Update OmniAvatar/models/model_manager.py

OmniAvatar/models/model_manager.py

@@ -254,49 +254,8 @@ class ModelDetectorFromSplitedSingleFile(ModelDetectorFromSingleFile):
                     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=[]):
@@ -357,7 +316,6 @@ class ModelManager:
         self.model_detector = [
             ModelDetectorFromSingleFile(model_loader_configs),
             ModelDetectorFromSplitedSingleFile(model_loader_configs),
-            ModelDetectorFromHuggingfaceFolder(huggingface_model_loader_configs),
         ]
         self.load_models(downloaded_files + file_path_list)