make model manager v2 ready for PR review

- Replace legacy model manager service with the v2 manager.

- Update invocations to use new load interface.

- Fixed many but not all type checking errors in the invocations. Most
  were unrelated to model manager

- Updated routes. All the new routes live under the route tag
  `model_manager_v2`. To avoid confusion with the old routes,
  they have the URL prefix `/api/v2/models`. The old routes
  have been de-registered.

- Added a pytest for the loader.

- Updated documentation in contributing/MODEL_MANAGER.md

invokeai/backend/model_manager/load/load_base.py

@@ -18,8 +18,16 @@ from pathlib import Path
 from typing import Any, Callable, Dict, Optional, Tuple, Type
 
 from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.backend.model_manager import AnyModelConfig, BaseModelType, ModelFormat, ModelType, SubModelType
-from invokeai.backend.model_manager.config import AnyModel, VaeCheckpointConfig, VaeDiffusersConfig
+from invokeai.backend.model_manager.config import (
+    AnyModel,
+    AnyModelConfig,
+    BaseModelType,
+    ModelFormat,
+    ModelType,
+    SubModelType,
+    VaeCheckpointConfig,
+    VaeDiffusersConfig,
+)
 from invokeai.backend.model_manager.load.convert_cache.convert_cache_base import ModelConvertCacheBase
 from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase, ModelLockerBase
 from invokeai.backend.util.logging import InvokeAILogger
@@ -32,7 +40,7 @@ class LoadedModel:
     config: AnyModelConfig
     locker: ModelLockerBase
 
-    def __enter__(self) -> AnyModel:  # I think load_file() always returns a dict
+    def __enter__(self) -> AnyModel:
         """Context entry."""
         self.locker.lock()
         return self.model
@@ -171,6 +179,10 @@ class AnyModelLoader:
         def decorator(subclass: Type[ModelLoaderBase]) -> Type[ModelLoaderBase]:
             cls._logger.debug(f"Registering class {subclass.__name__} to load models of type {base}/{type}/{format}")
             key = cls._to_registry_key(base, type, format)
+            if key in cls._registry:
+                raise Exception(
+                    f"{subclass.__name__} is trying to register as a loader for {base}/{type}/{format}, but this type of model has already been registered by {cls._registry[key].__name__}"
+                )
             cls._registry[key] = subclass
             return subclass
 

invokeai/backend/model_manager/load/load_default.py

@@ -169,7 +169,7 @@ class ModelLoader(ModelLoaderBase):
                 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, weights_path: Path, output_path: Path) -> Path:
+    def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path:
         raise NotImplementedError
 
     # This needs to be implemented in the subclass

invokeai/backend/model_manager/load/model_cache/model_cache_default.py

@@ -246,7 +246,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
 
     def move_model_to_device(self, cache_entry: CacheRecord[AnyModel], target_device: torch.device) -> None:
         """Move model into the indicated device."""
-        # These attributes are not in the base ModelMixin class but in derived classes.
+        # These attributes are not in the base ModelMixin class but in various derived classes.
         # Some models don't have these attributes, in which case they run in RAM/CPU.
         self.logger.debug(f"Called to move {cache_entry.key} to {target_device}")
         if not (hasattr(cache_entry.model, "device") and hasattr(cache_entry.model, "to")):

invokeai/backend/model_manager/load/model_loaders/controlnet.py

@@ -35,28 +35,28 @@ class ControlnetLoader(GenericDiffusersLoader):
         else:
             return True
 
-    def _convert_model(self, config: AnyModelConfig, weights_path: Path, output_path: Path) -> Path:
+    def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path:
         if config.base not in {BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2}:
             raise Exception(f"Vae conversion not supported for model type: {config.base}")
         else:
             assert hasattr(config, "config")
             config_file = config.config
 
-        if weights_path.suffix == ".safetensors":
-            checkpoint = safetensors.torch.load_file(weights_path, device="cpu")
+        if model_path.suffix == ".safetensors":
+            checkpoint = safetensors.torch.load_file(model_path, device="cpu")
         else:
-            checkpoint = torch.load(weights_path, map_location="cpu")
+            checkpoint = torch.load(model_path, map_location="cpu")
 
