logs = {

"loss/current": current_loss,

"loss/average": avr_loss,

"lr": lr_scheduler.get_last_lr()[0],

}

if args.optimizer_type.lower().startswith("DAdapt".lower()):

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

train_util.verify_training_args(args)

train_util.prepare_dataset_args(args, True)

sdxl_train_util.verify_sdxl_training_args(args)

cache_latents = args.cache_latents

use_user_config = args.dataset_config is not None

if args.seed is None:

args.seed = random.randint(0, 2**32)

set_seed(args.seed)

tokenizer1, tokenizer2 = sdxl_train_util.load_tokenizers(args)

# データセットを準備する

blueprint_generator = BlueprintGenerator(ConfigSanitizer(False, False, 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", "conditioning_data_dir"]

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": config_util.generate_controlnet_subsets_config_by_subdirs(

args.train_data_dir,

args.conditioning_data_dir,

args.caption_extension,

)

}

]

}

blueprint = blueprint_generator.generate(user_config, args, tokenizer=[tokenizer1, tokenizer2])

train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)

current_epoch = Value("i", 0)

current_step = Value("i", 0)

ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None

collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)

train_dataset_group.verify_bucket_reso_steps(32)

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"

else:

print("WARNING: random_crop is not supported yet for ControlNet training / ControlNetの学習ではrandom_cropはまだサポートされていません")

if args.cache_text_encoder_outputs:

assert (

train_dataset_group.is_text_encoder_output_cacheable()

), "when caching Text Encoder output, either caption_dropout_rate, shuffle_caption, token_warmup_step or caption_tag_dropout_rate cannot be used / Text Encoderの出力をキャッシュするときはcaption_dropout_rate, shuffle_caption, token_warmup_step, caption_tag_dropout_rateは使えません"

# acceleratorを準備する

print("prepare accelerator")

accelerator = 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)

vae_dtype = torch.float32 if args.no_half_vae else weight_dtype

# モデルを読み込む

(

load_stable_diffusion_format,

text_encoder1,

text_encoder2,

vae,

unet,

logit_scale,

ckpt_info,

) = sdxl_train_util.load_target_model(args, accelerator, sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, weight_dtype)

# 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, args.sdpa)

# 学習を準備する

if cache_latents:

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

# TextEncoderの出力をキャッシュする

if args.cache_text_encoder_outputs:

# Text Encodes are eval and no grad

with torch.no_grad():

train_dataset_group.cache_text_encoder_outputs(

(tokenizer1, tokenizer2),

(text_encoder1, text_encoder2),

accelerator.device,

None,

args.cache_text_encoder_outputs_to_disk,

accelerator.is_main_process,

)

accelerator.wait_for_everyone()

# prepare ControlNet

network = control_net_lllite.ControlNetLLLite(unet, args.cond_emb_dim, args.network_dim, args.network_dropout)

network.apply_to()

if args.network_weights is not None:

info = network.load_weights(args.network_weights)

accelerator.print(f"load ControlNet weights from {args.network_weights}: {info}")

if args.gradient_checkpointing:

unet.enable_gradient_checkpointing()

network.enable_gradient_checkpointing() # may have no effect

# 学習に必要なクラスを準備する

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

trainable_params = list(network.prepare_optimizer_params())

print(f"trainable params count: {len(trainable_params)}")

print(f"number of trainable parameters: {sum(p.numel() for p in trainable_params if p.requires_grad)}")

_, _, 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=collator,

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

)

accelerator.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/bf16学習を行う　モデル全体をfp16/bf16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。"

accelerator.print("enable full fp16 training.")

unet.to(weight_dtype)

network.to(weight_dtype)

elif args.full_bf16:

assert (

args.mixed_precision == "bf16"

), "full_bf16 requires mixed precision='bf16' / full_bf16を使う場合はmixed_precision='bf16'を指定してください。"

accelerator.print("enable full bf16 training.")

unet.to(weight_dtype)

network.to(weight_dtype)

# acceleratorがなんかよろしくやってくれるらしい

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

unet, network, optimizer, train_dataloader, lr_scheduler

)

network: control_net_lllite.ControlNetLLLite

if args.gradient_checkpointing:

unet.train() # according to TI example in Diffusers, train is required -> これオリジナルのU-Netしたので本当は外せる

else:

unet.eval()

network.prepare_grad_etc()

# TextEncoderの出力をキャッシュするときにはCPUへ移動する

if args.cache_text_encoder_outputs:

# move Text Encoders for sampling images. Text Encoder doesn't work on CPU with fp16

text_encoder1.to("cpu", dtype=torch.float32)

text_encoder2.to("cpu", dtype=torch.float32)

if torch.cuda.is_available():

torch.cuda.empty_cache()

else:

# make sure Text Encoders are on GPU

text_encoder1.to(accelerator.device)

text_encoder2.to(accelerator.device)

if not cache_latents:

vae.requires_grad_(False)

vae.eval()

vae.to(accelerator.device, dtype=vae_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

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

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

accelerator.print("running training / 学習開始")

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

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

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

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

accelerator.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}")

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

accelerator.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 args.zero_terminal_snr:

custom_train_functions.fix_noise_scheduler_betas_for_zero_terminal_snr(noise_scheduler)

if accelerator.is_main_process:

init_kwargs = {}

if args.log_tracker_config is not None:

init_kwargs = toml.load(args.log_tracker_config)

accelerator.init_trackers(

"lllite_control_net_train" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs

)

loss_recorder = train_util.LossRecorder()

del train_dataset_group

# function for saving/removing

def save_model(ckpt_name, unwrapped_nw, steps, epoch_no, force_sync_upload=False):

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

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

accelerator.print(f"\nsaving checkpoint: {ckpt_file}")

sai_metadata = train_util.get_sai_model_spec(None, args, True, True, False)

sai_metadata["modelspec.architecture"] = sai_model_spec.ARCH_SD_XL_V1_BASE + "/control-net-lllite"

unwrapped_nw.save_weights(ckpt_file, save_dtype, sai_metadata)

if args.huggingface_repo_id is not None:

huggingface_util.upload(args, ckpt_file, "/" + ckpt_name, force_sync_upload=force_sync_upload)

def remove_model(old_ckpt_name):

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

if os.path.exists(old_ckpt_file):

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

os.remove(old_ckpt_file)

# training loop

for epoch in range(num_train_epochs):

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

current_epoch.value = epoch + 1

network.on_epoch_start() # train()

for step, batch in enumerate(train_dataloader):

current_step.value = global_step

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=vae_dtype)).latent_dist.sample()

# NaNが含まれていれば警告を表示し0に置き換える

if torch.any(torch.isnan(latents)):

accelerator.print("NaN found in latents, replacing with zeros")

latents = torch.nan_to_num(latents, 0, out=latents)

latents = latents * sdxl_model_util.VAE_SCALE_FACTOR

if "text_encoder_outputs1_list" not in batch or batch["text_encoder_outputs1_list"] is None:

input_ids1 = batch["input_ids"]

input_ids2 = batch["input_ids2"]

with torch.no_grad():

# Get the text embedding for conditioning

input_ids1 = input_ids1.to(accelerator.device)

input_ids2 = input_ids2.to(accelerator.device)

encoder_hidden_states1, encoder_hidden_states2, pool2 = train_util.get_hidden_states_sdxl(

args.max_token_length,

input_ids1,

input_ids2,

tokenizer1,

tokenizer2,

text_encoder1,

text_encoder2,

None if not args.full_fp16 else weight_dtype,

)

else:

encoder_hidden_states1 = batch["text_encoder_outputs1_list"].to(accelerator.device).to(weight_dtype)

encoder_hidden_states2 = batch["text_encoder_outputs2_list"].to(accelerator.device).to(weight_dtype)

pool2 = batch["text_encoder_pool2_list"].to(accelerator.device).to(weight_dtype)

# get size embeddings

orig_size = batch["original_sizes_hw"]

crop_size = batch["crop_top_lefts"]

target_size = batch["target_sizes_hw"]

embs = sdxl_train_util.get_size_embeddings(orig_size, crop_size, target_size, accelerator.device).to(weight_dtype)

# concat embeddings

vector_embedding = torch.cat([pool2, embs], dim=1).to(weight_dtype)

text_embedding = torch.cat([encoder_hidden_states1, encoder_hidden_states2], dim=2).to(weight_dtype)

