convert custom VAEs into diffusers

- When a legacy checkpoint model is loaded via --convert_ckpt and its
  models.yaml stanza refers to a custom VAE path (using the 'vae:'
  key), the custom VAE will be converted and used within the diffusers
  model. Otherwise the VAE contained within the legacy model will be
  used.

- Note that the heuristic_import() method, which imports arbitrary
  legacy files on disk and URLs, will continue to default to the
  the standard stabilityai/sd-vae-ft-mse VAE. This can be fixed after
  the fact by editing the models.yaml stanza using the Web or CLI
  UIs.

- Fixes issue #2917

invokeai/backend/model_management/convert_ckpt_to_diffusers.py

@@ -1033,7 +1033,7 @@ def replace_checkpoint_vae(checkpoint, vae_path:str):
         vae_ckpt = torch.load(vae_path, map_location="cpu")
     for vae_key in vae_ckpt['state_dict']:
         new_key = f'first_stage_model.{vae_key}'
-    checkpoint[new_key] = vae_ckpt['state_dict'][vae_key]
+        checkpoint[new_key] = vae_ckpt['state_dict'][vae_key]
 
 def load_pipeline_from_original_stable_diffusion_ckpt(
     checkpoint_path: str,

invokeai/backend/model_management/model_manager.py

@@ -454,7 +454,12 @@ class ModelManager(object):
 
         from . import load_pipeline_from_original_stable_diffusion_ckpt
 
-        self.offload_model(self.current_model)
+        try:
+            if self.list_models()[self.current_model]['status'] == 'active':
+                self.offload_model(self.current_model)
+        except Exception as e:
+            pass
+        
         vae_path = None
         if vae:
             vae_path = vae if os.path.isabs(vae) else os.path.normpath(os.path.join(Globals.root, vae))
@@ -510,6 +515,7 @@ class ModelManager(object):
         print(f">> Offloading {model_name} to CPU")
         model = self.models[model_name]["model"]
         model.offload_all()
+        self.current_model = None
 
         gc.collect()
         if self._has_cuda():
@@ -790,14 +796,15 @@ v        Apply picklescanner to the indicated checkpoint and issue a warning
         return model_name
 
     def convert_and_import(
-        self,
-        ckpt_path: Path,
-        diffusers_path: Path,
-        model_name=None,
-        model_description=None,
-        vae=None,
-        original_config_file: Path = None,
-        commit_to_conf: Path = None,
+            self,
+            ckpt_path: Path,
+            diffusers_path: Path,
+            model_name=None,
+            model_description=None,
+            vae:dict=None,
+            vae_path:Path=None,
+            original_config_file: Path = None,
+            commit_to_conf: Path = None,
     ) -> str:
         """
         Convert a legacy ckpt weights file to diffuser model and import
@@ -825,13 +832,17 @@ v        Apply picklescanner to the indicated checkpoint and issue a warning
         try:
             # By passing the specified VAE to the conversion function, the autoencoder
             # will be built into the model rather than tacked on afterward via the config file
-            vae_model = self._load_vae(vae) if vae else None
+            vae_model=None
+            if vae:
+                vae_model=self._load_vae(vae)
+                vae_path=None
             convert_ckpt_to_diffusers(
                 ckpt_path,
                 diffusers_path,
                 extract_ema=True,
                 original_config_file=original_config_file,
                 vae=vae_model,
+                vae_path=vae_path,
             )
             print(
                 f"  | Success. Optimized model is now located at {str(diffusers_path)}"

invokeai/frontend/CLI/CLI.py

@@ -772,16 +772,10 @@ def convert_model(model_name_or_path: Union[Path, str], gen, opt, completer):
             original_config_file = Path(model_info["config"])
             model_name = model_name_or_path
             model_description = model_info["description"]
-            vae = model_info["vae"]
+            vae_path = model_info.get("vae")
         else:
             print(f"** {model_name_or_path} is not a legacy .ckpt weights file")
             return
-        if vae_repo := invokeai.backend.model_management.model_manager.VAE_TO_REPO_ID.get(
-            Path(vae).stem
-        ):
-            vae_repo = dict(repo_id=vae_repo)
-        else:
-            vae_repo = None
         model_name = manager.convert_and_import(
             ckpt_path,
             diffusers_path=Path(
@@ -790,7 +784,7 @@ def convert_model(model_name_or_path: Union[Path, str], gen, opt, completer):
             model_name=model_name,
             model_description=model_description,
             original_config_file=original_config_file,
-            vae=vae_repo,
+            vae_path=vae_path,
         )
     else:
         try: