, with_shape=False)
if keys_hash in self.keys_hash_dict:
return True
return False
def load(
self,
file_path="",
state_dict={},
device="cuda",
torch_dtype=torch.float16,
**kwargs,
):
if len(state_dict) == 0:
state_dict = load_state_dict(file_path)
keys_hash_with_shape = hash_state_dict_keys(state_dict, with_shape=True)
if keys_hash_with_shape in self.keys_hash_with_shape_dict:
model_names, model_classes, model_resource = self.keys_hash_with_shape_dict[
keys_hash_with_shape
]
loaded_model_names, loaded_models = load_model_from_single_file(
state_dict,
model_names,
model_classes,
model_resource,
torch_dtype,
device,
)
return (loaded_model_names, loaded_models)
keys_hash = hash_state_dict_keys(state_dict, with_shape=False)
if keys_hash in self.keys_hash_dict:
model_names, model_classes, model_resource = self.keys_hash_dict[keys_hash]
loaded_model_names, loaded_models = load_model_from_single_file(
state_dict,
model_names,
model_classes,
model_resource,
torch_dtype,
device,
)
return (loaded_model_names, loaded_models)
return (loaded_model_names, loaded_models)
class ModelDetectorFromSplitedSingleFile(ModelDetectorFromSingleFile):
def __init__(self, model_loader_configs=[]):
super().__init__(model_loader_configs)
def match(self, file_path="", state_dict={}):
if isinstance(file_path, str) and os.path.isdir(file_path):
return False
if len(state_dict) == 0:
state_dict = load_state_dict(file_path)
splited_state_dict = split_state_dict_with_prefix(state_dict)
for sub_state_dict in splited_state_dict:
if super().match(file_path, sub_state_dict):
return True
return False
def load(
self,
file_path="",
state_dict={},
device="cuda",
torch_dtype=torch.float16,
**kwargs,
):
splited_state_dict = split_state_dict_with_prefix(state_dict)
valid_state_dict = {}
for sub_state_dict in splited_state_dict:
if super().match(file_path, sub_state_dict):
valid_state_dict.update(sub_state_dict)
if super().match(file_path, valid_state_dict):
loaded_model_names, loaded_models = super().load(
file_path, valid_state_dict, device, torch_dtype
)
else:
loaded_model_names, loaded_models = ([], [])
for sub_state_dict in splited_state_dict:
if super().match(file_path, sub_state_dict):
loaded_model_names_, loaded_models_ = super().load(
file_path, valid_state_dict, device, torch_dtype
)
loaded_model_names += loaded_model_names_
loaded_models += loaded_models_
return (loaded_model_names, loaded_models)
class ModelManager:
def __init__(self, torch_dtype=torch.float16, device="cuda"):
self.torch_dtype = torch_dtype
self.device = device
self.model = []
self.model_path = []
self.model_name = []
self.model_detector = [
ModelDetectorFromSingleFile(model_loader_configs),
ModelDetectorFromSplitedSingleFile(model_loader_configs),
]
def load_model(self, file_path, model_names=None, device=None, torch_dtype=None):
print(f"Loading models from: {file_path}")
if device is None:
device = self.device
if torch_dtype is None:
torch_dtype = self.torch_dtype
_fp0 = file_path[0] if isinstance(file_path, list) and file_path else file_path
if isinstance(file_path, list):
state_dict = {}
for path in file_path:
state_dict.update(load_state_dict(path))
elif os.path.isfile(file_path):
state_dict = load_state_dict(file_path)
else:
state_dict = None
if isinstance(file_path, list):
_branch = "list"
elif isinstance(file_path, str) and os.path.isfile(file_path):
_branch = "file"
else:
_branch = "none"
for model_detector in self.model_detector:
if model_detector.match(file_path, state_dict):
model_names, models = model_detector.load(
file_path,
state_dict,
device=device,
torch_dtype=torch_dtype,
allowed_model_names=model_names,
model_manager=self,
)
for model_name, model in zip(model_names, models):
self.model.append(model)
self.model_path.append(file_path)
self.model_name.append(model_name)
print(f" The following models are loaded: {model_names}.")
break
else:
print(f" We cannot detect the model type. No models are loaded.")
def load_models(
self, file_path_list, model_names=None, device=None, torch_dtype=None
):
for file_path in file_path_list:
self.load_model(
file_path, model_names, device=device, torch_dtype=torch_dtype
)
def fetch_model(
self, model_name, file_path=None, require_model_path=False, index=None
):
fetched_models = []
fetched_model_paths = []
for model, model_path, model_name_ in zip(
self.model, self.model_path, self.model_name
):
if file_path is not None and file_path != model_path:
continue
if model_name == model_name_:
fetched_models.append(model)
fetched_model_paths.append(model_path)
if len(fetched_models) == 0:
print(f"No {model_name} models available.")
return None
if len(fetched_models) == 1:
print(f"Using {model_name} from {fetched_model_paths[0]}.")
model = fetched_models[0]
path = fetched_model_paths[0]
elif index is None:
model = fetched_models[0]
path = fetched_model_paths[0]
print(
f"More than one {model_name} models are loaded in model manager: {fetched_model_paths}. Using {model_name} from {fetched_model_paths[0]}."
)
elif isinstance(index, int):
model = fetched_models[:index]
path = fetched_model_paths[:index]
print(
f"More than one {model_name} models are loaded in model manager: {fetched_model_paths}. Using {model_name} from {fetched_model_paths[:index]}."
)
else:
model = fetched_models
path = fetched_model_paths
print(
f"More than one {model_name} models are loaded in model manager: {fetched_model_paths}. Using {model_name} from {fetched_model_paths}."
)
if require_model_path:
return (model, path)
else:
return model
def to(self, device):
for model in self.model:
model.to(device)
|