final tidying before marking PR as ready for review

- Replace AnyModelLoader with ModelLoaderRegistry
- Fix type check errors in multiple files
- Remove apparently unneeded `get_model_config_enum()` method from model manager
- Remove last vestiges of old model manager
- Updated tests and documentation

resolve conflict with seamless.py

invokeai/backend/model_manager/load/load_default.py

@@ -1,13 +1,9 @@
 # Copyright (c) 2024, Lincoln D. Stein and the InvokeAI Development Team
 """Default implementation of model loading in InvokeAI."""
 
-import sys
 from logging import Logger
 from pathlib import Path
-from typing import Any, Dict, Optional, Tuple
-
-from diffusers import ModelMixin
-from diffusers.configuration_utils import ConfigMixin
+from typing import Optional, Tuple
 
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.backend.model_manager import (
@@ -25,17 +21,6 @@ from invokeai.backend.model_manager.load.optimizations import skip_torch_weight_
 from invokeai.backend.util.devices import choose_torch_device, torch_dtype
 
 
-class ConfigLoader(ConfigMixin):
-    """Subclass of ConfigMixin for loading diffusers configuration files."""
-
-    @classmethod
-    def load_config(cls, *args: Any, **kwargs: Any) -> Dict[str, Any]:
-        """Load a diffusrs ConfigMixin configuration."""
-        cls.config_name = kwargs.pop("config_name")
-        # Diffusers doesn't provide typing info
-        return super().load_config(*args, **kwargs)  # type: ignore
-
-
 # TO DO: The loader is not thread safe!
 class ModelLoader(ModelLoaderBase):
     """Default implementation of ModelLoaderBase."""
@@ -137,43 +122,6 @@ class ModelLoader(ModelLoaderBase):
             variant=config.repo_variant if hasattr(config, "repo_variant") else None,
         )
 
-    def _load_diffusers_config(self, model_path: Path, config_name: str = "config.json") -> Dict[str, Any]:
-        return ConfigLoader.load_config(model_path, config_name=config_name)
-
-    # TO DO: Add exception handling
-    def _hf_definition_to_type(self, module: str, class_name: str) -> ModelMixin:  # fix with correct type
-        if module in ["diffusers", "transformers"]:
-            res_type = sys.modules[module]
-        else:
-            res_type = sys.modules["diffusers"].pipelines
-        result: ModelMixin = getattr(res_type, class_name)
-        return result
-
-    # TO DO: Add exception handling
-    def _get_hf_load_class(self, model_path: Path, submodel_type: Optional[SubModelType] = None) -> ModelMixin:
-        if submodel_type:
-            try:
-                config = self._load_diffusers_config(model_path, config_name="model_index.json")
-                module, class_name = config[submodel_type.value]
-                return self._hf_definition_to_type(module=module, class_name=class_name)
-            except KeyError as e:
-                raise InvalidModelConfigException(
-                    f'The "{submodel_type}" submodel is not available for this model.'
-                ) from e
-        else:
-            try:
-                config = self._load_diffusers_config(model_path, config_name="config.json")
-                class_name = config.get("_class_name", None)
-                if class_name:
-                    return self._hf_definition_to_type(module="diffusers", class_name=class_name)
-                if config.get("model_type", None) == "clip_vision_model":
-                    class_name = config.get("architectures")[0]
-                    return self._hf_definition_to_type(module="transformers", class_name=class_name)
-                if not class_name:
-                    raise InvalidModelConfigException("Unable to decifer Load Class based on given config.json")
-            except KeyError as e:
-                raise InvalidModelConfigException("An expected config.json file is missing from this model.") from e
-
     # This needs to be implemented in subclasses that handle checkpoints
     def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path:
         raise NotImplementedError