Tidy names and locations of modules

- Rename old "model_management" directory to "model_management_OLD" in order to catch
  dangling references to original model manager.
- Caught and fixed most dangling references (still checking)
- Rename lora, textual_inversion and model_patcher modules
- Introduce a RawModel base class to simplfy the Union returned by the
  model loaders.
- Tidy up the model manager 2-related tests. Add useful fixtures, and
  a finalizer to the queue and installer fixtures that will stop the
  services and release threads.

invokeai/backend/util/test_utils.py

@@ -5,10 +5,9 @@ from typing import Optional, Union
 import pytest
 import torch
 
-from invokeai.app.services.config.config_default import InvokeAIAppConfig
-from invokeai.backend.install.model_install_backend import ModelInstall
-from invokeai.backend.model_management.model_manager import LoadedModelInfo
-from invokeai.backend.model_management.models.base import BaseModelType, ModelNotFoundException, ModelType, SubModelType
+from invokeai.app.services.model_manager import ModelManagerServiceBase
+from invokeai.app.services.model_records import UnknownModelException
+from invokeai.backend.model_manager import BaseModelType, LoadedModel, ModelType, SubModelType
 
 
 @pytest.fixture(scope="session")
@@ -16,31 +15,20 @@ def torch_device():
     return "cuda" if torch.cuda.is_available() else "cpu"
 
 
-@pytest.fixture(scope="module")
-def model_installer():
-    """A global ModelInstall pytest fixture to be used by many tests."""
-    # HACK(ryand): InvokeAIAppConfig.get_config() returns a singleton config object. This can lead to weird interactions
-    # between tests that need to alter the config. For example, some tests change the 'root' directory in the config,
-    # which can cause `install_and_load_model(...)` to re-download the model unnecessarily. As a temporary workaround,
-    # we pass a kwarg to get_config, which causes the config to be re-loaded. To fix this properly, we should stop using
-    # a singleton.
-    return ModelInstall(InvokeAIAppConfig.get_config(log_level="info"))
-
-
 def install_and_load_model(
-    model_installer: ModelInstall,
+    model_manager: ModelManagerServiceBase,
     model_path_id_or_url: Union[str, Path],
     model_name: str,
     base_model: BaseModelType,
     model_type: ModelType,
     submodel_type: Optional[SubModelType] = None,
-) -> LoadedModelInfo:
-    """Install a model if it is not already installed, then get the LoadedModelInfo for that model.
+) -> LoadedModel:
+    """Install a model if it is not already installed, then get the LoadedModel for that model.
 
     This is intended as a utility function for tests.
 
     Args:
-        model_installer (ModelInstall): The model installer.
+        mm2_model_manager (ModelManagerServiceBase): The model manager
         model_path_id_or_url (Union[str, Path]): The path, HF ID, URL, etc. where the model can be installed from if it
             is not already installed.
         model_name (str): The model name, forwarded to ModelManager.get_model(...).
@@ -51,16 +39,23 @@ def install_and_load_model(
     Returns:
         LoadedModelInfo
     """
-    # If the requested model is already installed, return its LoadedModelInfo.
-    with contextlib.suppress(ModelNotFoundException):
-        return model_installer.mgr.get_model(model_name, base_model, model_type, submodel_type)
+    # If the requested model is already installed, return its LoadedModel
+    with contextlib.suppress(UnknownModelException):
+        # TODO: Replace with wrapper call
+        loaded_model: LoadedModel = model_manager.load.load_model_by_attr(
+            model_name=model_name, base_model=base_model, model_type=model_type
+        )
+        return loaded_model
 
     # Install the requested model.
-    model_installer.heuristic_import(model_path_id_or_url)
+    job = model_manager.install.heuristic_import(model_path_id_or_url)
+    model_manager.install.wait_for_job(job, timeout=10)
+    assert job.complete
 
     try:
-        return model_installer.mgr.get_model(model_name, base_model, model_type, submodel_type)
-    except ModelNotFoundException as e:
+        loaded_model = model_manager.load.load_model_by_config(job.config_out)
+        return loaded_model
+    except UnknownModelException as e:
         raise Exception(
             "Failed to get model info after installing it. There could be a mismatch between the requested model and"
             f" the installation id ('{model_path_id_or_url}'). Error: {e}"