flux_trai_orig.py

# training with captions

# Swap blocks between CPU and GPU:
# This implementation is inspired by and based on the work of 2kpr.
# Many thanks to 2kpr for the original concept and implementation of memory-efficient offloading.
# The original idea has been adapted and extended to fit the current project's needs.

# Key features:
# - CPU offloading during forward and backward passes
# - Use of fused optimizer and grad_hook for efficient gradient processing
# - Per-block fused optimizer instances

import argparse
import copy
import math
import os
from multiprocessing import Value
from typing import List
import toml

from tqdm import tqdm

import torch
from .library.device_utils import init_ipex, clean_memory_on_device

init_ipex()

from accelerate.utils import set_seed
from .library import deepspeed_utils, flux_train_utils, flux_utils, strategy_base, strategy_flux
from .library.sd3_train_utils import load_prompts, FlowMatchEulerDiscreteScheduler

from .library import train_util as train_util

from .library.utils import setup_logging, add_logging_arguments

setup_logging()
import logging

logger = logging.getLogger(__name__)

from .library import config_util as config_util

from .library.config_util import (
    ConfigSanitizer,
    BlueprintGenerator,
)
from .library.custom_train_functions import apply_masked_loss, add_custom_train_arguments


def train(args):
    train_util.verify_training_args(args)
    train_util.prepare_dataset_args(args, True)
    # sdxl_train_util.verify_sdxl_training_args(args)
    deepspeed_utils.prepare_deepspeed_args(args)
    setup_logging(args, reset=True)

    # assert (
    #     not args.weighted_captions
    # ), "weighted_captions is not supported currently / weighted_captionsは現在サポートされていません"
    if args.cache_text_encoder_outputs_to_disk and not args.cache_text_encoder_outputs:
        logger.warning(
            "cache_text_encoder_outputs_to_disk is enabled, so cache_text_encoder_outputs is also enabled / cache_text_encoder_outputs_to_diskが有効になっているため、cache_text_encoder_outputsも有効になります"
        )
        args.cache_text_encoder_outputs = True

    if args.cpu_offload_checkpointing and not args.gradient_checkpointing:
        logger.warning(
            "cpu_offload_checkpointing is enabled, so gradient_checkpointing is also enabled / cpu_offload_checkpointingが有効になっているため、gradient_checkpointingも有効になります"
        )
        args.gradient_checkpointing = True

    cache_latents = args.cache_latents
    use_dreambooth_method = args.in_json is None

    if args.seed is not None:
        set_seed(args.seed)  # 乱数系列を初期化する

    # prepare caching strategy: this must be set before preparing dataset. because dataset may use this strategy for initialization.
    if args.cache_latents:
        latents_caching_strategy = strategy_flux.FluxLatentsCachingStrategy(
            args.cache_latents_to_disk, args.vae_batch_size, args.skip_latents_validity_check
        )
        strategy_base.LatentsCachingStrategy.set_strategy(latents_caching_strategy)

    # データセットを準備する
    if args.dataset_class is None:
        blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, args.masked_loss, True))
        if args.dataset_config is not None:
            logger.info(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):
                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:
            if use_dreambooth_method:
                logger.info("Using DreamBooth method.")
                user_config = {
                    "datasets": [
                        {
                            "subsets": config_util.generate_dreambooth_subsets_config_by_subdirs(
                                args.train_data_dir, args.reg_data_dir
                            )
                        }
                    ]
                }
            else:
                logger.info("Training with captions.")
                user_config = {
                    "datasets": [
                        {
                            "subsets": [
                                {
                                    "image_dir": args.train_data_dir,
                                    "metadata_file": args.in_json,
                                }
                            ]
                        }
                    ]
                }

        blueprint = blueprint_generator.generate(user_config, args)
        train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
    else:
        train_dataset_group = train_util.load_arbitrary_dataset(args)

    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(16)  # TODO これでいいか確認

    if args.debug_dataset:
        if args.cache_text_encoder_outputs:
            strategy_base.TextEncoderOutputsCachingStrategy.set_strategy(
                strategy_flux.FluxTextEncoderOutputsCachingStrategy(
                    args.cache_text_encoder_outputs_to_disk, args.text_encoder_batch_size, False, False
                )
            )
        train_dataset_group.set_current_strategies()
        train_util.debug_dataset(train_dataset_group, True)
        return
    if len(train_dataset_group) == 0:
        logger.error(
            "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"

