diff --git a/fine_tune.py b/fine_tune.py
index 524b9b2a..ddc518f5 100644
--- a/fine_tune.py
+++ b/fine_tune.py
@@ -62,7 +62,7 @@ def train(args):
     return
 
   if cache_latents:
-    assert train_dataset_group.is_latent_cachable(), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
+    assert train_dataset_group.is_latent_cacheable(), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
 
   # acceleratorを準備する
   print("prepare accelerator")
diff --git a/library/train_util.py b/library/train_util.py
index a8cbd7d6..b191604c 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -547,7 +547,7 @@ class BaseDataset(torch.utils.data.Dataset):
     assert image.shape[0] == reso[1] and image.shape[1] == reso[0], f"internal error, illegal trimmed size: {image.shape}, {reso}"
     return image
 
-  def is_latent_cachable(self):
+  def is_latent_cacheable(self):
     return all([not subset.color_aug and not subset.random_crop for subset in self.subsets])
 
   def cache_latents(self, vae):
@@ -1062,8 +1062,8 @@ class DatasetGroup(torch.utils.data.ConcatDataset):
     for dataset in self.datasets:
       dataset.cache_latents(vae)
 
-  def is_latent_cachable(self) -> bool:
-    return all([dataset.is_latent_cachable() for dataset in self.datasets])
+  def is_latent_cacheable(self) -> bool:
+    return all([dataset.is_latent_cacheable() for dataset in self.datasets])
 
   def set_current_epoch(self, epoch):
     for dataset in self.datasets:
diff --git a/train_db.py b/train_db.py
index 2fa9f8bb..6ce1367e 100644
--- a/train_db.py
+++ b/train_db.py
@@ -62,7 +62,7 @@ def train(args):
     return
 
   if cache_latents:
-    assert train_dataset_group.is_latent_cachable(), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
+    assert train_dataset_group.is_latent_cacheable(), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
 
   # acceleratorを準備する
   print("prepare accelerator")
diff --git a/train_network.py b/train_network.py
index 24577c6f..34ce2160 100644
--- a/train_network.py
+++ b/train_network.py
@@ -99,7 +99,7 @@ def train(args):
     return
 
   if cache_latents:
-    assert train_dataset_group.is_latent_cachable(), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
+    assert train_dataset_group.is_latent_cacheable(), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
 
   # acceleratorを準備する
   print("prepare accelerator")
@@ -341,7 +341,7 @@ def train(args):
 
     metadata["ss_datasets"] = json.dumps(datasets_metadata)
   else:
-    # conserving backward compatiblity when using train_dataset_dir and reg_dataset_dir
+    # conserving backward compatibility when using train_dataset_dir and reg_dataset_dir
     assert len(train_dataset_group.datasets) == 1, f"There should be a single dataset but {len(train_dataset_group.datasets)} found. This seems to be a bug. / データセットは1個だけ存在するはずですが、実際には{len(train_dataset_group.datasets)}個でした。プログラムのバグかもしれません。"
 
     dataset = train_dataset_group.datasets[0]
diff --git a/train_textual_inversion.py b/train_textual_inversion.py
index 0f23dd55..669be7e0 100644
--- a/train_textual_inversion.py
+++ b/train_textual_inversion.py
@@ -197,7 +197,7 @@ def train(args):
     return
 
   if cache_latents:
-    assert train_dataset_group.is_latent_cachable(), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
+    assert train_dataset_group.is_latent_cacheable(), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
 
   # モデルに xformers とか memory efficient attention を組み込む
   train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers)