VictorMylle/Kohya-ss-sd-scripts
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Add dropout options

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This commit is contained in:
forestsource
2023-02-07 00:01:30 +09:00
parent d591891048
commit 7db98baa86
4 changed files with 43 additions and 5 deletions
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7
fine_tune.py
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@@ -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()

20
library/train_util.py
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@@ -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:
@@ -598,7 +602,17 @@ class BaseDataset(torch.utils.data.Dataset):
images.append(image)
latents_list.append(latents)
caption = self.process_caption(image_info.caption)
# 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
input_ids_list.append(self.get_input_ids(caption))
@@ -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

6
train_db.py
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@@ -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

15
train_network.py
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@@ -120,16 +120,16 @@ def train(args):
print("Use DreamBooth method.")
train_dataset = DreamBoothDataset(args.train_batch_size, args.train_data_dir, args.reg_data_dir,
tokenizer, args.max_token_length, args.caption_extension, args.shuffle_caption, args.keep_tokens,
args.resolution, args.enable_bucket, args.min_bucket_reso, args.max_bucket_reso,
args.bucket_reso_steps, args.bucket_no_upscale,
args.prior_loss_weight, args.flip_aug, args.color_aug, args.face_crop_aug_range,
args.resolution, args.enable_bucket, args.min_bucket_reso, args.max_bucket_reso,
args.bucket_reso_steps, args.bucket_no_upscale,
args.prior_loss_weight, args.flip_aug, args.color_aug, args.face_crop_aug_range,
args.random_crop, args.debug_dataset)
else:
print("Train with captions.")
train_dataset = FineTuningDataset(args.in_json, args.train_batch_size, args.train_data_dir,
tokenizer, args.max_token_length, args.shuffle_caption, args.keep_tokens,
args.resolution, args.enable_bucket, args.min_bucket_reso, args.max_bucket_reso,
args.bucket_reso_steps, args.bucket_no_upscale,
args.bucket_reso_steps, args.bucket_no_upscale,
args.flip_aug, args.color_aug, args.face_crop_aug_range, args.random_crop,
args.dataset_repeats, args.debug_dataset)
train_dataset.make_buckets()
@@ -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)