hv_train_network.py

import ast
import asyncio
from datetime import timedelta
import gc
import importlib
import argparse
import math
import os
import pathlib
import re
import sys
import random
import time
import json
from multiprocessing import Value
from typing import Any, Dict, List, Optional
import accelerate
import numpy as np
from packaging.version import Version
from PIL import Image

import huggingface_hub
import toml

import torch
from tqdm import tqdm
from accelerate.utils import set_seed
from accelerate import Accelerator, InitProcessGroupKwargs, DistributedDataParallelKwargs, PartialState
from safetensors.torch import load_file
import transformers
from diffusers.optimization import (
    SchedulerType as DiffusersSchedulerType,
    TYPE_TO_SCHEDULER_FUNCTION as DIFFUSERS_TYPE_TO_SCHEDULER_FUNCTION,
)
from transformers.optimization import SchedulerType, TYPE_TO_SCHEDULER_FUNCTION

from dataset import config_utils
from hunyuan_model.models import load_transformer, get_rotary_pos_embed_by_shape, HYVideoDiffusionTransformer
import hunyuan_model.text_encoder as text_encoder_module
from hunyuan_model.vae import load_vae, VAE_VER
import hunyuan_model.vae as vae_module
from modules.scheduling_flow_match_discrete import FlowMatchDiscreteScheduler
import networks.lora as lora_module
from dataset.config_utils import BlueprintGenerator, ConfigSanitizer
from hv_generate_video import save_images_grid, save_videos_grid, resize_image_to_bucket, encode_to_latents

import logging

from utils import huggingface_utils, model_utils, train_utils, sai_model_spec

logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)


BASE_MODEL_VERSION_HUNYUAN_VIDEO = "hunyuan_video"

SS_METADATA_KEY_BASE_MODEL_VERSION = "ss_base_model_version"
SS_METADATA_KEY_NETWORK_MODULE = "ss_network_module"
SS_METADATA_KEY_NETWORK_DIM = "ss_network_dim"
SS_METADATA_KEY_NETWORK_ALPHA = "ss_network_alpha"
SS_METADATA_KEY_NETWORK_ARGS = "ss_network_args"

SS_METADATA_MINIMUM_KEYS = [
    SS_METADATA_KEY_BASE_MODEL_VERSION,
    SS_METADATA_KEY_NETWORK_MODULE,
    SS_METADATA_KEY_NETWORK_DIM,
    SS_METADATA_KEY_NETWORK_ALPHA,
    SS_METADATA_KEY_NETWORK_ARGS,
]


def clean_memory_on_device(device: torch.device):
    r"""
    Clean memory on the specified device, will be called from training scripts.
    """
    gc.collect()

    # device may "cuda" or "cuda:0", so we need to check the type of device
    if device.type == "cuda":
        torch.cuda.empty_cache()
    if device.type == "xpu":
        torch.xpu.empty_cache()
    if device.type == "mps":
        torch.mps.empty_cache()


# for collate_fn: epoch and step is multiprocessing.Value
class collator_class:
    def __init__(self, epoch, step, dataset):
        self.current_epoch = epoch
        self.current_step = step
        self.dataset = dataset  # not used if worker_info is not None, in case of multiprocessing

    def __call__(self, examples):
        worker_info = torch.utils.data.get_worker_info()
        # worker_info is None in the main process
        if worker_info is not None:
            dataset = worker_info.dataset
        else:
            dataset = self.dataset

        # set epoch and step
        dataset.set_current_epoch(self.current_epoch.value)
        dataset.set_current_step(self.current_step.value)
        return examples[0]


def prepare_accelerator(args: argparse.Namespace) -> Accelerator:
    """
    DeepSpeed is not supported in this script currently.
    """
    if args.logging_dir is None:
        logging_dir = None
    else:
        log_prefix = "" if args.log_prefix is None else args.log_prefix
        logging_dir = args.logging_dir + "/" + log_prefix + time.strftime("%Y%m%d%H%M%S", time.localtime())

    if args.log_with is None:
        if logging_dir is not None:
            log_with = "tensorboard"
        else:
            log_with = None
    else:
        log_with = args.log_with
        if log_with in ["tensorboard", "all"]:
            if logging_dir is None:
                raise ValueError(
                    "logging_dir is required when log_with is tensorboard / Tensorboardを使う場合、logging_dirを指定してください"
                )
        if log_with in ["wandb", "all"]:
            try:
                import wandb
            except ImportError:
                raise ImportError("No wandb / wandb がインストールされていないようです")
            if logging_dir is not None:
                os.makedirs(logging_dir, exist_ok=True)
                os.environ["WANDB_DIR"] = logging_dir
            if args.wandb_api_key is not None:
                wandb.login(key=args.wandb_api_key)