         # sometimes weights are hidden under "state_dict", and sometimes not
         if "state_dict" in checkpoint:
             checkpoint = checkpoint["state_dict"]
 
         convert_controlnet_to_diffusers(
-            weights_path,
+            model_path,
             output_path,
             original_config_file=self._app_config.root_path / config_file,
             image_size=512,
             scan_needed=True,
-            from_safetensors=weights_path.suffix == ".safetensors",
+            from_safetensors=model_path.suffix == ".safetensors",
         )
         return output_path

invokeai/backend/model_manager/load/model_loaders/generic_diffusers.py

@@ -12,8 +12,9 @@ from invokeai.backend.model_manager import (
     ModelType,
     SubModelType,
 )
-from invokeai.backend.model_manager.load.load_base import AnyModelLoader
-from invokeai.backend.model_manager.load.load_default import ModelLoader
+
+from ..load_base import AnyModelLoader
+from ..load_default import ModelLoader
 
 
 @AnyModelLoader.register(base=BaseModelType.Any, type=ModelType.CLIPVision, format=ModelFormat.Diffusers)

invokeai/backend/model_manager/load/model_loaders/stable_diffusion.py

@@ -65,7 +65,7 @@ class StableDiffusionDiffusersModel(ModelLoader):
         else:
             return True
 
-    def _convert_model(self, config: AnyModelConfig, weights_path: Path, output_path: Path) -> Path:
+    def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path:
         assert isinstance(config, MainCheckpointConfig)
         variant = config.variant
         base = config.base
@@ -75,9 +75,9 @@ class StableDiffusionDiffusersModel(ModelLoader):
 
         config_file = config.config
 
-        self._logger.info(f"Converting {weights_path} to diffusers format")
+        self._logger.info(f"Converting {model_path} to diffusers format")
         convert_ckpt_to_diffusers(
-            weights_path,
+            model_path,
             output_path,
             model_type=self.model_base_to_model_type[base],
             model_version=base,
@@ -86,7 +86,7 @@ class StableDiffusionDiffusersModel(ModelLoader):
             extract_ema=True,
             scan_needed=True,
             pipeline_class=pipeline_class,
-            from_safetensors=weights_path.suffix == ".safetensors",
+            from_safetensors=model_path.suffix == ".safetensors",
             precision=self._torch_dtype,
             load_safety_checker=False,
         )

invokeai/backend/model_manager/load/model_loaders/vae.py

@@ -37,7 +37,7 @@ class VaeLoader(GenericDiffusersLoader):
         else:
             return True
 
-    def _convert_model(self, config: AnyModelConfig, weights_path: Path, output_path: Path) -> Path:
+    def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path:
         # TO DO: check whether sdxl VAE models convert.
         if config.base not in {BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2}:
             raise Exception(f"Vae conversion not supported for model type: {config.base}")
@@ -46,10 +46,10 @@ class VaeLoader(GenericDiffusersLoader):
                 "v1-inference.yaml" if config.base == BaseModelType.StableDiffusion1 else "v2-inference-v.yaml"
             )
 
-        if weights_path.suffix == ".safetensors":
-            checkpoint = safetensors.torch.load_file(weights_path, device="cpu")
+        if model_path.suffix == ".safetensors":
+            checkpoint = safetensors.torch.load_file(model_path, device="cpu")
         else:
-            checkpoint = torch.load(weights_path, map_location="cpu")
+            checkpoint = torch.load(model_path, map_location="cpu")
 
         # sometimes weights are hidden under "state_dict", and sometimes not
         if "state_dict" in checkpoint:

invokeai/backend/model_manager/load/model_util.py

@@ -65,7 +65,7 @@ def calc_model_size_by_fs(model_path: Path, subfolder: Optional[str] = None, var
     bit8_files = {f for f in all_files if ".8bit." in f.name or ".8bit-" in f.name}
     other_files = set(all_files) - fp16_files - bit8_files
 
-    if variant is None:
+    if not variant:  # ModelRepoVariant.DEFAULT evaluates to empty string for compatability with HF
         files = other_files
     elif variant == "fp16":
         files = fp16_files