sd-scripts/train_control_net.py

import argparse
import json
import math
import os
import random
import time
from multiprocessing import Value

# from omegaconf import OmegaConf
import toml

from tqdm import tqdm

import torch
from library import deepspeed_utils, strategy_base, strategy_sd
from library.device_utils import init_ipex, clean_memory_on_device

init_ipex()

from torch.nn.parallel import DistributedDataParallel as DDP
from accelerate.utils import set_seed
from diffusers import DDPMScheduler, ControlNetModel
from safetensors.torch import load_file

import library.model_util as model_util
import library.accelerator_setup as accelerator_setup
import library.args as args_util
import library.dataset as dataset_util
from library.hidden_states import get_hidden_states
import library.model_io as model_io
import library.optimizer as optimizer_util
import library.logging_util as logging_util
import library.loss as loss_util
import library.checkpoint_io as checkpoint_io
import library.sampling as sampling
import library.config_util as config_util
import library.sai_model_spec as sai_model_spec
from library.config_util import (
    ConfigSanitizer,
    BlueprintGenerator,
)
import library.huggingface_util as huggingface_util
import library.custom_train_functions as custom_train_functions
from library.custom_train_functions import (
    apply_snr_weight,
    pyramid_noise_like,
    apply_noise_offset,
)
from library.utils import setup_logging, add_logging_arguments

setup_logging()
import logging

logger = logging.getLogger(__name__)


# TODO 他のスクリプトと共通化する
def generate_step_logs(args: argparse.Namespace, current_loss, avr_loss, lr_scheduler):
    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"]

    return logs


def train(args):
    # session_id = random.randint(0, 2**32)
    # training_started_at = time.time()
    args_util.verify_training_args(args)
    accelerator_setup.prepare_dataset_args(args, True)
    setup_logging(args, reset=True)

    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)

    tokenize_strategy = strategy_sd.SdTokenizeStrategy(args.v2, args.max_token_length, args.tokenizer_cache_dir)
    strategy_base.TokenizeStrategy.set_strategy(tokenize_strategy)
    tokenizer = tokenize_strategy.tokenizer
    # prepare caching strategy: this must be set before preparing dataset. because dataset may use this strategy for initialization.
    latents_caching_strategy = strategy_sd.SdSdxlLatentsCachingStrategy(
        True, args.cache_latents_to_disk, args.vae_batch_size, False
    )
    strategy_base.LatentsCachingStrategy.set_strategy(latents_caching_strategy)

    # データセットを準備する
    blueprint_generator = BlueprintGenerator(ConfigSanitizer(False, False, True, True))
    if use_user_config:
        logger.info(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):
            logger.warning(
                "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)
    train_dataset_group, val_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 = dataset_util.collator_class(current_epoch, current_step, ds_for_collator)

    train_dataset_group.verify_bucket_reso_steps(64)

    if args.debug_dataset:
        dataset_util.debug_dataset(train_dataset_group)
        return
    if len(train_dataset_group) == 0:
        logger.error(
            "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
    logger.info("prepare accelerator")
    accelerator = accelerator_setup.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 = accelerator_setup.prepare_dtype(args)

    # モデルを読み込む
    text_encoder, vae, unet, _ = model_io.load_target_model(
        args, weight_dtype, accelerator, unet_use_linear_projection_in_v2=True
    )

    # DiffusersのControlNetが使用するデータを準備する
    if args.v2:
        unet.config = {
            "act_fn": "silu",
            "attention_head_dim": [5, 10, 20, 20],
            "block_out_channels": [320, 640, 1280, 1280],
            "center_input_sample": False,
            "cross_attention_dim": 1024,
            "down_block_types": ["CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D"],
            "downsample_padding": 1,
            "dual_cross_attention": False,
            "flip_sin_to_cos": True,
            "freq_shift": 0,
            "in_channels": 4,
            "layers_per_block": 2,
            "mid_block_scale_factor": 1,
            "mid_block_type": "UNetMidBlock2DCrossAttn",
            "norm_eps": 1e-05,
            "norm_num_groups": 32,
            "num_attention_heads": [5, 10, 20, 20],
            "num_class_embeds": None,
            "only_cross_attention": False,
            "out_channels": 4,
            "sample_size": 96,
            "up_block_types": ["UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"],
            "use_linear_projection": True,
            "upcast_attention": True,
            "only_cross_attention": False,
            "downsample_padding": 1,
            "use_linear_projection": True,
            "class_embed_type": None,
            "num_class_embeds": None,
            "resnet_time_scale_shift": "default",
            "projection_class_embeddings_input_dim": None,
        }
    else:
        unet.config = {
            "act_fn": "silu",
            "attention_head_dim": 8,
            "block_out_channels": [320, 640, 1280, 1280],
            "center_input_sample": False,
            "cross_attention_dim": 768,
            "down_block_types": ["CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D"],
            "downsample_padding": 1,
            "flip_sin_to_cos": True,
            "freq_shift": 0,
            "in_channels": 4,
            "layers_per_block": 2,
            "mid_block_scale_factor": 1,
            "mid_block_type": "UNetMidBlock2DCrossAttn",
            "norm_eps": 1e-05,
            "norm_num_groups": 32,
            "num_attention_heads": 8,
            "out_channels": 4,
            "sample_size": 64,
            "up_block_types": ["UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"],
            "only_cross_attention": False,
            "downsample_padding": 1,
            "use_linear_projection": False,
            "class_embed_type": None,
            "num_class_embeds": None,
            "upcast_attention": False,
            "resnet_time_scale_shift": "default",
            "projection_class_embeddings_input_dim": None,
        }
    # unet.config = OmegaConf.create(unet.config)

