logs = {"loss/current": current_loss, "loss/average": avr_loss}

if args.network_train_unet_only:

logs["lr/unet"] = float(lr_scheduler.get_last_lr()[0])

elif args.network_train_text_encoder_only:

logs["lr/textencoder"] = float(lr_scheduler.get_last_lr()[0])

else:

logs["lr/textencoder"] = float(lr_scheduler.get_last_lr()[0])

logs["lr/unet"] = float(lr_scheduler.get_last_lr()[-1]) # may be same to textencoder

if args.optimizer_type.lower() == "DAdaptation".lower(): # tracking d*lr value of unet.

logs["lr/d*lr"] = lr_scheduler.optimizers[-1].param_groups[0]["d"] * lr_scheduler.optimizers[-1].param_groups[0]["lr"]

session_id = random.randint(0, 2**32)

training_started_at = time.time()

train_util.verify_training_args(args)

train_util.prepare_dataset_args(args, True)

cache_latents = args.cache_latents

use_dreambooth_method = args.in_json is None

use_user_config = args.dataset_config is not None

if args.seed is not None:

set_seed(args.seed)

tokenizer = train_util.load_tokenizer(args)

# データセットを準備する

blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, True))

if use_user_config:

print(f"Load dataset config from {args.dataset_config}")

user_config = config_util.load_user_config(args.dataset_config)

ignored = ["train_data_dir", "reg_data_dir", "in_json"]

if any(getattr(args, attr) is not None for attr in ignored):

print(

"ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format(

", ".join(ignored)

)

)

else:

if use_dreambooth_method:

print("Use DreamBooth method.")

user_config = {

"datasets": [

{"subsets": config_util.generate_dreambooth_subsets_config_by_subdirs(args.train_data_dir, args.reg_data_dir)}

]

}

else:

print("Train with captions.")

user_config = {

"datasets": [

{

"subsets": [

{

"image_dir": args.train_data_dir,

"metadata_file": args.in_json,

}

]

}

]

}

blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)

train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)

if args.debug_dataset:

train_util.debug_dataset(train_dataset_group)

return

if len(train_dataset_group) == 0:

print(

"No data found. Please verify arguments (train_data_dir must be the parent of folders with images) / 画像がありません。引数指定を確認してください（train_data_dirには画像があるフォルダではなく、画像があるフォルダの親フォルダを指定する必要があります）"

)

return

if cache_latents:

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")

accelerator, unwrap_model = train_util.prepare_accelerator(args)

is_main_process = accelerator.is_main_process

# mixed precisionに対応した型を用意しておき適宜castする

weight_dtype, save_dtype = train_util.prepare_dtype(args)

# モデルを読み込む

text_encoder, vae, unet, _ = train_util.load_target_model(args, weight_dtype)

# work on low-ram device

if args.lowram:

text_encoder.to("cuda")

unet.to("cuda")

# 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)

# 学習を準備する

if cache_latents:

vae.to(accelerator.device, dtype=weight_dtype)

vae.requires_grad_(False)

vae.eval()

with torch.no_grad():

train_dataset_group.cache_latents(vae, args.vae_batch_size)

vae.to("cpu")

if torch.cuda.is_available():

torch.cuda.empty_cache()

gc.collect()

# prepare network

import sys

sys.path.append(os.path.dirname(__file__))

print("import network module:", args.network_module)

network_module = importlib.import_module(args.network_module)

net_kwargs = {}

if args.network_args is not None:

for net_arg in args.network_args:

key, value = net_arg.split("=")

net_kwargs[key] = value

# if a new network is added in future, add if ~ then blocks for each network (;'∀')

network = network_module.create_network(1.0, args.network_dim, args.network_alpha, vae, text_encoder, unet, **net_kwargs)

if network is None:

return

if args.network_weights is not None:

print("load network weights from:", args.network_weights)

network.load_weights(args.network_weights)

train_unet = not args.network_train_text_encoder_only

train_text_encoder = not args.network_train_unet_only

network.apply_to(text_encoder, unet, train_text_encoder, train_unet)

if args.gradient_checkpointing:

unet.enable_gradient_checkpointing()

text_encoder.gradient_checkpointing_enable()

network.enable_gradient_checkpointing() # may have no effect

# 学習に必要なクラスを準備する

print("prepare optimizer, data loader etc.")

