diff --git a/README.md b/README.md
index 9eabdaee..b67a2c4e 100644
--- a/README.md
+++ b/README.md
@@ -143,6 +143,8 @@ The majority of scripts is licensed under ASL 2.0 (including codes from Diffuser
   - transformers, accelerate and huggingface_hub are updated. 
   - If you encounter any issues, please report them.
 
+- Fixed a bug in the cache of latents. When `flip_aug`, `alpha_mask`, and `random_crop` are different in multiple subsets in the dataset configuration file (.toml), the last subset is used instead of reflecting them correctly.
+
 - Fixed an issue where the timesteps in the batch were the same when using Huber loss. PR [#1628](https://github.com/kohya-ss/sd-scripts/pull/1628) Thanks to recris!
 
 - Improvements in OFT (Orthogonal Finetuning) Implementation
diff --git a/library/train_util.py b/library/train_util.py
index 72d2d811..a31d00c6 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -998,9 +998,26 @@ class BaseDataset(torch.utils.data.Dataset):
         # sort by resolution
         image_infos.sort(key=lambda info: info.bucket_reso[0] * info.bucket_reso[1])
 
-        # split by resolution
-        batches = []
-        batch = []
+        # split by resolution and some conditions
+        class Condition:
+            def __init__(self, reso, flip_aug, alpha_mask, random_crop):
+                self.reso = reso
+                self.flip_aug = flip_aug
+                self.alpha_mask = alpha_mask
+                self.random_crop = random_crop
+
+            def __eq__(self, other):
+                return (
+                    self.reso == other.reso
+                    and self.flip_aug == other.flip_aug
+                    and self.alpha_mask == other.alpha_mask
+                    and self.random_crop == other.random_crop
+                )
+
+        batches: List[Tuple[Condition, List[ImageInfo]]] = []
+        batch: List[ImageInfo] = []
+        current_condition = None
+
         logger.info("checking cache validity...")
         for info in tqdm(image_infos):
             subset = self.image_to_subset[info.image_key]
@@ -1021,28 +1038,31 @@ class BaseDataset(torch.utils.data.Dataset):
                 if cache_available:  # do not add to batch
                     continue
 
-            # if last member of batch has different resolution, flush the batch
-            if len(batch) > 0 and batch[-1].bucket_reso != info.bucket_reso:
-                batches.append(batch)
+            # if batch is not empty and condition is changed, flush the batch. Note that current_condition is not None if batch is not empty
+            condition = Condition(info.bucket_reso, subset.flip_aug, subset.alpha_mask, subset.random_crop)
+            if len(batch) > 0 and current_condition != condition:
+                batches.append((current_condition, batch))
                 batch = []
 
             batch.append(info)
+            current_condition = condition
 
             # if number of data in batch is enough, flush the batch
             if len(batch) >= vae_batch_size:
-                batches.append(batch)
+                batches.append((current_condition, batch))
                 batch = []
+                current_condition = None
 
         if len(batch) > 0:
-            batches.append(batch)
+            batches.append((current_condition, batch))
 
         if cache_to_disk and not is_main_process:  # if cache to disk, don't cache latents in non-main process, set to info only
             return
 
         # iterate batches: batch doesn't have image, image will be loaded in cache_batch_latents and discarded
         logger.info("caching latents...")
-        for batch in tqdm(batches, smoothing=1, total=len(batches)):
-            cache_batch_latents(vae, cache_to_disk, batch, subset.flip_aug, subset.alpha_mask, subset.random_crop)
+        for condition, batch in tqdm(batches, smoothing=1, total=len(batches)):
+            cache_batch_latents(vae, cache_to_disk, batch, condition.flip_aug, condition.alpha_mask, condition.random_crop)
 
     # weight_dtypeを指定するとText Encoderそのもの、およひ出力がweight_dtypeになる
     # SDXLでのみ有効だが、datasetのメソッドとする必要があるので、sdxl_train_util.pyではなくこちらに実装する
@@ -2315,7 +2335,7 @@ def debug_dataset(train_dataset, show_input_ids=False):
                     if "alpha_masks" in example and example["alpha_masks"] is not None:
                         alpha_mask = example["alpha_masks"][j]
                         logger.info(f"alpha mask size: {alpha_mask.size()}")
-                        alpha_mask = (alpha_mask[0].numpy() * 255.0).astype(np.uint8)
+                        alpha_mask = (alpha_mask.numpy() * 255.0).astype(np.uint8)
                         if os.name == "nt":
                             cv2.imshow("alpha_mask", alpha_mask)
 
@@ -5124,7 +5144,7 @@ def save_sd_model_on_train_end_common(
 
 
 def get_timesteps_and_huber_c(args, min_timestep, max_timestep, noise_scheduler, b_size, device):
-    timesteps = torch.randint(min_timestep, max_timestep, (b_size,), device='cpu')
+    timesteps = torch.randint(min_timestep, max_timestep, (b_size,), device="cpu")
 
     if args.loss_type == "huber" or args.loss_type == "smooth_l1":
         if args.huber_schedule == "exponential":