multiple minor fixes

invokeai/app/services/config/invokeai_config.py

@@ -325,14 +325,6 @@ class InvokeAIAppConfig(InvokeAISettings):
             cls.singleton_init = kwargs
         return cls.singleton_config
 
-    @classmethod
-    def clear_config(cls):
-        """
-        This removes the singleton InvokeAIAppConfig configuration object.
-        """
-        cls.singleton_config = None
-        cls.singleton_config = None
-
     @property
     def root_path(self) -> Path:
         """

invokeai/app/services/download_manager.py

@@ -139,7 +139,7 @@ class DownloadQueueService(DownloadQueueServiceBase):
     ) -> DownloadJobBase:  # noqa D102
         event_handlers = event_handlers or []
         if self._event_bus:
-            event_handlers.append([self._event_bus.emit_model_download_event])
+            event_handlers.append([self._event_bus.emit_model_download_event])   # BUG! This is not a valid method call
         return self._queue.create_download_job(
             source,
             destdir,

invokeai/app/services/model_manager_service.py

@@ -7,6 +7,7 @@ from abc import ABC, abstractmethod
 from pathlib import Path
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
 
+from processor import Invoker
 from pydantic import Field, parse_obj_as
 from pydantic.networks import AnyHttpUrl
 
@@ -345,6 +346,9 @@ class ModelManagerService(ModelManagerServiceBase):
         if self._event_bus:
             kwargs.update(event_handlers=[self._event_bus.emit_model_event])
         self._loader = ModelLoad(config, **kwargs)
+
+    def start(self, invoker: Invoker):
+        """Call automatically at process start."""
         self._loader.installer.scan_models_directory()  # synchronize new/deleted models found in models directory
 
     def get_model(

invokeai/backend/model_manager/cache.py

@@ -95,7 +95,6 @@ class ModelCache(object):
         execution_device: torch.device = torch.device("cuda"),
         storage_device: torch.device = torch.device("cpu"),
         precision: torch.dtype = torch.float16,
-        sequential_offload: bool = False,
         lazy_offloading: bool = True,
         sha_chunksize: int = 16777216,
         logger: Logger = InvokeAILogger.get_logger(),
@@ -106,7 +105,6 @@ class ModelCache(object):
         :param storage_device: Torch device to save inactive model in [torch.device('cpu')]
         :param precision: Precision for loaded models [torch.float16]
         :param lazy_offloading: Keep model in VRAM until another model needs to be loaded
-        :param sequential_offload: Conserve VRAM by loading and unloading each stage of the pipeline sequentially
         :param sha_chunksize: Chunksize to use when calculating sha256 model hash
         """
         self.model_infos: Dict[str, ModelBase] = dict()

invokeai/backend/model_manager/config.py

@@ -240,7 +240,12 @@ AnyModelConfig = Union[
     TextualInversionConfig,
     ONNXSD1Config,
     ONNXSD2Config,
+    VaeCheckpointConfig,
+    VaeDiffusersConfig,
+    ControlNetDiffusersConfig,
+    ControlNetCheckpointConfig,
     ModelConfigBase,
+    IPAdapterConfig,
 ]
 
 

invokeai/backend/model_manager/install.py

@@ -293,6 +293,26 @@ class ModelInstallBase(ABC):
         """
         pass
 
+    @abstractmethod
+    def convert_model(
+        self,
+        key: str,
+        dest_directory: Optional[Path] = None,
+    ) -> ModelConfigBase:
+        """
+        Convert a checkpoint file into a diffusers folder.
+
+        It will delete the cached version ans well as the
+        original checkpoint file if it is in the models directory.
+        :param key: Unique key of model.
+        :dest_directory: Optional place to put converted file. If not specified,
+        will be stored in the `models_dir`.
+
+        This will raise a ValueError unless the model is a checkpoint.
+        This will raise an UnknownModelException if key is unknown.
+        """
+        pass
+
     @abstractmethod
     def sync_model_path(self, key) -> ModelConfigBase:
         """
@@ -456,6 +476,9 @@ class ModelInstall(ModelInstallBase):
             if not path.exists():
                 new_path = path
             counter += 1
+        self._logger.warning('Use shutil.move(), not Path.replace() here; hash before and after move')
+        # BUG! This won't work across filesystems.
+        # Rehash before and after moving.
         return old_path.replace(new_path)
 
     def _probe_model(self, model_path: Union[Path, str], overrides: Optional[Dict[str, Any]] = None) -> ModelProbeInfo:

invokeai/backend/model_manager/loader.py

@@ -78,6 +78,7 @@ class ModelLoadBase(ABC):
         pass
 
     @property
+    @abstractmethod
     def config(self) -> InvokeAIAppConfig:
         """Return the config object used by this installer."""
         pass
@@ -100,8 +101,8 @@ class ModelLoadBase(ABC):
 
     @property
     @abstractmethod
-    def precision(self) -> str:
-        """Return 'float32' or 'float16'."""
+    def precision(self) -> torch.dtype:
+        """Return torch.fp16 or torch.fp32."""
         pass
 
     @abstractmethod
@@ -168,7 +169,6 @@ class ModelLoad(ModelLoadBase):
             lazy_offloading=config.lazy_offload,
             execution_device=device,
             precision=dtype,
-            sequential_offload=config.sequential_guidance,
             logger=self._logger,
         )
 
@@ -178,8 +178,8 @@ class ModelLoad(ModelLoadBase):
         return self._store
 
     @property
-    def precision(self) -> str:
-        """Return 'float32' or 'float16'."""
+    def precision(self) -> torch.fp32:
+        """Return torch.fp16 or torch.fp32."""
         return self._cache.precision
 
     @property