    # make unet.config iterable and accessible by attribute
    class CustomConfig:
        def __init__(self, **kwargs):
            self.__dict__.update(kwargs)

        def __getattr__(self, name):
            if name in self.__dict__:
                return self.__dict__[name]
            else:
                raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{name}'")

        def __contains__(self, name):
            return name in self.__dict__

    unet.config = CustomConfig(**unet.config)

    controlnet = ControlNetModel.from_unet(unet)

    if args.controlnet_model_name_or_path:
        filename = args.controlnet_model_name_or_path
        if os.path.isfile(filename):
            if os.path.splitext(filename)[1] == ".safetensors":
                state_dict = load_file(filename)
            else:
                state_dict = torch.load(filename)
            state_dict = model_util.convert_controlnet_state_dict_to_diffusers(state_dict)
            controlnet.load_state_dict(state_dict)
        elif os.path.isdir(filename):
            controlnet = ControlNetModel.from_pretrained(filename)

    # モデルに xformers とか memory efficient attention を組み込む
    model_io.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=weight_dtype)
        vae.requires_grad_(False)
        vae.eval()
        with torch.no_grad():
            train_dataset_group.new_cache_latents(vae, accelerator)
        vae.to("cpu")
        clean_memory_on_device(accelerator.device)

        accelerator.wait_for_everyone()

    if args.gradient_checkpointing:
        unet.enable_gradient_checkpointing()
        controlnet.enable_gradient_checkpointing()

    # 学習に必要なクラスを準備する
    accelerator.print("prepare optimizer, data loader etc.")

    trainable_params = list(controlnet.parameters())

    _, _, optimizer = optimizer_util.get_optimizer(args, trainable_params)

    # dataloaderを準備する
    # DataLoaderのプロセス数：0 は persistent_workers が使えないので注意
    train_dataset_group.set_current_strategies()
    n_workers = min(args.max_data_loader_n_workers, os.cpu_count())  # cpu_count or max_data_loader_n_workers

    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 = optimizer_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。"
        accelerator.print("enable full fp16 training.")
        controlnet.to(weight_dtype)

    # acceleratorがなんかよろしくやってくれるらしい
    controlnet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
        controlnet, optimizer, train_dataloader, lr_scheduler
    )

    if args.fused_backward_pass:
        import library.adafactor_fused

        library.adafactor_fused.patch_adafactor_fused(optimizer)
        for param_group in optimizer.param_groups:
            for parameter in param_group["params"]:
                if parameter.requires_grad:

                    def __grad_hook(tensor: torch.Tensor, param_group=param_group):
                        if accelerator.sync_gradients and args.max_grad_norm != 0.0:
                            accelerator.clip_grad_norm_(tensor, args.max_grad_norm)
                        optimizer.step_param(tensor, param_group)
                        tensor.grad = None

                    parameter.register_post_accumulate_grad_hook(__grad_hook)

    unet.requires_grad_(False)
    text_encoder.requires_grad_(False)
    unet.to(accelerator.device)
    text_encoder.to(accelerator.device)

    # transform DDP after prepare
    controlnet = controlnet.module if isinstance(controlnet, DDP) else controlnet

    controlnet.train()

    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:
        accelerator_setup.patch_accelerator_for_fp16_training(accelerator)

    # resumeする
    args_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])}"
    )
    # logger.info(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,
    )
    if accelerator.is_main_process:
        init_kwargs = {}
        if args.wandb_run_name:
            init_kwargs["wandb"] = {"name": args.wandb_run_name}
        if args.log_tracker_config is not None:
            init_kwargs = toml.load(args.log_tracker_config)
        accelerator.init_trackers(
            "controlnet_train" if args.log_tracker_name is None else args.log_tracker_name,
            config=args_util.get_sanitized_config_or_none(args),
            init_kwargs=init_kwargs,
        )

    loss_recorder = logging_util.LossRecorder()
    del train_dataset_group

    # function for saving/removing
    def save_model(ckpt_name, model, 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}")

        state_dict = model_util.convert_controlnet_state_dict_to_sd(model.state_dict())

        if save_dtype is not None:
            for key in list(state_dict.keys()):
                v = state_dict[key]
                v = v.detach().clone().to("cpu").to(save_dtype)
                state_dict[key] = v

        if os.path.splitext(ckpt_file)[1] == ".safetensors":
            from safetensors.torch import save_file

            save_file(state_dict, ckpt_file)
        else:
            torch.save(state_dict, ckpt_file)

        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)

