train_util.verify_training_args(args)

train_util.prepare_dataset_args(args, False)

cache_latents = args.cache_latents

if args.tf32:

torch.backends.cuda.matmul.allow_tf32 = True

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)

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.prior_loss_weight,

args.flip_aug, args.color_aug, args.face_crop_aug_range, args.random_crop, args.debug_dataset)

if args.no_token_padding:

train_dataset.disable_token_padding()

train_dataset.make_buckets()

if args.debug_dataset:

train_util.debug_dataset(train_dataset)

return

# acceleratorを準備する

print("prepare accelerator")

if args.gradient_accumulation_steps > 1:

print(f"gradient_accumulation_steps is {args.gradient_accumulation_steps}. accelerate does not support gradient_accumulation_steps when training multiple models (U-Net and Text Encoder), so something might be wrong")

print(

f"gradient_accumulation_stepsが{args.gradient_accumulation_steps}に設定されています。accelerateは複数モデル（U-NetおよびText Encoder）の学習時にgradient_accumulation_stepsをサポートしていないため結果は未知数です")

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)

# 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())

# 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.cache_latents(vae)

vae.to("cpu")

if torch.cuda.is_available():

torch.cuda.empty_cache()

gc.collect()

# 学習を準備する：モデルを適切な状態にする

train_text_encoder = args.stop_text_encoder_training is None or args.stop_text_encoder_training >= 0

unet.requires_grad_(True) # 念のため追加

text_encoder.requires_grad_(train_text_encoder)

if not train_text_encoder:

print("Text Encoder is not trained.")

if args.gradient_checkpointing:

unet.enable_gradient_checkpointing()

text_encoder.gradient_checkpointing_enable()

if not cache_latents:

vae.requires_grad_(False)

vae.eval()

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

# 学習に必要なクラスを準備する

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

# 8-bit Adamを使う

if args.use_8bit_adam:

try:

import bitsandbytes as bnb

except ImportError:

raise ImportError("No bitsand bytes / bitsandbytesがインストールされていないようです")

print("use 8-bit Adam optimizer")

optimizer_class = bnb.optim.AdamW8bit

else:

optimizer_class = torch.optim.AdamW

if train_text_encoder:

trainable_params = (itertools.chain(unet.parameters(), text_encoder.parameters()))

else:

trainable_params = unet.parameters()

# betaやweight decayはdiffusers DreamBoothもDreamBooth SDもデフォルト値のようなのでオプションはとりあえず省略

optimizer = optimizer_class(trainable_params, lr=args.learning_rate)

# 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, batch_size=1, shuffle=False, collate_fn=collate_fn, num_workers=n_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 * 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}")

if args.stop_text_encoder_training is None:

args.stop_text_encoder_training = args.max_train_steps + 1 # do not stop until end

# 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)

# 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 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)

if not train_text_encoder:

text_encoder.to(accelerator.device, dtype=weight_dtype) # to avoid 'cpu' vs 'cuda' error

# 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)

# 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 train images * repeats / 学習画像の数×繰り返し回数: {train_dataset.num_train_images}")

print(f" num reg images / 正則化画像の数: {train_dataset.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: {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 ccumulation 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)

if accelerator.is_main_process:

accelerator.init_trackers("dreambooth")

for epoch in range(num_train_epochs):

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

# 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

if args.gradient_checkpointing or global_step < args.stop_text_encoder_training:

text_encoder.train()

loss_total = 0

for step, batch in enumerate(train_dataloader):

# 指定したステップ数でText Encoderの学習を止める

if global_step == args.stop_text_encoder_training:

print(f"stop text encoder training at step {global_step}")

if not args.gradient_checkpointing:

text_encoder.train(False)

text_encoder.requires_grad_(False)

with accelerator.accumulate(unet):

with torch.no_grad():

# latentに変換

if cache_latents:

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

else:

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

latents = latents * 0.18215

# Sample noise that we'll add to the latents

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

b_size = latents.shape[0]

# Get the text embedding for conditioning

with torch.set_grad_enabled(global_step < args.stop_text_encoder_training):

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 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

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:

if train_text_encoder:

params_to_clip = (itertools.chain(unet.parameters(), text_encoder.parameters()))

else:

params_to_clip = unet.parameters()

accelerator.clip_grad_norm_(params_to_clip, 1.0) # 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

current_loss = loss.detach().item()

if args.logging_dir is not None:

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

accelerator.log(logs, step=global_step)

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 = {"epoch_loss": loss_total / len(train_dataloader)}

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

accelerator.wait_for_everyone()

if args.save_every_n_epochs is not None:

src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path

train_util.save_sd_model_on_epoch_end(args, 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)

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:

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)