    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
    logger.info("prepare accelerator")
    accelerator = 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
    name = "schnell" if "schnell" in args.pretrained_model_name_or_path else "dev"

    # load VAE for caching latents
    ae = None
    if cache_latents:
        ae = flux_utils.load_ae(name, args.ae, weight_dtype, "cpu")
        ae.to(accelerator.device, dtype=weight_dtype)
        ae.requires_grad_(False)
        ae.eval()

        train_dataset_group.new_cache_latents(ae, accelerator.is_main_process)

        ae.to("cpu")  # if no sampling, vae can be deleted
        clean_memory_on_device(accelerator.device)

        accelerator.wait_for_everyone()

    # prepare tokenize strategy
    if args.t5xxl_max_token_length is None:
        if name == "schnell":
            t5xxl_max_token_length = 256
        else:
            t5xxl_max_token_length = 512
    else:
        t5xxl_max_token_length = args.t5xxl_max_token_length

    flux_tokenize_strategy = strategy_flux.FluxTokenizeStrategy(t5xxl_max_token_length)
    strategy_base.TokenizeStrategy.set_strategy(flux_tokenize_strategy)

    # load clip_l, t5xxl for caching text encoder outputs
    clip_l = flux_utils.load_clip_l(args.clip_l, weight_dtype, "cpu")
    t5xxl = flux_utils.load_t5xxl(args.t5xxl, weight_dtype, "cpu")
    clip_l.eval()
    t5xxl.eval()
    clip_l.requires_grad_(False)
    t5xxl.requires_grad_(False)

    text_encoding_strategy = strategy_flux.FluxTextEncodingStrategy(args.apply_t5_attn_mask)
    strategy_base.TextEncodingStrategy.set_strategy(text_encoding_strategy)

    # cache text encoder outputs
    sample_prompts_te_outputs = None
    if args.cache_text_encoder_outputs:
        # Text Encodes are eval and no grad here
        clip_l.to(accelerator.device)
        t5xxl.to(accelerator.device)

        text_encoder_caching_strategy = strategy_flux.FluxTextEncoderOutputsCachingStrategy(
            args.cache_text_encoder_outputs_to_disk, args.text_encoder_batch_size, False, False, args.apply_t5_attn_mask
        )
        strategy_base.TextEncoderOutputsCachingStrategy.set_strategy(text_encoder_caching_strategy)

        with accelerator.autocast():
            train_dataset_group.new_cache_text_encoder_outputs([clip_l, t5xxl], accelerator.is_main_process)

        # cache sample prompt's embeddings to free text encoder's memory
        if args.sample_prompts is not None:
            logger.info(f"cache Text Encoder outputs for sample prompt: {args.sample_prompts}")

            tokenize_strategy: strategy_flux.FluxTokenizeStrategy = strategy_base.TokenizeStrategy.get_strategy()
            text_encoding_strategy: strategy_flux.FluxTextEncodingStrategy = strategy_base.TextEncodingStrategy.get_strategy()

            prompts = load_prompts(args.sample_prompts)
            sample_prompts_te_outputs = {}  # key: prompt, value: text encoder outputs
            with accelerator.autocast(), torch.no_grad():
                for prompt_dict in prompts:
                    for p in [prompt_dict.get("prompt", ""), prompt_dict.get("negative_prompt", "")]:
                        if p not in sample_prompts_te_outputs:
                            logger.info(f"cache Text Encoder outputs for prompt: {p}")
                            tokens_and_masks = tokenize_strategy.tokenize(p)
                            sample_prompts_te_outputs[p] = text_encoding_strategy.encode_tokens(
                                tokenize_strategy, [clip_l, t5xxl], tokens_and_masks, args.apply_t5_attn_mask
                            )

        accelerator.wait_for_everyone()

        # now we can delete Text Encoders to free memory
        clip_l = None
        t5xxl = None
        clean_memory_on_device(accelerator.device)