    kwargs_handlers = [
        (
            InitProcessGroupKwargs(
                backend="gloo" if os.name == "nt" or not torch.cuda.is_available() else "nccl",
                init_method=(
                    "env://?use_libuv=False" if os.name == "nt" and Version(torch.__version__) >= Version("2.4.0") else None
                ),
                timeout=timedelta(minutes=args.ddp_timeout) if args.ddp_timeout else None,
            )
            if torch.cuda.device_count() > 1
            else None
        ),
        (
            DistributedDataParallelKwargs(
                gradient_as_bucket_view=args.ddp_gradient_as_bucket_view, static_graph=args.ddp_static_graph
            )
            if args.ddp_gradient_as_bucket_view or args.ddp_static_graph
            else None
        ),
    ]
    kwargs_handlers = [i for i in kwargs_handlers if i is not None]

    accelerator = Accelerator(
        gradient_accumulation_steps=args.gradient_accumulation_steps,
        mixed_precision=args.mixed_precision,
        log_with=log_with,
        project_dir=logging_dir,
        kwargs_handlers=kwargs_handlers,
    )
    print("accelerator device:", accelerator.device)
    return accelerator


def line_to_prompt_dict(line: str) -> dict:
    # subset of gen_img_diffusers
    prompt_args = line.split(" --")
    prompt_dict = {}
    prompt_dict["prompt"] = prompt_args[0]

    for parg in prompt_args:
        try:
            m = re.match(r"w (\d+)", parg, re.IGNORECASE)
            if m:
                prompt_dict["width"] = int(m.group(1))
                continue

            m = re.match(r"h (\d+)", parg, re.IGNORECASE)
            if m:
                prompt_dict["height"] = int(m.group(1))
                continue

            m = re.match(r"f (\d+)", parg, re.IGNORECASE)
            if m:
                prompt_dict["frame_count"] = int(m.group(1))
                continue

            m = re.match(r"d (\d+)", parg, re.IGNORECASE)
            if m:
                prompt_dict["seed"] = int(m.group(1))
                continue

            m = re.match(r"s (\d+)", parg, re.IGNORECASE)
            if m:  # steps
                prompt_dict["sample_steps"] = max(1, min(1000, int(m.group(1))))
                continue

            m = re.match(r"g ([\d\.]+)", parg, re.IGNORECASE)
            if m:  # scale
                prompt_dict["guidance_scale"] = float(m.group(1))
                continue

            m = re.match(r"fs ([\d\.]+)", parg, re.IGNORECASE)
            if m:  # scale
                prompt_dict["discrete_flow_shift"] = float(m.group(1))
                continue

            m = re.match(r"l ([\d\.]+)", parg, re.IGNORECASE)
            if m:  # scale
                prompt_dict["cfg_scale"] = float(m.group(1))
                continue

            m = re.match(r"n (.+)", parg, re.IGNORECASE)
            if m:  # negative prompt
                prompt_dict["negative_prompt"] = m.group(1)
                continue

            m = re.match(r"i (.+)", parg, re.IGNORECASE)
            if m:  # negative prompt
                prompt_dict["image_path"] = m.group(1)
                continue

        except ValueError as ex:
            logger.error(f"Exception in parsing / 解析エラー: {parg}")
            logger.error(ex)

    return prompt_dict


def load_prompts(prompt_file: str) -> list[Dict]:
    # read prompts
    if prompt_file.endswith(".txt"):
        with open(prompt_file, "r", encoding="utf-8") as f:
            lines = f.readlines()
        prompts = [line.strip() for line in lines if len(line.strip()) > 0 and line[0] != "#"]
    elif prompt_file.endswith(".toml"):
        with open(prompt_file, "r", encoding="utf-8") as f:
            data = toml.load(f)
        prompts = [dict(**data["prompt"], **subset) for subset in data["prompt"]["subset"]]
    elif prompt_file.endswith(".json"):
        with open(prompt_file, "r", encoding="utf-8") as f:
            prompts = json.load(f)

    # preprocess prompts
    for i in range(len(prompts)):
        prompt_dict = prompts[i]
        if isinstance(prompt_dict, str):
            prompt_dict = line_to_prompt_dict(prompt_dict)
            prompts[i] = prompt_dict
        assert isinstance(prompt_dict, dict)

        # Adds an enumerator to the dict based on prompt position. Used later to name image files. Also cleanup of extra data in original prompt dict.
        prompt_dict["enum"] = i
        prompt_dict.pop("subset", None)

    return prompts


def compute_density_for_timestep_sampling(
    weighting_scheme: str, batch_size: int, logit_mean: float = None, logit_std: float = None, mode_scale: float = None
):
    """Compute the density for sampling the timesteps when doing SD3 training.

    Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528.

