diff --git a/library/leco_train_util.py b/library/leco_train_util.py
index 987f1233..7a67c904 100644
--- a/library/leco_train_util.py
+++ b/library/leco_train_util.py
@@ -320,10 +320,10 @@ def load_prompt_settings(path: Union[str, Path]) -> List[PromptSettings]:
 
     if isinstance(data, list):
         for item in data:
-                if "target_class" in item:
-                    append_slider_item(item, default_prompt_values, [str(item.get("neutral", "") or "")])
-                else:
-                    append_prompt_item(item, default_prompt_values)
+            if "target_class" in item:
+                append_slider_item(item, default_prompt_values, [str(item.get("neutral", "") or "")])
+            else:
+                append_prompt_item(item, default_prompt_values)
     elif isinstance(data, dict):
         if "prompts" in data:
             defaults = {**default_prompt_values, **{k: v for k, v in data.items() if k in _recognized_prompt_keys()}}
@@ -406,11 +406,11 @@ def get_initial_latents(scheduler, batch_size: int, height: int, width: int, n_p
 
 def concat_embeddings(unconditional: torch.Tensor, conditional: torch.Tensor, batch_size: int) -> torch.Tensor:
     return torch.cat([unconditional, conditional], dim=0).repeat_interleave(batch_size, dim=0)
+
+
 def concat_embeddings_xl(unconditional: PromptEmbedsXL, conditional: PromptEmbedsXL, batch_size: int) -> PromptEmbedsXL:
     text_embeds = torch.cat([unconditional.text_embeds, conditional.text_embeds], dim=0).repeat_interleave(batch_size, dim=0)
-    pooled_embeds = torch.cat([unconditional.pooled_embeds, conditional.pooled_embeds], dim=0).repeat_interleave(
-        batch_size, dim=0
-    )
+    pooled_embeds = torch.cat([unconditional.pooled_embeds, conditional.pooled_embeds], dim=0).repeat_interleave(batch_size, dim=0)
     return PromptEmbedsXL(text_embeds=text_embeds, pooled_embeds=pooled_embeds)
 
 
@@ -499,7 +499,6 @@ def predict_noise_xl(
     return noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
 
 
-
 def diffusion_xl(
     unet,
     scheduler,
diff --git a/library/train_util.py b/library/train_util.py
index 0df51852..d87a73a5 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -1106,7 +1106,8 @@ class BaseDataset(torch.utils.data.Dataset):
         return all(
             [
                 not (
-                    subset.caption_dropout_rate > 0 and not cache_supports_dropout
+                    subset.caption_dropout_rate > 0
+                    and not cache_supports_dropout
                     or subset.shuffle_caption
                     or subset.token_warmup_step > 0
                     or subset.caption_tag_dropout_rate > 0
@@ -2056,7 +2057,9 @@ class DreamBoothDataset(BaseDataset):
                     filtered_img_paths.append(img_path)
                     filtered_sizes.append(size)
                 if len(filtered_img_paths) < len(img_paths):
-                    logger.info(f"filtered {len(img_paths) - len(filtered_img_paths)} images by original resolution from {subset.image_dir}")
+                    logger.info(
+                        f"filtered {len(img_paths) - len(filtered_img_paths)} images by original resolution from {subset.image_dir}"
+                    )
                 img_paths = filtered_img_paths
                 sizes = filtered_sizes
 
@@ -2542,9 +2545,7 @@ class ControlNetDataset(BaseDataset):
             len(missing_imgs) == 0
         ), f"missing conditioning data for {len(missing_imgs)} images / 制御用画像が見つかりませんでした: {missing_imgs}"
         if len(extra_imgs) > 0:
-            logger.warning(
-                f"extra conditioning data for {len(extra_imgs)} images / 余分な制御用画像があります: {extra_imgs}"
-            )
+            logger.warning(f"extra conditioning data for {len(extra_imgs)} images / 余分な制御用画像があります: {extra_imgs}")
 
         self.conditioning_image_transforms = IMAGE_TRANSFORMS