diff --git a/library/train_util.py b/library/train_util.py
index 41afc13b..05ec7f84 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -348,6 +348,8 @@ class DreamBoothSubset(BaseSubset):
         self.is_reg = is_reg
         self.class_tokens = class_tokens
         self.caption_extension = caption_extension
+        if self.caption_extension and not self.caption_extension.startswith("."):
+            self.caption_extension = "." + self.caption_extension
 
     def __eq__(self, other) -> bool:
         if not isinstance(other, DreamBoothSubset):
@@ -1069,7 +1071,7 @@ class DreamBoothDataset(BaseDataset):
                         assert len(lines) > 0, f"caption file is empty / キャプションファイルが空です: {cap_path}"
                         caption = lines[0].strip()
                     break
-            return caption
+                return caption
 
         def load_dreambooth_dir(subset: DreamBoothSubset):
             if not os.path.isdir(subset.image_dir):
@@ -1081,16 +1083,33 @@ class DreamBoothDataset(BaseDataset):
 
             # 画像ファイルごとにプロンプトを読み込み、もしあればそちらを使う
             captions = []
+            missing_captions = []
             for img_path in img_paths:
                 cap_for_img = read_caption(img_path, subset.caption_extension)
                 if cap_for_img is None and subset.class_tokens is None:
                     print(f"neither caption file nor class tokens are found. use empty caption for {img_path}")
                     captions.append("")
                 else:
-                    captions.append(subset.class_tokens if cap_for_img is None else cap_for_img)
+                    if cap_for_img is None:
+                        captions.append(subset.class_tokens)
+                        missing_captions.append(img_path)
+                    else:
+                        captions.append(cap_for_img)
 
             self.set_tag_frequency(os.path.basename(subset.image_dir), captions)  # タグ頻度を記録
 
+            if missing_captions:
+                number_of_missing_captions = len(missing_captions)
+                number_of_missing_captions_to_show = 5
+                remaining_missing_captions = number_of_missing_captions - number_of_missing_captions_to_show
+
+                print(f"No caption file found for {number_of_missing_captions} images. Training will continue without captions for these images")
+                for i, missing_caption in enumerate(missing_captions):
+                    if i >= number_of_missing_captions_to_show:
+                        print(missing_caption+f"... and {remaining_missing_captions} more")
+                        break
+                    print(missing_caption)
+                time.sleep(5)
             return img_paths, captions
 
         print("prepare images.")