    SD3 paper reference: https://arxiv.org/abs/2403.03206v1.
    """
    if weighting_scheme == "logit_normal":
        # See 3.1 in the SD3 paper ($rf/lognorm(0.00,1.00)$).
        u = torch.normal(mean=logit_mean, std=logit_std, size=(batch_size,), device="cpu")
        u = torch.nn.functional.sigmoid(u)
    elif weighting_scheme == "mode":
        u = torch.rand(size=(batch_size,), device="cpu")
        u = 1 - u - mode_scale * (torch.cos(math.pi * u / 2) ** 2 - 1 + u)
    else:
        u = torch.rand(size=(batch_size,), device="cpu")
    return u


def get_sigmas(noise_scheduler, timesteps, device, n_dim=4, dtype=torch.float32):
    sigmas = noise_scheduler.sigmas.to(device=device, dtype=dtype)
    schedule_timesteps = noise_scheduler.timesteps.to(device)
    timesteps = timesteps.to(device)

    # if sum([(schedule_timesteps == t) for t in timesteps]) < len(timesteps):
    if any([(schedule_timesteps == t).sum() == 0 for t in timesteps]):
        # raise ValueError("Some timesteps are not in the schedule / 一部のtimestepsがスケジュールに含まれていません")
        # round to nearest timestep
        logger.warning("Some timesteps are not in the schedule / 一部のtimestepsがスケジュールに含まれていません")
        step_indices = [torch.argmin(torch.abs(schedule_timesteps - t)).item() for t in timesteps]
    else:
        step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]

    sigma = sigmas[step_indices].flatten()
    while len(sigma.shape) < n_dim:
        sigma = sigma.unsqueeze(-1)
    return sigma


def compute_loss_weighting_for_sd3(weighting_scheme: str, noise_scheduler, timesteps, device, dtype):
    """Computes loss weighting scheme for SD3 training.

    Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528.

    SD3 paper reference: https://arxiv.org/abs/2403.03206v1.
    """
    if weighting_scheme == "sigma_sqrt" or weighting_scheme == "cosmap":
        sigmas = get_sigmas(noise_scheduler, timesteps, device, n_dim=5, dtype=dtype)
        if weighting_scheme == "sigma_sqrt":
            weighting = (sigmas**-2.0).float()
        else:
            bot = 1 - 2 * sigmas + 2 * sigmas**2
            weighting = 2 / (math.pi * bot)
    else:
        weighting = None  # torch.ones_like(sigmas)
    return weighting


def should_sample_images(args, steps, epoch=None):
    if steps == 0:
        if not args.sample_at_first:
            return False
    else:
        should_sample_by_steps = args.sample_every_n_steps is not None and steps % args.sample_every_n_steps == 0
        should_sample_by_epochs = (
            args.sample_every_n_epochs is not None and epoch is not None and epoch % args.sample_every_n_epochs == 0
        )
        if not should_sample_by_steps and not should_sample_by_epochs:
            return False
    return True


def sample_images(accelerator, args, epoch, steps, vae, transformer, sample_parameters, dit_dtype):
    if not should_sample_images(args, steps, epoch):
        return

    logger.info("")
    logger.info(f"generating sample images at step / サンプル画像生成 ステップ: {steps}")
    if sample_parameters is None:
        logger.error(f"No prompt file / プロンプトファイルがありません: {args.sample_prompts}")
        return

    distributed_state = PartialState()  # for multi gpu distributed inference. this is a singleton, so it's safe to use it here

    # Use the unwrapped model
    transformer: HYVideoDiffusionTransformer = accelerator.unwrap_model(transformer)
    transformer.switch_block_swap_for_inference()

    # Create a directory to save the samples
    save_dir = args.output_dir + "/sample"
    os.makedirs(save_dir, exist_ok=True)

    # save random state to restore later
    rng_state = torch.get_rng_state()
    cuda_rng_state = None
    try:
        cuda_rng_state = torch.cuda.get_rng_state() if torch.cuda.is_available() else None
    except Exception:
        pass

    if distributed_state.num_processes <= 1:
        # If only one device is available, just use the original prompt list. We don't need to care about the distribution of prompts.
        with torch.no_grad(), accelerator.autocast():
            for sample_parameter in sample_parameters:
                sample_image_inference(accelerator, args, transformer, dit_dtype, vae, save_dir, sample_parameter, epoch, steps)
                clean_memory_on_device(accelerator.device)
    else:
        # Creating list with N elements, where each element is a list of prompt_dicts, and N is the number of processes available (number of devices available)
        # prompt_dicts are assigned to lists based on order of processes, to attempt to time the image creation time to match enum order. Probably only works when steps and sampler are identical.
        per_process_params = []  # list of lists
        for i in range(distributed_state.num_processes):
            per_process_params.append(sample_parameters[i :: distributed_state.num_processes])

        with torch.no_grad():
            with distributed_state.split_between_processes(per_process_params) as sample_parameter_lists:
                for sample_parameter in sample_parameter_lists[0]:
                    sample_image_inference(accelerator, args, transformer, dit_dtype, vae, save_dir, sample_parameter, epoch, steps)
                    clean_memory_on_device(accelerator.device)

    torch.set_rng_state(rng_state)
    if cuda_rng_state is not None:
        torch.cuda.set_rng_state(cuda_rng_state)

    transformer.switch_block_swap_for_training()
    clean_memory_on_device(accelerator.device)


