import os, torch, json, importlib from typing import List from .downloader import download_models, download_customized_models, Preset_model_id, Preset_model_website from ..configs.model_config import model_loader_configs, huggingface_model_loader_configs, patch_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 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)