    # load FLUX
    # if we load to cpu, flux.to(fp8) takes a long time
    flux = flux_utils.load_flow_model(name, args.pretrained_model_name_or_path, weight_dtype, "cpu")

    if args.gradient_checkpointing:
        flux.enable_gradient_checkpointing(args.cpu_offload_checkpointing)

    flux.requires_grad_(True)

    if args.double_blocks_to_swap is not None or args.single_blocks_to_swap is not None:
        # Swap blocks between CPU and GPU to reduce memory usage, in forward and backward passes.
        # This idea is based on 2kpr's great work. Thank you!
        logger.info(
            f"enable block swap: double_blocks_to_swap={args.double_blocks_to_swap}, single_blocks_to_swap={args.single_blocks_to_swap}"
        )
        flux.enable_block_swap(args.double_blocks_to_swap, args.single_blocks_to_swap)

    if not cache_latents:
        # load VAE here if not cached
        ae = flux_utils.load_ae(name, args.ae, weight_dtype, "cpu")
        ae.requires_grad_(False)
        ae.eval()
        ae.to(accelerator.device, dtype=weight_dtype)

    training_models = []
    params_to_optimize = []
    training_models.append(flux)
    params_to_optimize.append({"params": list(flux.parameters()), "lr": args.learning_rate})

    # calculate number of trainable parameters
    n_params = 0
    for group in params_to_optimize:
        for p in group["params"]:
            n_params += p.numel()

    accelerator.print(f"number of trainable parameters: {n_params}")

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

    if args.blockwise_fused_optimizers:
        # fused backward pass: https://pytorch.org/tutorials/intermediate/optimizer_step_in_backward_tutorial.html
        # Instead of creating an optimizer for all parameters as in the tutorial, we create an optimizer for each block of parameters.
        # This balances memory usage and management complexity.

        # split params into groups. currently different learning rates are not supported
        grouped_params = []
        param_group = {}
        for group in params_to_optimize:
            named_parameters = list(flux.named_parameters())
            assert len(named_parameters) == len(group["params"]), "number of parameters does not match"
            for p, np in zip(group["params"], named_parameters):
                # determine target layer and block index for each parameter
                block_type = "other"  # double, single or other
                if np[0].startswith("double_blocks"):
                    block_idx = int(np[0].split(".")[1])
                    block_type = "double"
                elif np[0].startswith("single_blocks"):
                    block_idx = int(np[0].split(".")[1])
                    block_type = "single"
                else:
                    block_idx = -1

                param_group_key = (block_type, block_idx)
                if param_group_key not in param_group:
                    param_group[param_group_key] = []
                param_group[param_group_key].append(p)

        block_types_and_indices = []
        for param_group_key, param_group in param_group.items():
            block_types_and_indices.append(param_group_key)
            grouped_params.append({"params": param_group, "lr": args.learning_rate})

            num_params = 0
            for p in param_group:
                num_params += p.numel()
            accelerator.print(f"block {param_group_key}: {num_params} parameters")

        # prepare optimizers for each group
        optimizers = []
        for group in grouped_params:
            _, _, optimizer = train_util.get_optimizer(args, trainable_params=[group])
            optimizers.append(optimizer)
        optimizer = optimizers[0]  # avoid error in the following code

        logger.info(f"using {len(optimizers)} optimizers for blockwise fused optimizers")

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

    # prepare dataloader
    # strategies are set here because they cannot be referenced in another process. Copy them with the dataset
    # some strategies can be None
    train_dataset_group.set_current_strategies()

    # DataLoaderのプロセス数：0 は persistent_workers が使えないので注意
    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
    if args.blockwise_fused_optimizers:
        # prepare lr schedulers for each optimizer
        lr_schedulers = [train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes) for optimizer in optimizers]
        lr_scheduler = lr_schedulers[0]  # avoid error in the following code
    else:
        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.")
        flux.to(weight_dtype)
        if clip_l is not None:
            clip_l.to(weight_dtype)
            t5xxl.to(weight_dtype)  # TODO check works with fp16 or not
    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.")
        flux.to(weight_dtype)
        if clip_l is not None:
            clip_l.to(weight_dtype)
            t5xxl.to(weight_dtype)

    # if we don't cache text encoder outputs, move them to device
    if not args.cache_text_encoder_outputs:
        clip_l.to(accelerator.device)
        t5xxl.to(accelerator.device)

    clean_memory_on_device(accelerator.device)

    if args.deepspeed:
        ds_model = deepspeed_utils.prepare_deepspeed_model(args, mmdit=flux)
        # most of ZeRO stage uses optimizer partitioning, so we have to prepare optimizer and ds_model at the same time. # pull/1139#issuecomment-1986790007
        ds_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
            ds_model, optimizer, train_dataloader, lr_scheduler
        )
        training_models = [ds_model]