def sample_image_inference(
    accelerator, args, transformer: HYVideoDiffusionTransformer, dit_dtype, vae, save_dir, sample_parameter, epoch, steps
):
    sample_steps = sample_parameter.get("sample_steps", 20)
    width = sample_parameter.get("width", 256)  # make smaller for faster and memory saving inference
    height = sample_parameter.get("height", 256)
    frame_count = sample_parameter.get("frame_count", 1)
    guidance_scale = sample_parameter.get("guidance_scale", 6.0)
    discrete_flow_shift = sample_parameter.get("discrete_flow_shift", 14.5)
    seed = sample_parameter.get("seed")
    prompt: str = sample_parameter.get("prompt", "")
    cfg_scale = sample_parameter.get("cfg_scale", 1.0)
    negative_prompt = sample_parameter.get("negative_prompt", None)

    i2v_model = transformer.in_channels == 32
    if i2v_model:
        image_path = sample_parameter.get("image_path", None)
        if image_path is None:
            logger.error("No image_path for i2v model / i2vモデルのサンプル画像生成にはimage_pathが必要です")
            return
    else:
        image_path = None
        image_latents = None

    # Calculate latent video length based on VAE version
    if "884" in VAE_VER:
        latent_video_length = (frame_count - 1) // 4 + 1
    elif "888" in VAE_VER:
        latent_video_length = (frame_count - 1) // 8 + 1
    else:
        latent_video_length = frame_count

    device = accelerator.device
    if seed is not None:
        torch.manual_seed(seed)
        torch.cuda.manual_seed(seed)
        generator = torch.Generator(device=device).manual_seed(seed)
    else:
        # True random sample image generation
        torch.seed()
        torch.cuda.seed()
        generator = torch.Generator(device=device).manual_seed(torch.initial_seed())

    logger.info(f"prompt: {prompt}")
    logger.info(f"height: {height}")
    logger.info(f"width: {width}")
    logger.info(f"frame count: {frame_count}")
    logger.info(f"sample steps: {sample_steps}")
    logger.info(f"guidance scale: {guidance_scale}")
    logger.info(f"discrete flow shift: {discrete_flow_shift}")
    if seed is not None:
        logger.info(f"seed: {seed}")

    do_classifier_free_guidance = False
    if negative_prompt is not None:
        do_classifier_free_guidance = True
        logger.info(f"negative prompt: {negative_prompt}")
        logger.info(f"cfg scale: {cfg_scale}")

    if i2v_model:
        logger.info(f"image path: {image_path}")

    # Prepare scheduler for each prompt
    scheduler = FlowMatchDiscreteScheduler(shift=discrete_flow_shift, reverse=True, solver="euler")

    # Number of inference steps for sampling
    scheduler.set_timesteps(sample_steps, device=device)
    timesteps = scheduler.timesteps

    # Get embeddings
    prompt_embeds = sample_parameter["llm_embeds"].to(device=device, dtype=dit_dtype)
    prompt_mask = sample_parameter["llm_mask"].to(device=device)
    prompt_embeds_2 = sample_parameter["clipL_embeds"].to(device=device, dtype=dit_dtype)

    if do_classifier_free_guidance:
        negative_prompt_embeds = sample_parameter["negative_llm_embeds"].to(device=device, dtype=dit_dtype)
        negative_prompt_mask = sample_parameter["negative_llm_mask"].to(device=device)
        negative_prompt_embeds_2 = sample_parameter["negative_clipL_embeds"].to(device=device, dtype=dit_dtype)
        prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
        prompt_mask = torch.cat([negative_prompt_mask, prompt_mask], dim=0)
        prompt_embeds_2 = torch.cat([negative_prompt_embeds_2, prompt_embeds_2], dim=0)

    num_channels_latents = 16  # transformer.config.in_channels
    vae_scale_factor = 2 ** (4 - 1)  # Assuming 4 VAE blocks

    # Initialize latents
    shape_or_frame = (
        1,
        num_channels_latents,
        1,
        height // vae_scale_factor,
        width // vae_scale_factor,
    )
    latents = []
    for _ in range(latent_video_length):
        latents.append(torch.randn(shape_or_frame, generator=generator, device=device, dtype=dit_dtype))
    latents = torch.cat(latents, dim=2)

    if i2v_model:
        # Move VAE to the appropriate device for sampling
        vae.to(device)
        vae.eval()

        image = Image.open(image_path)
        image = resize_image_to_bucket(image, (width, height))  # returns a numpy array
        image = torch.from_numpy(image).permute(2, 0, 1).unsqueeze(0).unsqueeze(2).float()  # 1, C, 1, H, W
        image = image / 255.0

        logger.info(f"Encoding image to latents")
        image_latents = encode_to_latents(args, image, device)  # 1, C, 1, H, W
        image_latents = image_latents.to(device=device, dtype=dit_dtype)

        vae.to("cpu")
        clean_memory_on_device(device)

        zero_latents = torch.zeros_like(latents)
        zero_latents[:, :, :1, :, :] = image_latents
        image_latents = zero_latents

    # Guidance scale
    guidance_expand = torch.tensor([guidance_scale * 1000.0], dtype=torch.float32, device=device).to(dit_dtype)

