diff --git a/invokeai/backend/model_management/lora.py b/invokeai/backend/model_management/lora.py
index f9c40f8386..acd1f6bab6 100644
--- a/invokeai/backend/model_management/lora.py
+++ b/invokeai/backend/model_management/lora.py
@@ -195,7 +195,11 @@ class ModelPatcher:
             def _get_ti_embedding(model_embeddings, ti):
                 # for SDXL models, select the embedding that matches the text encoder's dimensions
                 if ti.embedding_2 is not None:
-                    return ti.embedding_2 if ti.embedding_2.shape[1] == model_embeddings.weight.data[0].shape[0] else ti.embedding
+                    return (
+                        ti.embedding_2
+                        if ti.embedding_2.shape[1] == model_embeddings.weight.data[0].shape[0]
+                        else ti.embedding
+                    )
                 else:
                     return ti.embedding
 
@@ -212,7 +216,6 @@ class ModelPatcher:
             model_embeddings = text_encoder.get_input_embeddings()
 
             for ti_name, ti in ti_list:
-
                 ti_tokens = []
                 for i in range(ti_embedding.shape[0]):
                     embedding = ti_embedding[i]
@@ -282,7 +285,7 @@ class ModelPatcher:
 
 
 class TextualInversionModel:
-    embedding: torch.Tensor                     # [n, 768]|[n, 1280]
+    embedding: torch.Tensor  # [n, 768]|[n, 1280]
     embedding_2: Optional[torch.Tensor] = None  # [n, 768]|[n, 1280]   - for SDXL models
 
     @classmethod
@@ -308,7 +311,7 @@ class TextualInversionModel:
             if len(state_dict["string_to_param"]) > 1:
                 print(
                     f'Warn: Embedding "{file_path.name}" contains multiple tokens, which is not supported. The first',
-                    " token will be used."
+                    " token will be used.",
                 )
 
             result.embedding = next(iter(state_dict["string_to_param"].values()))
@@ -319,8 +322,8 @@ class TextualInversionModel:
 
         # v5(sdxl safetensors file)
         elif "clip_g" in state_dict and "clip_l" in state_dict:
-                result.embedding = state_dict["clip_g"]
-                result.embedding_2 = state_dict["clip_l"]
+            result.embedding = state_dict["clip_g"]
+            result.embedding_2 = state_dict["clip_l"]
 
         # v4(diffusers bin files)
         else:
@@ -332,7 +335,6 @@ class TextualInversionModel:
             if not isinstance(result.embedding, torch.Tensor):
                 raise ValueError(f"Invalid embeddings file: {file_path.name}")
 
-
         return result
 
 
@@ -520,9 +522,10 @@ class ONNXModelPatcher:
             # modify tokenizer
             new_tokens_added = 0
             for ti_name, ti in ti_list:
-
                 if ti.embedding_2 is not None:
-                    ti_embedding = ti.embedding_2 if ti.embedding_2.shape[1] == orig_embeddings.shape[0] else ti.embedding
+                    ti_embedding = (
+                        ti.embedding_2 if ti.embedding_2.shape[1] == orig_embeddings.shape[0] else ti.embedding
+                    )
                 else:
                     ti_embedding = ti.embedding