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

    # 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:
        # During deepseed training, accelerate not handles fp16/bf16|mixed precision directly via scaler. Let deepspeed engine do.
        # -> But we think it's ok to patch accelerator even if deepspeed is enabled.
        train_util.patch_accelerator_for_fp16_training(accelerator)

    # resumeする
    train_util.resume_from_local_or_hf_if_specified(accelerator, args)

    if args.fused_backward_pass:
        # use fused optimizer for backward pass: other optimizers will be supported in the future
        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)

    elif args.blockwise_fused_optimizers:
        # prepare for additional optimizers and lr schedulers
        for i in range(1, len(optimizers)):
            optimizers[i] = accelerator.prepare(optimizers[i])
            lr_schedulers[i] = accelerator.prepare(lr_schedulers[i])

        # counters are used to determine when to step the optimizer
        global optimizer_hooked_count
        global num_parameters_per_group
        global parameter_optimizer_map

        optimizer_hooked_count = {}
        num_parameters_per_group = [0] * len(optimizers)
        parameter_optimizer_map = {}

        double_blocks_to_swap = args.double_blocks_to_swap
        single_blocks_to_swap = args.single_blocks_to_swap
        num_double_blocks = len(flux.double_blocks)
        num_single_blocks = len(flux.single_blocks)

        for opt_idx, optimizer in enumerate(optimizers):
            for param_group in optimizer.param_groups:
                for parameter in param_group["params"]:
                    if parameter.requires_grad:
                        block_type, block_idx = block_types_and_indices[opt_idx]

                        def create_optimizer_hook(btype, bidx):
                            def optimizer_hook(parameter: torch.Tensor):
                                # print(f"optimizer_hook: {btype}, {bidx}")
                                if accelerator.sync_gradients and args.max_grad_norm != 0.0:
                                    accelerator.clip_grad_norm_(parameter, args.max_grad_norm)

                                i = parameter_optimizer_map[parameter]
                                optimizer_hooked_count[i] += 1
                                if optimizer_hooked_count[i] == num_parameters_per_group[i]:
                                    optimizers[i].step()
                                    optimizers[i].zero_grad(set_to_none=True)

                                    # swap blocks if necessary
                                    if btype == "double" and double_blocks_to_swap:
                                        if bidx >= num_double_blocks - double_blocks_to_swap:
                                            bidx_cuda = double_blocks_to_swap - (num_double_blocks - bidx)
                                            flux.double_blocks[bidx].to("cpu")
                                            flux.double_blocks[bidx_cuda].to(accelerator.device)
                                            # print(f"Move double block {bidx} to cpu and {bidx_cuda} to device")
                                    elif btype == "single" and single_blocks_to_swap:
                                        if bidx >= num_single_blocks - single_blocks_to_swap:
                                            bidx_cuda = single_blocks_to_swap - (num_single_blocks - bidx)
                                            flux.single_blocks[bidx].to("cpu")
                                            flux.single_blocks[bidx_cuda].to(accelerator.device)
                                            # print(f"Move single block {bidx} to cpu and {bidx_cuda} to device")

                            return optimizer_hook

                        parameter.register_post_accumulate_grad_hook(create_optimizer_hook(block_type, block_idx))
                        parameter_optimizer_map[parameter] = opt_idx
                        num_parameters_per_group[opt_idx] += 1

    # 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
    accelerator.print("running training / 学習開始")
    accelerator.print(f"  num examples / サンプル数: {train_dataset_group.num_train_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])}"
    )
    # accelerator.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 = FlowMatchEulerDiscreteScheduler(num_train_timesteps=1000, shift=args.discrete_flow_shift)
    noise_scheduler_copy = copy.deepcopy(noise_scheduler)

    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(
            "finetuning" if args.log_tracker_name is None else args.log_tracker_name,
            config=train_util.get_sanitized_config_or_none(args),
            init_kwargs=init_kwargs,
        )

    if args.double_blocks_to_swap is not None or args.single_blocks_to_swap is not None:
        flux.prepare_block_swap_before_forward()

