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import importlib
import json
import os
from typing import List
import torch
from ..configs.model_config import (huggingface_model_loader_configs,
model_loader_configs,
patch_model_loader_configs)
from .downloader import (Preset_model_id, Preset_model_website,
download_customized_models, download_models)
from .utils import (hash_state_dict_keys, init_weights_on_device,
load_state_dict, 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
def load_model_from_huggingface_folder(
file_path, model_names, model_classes, torch_dtype, device
):
loaded_model_names, loaded_models = [], []
for model_name, model_class in zip(model_names, model_classes):
if torch_dtype in [torch.float32, torch.float16, torch.bfloat16]:
model = model_class.from_pretrained(
file_path, torch_dtype=torch_dtype
).eval()
else:
model = model_class.from_pretrained(file_path).eval().to(dtype=torch_dtype)
if torch_dtype == torch.float16 and hasattr(model, "half"):
model = model.half()
try:
model = model.to(device=device)
except:
pass
loaded_model_names.append(model_name)
loaded_models.append(model)
return loaded_model_names, loaded_models
def load_single_patch_model_from_single_file(
state_dict, model_name, model_class, base_model, extra_kwargs, torch_dtype, device
):
print(
f" model_name: {model_name} model_class: {model_class.__name__} extra_kwargs: {extra_kwargs}"
)
base_state_dict = base_model.state_dict()
base_model.to("cpu")
del base_model
model = model_class(**extra_kwargs)
model.load_state_dict(base_state_dict, strict=False)
model.load_state_dict(state_dict, strict=False)
model.to(dtype=torch_dtype, device=device)
return model
def load_patch_model_from_single_file(
state_dict,
model_names,
model_classes,
extra_kwargs,
model_manager,
torch_dtype,
device,
):
loaded_model_names, loaded_models = [], []
for model_name, model_class in zip(model_names, model_classes):
while True:
for model_id in range(len(model_manager.model)):
base_model_name = model_manager.model_name[model_id]
if base_model_name == model_name:
base_model_path = model_manager.model_path[model_id]
base_model = model_manager.model[model_id]
print(
f" Adding patch model to {base_model_name} ({base_model_path})"
)
patched_model = load_single_patch_model_from_single_file(
state_dict,
model_name,
model_class,
base_model,
extra_kwargs,
torch_dtype,
device,
)
loaded_model_names.append(base_model_name)
loaded_models.append(patched_model)
model_manager.model.pop(model_id)
model_manager.model_path.pop(model_id)
model_manager.model_name.pop(model_id)
break
else:
break
return loaded_model_names, loaded_models
class ModelDetectorTemplate:
def __init__(self):
pass
def match(self, file_path="", state_dict={}):
return False
def load(
self,
file_path="",
state_dict={},
device="cuda",
torch_dtype=torch.float16,
**kwargs,
):
return [], []
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)
# Load models with strict matching
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
# Load models without strict matching
# (the shape of parameters may be inconsistent, and the state_dict_converter will modify the model architecture)
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,
):
# Split the state_dict and load from each component
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 ModelDetectorFromHuggingfaceFolder:
def __init__(self, model_loader_configs=[]):
self.architecture_dict = {}
for metadata in model_loader_configs:
self.add_model_metadata(*metadata)
def add_model_metadata(
self, architecture, huggingface_lib, model_name, redirected_architecture
):
self.architecture_dict[architecture] = (
huggingface_lib,
model_name,
redirected_architecture,
)
def match(self, file_path="", state_dict={}):
if not isinstance(file_path, str) or os.path.isfile(file_path):
return False
file_list = os.listdir(file_path)
if "config.json" not in file_list:
return False
with open(os.path.join(file_path, "config.json"), "r") as f:
config = json.load(f)
if "architectures" not in config and "_class_name" not in config:
return False
return True
def load(
self,
file_path="",
state_dict={},
device="cuda",
torch_dtype=torch.float16,
**kwargs,
):
with open(os.path.join(file_path, "config.json"), "r") as f:
config = json.load(f)
loaded_model_names, loaded_models = [], []
architectures = (
config["architectures"]
if "architectures" in config
else [config["_class_name"]]
)
for architecture in architectures:
(
huggingface_lib,
model_name,
redirected_architecture,
) = self.architecture_dict[architecture]
if redirected_architecture is not None:
architecture = redirected_architecture
model_class = importlib.import_module(huggingface_lib).__getattribute__(