    # For --sample_at_first
    sampling.sample_images(
        accelerator, args, 0, global_step, accelerator.device, vae, tokenizer, text_encoder, unet, controlnet=controlnet
    )
    if len(accelerator.trackers) > 0:
        # log empty object to commit the sample images to wandb
        accelerator.log({}, step=0)

    # training loop
    for epoch in range(num_train_epochs):
        if is_main_process:
            accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}")
        current_epoch.value = epoch + 1

        for step, batch in enumerate(train_dataloader):
            current_step.value = global_step
            with accelerator.accumulate(controlnet):
                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]

                input_ids = batch["input_ids_list"][0].to(accelerator.device)
                encoder_hidden_states = 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:
                    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 = loss_util.get_timesteps(0, noise_scheduler.config.num_train_timesteps, b_size, latents.device)

                # 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)

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

                with accelerator.autocast():
                    down_block_res_samples, mid_block_res_sample = controlnet(
                        noisy_latents,
                        timesteps,
                        encoder_hidden_states=encoder_hidden_states,
                        controlnet_cond=controlnet_image,
                        return_dict=False,
                    )

                    # Predict the noise residual
                    noise_pred = unet(
                        noisy_latents,
                        timesteps,
                        encoder_hidden_states,
                        down_block_additional_residuals=[sample.to(dtype=weight_dtype) for sample in down_block_res_samples],
                        mid_block_additional_residual=mid_block_res_sample.to(dtype=weight_dtype),
                    ).sample

                if args.v_parameterization:
                    # v-parameterization training
                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
                else:
                    target = noise

                huber_c = loss_util.get_huber_threshold_if_needed(args, timesteps, noise_scheduler)
                loss = loss_util.conditional_loss(noise_pred.float(), target.float(), args.loss_type, "none", huber_c)
                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)

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

                accelerator.backward(loss)
                if not args.fused_backward_pass:
                    if accelerator.sync_gradients and args.max_grad_norm != 0.0:
                        params_to_clip = controlnet.parameters()
                        accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)

                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad(set_to_none=True)
                else:
                    # optimizer.step() and optimizer.zero_grad() are called in the optimizer hook
                    lr_scheduler.step()

            # Checks if the accelerator has performed an optimization step behind the scenes
            if accelerator.sync_gradients:
                progress_bar.update(1)
                global_step += 1

                sampling.sample_images(
                    accelerator,
                    args,
                    None,
                    global_step,
                    accelerator.device,
                    vae,
                    tokenizer,
                    text_encoder,
                    unet,
                    controlnet=controlnet,
                )

                # 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 = checkpoint_io.get_step_ckpt_name(args, "." + args.save_model_as, global_step)
                        save_model(
                            ckpt_name,
                            accelerator.unwrap_model(controlnet),
                        )

                        if args.save_state:
                            checkpoint_io.save_and_remove_state_stepwise(args, accelerator, global_step)

                        remove_step_no = checkpoint_io.get_remove_step_no(args, global_step)
                        if remove_step_no is not None:
                            remove_ckpt_name = checkpoint_io.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 len(accelerator.trackers) > 0:
                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 len(accelerator.trackers) > 0:
            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 = checkpoint_io.get_epoch_ckpt_name(args, "." + args.save_model_as, epoch + 1)
                save_model(ckpt_name, accelerator.unwrap_model(controlnet))

                remove_epoch_no = checkpoint_io.get_remove_epoch_no(args, epoch + 1)
                if remove_epoch_no is not None:
                    remove_ckpt_name = checkpoint_io.get_epoch_ckpt_name(args, "." + args.save_model_as, remove_epoch_no)
                    remove_model(remove_ckpt_name)

                if args.save_state:
                    checkpoint_io.save_and_remove_state_on_epoch_end(args, accelerator, epoch + 1)

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

        # end of epoch
    if is_main_process:
        controlnet = accelerator.unwrap_model(controlnet)

    accelerator.end_training()

    if is_main_process and (args.save_state or args.save_state_on_train_end):
        checkpoint_io.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→printで使うので消さずにおく

    if is_main_process:
        ckpt_name = checkpoint_io.get_last_ckpt_name(args, "." + args.save_model_as)
        save_model(ckpt_name, controlnet, force_sync_upload=True)

        logger.info("model saved.")


def setup_parser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser()

    add_logging_arguments(parser)
    args_util.add_sd_models_arguments(parser)
    args_util.add_dataset_arguments(parser, False, True, True)
    args_util.add_training_arguments(parser, False)
    deepspeed_utils.add_deepspeed_arguments(parser)
    args_util.add_optimizer_arguments(parser)
    config_util.add_config_arguments(parser)
    custom_train_functions.add_custom_train_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(
        "--controlnet_model_name_or_path",
        type=str,
        default=None,
        help="controlnet model name or path / controlnetのモデル名またはパス",
    )
    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",
    )

    return parser


if __name__ == "__main__":
    parser = setup_parser()

    args = parser.parse_args()
    args_util.verify_command_line_training_args(args)
    args = args_util.read_config_from_file(args, parser)

    train(args)