    # For --sample_at_first
    #flux_train_utils.sample_images(accelerator, args, 0, global_step, flux, ae, [clip_l, t5xxl], sample_prompts_te_outputs)

    loss_recorder = train_util.LossRecorder()
    epoch = 0  # avoid error when max_train_steps is 0
    for epoch in range(num_train_epochs):
        accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}")
        current_epoch.value = epoch + 1

        for m in training_models:
            m.train()

        for step, batch in enumerate(train_dataloader):
            current_step.value = global_step

            if args.blockwise_fused_optimizers:
                optimizer_hooked_count = {i: 0 for i in range(len(optimizers))}  # reset counter for each step

            with accelerator.accumulate(*training_models):
                if "latents" in batch and batch["latents"] is not None:
                    latents = batch["latents"].to(accelerator.device, dtype=weight_dtype)
                else:
                    with torch.no_grad():
                        # encode images to latents. images are [-1, 1]
                        latents = ae.encode(batch["images"])

                    # 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)

                text_encoder_outputs_list = batch.get("text_encoder_outputs_list", None)
                if text_encoder_outputs_list is not None:
                    text_encoder_conds = text_encoder_outputs_list
                else:
                    # not cached or training, so get from text encoders
                    tokens_and_masks = batch["input_ids_list"]
                    with torch.no_grad():
                        input_ids = [ids.to(accelerator.device) for ids in batch["input_ids_list"]]
                        text_encoder_conds = text_encoding_strategy.encode_tokens(
                            tokenize_strategy, [clip_l, t5xxl], input_ids, args.apply_t5_attn_mask
                        )
                        if args.full_fp16:
                            text_encoder_conds = [c.to(weight_dtype) for c in text_encoder_conds]

                # TODO support some features for noise implemented in get_noise_noisy_latents_and_timesteps

                # Sample noise that we'll add to the latents
                noise = torch.randn_like(latents)
                bsz = latents.shape[0]

                # get noisy model input and timesteps
                noisy_model_input, timesteps, sigmas = flux_train_utils.get_noisy_model_input_and_timesteps(
                    args, noise_scheduler, latents, noise, accelerator.device, weight_dtype
                )

                # pack latents and get img_ids
                packed_noisy_model_input = flux_utils.pack_latents(noisy_model_input)  # b, c, h*2, w*2 -> b, h*w, c*4
                packed_latent_height, packed_latent_width = noisy_model_input.shape[2] // 2, noisy_model_input.shape[3] // 2
                img_ids = flux_utils.prepare_img_ids(bsz, packed_latent_height, packed_latent_width).to(device=accelerator.device)

                # get guidance
                guidance_vec = torch.full((bsz,), args.guidance_scale, device=accelerator.device)

                # call model
                l_pooled, t5_out, txt_ids = text_encoder_conds
                with accelerator.autocast():
                    # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transformer model (we should not keep it but I want to keep the inputs same for the model for testing)
                    model_pred = flux(
                        img=packed_noisy_model_input,
                        img_ids=img_ids,
                        txt=t5_out,
                        txt_ids=txt_ids,
                        y=l_pooled,
                        timesteps=timesteps / 1000,
                        guidance=guidance_vec,
                    )

                # unpack latents
                model_pred = flux_utils.unpack_latents(model_pred, packed_latent_height, packed_latent_width)

                # apply model prediction type
                model_pred, weighting = flux_train_utils.apply_model_prediction_type(args, model_pred, noisy_model_input, sigmas)

                # flow matching loss: this is different from SD3
                target = noise - latents

                # calculate loss
                loss = train_util.conditional_loss(
                    model_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=None
                )
                if weighting is not None:
                    loss = loss * weighting
                if args.masked_loss or ("alpha_masks" in batch and batch["alpha_masks"] is not None):
                    loss = apply_masked_loss(loss, batch)
                loss = loss.mean([1, 2, 3])

                loss_weights = batch["loss_weights"]  # 各sampleごとのweight
                loss = loss * loss_weights
                loss = loss.mean()

                # backward
                accelerator.backward(loss)

                if not (args.fused_backward_pass or args.blockwise_fused_optimizers):
                    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)
                else:
                    # optimizer.step() and optimizer.zero_grad() are called in the optimizer hook
                    lr_scheduler.step()
                    if args.blockwise_fused_optimizers:
                        for i in range(1, len(optimizers)):
                            lr_schedulers[i].step()

