diff --git a/finetune/prepare_buckets_latents.py b/finetune/prepare_buckets_latents.py
index 537626d8..d1b9ea25 100644
--- a/finetune/prepare_buckets_latents.py
+++ b/finetune/prepare_buckets_latents.py
@@ -63,25 +63,6 @@ def main(args):
     print(f"no metadata / メタデータファイルがありません: {args.in_json}")
     return
 
-  # 既に存在するファイルをfilterする
-  if args.skip_existing:
-    filtered = []
-    for image_path in image_paths:
-      image_key = image_path if args.full_path else os.path.splitext(os.path.basename(image_path))[0]
-
-      npz_file_name_flip = get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, False) + ".npz"
-      if os.path.exists(npz_file_name_flip):
-        if not args.flip_aug:
-          continue
-
-        npz_file_name_flip = get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, True) + ".npz"
-        if os.path.exists(npz_file_name_flip):
-          continue
-
-      filtered.apppend(image_path)
-    print(f"number of skipped images (npz already exists) / npzファイルが存在するためスキップした画像数: {len(image_paths) - len(filtered)}")
-    image_paths = filtered
-
   weight_dtype = torch.float32
   if args.mixed_precision == "fp16":
     weight_dtype = torch.float16
@@ -128,8 +109,8 @@ def main(args):
   # 読み込みの高速化のためにDataLoaderを使うオプション
   if args.max_data_loader_n_workers is not None:
     dataset = train_util.ImageLoadingDataset(image_paths)
-    data = torch.util.data.DataLoader(dataset, batch_size=1, shuffle=False,
-                                      num_workers=args.max_data_loader_n_workers, collate_fn=collate_fn_remove_corrupted, drop_last=False)
+    data = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False,
+                                       num_workers=args.max_data_loader_n_workers, collate_fn=collate_fn_remove_corrupted, drop_last=False)
   else:
     data = [[(None, ip)] for ip in image_paths]
 
@@ -153,6 +134,7 @@ def main(args):
     if image_key not in metadata:
       metadata[image_key] = {}
 
+    # 本当はこの部分もDataSetに持っていけば高速化できるがいろいろ大変
     aspect_ratio = image.width / image.height
     ar_errors = bucket_aspect_ratios - aspect_ratio
     bucket_id = np.abs(ar_errors).argmin()
@@ -176,6 +158,25 @@ def main(args):
     assert resized_size[0] >= reso[0] and resized_size[1] >= reso[
         1], f"internal error, resized size too small: {reso}, {resized_size}, {image.width}, {image.height}"
 
+    # 既に存在するファイルがあればshapeを確認して同じならskipする
+    if args.skip_existing:
+      npz_files = [get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, False) + ".npz"]
+      if args.flip_aug:
+        npz_files.append(get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, True) + ".npz")
+
+      found = True
+      for npz_file in npz_files:
+        if not os.path.exists(npz_file):
+          found = False
+          break
+
+        dat = np.load(npz_file)['arr_0']
+        if dat.shape[1] != reso[1] // 8 or dat.shape[2] != reso[0] // 8:     # latentsのshapeを確認
+          found = False
+          break
+      if found:
+        continue
+
     # 画像をリサイズしてトリミングする
     # PILにinter_areaがないのでcv2で……
     image = np.array(image)