models/model_manager.py

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}")
            is_loaded = False
            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,
                        )
                        is_loaded = True
                        break
            if not is_loaded:
                print(f"    Cannot load LoRA: {file_path}")

    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, 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]
        else:
            if 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)