    # Get rotary positional embeddings
    freqs_cos, freqs_sin = get_rotary_pos_embed_by_shape(transformer, latents.shape[2:])
    freqs_cos = freqs_cos.to(device=device, dtype=dit_dtype)
    freqs_sin = freqs_sin.to(device=device, dtype=dit_dtype)

    # Wrap the inner loop with tqdm to track progress over timesteps
    prompt_idx = sample_parameter.get("enum", 0)
    with torch.no_grad():
        for i, t in enumerate(tqdm(timesteps, desc=f"Sampling timesteps for prompt {prompt_idx+1}")):
            latents_input = scheduler.scale_model_input(latents, t)

            if do_classifier_free_guidance:
                latents_input = torch.cat([latents_input, latents_input], dim=0)  # 2, C, F, H, W

            if image_latents is not None:
                latents_image_input = (
                    image_latents if not do_classifier_free_guidance else torch.cat([image_latents, image_latents], dim=0)
                )
                latents_input = torch.cat([latents_input, latents_image_input], dim=1)  # 1 or 2, C*2, F, H, W

            noise_pred = transformer(
                latents_input,
                t.repeat(latents.shape[0]).to(device=device, dtype=dit_dtype),
                text_states=prompt_embeds,
                text_mask=prompt_mask,
                text_states_2=prompt_embeds_2,
                freqs_cos=freqs_cos,
                freqs_sin=freqs_sin,
                guidance=guidance_expand,
                return_dict=True,
            )["x"]

            # perform classifier free guidance
            if do_classifier_free_guidance:
                noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)
                noise_pred = noise_pred_uncond + cfg_scale * (noise_pred_cond - noise_pred_uncond)

            # Compute the previous noisy sample x_t -> x_t-1
            latents = scheduler.step(noise_pred, t, latents, return_dict=False)[0]

    # Move VAE to the appropriate device for sampling
    vae.to(device)
    vae.eval()

    # Decode latents to video
    if hasattr(vae.config, "shift_factor") and vae.config.shift_factor:
        latents = latents / vae.config.scaling_factor + vae.config.shift_factor
    else:
        latents = latents / vae.config.scaling_factor

    latents = latents.to(device=device, dtype=vae.dtype)
    with torch.no_grad():
        video = vae.decode(latents, return_dict=False)[0]
    video = (video / 2 + 0.5).clamp(0, 1)
    video = video.cpu().float()

    # Save video
    ts_str = time.strftime("%Y%m%d%H%M%S", time.localtime())
    num_suffix = f"e{epoch:06d}" if epoch is not None else f"{steps:06d}"
    seed_suffix = "" if seed is None else f"_{seed}"
    save_path = f"{'' if args.output_name is None else args.output_name + '_'}{num_suffix}_{prompt_idx:02d}_{ts_str}{seed_suffix}"
    if video.shape[2] == 1:
        save_images_grid(video, save_dir, save_path, create_subdir=False)
    else:
        save_videos_grid(video, os.path.join(save_dir, save_path) + ".mp4")

    # Move models back to initial state
    vae.to("cpu")


class NetworkTrainer:
    def __init__(self):
        pass

    # TODO 他のスクリプトと共通化する
    def generate_step_logs(
        self,
        args: argparse.Namespace,
        current_loss,
        avr_loss,
        lr_scheduler,
        lr_descriptions,
        optimizer=None,
        keys_scaled=None,
        mean_norm=None,
        maximum_norm=None,
    ):
        network_train_unet_only = True
        logs = {"loss/current": current_loss, "loss/average": avr_loss}

        if keys_scaled is not None:
            logs["max_norm/keys_scaled"] = keys_scaled
            logs["max_norm/average_key_norm"] = mean_norm
            logs["max_norm/max_key_norm"] = maximum_norm

        lrs = lr_scheduler.get_last_lr()
        for i, lr in enumerate(lrs):
            if lr_descriptions is not None:
                lr_desc = lr_descriptions[i]
            else:
                idx = i - (0 if network_train_unet_only else -1)
                if idx == -1:
                    lr_desc = "textencoder"
                else:
                    if len(lrs) > 2:
                        lr_desc = f"group{idx}"
                    else:
                        lr_desc = "unet"

            logs[f"lr/{lr_desc}"] = lr

            if args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower():
                # tracking d*lr value
                logs[f"lr/d*lr/{lr_desc}"] = (
                    lr_scheduler.optimizers[-1].param_groups[i]["d"] * lr_scheduler.optimizers[-1].param_groups[i]["lr"]
                )
            if (
                args.optimizer_type.lower().endswith("ProdigyPlusScheduleFree".lower()) and optimizer is not None
            ):  # tracking d*lr value of unet.
                logs["lr/d*lr"] = optimizer.param_groups[0]["d"] * optimizer.param_groups[0]["lr"]
        else:
            idx = 0
            if not network_train_unet_only:
                logs["lr/textencoder"] = float(lrs[0])
                idx = 1

