VictorMylle/Kohya-ss-sd-scripts
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support disk cache: same as #164, might fix #407

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Kohya S
2023-04-13 21:40:34 +09:00
parent 2de9a51591
commit 9fc27403b2
3 changed files with 17 additions and 12 deletions
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3
fine_tune.py
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@@ -275,7 +275,7 @@ def train(args):
with accelerator.accumulate(training_models[0]): # 複数モデルに対応していない模様だがとりあえずこうしておく with accelerator.accumulate(training_models[0]): # 複数モデルに対応していない模様だがとりあえずこうしておく
with torch.no_grad(): with torch.no_grad():
if "latents" in batch and batch["latents"] is not None: if "latents" in batch and batch["latents"] is not None:
latents = batch["latents"].to(accelerator.device).to(dtype=weight_dtype) latents = batch["latents"].to(accelerator.device) # .to(dtype=weight_dtype)
else: else:
# latentに変換 # latentに変換
latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample() latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
@@ -313,6 +313,7 @@ def train(args):
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Predict the noise residual # Predict the noise residual
with accelerator.autocast():
noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
if args.v_parameterization: if args.v_parameterization:

11
train_textual_inversion.py
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@@ -185,8 +185,8 @@ def train(args):
blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer) blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group) train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
current_epoch = Value('i',0) current_epoch = Value("i", 0)
current_step = Value('i',0) current_step = Value("i", 0)
ds_for_collater = train_dataset_group if args.max_data_loader_n_workers == 0 else None ds_for_collater = train_dataset_group if args.max_data_loader_n_workers == 0 else None
collater = train_util.collater_class(current_epoch, current_step, ds_for_collater) collater = train_util.collater_class(current_epoch, current_step, ds_for_collater)
@@ -264,7 +264,9 @@ def train(args):
# 学習ステップ数を計算する # 学習ステップ数を計算する
if args.max_train_epochs is not None: if args.max_train_epochs is not None:
args.max_train_steps = args.max_train_epochs * math.ceil(len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps) args.max_train_steps = args.max_train_epochs * math.ceil(
len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps
)
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}") 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}")
# データセット側にも学習ステップを送信 # データセット側にも学習ステップを送信
@@ -359,7 +361,7 @@ def train(args):
# Get the text embedding for conditioning # Get the text embedding for conditioning
input_ids = batch["input_ids"].to(accelerator.device) input_ids = batch["input_ids"].to(accelerator.device)
# weight_dtype) use float instead of fp16/bf16 because text encoder is float # use float instead of fp16/bf16 because text encoder is float
encoder_hidden_states = train_util.get_hidden_states(args, input_ids, tokenizer, text_encoder, torch.float) encoder_hidden_states = train_util.get_hidden_states(args, input_ids, tokenizer, text_encoder, torch.float)
# Sample noise that we'll add to the latents # Sample noise that we'll add to the latents
@@ -377,6 +379,7 @@ def train(args):
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Predict the noise residual # Predict the noise residual
with accelerator.autocast():
noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
if args.v_parameterization: if args.v_parameterization:

1
train_textual_inversion_XTI.py
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@@ -418,6 +418,7 @@ def train(args):
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Predict the noise residual # Predict the noise residual
with accelerator.autocast():
noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states=encoder_hidden_states).sample noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states=encoder_hidden_states).sample
if args.v_parameterization: if args.v_parameterization: