Add dropout options

fine_tune.py

@@ -171,6 +171,10 @@ def train(args):
     args.max_train_steps = args.max_train_epochs * len(train_dataloader)
     print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
 
+  # 学習データのdropout率を設定する
+  train_dataset.dropout_rate = args.dropout_rate
+  train_dataset.dropout_every_n_epochs = args.dropout_every_n_epochs
+
   # lr schedulerを用意する
   lr_scheduler = diffusers.optimization.get_scheduler(
       args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps)
@@ -226,6 +230,9 @@ def train(args):
 
   for epoch in range(num_train_epochs):
     print(f"epoch {epoch+1}/{num_train_epochs}")
+
+    train_dataset.epoch_current = epoch + 1
+
     for m in training_models:
       m.train()
 

library/train_util.py

@@ -223,6 +223,10 @@ class BaseDataset(torch.utils.data.Dataset):
 
     self.tokenizer_max_length = self.tokenizer.model_max_length if max_token_length is None else max_token_length + 2
 
+    self.epoch_current:int = int(0)
+    self.dropout_rate:float = 0
+    self.dropout_every_n_epochs:int = 0
+
     # augmentation
     flip_p = 0.5 if flip_aug else 0.0
     if color_aug:
@@ -598,6 +602,16 @@ class BaseDataset(torch.utils.data.Dataset):
       images.append(image)
       latents_list.append(latents)
 
+      # dropoutの決定
+      is_drop_out = False
+      if self.dropout_rate > 0 and self.dropout_rate < random.random() :
+        is_drop_out = True
+      if self.dropout_every_n_epochs > 0 and self.epoch_current % self.dropout_every_n_epochs == 0 :
+        is_drop_out = True
+
+      if is_drop_out:
+        caption = ""
+      else:
         caption = self.process_caption(image_info.caption)
       captions.append(caption)
       if not self.token_padding_disabled:                     # this option might be omitted in future
@@ -1407,6 +1421,10 @@ def add_dataset_arguments(parser: argparse.ArgumentParser, support_dreambooth: b
                       help="steps of resolution for buckets, divisible by 8 is recommended / bucketの解像度の単位、8で割り切れる値を推奨します")
   parser.add_argument("--bucket_no_upscale", action="store_true",
                       help="make bucket for each image without upscaling / 画像を拡大せずbucketを作成します")
+  parser.add_argument("--dropout_rate", type=float, default=0,
+                      help="Rate out dropout caption(0.0~1.0) / captionをdropoutする割合")
+  parser.add_argument("--dropout_every_n_epochs", type=int, default=0,
+                      help="Dropout all captions every N epochs / captionを指定エポックごとにdropoutする")
 
   if support_dreambooth:
     # DreamBooth dataset

train_db.py

@@ -136,6 +136,10 @@ def train(args):
   train_dataloader = torch.utils.data.DataLoader(
       train_dataset, batch_size=1, shuffle=False, collate_fn=collate_fn, num_workers=n_workers, persistent_workers=args.persistent_data_loader_workers)
 
+  # 学習データのdropout率を設定する
+  train_dataset.dropout_rate = args.dropout_rate
+  train_dataset.dropout_every_n_epochs = args.dropout_every_n_epochs
+
   # 学習ステップ数を計算する
   if args.max_train_epochs is not None:
     args.max_train_steps = args.max_train_epochs * len(train_dataloader)
@@ -204,6 +208,8 @@ def train(args):
   for epoch in range(num_train_epochs):
     print(f"epoch {epoch+1}/{num_train_epochs}")
 
+    train_dataset.epoch_current = epoch + 1
+
     # 指定したステップ数までText Encoderを学習する：epoch最初の状態
     unet.train()
     # train==True is required to enable gradient_checkpointing

train_network.py

@@ -219,6 +219,10 @@ def train(args):
   train_dataloader = torch.utils.data.DataLoader(
       train_dataset, batch_size=1, shuffle=False, collate_fn=collate_fn, num_workers=n_workers, persistent_workers=args.persistent_data_loader_workers)
 
+  # 学習データのdropout率を設定する
+  train_dataset.dropout_rate = args.dropout_rate
+  train_dataset.dropout_every_n_epochs = args.dropout_every_n_epochs
+
   # 学習ステップ数を計算する
   if args.max_train_epochs is not None:
     args.max_train_steps = args.max_train_epochs * len(train_dataloader)
@@ -376,6 +380,9 @@ def train(args):
 
   for epoch in range(num_train_epochs):
     print(f"epoch {epoch+1}/{num_train_epochs}")
+
+    train_dataset.epoch_current = epoch + 1
+
     metadata["ss_epoch"] = str(epoch+1)
 
     network.on_epoch_start(text_encoder, unet)