            for i in range(idx, len(lrs)):
                logs[f"lr/group{i}"] = float(lrs[i])
                if args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower():
                    logs[f"lr/d*lr/group{i}"] = (
                        lr_scheduler.optimizers[-1].param_groups[i]["d"] * lr_scheduler.optimizers[-1].param_groups[i]["lr"]
                    )
                if args.optimizer_type.lower().endswith("ProdigyPlusScheduleFree".lower()) and optimizer is not None:
                    logs[f"lr/d*lr/group{i}"] = optimizer.param_groups[i]["d"] * optimizer.param_groups[i]["lr"]

        return logs

    def process_sample_prompts(
        self,
        args: argparse.Namespace,
        accelerator: Accelerator,
        sample_prompts: str,
        text_encoder1: str,
        text_encoder2: str,
        fp8_llm: bool,
    ):
        logger.info(f"cache Text Encoder outputs for sample prompt: {sample_prompts}")
        prompts = load_prompts(sample_prompts)

        def encode_for_text_encoder(text_encoder, is_llm=True):
            sample_prompts_te_outputs = {}  # (prompt) -> (embeds, mask)
            with accelerator.autocast(), torch.no_grad():
                for prompt_dict in prompts:
                    for p in [prompt_dict.get("prompt", ""), prompt_dict.get("negative_prompt", None)]:
                        if p is None:
                            continue
                        if p not in sample_prompts_te_outputs:
                            logger.info(f"cache Text Encoder outputs for prompt: {p}")

                            data_type = "video"
                            text_inputs = text_encoder.text2tokens(p, data_type=data_type)

                            prompt_outputs = text_encoder.encode(text_inputs, data_type=data_type)
                            sample_prompts_te_outputs[p] = (prompt_outputs.hidden_state, prompt_outputs.attention_mask)

            return sample_prompts_te_outputs

        # Load Text Encoder 1 and encode
        text_encoder_dtype = torch.float16 if args.text_encoder_dtype is None else model_utils.str_to_dtype(args.text_encoder_dtype)
        logger.info(f"loading text encoder 1: {text_encoder1}")
        text_encoder_1 = text_encoder_module.load_text_encoder_1(text_encoder1, accelerator.device, fp8_llm, text_encoder_dtype)

        logger.info("encoding with Text Encoder 1")
        te_outputs_1 = encode_for_text_encoder(text_encoder_1)
        del text_encoder_1

        # Load Text Encoder 2 and encode
        logger.info(f"loading text encoder 2: {text_encoder2}")
        text_encoder_2 = text_encoder_module.load_text_encoder_2(text_encoder2, accelerator.device, text_encoder_dtype)

        logger.info("encoding with Text Encoder 2")
        te_outputs_2 = encode_for_text_encoder(text_encoder_2, is_llm=False)
        del text_encoder_2

        # prepare sample parameters
        sample_parameters = []
        for prompt_dict in prompts:
            prompt_dict_copy = prompt_dict.copy()

            p = prompt_dict.get("prompt", "")
            prompt_dict_copy["llm_embeds"] = te_outputs_1[p][0]
            prompt_dict_copy["llm_mask"] = te_outputs_1[p][1]
            prompt_dict_copy["clipL_embeds"] = te_outputs_2[p][0]
            prompt_dict_copy["clipL_mask"] = te_outputs_2[p][1]

            p = prompt_dict.get("negative_prompt", None)
            if p is not None:
                prompt_dict_copy["negative_llm_embeds"] = te_outputs_1[p][0]
                prompt_dict_copy["negative_llm_mask"] = te_outputs_1[p][1]
                prompt_dict_copy["negative_clipL_embeds"] = te_outputs_2[p][0]
                prompt_dict_copy["negative_clipL_mask"] = te_outputs_2[p][1]

            sample_parameters.append(prompt_dict_copy)

        clean_memory_on_device(accelerator.device)

        return sample_parameters

    def get_optimizer(self, args, trainable_params: list[torch.nn.Parameter]) -> tuple[str, str, torch.optim.Optimizer]:
        # adamw, adamw8bit, adafactor

        optimizer_type = args.optimizer_type.lower()

        # split optimizer_type and optimizer_args
        optimizer_kwargs = {}
        if args.optimizer_args is not None and len(args.optimizer_args) > 0:
            for arg in args.optimizer_args:
                key, value = arg.split("=")
                value = ast.literal_eval(value)
                optimizer_kwargs[key] = value

        lr = args.learning_rate
        optimizer = None
        optimizer_class = None

        if optimizer_type.endswith("8bit".lower()):
            try:
                import bitsandbytes as bnb
            except ImportError:
                raise ImportError("No bitsandbytes / bitsandbytesがインストールされていないようです")

            if optimizer_type == "AdamW8bit".lower():
                logger.info(f"use 8-bit AdamW optimizer | {optimizer_kwargs}")
                optimizer_class = bnb.optim.AdamW8bit
                optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)