# Sample noise, sample a random timestep for each image, and add noise to the latents,

# with noise offset and/or multires noise if specified

noise, noisy_latents, timesteps = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents)

noisy_latents = noisy_latents.to(weight_dtype) # TODO check why noisy_latents is not weight_dtype

controlnet_image = batch["conditioning_images"].to(dtype=weight_dtype)

with accelerator.autocast():

# conditioning imageをControlNetに渡す / pass conditioning image to ControlNet

# 内部でcond_embに変換される / it will be converted to cond_emb inside

network.set_cond_image(controlnet_image)

# それらの値を使いつつ、U-Netでノイズを予測する / predict noise with U-Net using those values

noise_pred = unet(noisy_latents, timesteps, text_embedding, vector_embedding)

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

if args.min_snr_gamma:

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

if args.scale_v_pred_loss_like_noise_pred:

loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)

if args.v_pred_like_loss:

loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)

if args.debiased_estimation_loss:

loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)

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

# sdxl_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:

ckpt_name = train_util.get_step_ckpt_name(args, "." + args.save_model_as, global_step)

save_model(ckpt_name, accelerator.unwrap_model(network), global_step, epoch)

if args.save_state:

train_util.save_and_remove_state_stepwise(args, accelerator, global_step)

remove_step_no = train_util.get_remove_step_no(args, global_step)

if remove_step_no is not None:

remove_ckpt_name = train_util.get_step_ckpt_name(args, "." + args.save_model_as, remove_step_no)

remove_model(remove_ckpt_name)

current_loss = loss.detach().item()

loss_recorder.add(epoch=epoch, step=step, loss=current_loss)

avr_loss: float = loss_recorder.moving_average

logs = {"avr_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_recorder.moving_average}

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

accelerator.wait_for_everyone()

# 指定エポックごとにモデルを保存

if args.save_every_n_epochs is not None:

saving = (epoch + 1) % args.save_every_n_epochs == 0 and (epoch + 1) < num_train_epochs

if is_main_process and saving:

ckpt_name = train_util.get_epoch_ckpt_name(args, "." + args.save_model_as, epoch + 1)

save_model(ckpt_name, accelerator.unwrap_model(network), global_step, epoch + 1)

remove_epoch_no = train_util.get_remove_epoch_no(args, epoch + 1)

if remove_epoch_no is not None:

remove_ckpt_name = train_util.get_epoch_ckpt_name(args, "." + args.save_model_as, remove_epoch_no)

remove_model(remove_ckpt_name)

if args.save_state:

train_util.save_and_remove_state_on_epoch_end(args, accelerator, epoch + 1)

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

# end of epoch

if is_main_process:

network = accelerator.unwrap_model(network)

accelerator.end_training()

if is_main_process and args.save_state:

train_util.save_state_on_train_end(args, accelerator)

if is_main_process:

ckpt_name = train_util.get_last_ckpt_name(args, "." + args.save_model_as)

save_model(ckpt_name, network, global_step, num_train_epochs, force_sync_upload=True)

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_optimizer_arguments(parser)

config_util.add_config_arguments(parser)

custom_train_functions.add_custom_train_arguments(parser)

sdxl_train_util.add_sdxl_training_arguments(parser)

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("--cond_emb_dim", type=int, default=None, help="conditioning embedding dimension / 条件付け埋め込みの次元数")

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_dim", type=int, default=None, help="network dimensions (rank) / モジュールの次元数")

parser.add_argument(

"--network_dropout",

type=float,

default=None,

help="Drops neurons out of training every step (0 or None is default behavior (no dropout), 1 would drop all neurons) / 訓練時に毎ステップでニューロンをdropする（0またはNoneはdropoutなし、1は全ニューロンをdropout）",

)

parser.add_argument(

"--conditioning_data_dir",

type=str,

default=None,

help="conditioning data directory / 条件付けデータのディレクトリ",

)

parser.add_argument(

"--no_half_vae",

action="store_true",

help="do not use fp16/bf16 VAE in mixed precision (use float VAE) / mixed precisionでも fp16/bf16 VAEを使わずfloat VAEを使う",

)