diff --git a/api/onnx_web/convert/diffusion/textual_inversion.py b/api/onnx_web/convert/diffusion/textual_inversion.py
index 1427606e..25e8e18b 100644
--- a/api/onnx_web/convert/diffusion/textual_inversion.py
+++ b/api/onnx_web/convert/diffusion/textual_inversion.py
@@ -24,13 +24,13 @@ def blend_textual_inversions(
     dtype = np.float
     embeds = {}
 
-    for name, weight, base_token, format in inversions:
+    for name, weight, base_token, inversion_format in inversions:
         if base_token is None:
             base_token = name
 
-        if format is None:
+        if inversion_format is None:
             # TODO: detect concept format
-            format = "embeddings"
+            inversion_format = "embeddings"
 
         logger.info(
             "blending Textual Inversion %s with weight of %s for token %s",
@@ -39,7 +39,7 @@ def blend_textual_inversions(
             base_token,
         )
 
-        if format == "concept":
+        if inversion_format == "concept":
             # TODO: this should be done in fetch, maybe
             embeds_file = hf_hub_download(repo_id=name, filename="learned_embeds.bin")
             token_file = hf_hub_download(repo_id=name, filename="token_identifier.txt")
@@ -58,7 +58,7 @@ def blend_textual_inversions(
                 embeds[token] += layer
             else:
                 embeds[token] = layer
-        elif format == "embeddings":
+        elif inversion_format == "embeddings":
             loaded_embeds = torch.load(name)
 
             string_to_token = loaded_embeds["string_to_token"]
@@ -91,7 +91,7 @@ def blend_textual_inversions(
             else:
                 embeds[sum_token] = sum_layer
         else:
-            raise ValueError(f"unknown Textual Inversion format: {format}")
+            raise ValueError(f"unknown Textual Inversion format: {inversion_format}")
 
         # add the tokens to the tokenizer
         logger.debug(