        elif optimizer_type == "Adafactor".lower():
            # Adafactor: check relative_step and warmup_init
            if "relative_step" not in optimizer_kwargs:
                optimizer_kwargs["relative_step"] = True  # default
            if not optimizer_kwargs["relative_step"] and optimizer_kwargs.get("warmup_init", False):
                logger.info(
                    f"set relative_step to True because warmup_init is True / warmup_initがTrueのためrelative_stepをTrueにします"
                )
                optimizer_kwargs["relative_step"] = True
            logger.info(f"use Adafactor optimizer | {optimizer_kwargs}")

            if optimizer_kwargs["relative_step"]:
                logger.info(f"relative_step is true / relative_stepがtrueです")
                if lr != 0.0:
                    logger.warning(f"learning rate is used as initial_lr / 指定したlearning rateはinitial_lrとして使用されます")
                args.learning_rate = None

                if args.lr_scheduler != "adafactor":
                    logger.info(f"use adafactor_scheduler / スケジューラにadafactor_schedulerを使用します")
                args.lr_scheduler = f"adafactor:{lr}"  # ちょっと微妙だけど

                lr = None
            else:
                if args.max_grad_norm != 0.0:
                    logger.warning(
                        f"because max_grad_norm is set, clip_grad_norm is enabled. consider set to 0 / max_grad_normが設定されているためclip_grad_normが有効になります。0に設定して無効にしたほうがいいかもしれません"
                    )
                if args.lr_scheduler != "constant_with_warmup":
                    logger.warning(f"constant_with_warmup will be good / スケジューラはconstant_with_warmupが良いかもしれません")
                if optimizer_kwargs.get("clip_threshold", 1.0) != 1.0:
                    logger.warning(f"clip_threshold=1.0 will be good / clip_thresholdは1.0が良いかもしれません")

            optimizer_class = transformers.optimization.Adafactor
            optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)

        elif optimizer_type == "AdamW".lower():
            logger.info(f"use AdamW optimizer | {optimizer_kwargs}")
            optimizer_class = torch.optim.AdamW
            optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)

        if optimizer is None:
            # 任意のoptimizerを使う
            case_sensitive_optimizer_type = args.optimizer_type  # not lower
            logger.info(f"use {case_sensitive_optimizer_type} | {optimizer_kwargs}")

            if "." not in case_sensitive_optimizer_type:  # from torch.optim
                optimizer_module = torch.optim
            else:  # from other library
                values = case_sensitive_optimizer_type.split(".")
                optimizer_module = importlib.import_module(".".join(values[:-1]))
                case_sensitive_optimizer_type = values[-1]

            optimizer_class = getattr(optimizer_module, case_sensitive_optimizer_type)
            optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)

        # for logging
        optimizer_name = optimizer_class.__module__ + "." + optimizer_class.__name__
        optimizer_args = ",".join([f"{k}={v}" for k, v in optimizer_kwargs.items()])

        # get train and eval functions
        if hasattr(optimizer, "train") and callable(optimizer.train):
            train_fn = optimizer.train
            eval_fn = optimizer.eval
        else:
            train_fn = lambda: None
            eval_fn = lambda: None

        return optimizer_name, optimizer_args, optimizer, train_fn, eval_fn

    def is_schedulefree_optimizer(self, optimizer: torch.optim.Optimizer, args: argparse.Namespace) -> bool:
        return args.optimizer_type.lower().endswith("schedulefree".lower())  # or args.optimizer_schedulefree_wrapper

    def get_dummy_scheduler(optimizer: torch.optim.Optimizer) -> Any:
        # dummy scheduler for schedulefree optimizer. supports only empty step(), get_last_lr() and optimizers.
        # this scheduler is used for logging only.
        # this isn't be wrapped by accelerator because of this class is not a subclass of torch.optim.lr_scheduler._LRScheduler
        class DummyScheduler:
            def __init__(self, optimizer: torch.optim.Optimizer):
                self.optimizer = optimizer

            def step(self):
                pass

            def get_last_lr(self):
                return [group["lr"] for group in self.optimizer.param_groups]

        return DummyScheduler(optimizer)

    def get_scheduler(self, args, optimizer: torch.optim.Optimizer, num_processes: int):
        """
        Unified API to get any scheduler from its name.
        """
        # if schedulefree optimizer, return dummy scheduler
        if self.is_schedulefree_optimizer(optimizer, args):
            return self.get_dummy_scheduler(optimizer)

        name = args.lr_scheduler
        num_training_steps = args.max_train_steps * num_processes  # * args.gradient_accumulation_steps
        num_warmup_steps: Optional[int] = (
            int(args.lr_warmup_steps * num_training_steps) if isinstance(args.lr_warmup_steps, float) else args.lr_warmup_steps
        )
        num_decay_steps: Optional[int] = (
            int(args.lr_decay_steps * num_training_steps) if isinstance(args.lr_decay_steps, float) else args.lr_decay_steps
        )
        num_stable_steps = num_training_steps - num_warmup_steps - num_decay_steps
        num_cycles = args.lr_scheduler_num_cycles
        power = args.lr_scheduler_power
        timescale = args.lr_scheduler_timescale
        min_lr_ratio = args.lr_scheduler_min_lr_ratio

        lr_scheduler_kwargs = {}  # get custom lr_scheduler kwargs
        if args.lr_scheduler_args is not None and len(args.lr_scheduler_args) > 0:
            for arg in args.lr_scheduler_args:
                key, value = arg.split("=")
                value = ast.literal_eval(value)
                lr_scheduler_kwargs[key] = value

        def wrap_check_needless_num_warmup_steps(return_vals):
            if num_warmup_steps is not None and num_warmup_steps != 0:
                raise ValueError(f"{name} does not require `num_warmup_steps`. Set None or 0.")
            return return_vals

