diff --git a/api/onnx_web/convert/diffusion/diffusers.py b/api/onnx_web/convert/diffusion/diffusers.py
index d1d3e591..090335c5 100644
--- a/api/onnx_web/convert/diffusion/diffusers.py
+++ b/api/onnx_web/convert/diffusion/diffusers.py
@@ -137,12 +137,10 @@ def convert_diffusion_diffusers(
         # casting to torch.int32 until the CLIP fix is released: https://github.com/huggingface/transformers/pull/18515/files
         model_args=(
             text_input.input_ids.to(device=ctx.training_device, dtype=torch.int32),
-            None,   # attention mask
-            None,   # position ids
-            None,   # output attentions
-            torch.tensor(True).to(
-                device=ctx.training_device, dtype=torch.bool
-            ),
+            None,  # attention mask
+            None,  # position ids
+            None,  # output attentions
+            torch.tensor(True).to(device=ctx.training_device, dtype=torch.bool),
         ),
         output_path=output_path / "text_encoder" / "model.onnx",
         ordered_input_names=["input_ids"],
diff --git a/api/onnx_web/diffusers/utils.py b/api/onnx_web/diffusers/utils.py
index 7c0cb2a5..958f672a 100644
--- a/api/onnx_web/diffusers/utils.py
+++ b/api/onnx_web/diffusers/utils.py
@@ -3,8 +3,8 @@ from math import ceil
 from re import Pattern, compile
 from typing import List, Optional, Tuple
 
-import torch
 import numpy as np
+import torch
 from diffusers import OnnxStableDiffusionPipeline
 
 logger = getLogger(__name__)
@@ -78,13 +78,21 @@ def expand_prompt(
         logger.trace("encoding group: %s", group.shape)
 
         text_result = self.text_encoder(input_ids=group.astype(np.int32))
-        logger.trace("text encoder produced %s outputs: %s", len(text_result), text_result)
+        logger.trace(
+            "text encoder produced %s outputs: %s", len(text_result), text_result
+        )
 
         last_state, _pooled_output, *hidden_states = text_result
         if skip_clip_states > 0:
             layer_norm = torch.nn.LayerNorm(last_state.shape[2])
-            norm_state = layer_norm(torch.from_numpy(hidden_states[-skip_clip_states]).detach())
-            logger.trace("normalized results after skipping %s layers: %s", skip_clip_states, norm_state.shape)
+            norm_state = layer_norm(
+                torch.from_numpy(hidden_states[-skip_clip_states]).detach()
+            )
+            logger.trace(
+                "normalized results after skipping %s layers: %s",
+                skip_clip_states,
+                norm_state.shape,
+            )
             group_embeds.append(norm_state)
         else:
             group_embeds.append(last_state)