Update train_util.py

library/train_util.py

@@ -453,7 +453,6 @@ def __init__(
         caption_suffix: Optional[str],
         token_warmup_min: int,
         token_warmup_step: Union[float, int],
-        sample_weight: bool,
     ) -> None:
         self.image_dir = image_dir
         self.num_repeats = num_repeats
@@ -477,7 +476,6 @@ def __init__(
 
         self.token_warmup_min = token_warmup_min  # step=0におけるタグの数
         self.token_warmup_step = token_warmup_step  # N（N<1ならN*max_train_steps）ステップ目でタグの数が最大になる
-        self.sample_weight = sample_weight
         self.img_count = 0
 
 
@@ -508,7 +506,6 @@ def __init__(
         caption_suffix,
         token_warmup_min,
         token_warmup_step,
-        sample_weight,
     ) -> None:
         assert image_dir is not None, "image_dir must be specified / image_dirは指定が必須です"
 
@@ -534,7 +531,6 @@ def __init__(
             caption_suffix,
             token_warmup_min,
             token_warmup_step,
-            sample_weight,
         )
 
         self.is_reg = is_reg
@@ -574,7 +570,6 @@ def __init__(
         caption_suffix,
         token_warmup_min,
         token_warmup_step,
-        sample_weight,
     ) -> None:
         assert metadata_file is not None, "metadata_file must be specified / metadata_fileは指定が必須です"
 
@@ -600,7 +595,6 @@ def __init__(
             caption_suffix,
             token_warmup_min,
             token_warmup_step,
-            sample_weight,
         )
 
         self.metadata_file = metadata_file
@@ -637,7 +631,6 @@ def __init__(
         caption_suffix,
         token_warmup_min,
         token_warmup_step,
-        sample_weight,
     ) -> None:
         assert image_dir is not None, "image_dir must be specified / image_dirは指定が必須です"
 
@@ -663,7 +656,6 @@ def __init__(
             caption_suffix,
             token_warmup_min,
             token_warmup_step,
-            sample_weight,
         )
 
         self.conditioning_data_dir = conditioning_data_dir
@@ -695,7 +687,6 @@ def __init__(
         self.width, self.height = (None, None) if resolution is None else resolution
         self.network_multiplier = network_multiplier
         self.debug_dataset = debug_dataset
-        self.sample_weight = sample_weight
         self.subsets: List[Union[DreamBoothSubset, FineTuningSubset]] = []
 
         self.token_padding_disabled = False
@@ -1271,13 +1262,12 @@ def __getitem__(self, index):
             image_info = self.image_data[image_key]
             subset = self.image_to_subset[image_key]
             sample_weight = 1.0
-            if subset.sample_weight is not None:
-                sample_weight_path = os.path.splitext(info.absolute_path)[0] + ".weight"
-                try:
-                    with open(sample_weight_path, 'r', encoding='utf-8') as file:
-                        sample_weight = float(file.readline().strip())
-                except (OSError, ValueError):
-                    pass
+            sample_weight_path = os.path.splitext(info.absolute_path)[0] + ".weight"
+            try:
+                with open(sample_weight_path, 'r', encoding='utf-8') as file:
+                    sample_weight = float(file.readline().strip())
+            except (OSError, ValueError):
+                pass
             loss_weights.append(sample_weight * (self.prior_loss_weight if image_info.is_reg else 1.0))  # in case of fine tuning, is_reg is always False
 
             flipped = subset.flip_aug and random.random() < 0.5  # not flipped or flipped with 50% chance
@@ -1533,7 +1523,6 @@ def __init__(
         validation_split: float,
         validation_seed: Optional[int],        
         debug_dataset: bool,
-        sample_weight: bool,
     ) -> None:
         super().__init__(tokenizer, max_token_length, resolution, network_multiplier, debug_dataset)
 
@@ -1545,7 +1534,6 @@ def __init__(
         self.batch_size = batch_size
         self.size = min(self.width, self.height)  # 短いほう
         self.prior_loss_weight = prior_loss_weight
-        self.sample_weight = sample_weight
         self.latents_cache = None
         self.enable_bucket = enable_bucket
         if self.enable_bucket:
@@ -1963,7 +1951,6 @@ def __init__(
                 subset.caption_suffix,
                 subset.token_warmup_min,
                 subset.token_warmup_step,
-                subset.sample_weight,
             )
             db_subsets.append(db_subset)
 
@@ -1984,7 +1971,6 @@ def __init__(
             validation_split,
             validation_seed,
             debug_dataset,
-            sample_weight,
         )
 
         # config_util等から参照される値をいれておく（若干微妙なのでなんとかしたい）
@@ -3514,12 +3500,6 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
             "--prior_loss_weight", type=float, default=1.0, help="loss weight for regularization images / 正則化画像のlossの重み"
         )
 
-    parser.add_argument(
-        "--sample_weight",
-        action="store_true",
-        help="Enables the use of sample weights for images, where each weight is read from a `.weight` file corresponding to the image. / 画像に対してサンプルウェイトを使用することを可能にします。各ウェイトは、画像に対応する .weight ファイルから読み取られます。",
-    )
-
 def verify_training_args(args: argparse.Namespace):
     r"""
     Verify training arguments. Also reflect highvram option to global variable