trainable_params = network.prepare_optimizer_params(args.text_encoder_lr, args.unet_lr)

optimizer_name, optimizer_args, optimizer = train_util.get_optimizer(args, trainable_params)

# dataloaderを準備する

# DataLoaderのプロセス数：0はメインプロセスになる

n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1) # cpu_count-1 ただし最大で指定された数まで

train_dataloader = torch.utils.data.DataLoader(

train_dataset_group,

batch_size=1,

shuffle=True,

collate_fn=collate_fn,

num_workers=n_workers,

persistent_workers=args.persistent_data_loader_workers,

)

# 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 * math.ceil(len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps)

if is_main_process:

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}")

# lr schedulerを用意する

lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)

# 実験的機能：勾配も含めたfp16学習を行う　モデル全体をfp16にする

if args.full_fp16:

assert (

args.mixed_precision == "fp16"

), "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。"

print("enable full fp16 training.")

network.to(weight_dtype)

# acceleratorがなんかよろしくやってくれるらしい

if train_unet and train_text_encoder:

unet, text_encoder, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(

unet, text_encoder, network, optimizer, train_dataloader, lr_scheduler

)

elif train_unet:

unet, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(

unet, network, optimizer, train_dataloader, lr_scheduler

)

elif train_text_encoder:

text_encoder, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(

text_encoder, network, optimizer, train_dataloader, lr_scheduler

)

else:

network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(network, optimizer, train_dataloader, lr_scheduler)

unet.requires_grad_(False)

unet.to(accelerator.device, dtype=weight_dtype)

text_encoder.requires_grad_(False)

text_encoder.to(accelerator.device)

if args.gradient_checkpointing: # according to TI example in Diffusers, train is required

unet.train()

text_encoder.train()

# set top parameter requires_grad = True for gradient checkpointing works

if type(text_encoder) == DDP:

text_encoder.module.text_model.embeddings.requires_grad_(True)

else:

text_encoder.text_model.embeddings.requires_grad_(True)

else:

unet.eval()

text_encoder.eval()

# support DistributedDataParallel

if type(text_encoder) == DDP:

text_encoder = text_encoder.module

unet = unet.module

network = network.module

network.prepare_grad_etc(text_encoder, unet)

if not cache_latents:

vae.requires_grad_(False)

vae.eval()

vae.to(accelerator.device, dtype=weight_dtype)

# 実験的機能：勾配も含めたfp16学習を行う　PyTorchにパッチを当ててfp16でのgrad scaleを有効にする

if args.full_fp16:

train_util.patch_accelerator_for_fp16_training(accelerator)

# resumeする

if args.resume is not None:

print(f"resume training from state: {args.resume}")

accelerator.load_state(args.resume)

# epoch数を計算する

num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)

num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)

if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0):

args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1

# 学習する

# TODO: find a way to handle total batch size when there are multiple datasets

total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps

if is_main_process:

print("running training / 学習開始")

print(f" num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}")

print(f" num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}")

print(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")

print(f" num epochs / epoch数: {num_train_epochs}")

print(f" batch size per device / バッチサイズ: {', '.join([str(d.batch_size) for d in train_dataset_group.datasets])}")

# print(f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}")

print(f" gradient accumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")

print(f" total optimization steps / 学習ステップ数: {args.max_train_steps}")

# TODO refactor metadata creation and move to util

metadata = {

"ss_session_id": session_id, # random integer indicating which group of epochs the model came from

"ss_training_started_at": training_started_at, # unix timestamp

"ss_output_name": args.output_name,

"ss_learning_rate": args.learning_rate,

"ss_text_encoder_lr": args.text_encoder_lr,

"ss_unet_lr": args.unet_lr,

"ss_num_train_images": train_dataset_group.num_train_images,

"ss_num_reg_images": train_dataset_group.num_reg_images,

"ss_num_batches_per_epoch": len(train_dataloader),

"ss_num_epochs": num_train_epochs,

"ss_gradient_checkpointing": args.gradient_checkpointing,

"ss_gradient_accumulation_steps": args.gradient_accumulation_steps,

"ss_max_train_steps": args.max_train_steps,

"ss_lr_warmup_steps": args.lr_warmup_steps,

"ss_lr_scheduler": args.lr_scheduler,

"ss_network_module": args.network_module,

"ss_network_dim": args.network_dim, # None means default because another network than LoRA may have another default dim

"ss_network_alpha": args.network_alpha, # some networks may not use this value

"ss_mixed_precision": args.mixed_precision,

"ss_full_fp16": bool(args.full_fp16),

"ss_v2": bool(args.v2),

"ss_clip_skip": args.clip_skip,

"ss_max_token_length": args.max_token_length,

"ss_cache_latents": bool(args.cache_latents),

"ss_seed": args.seed,

"ss_lowram": args.lowram,

"ss_noise_offset": args.noise_offset,

"ss_training_comment": args.training_comment, # will not be updated after training

"ss_sd_scripts_commit_hash": train_util.get_git_revision_hash(),

"ss_optimizer": optimizer_name + (f"({optimizer_args})" if len(optimizer_args) > 0 else ""),

"ss_max_grad_norm": args.max_grad_norm,

"ss_caption_dropout_rate": args.caption_dropout_rate,

"ss_caption_dropout_every_n_epochs": args.caption_dropout_every_n_epochs,

"ss_caption_tag_dropout_rate": args.caption_tag_dropout_rate,

"ss_face_crop_aug_range": args.face_crop_aug_range,

"ss_prior_loss_weight": args.prior_loss_weight,

}

if use_user_config:

# save metadata of multiple datasets

# NOTE: pack "ss_datasets" value as json one time

# or should also pack nested collections as json?

datasets_metadata = []

tag_frequency = {} # merge tag frequency for metadata editor

dataset_dirs_info = {} # merge subset dirs for metadata editor

for dataset in train_dataset_group.datasets:

is_dreambooth_dataset = isinstance(dataset, DreamBoothDataset)

dataset_metadata = {

"is_dreambooth": is_dreambooth_dataset,

"batch_size_per_device": dataset.batch_size,

"num_train_images": dataset.num_train_images, # includes repeating

"num_reg_images": dataset.num_reg_images,

"resolution": (dataset.width, dataset.height),

"enable_bucket": bool(dataset.enable_bucket),

"min_bucket_reso": dataset.min_bucket_reso,

"max_bucket_reso": dataset.max_bucket_reso,

"tag_frequency": dataset.tag_frequency,

"bucket_info": dataset.bucket_info,

}

subsets_metadata = []

for subset in dataset.subsets:

subset_metadata = {

"img_count": subset.img_count,

"num_repeats": subset.num_repeats,

"color_aug": bool(subset.color_aug),

"flip_aug": bool(subset.flip_aug),

"random_crop": bool(subset.random_crop),

"shuffle_caption": bool(subset.shuffle_caption),

"keep_tokens": subset.keep_tokens,

}

image_dir_or_metadata_file = None

if subset.image_dir:

image_dir = os.path.basename(subset.image_dir)

subset_metadata["image_dir"] = image_dir

image_dir_or_metadata_file = image_dir

if is_dreambooth_dataset:

subset_metadata["class_tokens"] = subset.class_tokens

subset_metadata["is_reg"] = subset.is_reg

if subset.is_reg:

image_dir_or_metadata_file = None # not merging reg dataset

else:

metadata_file = os.path.basename(subset.metadata_file)

subset_metadata["metadata_file"] = metadata_file

image_dir_or_metadata_file = metadata_file # may overwrite

subsets_metadata.append(subset_metadata)

# merge dataset dir: not reg subset only

# TODO update additional-network extension to show detailed dataset config from metadata

if image_dir_or_metadata_file is not None:

# datasets may have a certain dir multiple times

v = image_dir_or_metadata_file

i = 2

while v in dataset_dirs_info:

v = image_dir_or_metadata_file + f" ({i})"

i += 1

image_dir_or_metadata_file = v

dataset_dirs_info[image_dir_or_metadata_file] = {"n_repeats": subset.num_repeats, "img_count": subset.img_count}

dataset_metadata["subsets"] = subsets_metadata

datasets_metadata.append(dataset_metadata)

# merge tag frequency:

for ds_dir_name, ds_freq_for_dir in dataset.tag_frequency.items():

# あるディレクトリが複数のdatasetで使用されている場合、一度だけ数える

# もともと繰り返し回数を指定しているので、キャプション内でのタグの出現回数と、それが学習で何度使われるかは一致しない

# なので、ここで複数datasetの回数を合算してもあまり意味はない

if ds_dir_name in tag_frequency:

continue

tag_frequency[ds_dir_name] = ds_freq_for_dir

metadata["ss_datasets"] = json.dumps(datasets_metadata)

metadata["ss_tag_frequency"] = json.dumps(tag_frequency)

metadata["ss_dataset_dirs"] = json.dumps(dataset_dirs_info)

else:

# 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]

dataset_dirs_info = {}

reg_dataset_dirs_info = {}

if use_dreambooth_method:

for subset in dataset.subsets:

info = reg_dataset_dirs_info if subset.is_reg else dataset_dirs_info

info[os.path.basename(subset.image_dir)] = {"n_repeats": subset.num_repeats, "img_count": subset.img_count}

else:

for subset in dataset.subsets:

dataset_dirs_info[os.path.basename(subset.metadata_file)] = {

"n_repeats": subset.num_repeats,

"img_count": subset.img_count,

}

metadata.update(

{

"ss_batch_size_per_device": args.train_batch_size,

"ss_total_batch_size": total_batch_size,

"ss_resolution": args.resolution,

"ss_color_aug": bool(args.color_aug),

"ss_flip_aug": bool(args.flip_aug),

"ss_random_crop": bool(args.random_crop),

"ss_shuffle_caption": bool(args.shuffle_caption),

"ss_enable_bucket": bool(dataset.enable_bucket),

"ss_bucket_no_upscale": bool(dataset.bucket_no_upscale),

"ss_min_bucket_reso": dataset.min_bucket_reso,

"ss_max_bucket_reso": dataset.max_bucket_reso,

"ss_keep_tokens": args.keep_tokens,

"ss_dataset_dirs": json.dumps(dataset_dirs_info),

"ss_reg_dataset_dirs": json.dumps(reg_dataset_dirs_info),

"ss_tag_frequency": json.dumps(dataset.tag_frequency),

"ss_bucket_info": json.dumps(dataset.bucket_info),

}

)

# add extra args

if args.network_args:

metadata["ss_network_args"] = json.dumps(net_kwargs)

# for key, value in net_kwargs.items():

# metadata["ss_arg_" + key] = value

# model name and hash

if args.pretrained_model_name_or_path is not None:

sd_model_name = args.pretrained_model_name_or_path

if os.path.exists(sd_model_name):

metadata["ss_sd_model_hash"] = train_util.model_hash(sd_model_name)

metadata["ss_new_sd_model_hash"] = train_util.calculate_sha256(sd_model_name)

sd_model_name = os.path.basename(sd_model_name)

metadata["ss_sd_model_name"] = sd_model_name

if args.vae is not None:

vae_name = args.vae

if os.path.exists(vae_name):

metadata["ss_vae_hash"] = train_util.model_hash(vae_name)

metadata["ss_new_vae_hash"] = train_util.calculate_sha256(vae_name)

vae_name = os.path.basename(vae_name)

metadata["ss_vae_name"] = vae_name

metadata = {k: str(v) for k, v in metadata.items()}

# make minimum metadata for filtering

minimum_keys = ["ss_network_module", "ss_network_dim", "ss_network_alpha", "ss_network_args"]

minimum_metadata = {}

for key in minimum_keys:

if key in metadata:

minimum_metadata[key] = metadata[key]

progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")

global_step = 0

noise_scheduler = DDPMScheduler(

beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, clip_sample=False

)

if accelerator.is_main_process:

accelerator.init_trackers("network_train")

loss_list = []

loss_total = 0.0

for epoch in range(num_train_epochs):

if is_main_process:

print(f"epoch {epoch+1}/{num_train_epochs}")

train_dataset_group.set_current_epoch(epoch + 1)

metadata["ss_epoch"] = str(epoch + 1)

network.on_epoch_start(text_encoder, unet)

for step, batch in enumerate(train_dataloader):

with accelerator.accumulate(network):

with torch.no_grad():

if "latents" in batch and batch["latents"] is not None:

latents = batch["latents"].to(accelerator.device)

else:

# latentに変換

latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()

latents = latents * 0.18215

b_size = latents.shape[0]

with torch.set_grad_enabled(train_text_encoder):

# Get the text embedding for conditioning

input_ids = batch["input_ids"].to(accelerator.device)

encoder_hidden_states = train_util.get_hidden_states(args, input_ids, tokenizer, text_encoder, weight_dtype)

# Sample noise that we'll add to the latents

noise = torch.randn_like(latents, device=latents.device)

if args.noise_offset:

# https://www.crosslabs.org//blog/diffusion-with-offset-noise

noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), device=latents.device)

# Sample a random timestep for each image

timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device)

timesteps = timesteps.long()

# Add noise to the latents according to the noise magnitude at each timestep

# (this is the forward diffusion process)

noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)

# Predict the noise residual

with accelerator.autocast():

noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample

if args.v_parameterization:

# v-parameterization training

target = noise_scheduler.get_velocity(latents, noise, timesteps)

else:

target = noise

loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")

loss = loss.mean([1, 2, 3])

loss_weights = batch["loss_weights"] # 各sampleごとのweight

loss = loss * loss_weights

loss = loss.mean() # 平均なのでbatch_sizeで割る必要なし

accelerator.backward(loss)

if accelerator.sync_gradients and args.max_grad_norm != 0.0:

params_to_clip = network.get_trainable_params()

accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)

optimizer.step()

lr_scheduler.step()

optimizer.zero_grad(set_to_none=True)

# Checks if the accelerator has performed an optimization step behind the scenes

if accelerator.sync_gradients:

progress_bar.update(1)

global_step += 1

train_util.sample_images(

accelerator, args, None, global_step, accelerator.device, vae, tokenizer, text_encoder, unet

)

current_loss = loss.detach().item()

if epoch == 0:

loss_list.append(current_loss)

else:

loss_total -= loss_list[step]

loss_list[step] = current_loss

loss_total += current_loss

avr_loss = loss_total / len(loss_list)

logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}

progress_bar.set_postfix(**logs)

if args.logging_dir is not None:

logs = generate_step_logs(args, current_loss, avr_loss, lr_scheduler)

accelerator.log(logs, step=global_step)

if global_step >= args.max_train_steps:

break

if args.logging_dir is not None:

logs = {"loss/epoch": loss_total / len(loss_list)}

accelerator.log(logs, step=epoch + 1)

accelerator.wait_for_everyone()

if args.save_every_n_epochs is not None:

model_name = train_util.DEFAULT_EPOCH_NAME if args.output_name is None else args.output_name

def save_func():

ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, epoch + 1) + "." + args.save_model_as

ckpt_file = os.path.join(args.output_dir, ckpt_name)

metadata["ss_training_finished_at"] = str(time.time())

print(f"saving checkpoint: {ckpt_file}")

unwrap_model(network).save_weights(ckpt_file, save_dtype, minimum_metadata if args.no_metadata else metadata)

def remove_old_func(old_epoch_no):

old_ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, old_epoch_no) + "." + args.save_model_as

old_ckpt_file = os.path.join(args.output_dir, old_ckpt_name)

if os.path.exists(old_ckpt_file):

print(f"removing old checkpoint: {old_ckpt_file}")

os.remove(old_ckpt_file)

if is_main_process:

saving = train_util.save_on_epoch_end(args, save_func, remove_old_func, epoch + 1, num_train_epochs)

if saving and args.save_state:

train_util.save_state_on_epoch_end(args, accelerator, model_name, epoch + 1)

train_util.sample_images(accelerator, args, epoch + 1, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)

# end of epoch

metadata["ss_epoch"] = str(num_train_epochs)

metadata["ss_training_finished_at"] = str(time.time())

if is_main_process:

network = unwrap_model(network)

accelerator.end_training()

if args.save_state:

train_util.save_state_on_train_end(args, accelerator)

del accelerator # この後メモリを使うのでこれは消す

if is_main_process:

os.makedirs(args.output_dir, exist_ok=True)

model_name = train_util.DEFAULT_LAST_OUTPUT_NAME if args.output_name is None else args.output_name

ckpt_name = model_name + "." + args.save_model_as

ckpt_file = os.path.join(args.output_dir, ckpt_name)

print(f"save trained model to {ckpt_file}")

network.save_weights(ckpt_file, save_dtype, minimum_metadata if args.no_metadata else metadata)

parser = argparse.ArgumentParser()

train_util.add_sd_models_arguments(parser)

train_util.add_dataset_arguments(parser, True, True, True)

train_util.add_training_arguments(parser, True)

train_util.add_optimizer_arguments(parser)

config_util.add_config_arguments(parser)

parser.add_argument("--no_metadata", action="store_true", help="do not save metadata in output model / メタデータを出力先モデルに保存しない")

parser.add_argument(

"--save_model_as",

type=str,

default="safetensors",

choices=[None, "ckpt", "pt", "safetensors"],

help="format to save the model (default is .safetensors) / モデル保存時の形式（デフォルトはsafetensors）",

)

parser.add_argument("--unet_lr", type=float, default=None, help="learning rate for U-Net / U-Netの学習率")

parser.add_argument("--text_encoder_lr", type=float, default=None, help="learning rate for Text Encoder / Text Encoderの学習率")

parser.add_argument("--network_weights", type=str, default=None, help="pretrained weights for network / 学習するネットワークの初期重み")

parser.add_argument("--network_module", type=str, default=None, help="network module to train / 学習対象のネットワークのモジュール")

parser.add_argument(

"--network_dim", type=int, default=None, help="network dimensions (depends on each network) / モジュールの次元数（ネットワークにより定義は異なります）"

)

parser.add_argument(

"--network_alpha",

type=float,

default=1,

help="alpha for LoRA weight scaling, default 1 (same as network_dim for same behavior as old version) / LoRaの重み調整のalpha値、デフォルト1（旧バージョンと同じ動作をするにはnetwork_dimと同じ値を指定）",

)

parser.add_argument(

"--network_args", type=str, default=None, nargs="*", help="additional argmuments for network (key=value) / ネットワークへの追加の引数"

)

parser.add_argument("--network_train_unet_only", action="store_true", help="only training U-Net part / U-Net関連部分のみ学習する")

parser.add_argument(

"--network_train_text_encoder_only", action="store_true", help="only training Text Encoder part / Text Encoder関連部分のみ学習する"

)

parser.add_argument(

"--training_comment", type=str, default=None, help="arbitrary comment string stored in metadata / メタデータに記録する任意のコメント文字列"

)