architecture
)
loaded_model_names_, loaded_models_ = load_model_from_huggingface_folder(
file_path, [model_name], [model_class], torch_dtype, device
)
loaded_model_names += loaded_model_names_
loaded_models += loaded_models_
return loaded_model_names, loaded_models
class ModelDetectorFromPatchedSingleFile:
def __init__(self, model_loader_configs=[]):
self.keys_hash_with_shape_dict = {}
for metadata in model_loader_configs:
self.add_model_metadata(*metadata)
def add_model_metadata(
self, keys_hash_with_shape, model_name, model_class, extra_kwargs
):
self.keys_hash_with_shape_dict[keys_hash_with_shape] = (
model_name,
model_class,
extra_kwargs,
)
def match(self, file_path="", state_dict={}):
if not isinstance(file_path, str) or 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
return False
def load(
self,
file_path="",
state_dict={},
device="cuda",
torch_dtype=torch.float16,
model_manager=None,
**kwargs,
):
if len(state_dict) == 0:
state_dict = load_state_dict(file_path)
# Load models with strict matching
loaded_model_names, loaded_models = [], []
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, extra_kwargs = self.keys_hash_with_shape_dict[
keys_hash_with_shape
]
loaded_model_names_, loaded_models_ = load_patch_model_from_single_file(
state_dict,
model_names,
model_classes,
extra_kwargs,
model_manager,
torch_dtype,
device,
)
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",
model_id_list: List[Preset_model_id] = [],
downloading_priority: List[Preset_model_website] = [
"ModelScope",
"HuggingFace",
],
file_path_list: List[str] = [],
):
self.torch_dtype = torch_dtype
self.device = device
self.model = []
self.model_path = []
self.model_name = []
downloaded_files = (
download_models(model_id_list, downloading_priority)
if len(model_id_list) > 0
else []
)
self.model_detector = [
ModelDetectorFromSingleFile(model_loader_configs),
ModelDetectorFromSplitedSingleFile(model_loader_configs),
ModelDetectorFromHuggingfaceFolder(huggingface_model_loader_configs),
ModelDetectorFromPatchedSingleFile(patch_model_loader_configs),
]
self.load_models(downloaded_files + file_path_list)
def load_model_from_single_file(
self,
file_path="",
state_dict={},
model_names=[],
model_classes=[],
model_resource=None,
):
print(f"Loading models from file: {file_path}")
if len(state_dict) == 0:
state_dict = load_state_dict(file_path)
model_names, models = load_model_from_single_file(
state_dict,
model_names,
model_classes,
model_resource,
self.torch_dtype,
self.device,
)
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}.")
def load_model_from_huggingface_folder(
self, file_path="", model_names=[], model_classes=[]
):
print(f"Loading models from folder: {file_path}")
model_names, models = load_model_from_huggingface_folder(
file_path, model_names, model_classes, self.torch_dtype, self.device
)
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}.")
def load_patch_model_from_single_file(
self,
file_path="",
state_dict={},
model_names=[],
model_classes=[],
extra_kwargs={},
):
print(f"Loading patch models from file: {file_path}")
model_names, models = load_patch_model_from_single_file(
state_dict,
model_names,
model_classes,
extra_kwargs,
self,
self.torch_dtype,
self.device,
)
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 patched models are loaded: {model_names}.")
def load_lora(self, file_path="", state_dict={}, lora_alpha=1.0):
if isinstance(file_path, list):
for file_path_ in file_path:
self.load_lora(file_path_, state_dict=state_dict, lora_alpha=lora_alpha)
else:
print(f"Loading LoRA models from file: {file_path}")
if len(state_dict) == 0:
state_dict = load_state_dict(file_path)
for model_name, model, model_path in zip(
self.model_name, self.model, self.model_path
):
for lora in get_lora_loaders():
match_results = lora.match(model, state_dict)
if match_results is not None:
print(f" Adding LoRA to {model_name} ({model_path}).")
lora_prefix, model_resource = match_results
lora.load(
model,
state_dict,
lora_prefix,
alpha=lora_alpha,
model_resource=model_resource,
)
break
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
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
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):
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]}.")
else:
print(
f"More than one {model_name} models are loaded in model manager: {fetched_model_paths}. Using {model_name} from {fetched_model_paths[0]}."
)
if require_model_path:
return fetched_models[0], fetched_model_paths[0]
else:
return fetched_models[0]
def to(self, device):
for model in self.model:
model.to(device)
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