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

                flux_train_utils.sample_images(
                    accelerator, args, None, global_step, flux, ae, [clip_l, t5xxl], sample_prompts_te_outputs
                )

                # 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:
                        flux_train_utils.save_flux_model_on_epoch_end_or_stepwise(
                            args,
                            False,
                            accelerator,
                            save_dtype,
                            epoch,
                            num_train_epochs,
                            global_step,
                            accelerator.unwrap_model(flux),
                        )

            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}
                train_util.append_lr_to_logs(logs, lr_scheduler, args.optimizer_type, including_unet=True)

                accelerator.log(logs, step=global_step)

            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 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()

        if args.save_every_n_epochs is not None:
            if accelerator.is_main_process:
                flux_train_utils.save_flux_model_on_epoch_end_or_stepwise(
                    args,
                    True,
                    accelerator,
                    save_dtype,
                    epoch,
                    num_train_epochs,
                    global_step,
                    accelerator.unwrap_model(flux),
                )

        flux_train_utils.sample_images(
            accelerator, args, epoch + 1, global_step, flux, ae, [clip_l, t5xxl], sample_prompts_te_outputs
        )

    is_main_process = accelerator.is_main_process
    # if is_main_process:
    flux = accelerator.unwrap_model(flux)

    accelerator.end_training()

    if args.save_state or args.save_state_on_train_end:
        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:
        flux_train_utils.save_flux_model_on_train_end(args, save_dtype, epoch, global_step, flux)
        logger.info("model saved.")


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

    add_logging_arguments(parser)
    train_util.add_sd_models_arguments(parser)  # TODO split this
    train_util.add_dataset_arguments(parser, True, True, True)
    train_util.add_training_arguments(parser, False)
    train_util.add_masked_loss_arguments(parser)
    deepspeed_utils.add_deepspeed_arguments(parser)
    train_util.add_sd_saving_arguments(parser)
    train_util.add_optimizer_arguments(parser)
    config_util.add_config_arguments(parser)
    add_custom_train_arguments(parser)  # TODO remove this from here
    flux_train_utils.add_flux_train_arguments(parser)

    parser.add_argument(
        "--fused_optimizer_groups",
        type=int,
        default=None,
        help="**this option is not working** will be removed in the future / このオプションは動作しません。将来削除されます",
    )
    parser.add_argument(
        "--blockwise_fused_optimizers",
        action="store_true",
        help="enable blockwise optimizers for fused backward pass and optimizer step / fused backward passとoptimizer step のためブロック単位のoptimizerを有効にする",
    )
    parser.add_argument(
        "--skip_latents_validity_check",
        action="store_true",
        help="skip latents validity check / latentsの正当性チェックをスキップする",
    )
    parser.add_argument(
        "--double_blocks_to_swap",
        type=int,
        default=None,
        help="[EXPERIMENTAL] "
        "Sets the number of 'double_blocks' (~640MB) to swap during the forward and backward passes."
        "Increasing this number lowers the overall VRAM used during training at the expense of training speed (s/it)."
        " / 順伝播および逆伝播中にスワップする'変換ブロック'（約640MB）の数を設定します。"
        "この数を増やすと、トレーニング中のVRAM使用量が減りますが、トレーニング速度（s/it）も低下します。",
    )
    parser.add_argument(
        "--single_blocks_to_swap",
        type=int,
        default=None,
        help="[EXPERIMENTAL] "
        "Sets the number of 'single_blocks' (~320MB) to swap during the forward and backward passes."
        "Increasing this number lowers the overall VRAM used during training at the expense of training speed (s/it)."
        " / 順伝播および逆伝播中にスワップする'変換ブロック'（約320MB）の数を設定します。"
        "この数を増やすと、トレーニング中のVRAM使用量が減りますが、トレーニング速度（s/it）も低下します。",
    )
    parser.add_argument(
        "--cpu_offload_checkpointing",
        action="store_true",
        help="[EXPERIMENTAL] enable offloading of tensors to CPU during checkpointing / チェックポイント時にテンソルをCPUにオフロードする",
    )
    return parser


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

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

    train(args)