adjust the convert api - not right just yet

invokeai/app/api/routers/model_manager.py

@@ -619,17 +619,12 @@ async def convert_model(
         logger.error(f"The model with key {key} is not a main checkpoint model.")
         raise HTTPException(400, f"The model with key {key} is not a main checkpoint model.")
 
-    # loading the model will convert it into a cached diffusers file
-    try:
-        cc_size = loader.convert_cache.max_size
-        if cc_size == 0:  # temporary set the convert cache to a positive number so that cached model is written
-            loader._convert_cache.max_size = 1.0
-        loader.load_model(model_config, submodel_type=SubModelType.Scheduler)
-    finally:
-        loader._convert_cache.max_size = cc_size
-
-    # Get the path of the converted model from the loader
     cache_path = loader.convert_cache.cache_path(key)
+    converted_model = loader.load_model(model_config, submodel_type=SubModelType.Scheduler)
+    # write the converted file to the model cache directory
+    raw_model = converted_model.model
+    assert hasattr(raw_model, 'save_pretrained')
+    raw_model.save_pretrained(cache_path)
     assert cache_path.exists()
 
     # temporarily rename the original safetensors file so that there is no naming conflict

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

@@ -28,22 +28,14 @@ class ControlNetLoader(GenericDiffusersLoader):
         submodel_type: Optional[SubModelType] = None,
     ) -> AnyModel:
         if isinstance(config, ControlNetCheckpointConfig):
-            return ControlNetModel.from_single_file(config.path, config=self._app_config.legacy_conf_path / config.config_path)
+            return ControlNetModel.from_single_file(config.path,
+                                                    config=self._app_config.legacy_conf_path / config.config_path,
+                                                    torch_dtype=self._torch_dtype,
+                                                    local_files_only=True,
+                                                    )
         else:
             return super()._load_model(config, submodel_type)
 
-    # def _needs_conversion(self, config: AnyModelConfig, model_path: Path, dest_path: Path) -> bool:
-    #     if not isinstance(config, CheckpointConfigBase):
-    #         return False
-    #     elif (
-    #         dest_path.exists()
-    #         and (dest_path / "config.json").stat().st_mtime >= (config.converted_at or 0.0)
-    #         and (dest_path / "config.json").stat().st_mtime >= model_path.stat().st_mtime
-    #     ):
-    #         return False
-    #     else:
-    #         return True
-
     # def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Optional[Path] = None) -> AnyModel:
     #     assert isinstance(config, CheckpointConfigBase)
     #     image_size = (

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

@@ -107,9 +107,12 @@ class StableDiffusionDiffusersModel(GenericDiffusersLoader):
             load_class = load_classes[config.base][config.variant]
         except KeyError as e:
             raise Exception(f'No diffusers pipeline known for base={config.base}, variant={config.variant}') from e
-        print(f'DEBUG: load_class={load_class}')
         original_config_file=self._app_config.legacy_conf_path / config.config_path   # should try without using this...
-        pipeline = load_class.from_single_file(config.path, config=original_config_file)
+        pipeline = load_class.from_single_file(config.path,
+                                               config=original_config_file,
+                                               torch_dtype=self._torch_dtype,
+                                               local_files_only=True,
+                                               )
 
         # Proactively load the various submodels into the RAM cache so that we don't have to re-convert
         # the entire pipeline every time a new submodel is needed.

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

@@ -34,22 +34,14 @@ class VAELoader(GenericDiffusersLoader):
         submodel_type: Optional[SubModelType] = None,
     ) -> AnyModel:
         if isinstance(config, VAECheckpointConfig):
-            return AutoencoderKL.from_single_file(config.path, config=self._app_config.legacy_conf_path / config.config_path)
+            return AutoencoderKL.from_single_file(config.path,
+                                                  config=self._app_config.legacy_conf_path / config.config_path,
+                                                  torch_dtype=self._torch_dtype,
+                                                  local_files_only=True,
+                                                  )
         else:
             return super()._load_model(config, submodel_type)
 
-    # def _needs_conversion(self, config: AnyModelConfig, model_path: Path, dest_path: Path) -> bool:
-    #     if not isinstance(config, CheckpointConfigBase):
-    #         return False
-    #     elif (
-    #         dest_path.exists()
-    #         and (dest_path / "config.json").stat().st_mtime >= (config.converted_at or 0.0)
-    #         and (dest_path / "config.json").stat().st_mtime >= model_path.stat().st_mtime
-    #     ):
-    #         return False
-    #     else:
-    #         return True
-
     # def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Optional[Path] = None) -> AnyModel:
     #     # TODO(MM2): check whether sdxl VAE models convert.
     #     if config.base not in {BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2}: