diff --git a/invokeai/app/services/model_install/model_install_base.py b/invokeai/app/services/model_install/model_install_base.py
index a1ad0e1a87..b7385495e5 100644
--- a/invokeai/app/services/model_install/model_install_base.py
+++ b/invokeai/app/services/model_install/model_install_base.py
@@ -131,9 +131,7 @@ class URLModelSource(StringLikeSource):
         return str(self.url)
 
 
-ModelSource = Annotated[
-    Union[LocalModelSource, HFModelSource, URLModelSource], Field(discriminator="type")
-]
+ModelSource = Annotated[Union[LocalModelSource, HFModelSource, URLModelSource], Field(discriminator="type")]
 
 MODEL_SOURCE_TO_TYPE_MAP = {
     URLModelSource: ModelSourceType.Url,
diff --git a/invokeai/app/services/shared/sqlite_migrator/sqlite_migrator_common.py b/invokeai/app/services/shared/sqlite_migrator/sqlite_migrator_common.py
index 9b2444dae4..47ed5da505 100644
--- a/invokeai/app/services/shared/sqlite_migrator/sqlite_migrator_common.py
+++ b/invokeai/app/services/shared/sqlite_migrator/sqlite_migrator_common.py
@@ -17,7 +17,8 @@ class MigrateCallback(Protocol):
     See :class:`Migration` for an example.
     """
 
-    def __call__(self, cursor: sqlite3.Cursor) -> None: ...
+    def __call__(self, cursor: sqlite3.Cursor) -> None:
+        ...
 
 
 class MigrationError(RuntimeError):
diff --git a/invokeai/backend/training/textual_inversion_training.py b/invokeai/backend/training/textual_inversion_training.py
index 7ddcf14367..9a38c006a5 100644
--- a/invokeai/backend/training/textual_inversion_training.py
+++ b/invokeai/backend/training/textual_inversion_training.py
@@ -858,9 +858,9 @@ def do_textual_inversion_training(
                 # Let's make sure we don't update any embedding weights besides the newly added token
                 index_no_updates = torch.arange(len(tokenizer)) != placeholder_token_id
                 with torch.no_grad():
-                    accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[index_no_updates] = (
-                        orig_embeds_params[index_no_updates]
-                    )
+                    accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[
+                        index_no_updates
+                    ] = orig_embeds_params[index_no_updates]
 
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients: