train_util.verify_training_args(args)

train_util.prepare_dataset_args(args, True)

cache_latents = args.cache_latents

if args.seed is not None:

set_seed(args.seed) # 乱数系列を初期化する

tokenizer = train_util.load_tokenizer(args)

# データセットを準備する

if args.dataset_class is None:

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

if args.dataset_config is not None:

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

user_config = config_util.load_user_config(args.dataset_config)

ignored = ["train_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:

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)

else:

train_dataset_group = train_util.load_arbitrary_dataset(args, tokenizer)

current_epoch = Value("i", 0)

current_step = Value("i", 0)

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)

if args.debug_dataset:

train_util.debug_dataset(train_dataset_group)

return

if len(train_dataset_group) == 0:

print(

"No data found. Please verify the metadata file and train_data_dir option. / 画像がありません。メタデータおよびtrain_data_dirオプションを確認してください。"

)

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)

# 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, load_stable_diffusion_format = train_util.load_target_model(args, weight_dtype, accelerator)

# verify load/save model formats

if load_stable_diffusion_format:

src_stable_diffusion_ckpt = args.pretrained_model_name_or_path

src_diffusers_model_path = None

else:

src_stable_diffusion_ckpt = None

src_diffusers_model_path = args.pretrained_model_name_or_path

if args.save_model_as is None:

save_stable_diffusion_format = load_stable_diffusion_format

use_safetensors = args.use_safetensors

else:

save_stable_diffusion_format = args.save_model_as.lower() == "ckpt" or args.save_model_as.lower() == "safetensors"

use_safetensors = args.use_safetensors or ("safetensors" in args.save_model_as.lower())

# Diffusers版のxformers使用フラグを設定する関数

def set_diffusers_xformers_flag(model, valid):

# model.set_use_memory_efficient_attention_xformers(valid) # 次のリリースでなくなりそう

# pipeが自動で再帰的にset_use_memory_efficient_attention_xformersを探すんだって(;´Д｀)

# U-Netだけ使う時にはどうすればいいのか……仕方ないからコピって使うか

# 0.10.2でなんか巻き戻って個別に指定するようになった(;^ω^)

# Recursively walk through all the children.

# Any children which exposes the set_use_memory_efficient_attention_xformers method

# gets the message

def fn_recursive_set_mem_eff(module: torch.nn.Module):

if hasattr(module, "set_use_memory_efficient_attention_xformers"):

module.set_use_memory_efficient_attention_xformers(valid)

for child in module.children():

fn_recursive_set_mem_eff(child)

fn_recursive_set_mem_eff(model)

# モデルに xformers とか memory efficient attention を組み込む

if args.diffusers_xformers:

print("Use xformers by Diffusers")

set_diffusers_xformers_flag(unet, True)

else:

# Windows版のxformersはfloatで学習できないのでxformersを使わない設定も可能にしておく必要がある

print("Disable Diffusers' xformers")

set_diffusers_xformers_flag(unet, False)

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, args.cache_latents_to_disk, accelerator.is_main_process)

vae.to("cpu")

if torch.cuda.is_available():

torch.cuda.empty_cache()

gc.collect()

accelerator.wait_for_everyone()

# 学習を準備する：モデルを適切な状態にする

training_models = []

if args.gradient_checkpointing:

unet.enable_gradient_checkpointing()

training_models.append(unet)

if args.train_text_encoder:

print("enable text encoder training")

if args.gradient_checkpointing:

text_encoder.gradient_checkpointing_enable()

training_models.append(text_encoder)

else:

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

text_encoder.requires_grad_(False) # text encoderは学習しない

if args.gradient_checkpointing:

text_encoder.gradient_checkpointing_enable()

text_encoder.train() # required for gradient_checkpointing

else:

text_encoder.eval()

if not cache_latents:

vae.requires_grad_(False)

vae.eval()

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

for m in training_models:

m.requires_grad_(True)

params = []

for m in training_models:

params.extend(m.parameters())

params_to_optimize = params

# 学習に必要なクラスを準備する

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

_, _, optimizer = train_util.get_optimizer(args, trainable_params=params_to_optimize)

# 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=collater,

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

)

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

train_dataset_group.set_max_train_steps(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.")

unet.to(weight_dtype)

text_encoder.to(weight_dtype)

# acceleratorがなんかよろしくやってくれるらしい

if args.train_text_encoder:

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

unet, text_encoder, optimizer, train_dataloader, lr_scheduler

)

else:

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

# transform DDP after prepare

text_encoder, unet = train_util.transform_if_model_is_DDP(text_encoder, unet)

# 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

train_util.resume_from_local_or_hf_if_specified(accelerator, args)

# 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

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

print("running training / 学習開始")

print(f" num examples / サンプル数: {train_dataset_group.num_train_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: {args.train_batch_size}")

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}")

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

)

prepare_scheduler_for_custom_training(noise_scheduler, accelerator.device)

if accelerator.is_main_process:

accelerator.init_trackers("finetuning" if args.log_tracker_name is None else args.log_tracker_name)

for epoch in range(num_train_epochs):

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

current_epoch.value = epoch + 1

for m in training_models:

m.train()

loss_total = 0

for step, batch in enumerate(train_dataloader):

current_step.value = global_step

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():

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

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

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(args.train_text_encoder):

# Get the text embedding for conditioning

if args.weighted_captions:

encoder_hidden_states = get_weighted_text_embeddings(

tokenizer,

text_encoder,

batch["captions"],

accelerator.device,

args.max_token_length // 75 if args.max_token_length else 1,

clip_skip=args.clip_skip,

)

else:

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

encoder_hidden_states = train_util.get_hidden_states(

args, input_ids, tokenizer, text_encoder, None if not args.full_fp16 else weight_dtype

)

# Sample noise that we'll add to the latents

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

if args.noise_offset:

noise = apply_noise_offset(latents, noise, args.noise_offset, args.adaptive_noise_scale)

elif args.multires_noise_iterations:

noise = pyramid_noise_like(noise, latents.device, args.multires_noise_iterations, args.multires_noise_discount)

# 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

if args.min_snr_gamma or args.scale_v_pred_loss_like_noise_pred:

# do not mean over batch dimension for snr weight or scale v-pred loss

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

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

if args.min_snr_gamma:

loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)

if args.scale_v_pred_loss_like_noise_pred:

loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)

loss = loss.mean() # mean over batch dimension

else:

loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="mean")

accelerator.backward(loss)

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

params_to_clip = []

for m in training_models:

params_to_clip.extend(m.parameters())

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

)

# 指定ステップごとにモデルを保存

if args.save_every_n_steps is not None and global_step % args.save_every_n_steps == 0:

accelerator.wait_for_everyone()

if accelerator.is_main_process:

src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path

train_util.save_sd_model_on_epoch_end_or_stepwise(

args,

False,

accelerator,

src_path,

save_stable_diffusion_format,

use_safetensors,

save_dtype,

epoch,

num_train_epochs,

global_step,

unwrap_model(text_encoder),

unwrap_model(unet),

vae,

)

current_loss = loss.detach().item() # 平均なのでbatch sizeは関係ないはず

if args.logging_dir is not None:

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

if args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower(): # tracking d*lr value

logs["lr/d*lr"] = (

lr_scheduler.optimizers[0].param_groups[0]["d"] * lr_scheduler.optimizers[0].param_groups[0]["lr"]

)

accelerator.log(logs, step=global_step)

# TODO moving averageにする

loss_total += current_loss

avr_loss = loss_total / (step + 1)

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

progress_bar.set_postfix(**logs)

if global_step >= args.max_train_steps:

break

if args.logging_dir is not None:

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

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

accelerator.wait_for_everyone()

if args.save_every_n_epochs is not None:

if accelerator.is_main_process:

src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path

train_util.save_sd_model_on_epoch_end_or_stepwise(

args,

True,

accelerator,

src_path,

save_stable_diffusion_format,

use_safetensors,

save_dtype,

epoch,

num_train_epochs,

global_step,

unwrap_model(text_encoder),

unwrap_model(unet),

vae,

)

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

is_main_process = accelerator.is_main_process

if is_main_process:

unet = unwrap_model(unet)

text_encoder = unwrap_model(text_encoder)

accelerator.end_training()

if args.save_state and is_main_process:

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:

src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path

train_util.save_sd_model_on_train_end(

args, src_path, save_stable_diffusion_format, use_safetensors, save_dtype, epoch, global_step, text_encoder, unet, vae

)

parser = argparse.ArgumentParser()

train_util.add_sd_models_arguments(parser)

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

train_util.add_training_arguments(parser, False)

train_util.add_sd_saving_arguments(parser)

train_util.add_optimizer_arguments(parser)

config_util.add_config_arguments(parser)

custom_train_functions.add_custom_train_arguments(parser)

parser.add_argument("--diffusers_xformers", action="store_true", help="use xformers by diffusers / Diffusersでxformersを使用する")

parser.add_argument("--train_text_encoder", action="store_true", help="train text encoder / text encoderも学習する")