        # using any lr_scheduler from other library
        if args.lr_scheduler_type:
            lr_scheduler_type = args.lr_scheduler_type
            logger.info(f"use {lr_scheduler_type} | {lr_scheduler_kwargs} as lr_scheduler")
            if "." not in lr_scheduler_type:  # default to use torch.optim
                lr_scheduler_module = torch.optim.lr_scheduler
            else:
                values = lr_scheduler_type.split(".")
                lr_scheduler_module = importlib.import_module(".".join(values[:-1]))
                lr_scheduler_type = values[-1]
            lr_scheduler_class = getattr(lr_scheduler_module, lr_scheduler_type)
            lr_scheduler = lr_scheduler_class(optimizer, **lr_scheduler_kwargs)
            return lr_scheduler

        if name.startswith("adafactor"):
            assert (
                type(optimizer) == transformers.optimization.Adafactor
            ), f"adafactor scheduler must be used with Adafactor optimizer / adafactor schedulerはAdafactorオプティマイザと同時に使ってください"
            initial_lr = float(name.split(":")[1])
            # logger.info(f"adafactor scheduler init lr {initial_lr}")
            return wrap_check_needless_num_warmup_steps(transformers.optimization.AdafactorSchedule(optimizer, initial_lr))

        if name == DiffusersSchedulerType.PIECEWISE_CONSTANT.value:
            name = DiffusersSchedulerType(name)
            schedule_func = DIFFUSERS_TYPE_TO_SCHEDULER_FUNCTION[name]
            return schedule_func(optimizer, **lr_scheduler_kwargs)  # step_rules and last_epoch are given as kwargs

        name = SchedulerType(name)
        schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name]

        if name == SchedulerType.CONSTANT:
            return wrap_check_needless_num_warmup_steps(schedule_func(optimizer, **lr_scheduler_kwargs))

        # All other schedulers require `num_warmup_steps`
        if num_warmup_steps is None:
            raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument.")

        if name == SchedulerType.CONSTANT_WITH_WARMUP:
            return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, **lr_scheduler_kwargs)

        if name == SchedulerType.INVERSE_SQRT:
            return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, timescale=timescale, **lr_scheduler_kwargs)

        # All other schedulers require `num_training_steps`
        if num_training_steps is None:
            raise ValueError(f"{name} requires `num_training_steps`, please provide that argument.")

        if name == SchedulerType.COSINE_WITH_RESTARTS:
            return schedule_func(
                optimizer,
                num_warmup_steps=num_warmup_steps,
                num_training_steps=num_training_steps,
                num_cycles=num_cycles,
                **lr_scheduler_kwargs,
            )

        if name == SchedulerType.POLYNOMIAL:
            return schedule_func(
                optimizer,
                num_warmup_steps=num_warmup_steps,
                num_training_steps=num_training_steps,
                power=power,
                **lr_scheduler_kwargs,
            )

        if name == SchedulerType.COSINE_WITH_MIN_LR:
            return schedule_func(
                optimizer,
                num_warmup_steps=num_warmup_steps,
                num_training_steps=num_training_steps,
                num_cycles=num_cycles / 2,
                min_lr_rate=min_lr_ratio,
                **lr_scheduler_kwargs,
            )

        # these schedulers do not require `num_decay_steps`
        if name == SchedulerType.LINEAR or name == SchedulerType.COSINE:
            return schedule_func(
                optimizer,
                num_warmup_steps=num_warmup_steps,
                num_training_steps=num_training_steps,
                **lr_scheduler_kwargs,
            )

        # All other schedulers require `num_decay_steps`
        if num_decay_steps is None:
            raise ValueError(f"{name} requires `num_decay_steps`, please provide that argument.")
        if name == SchedulerType.WARMUP_STABLE_DECAY:
            return schedule_func(
                optimizer,
                num_warmup_steps=num_warmup_steps,
                num_stable_steps=num_stable_steps,
                num_decay_steps=num_decay_steps,
                num_cycles=num_cycles / 2,
                min_lr_ratio=min_lr_ratio if min_lr_ratio is not None else 0.0,
                **lr_scheduler_kwargs,
            )

        return schedule_func(
            optimizer,
            num_warmup_steps=num_warmup_steps,
            num_training_steps=num_training_steps,
            num_decay_steps=num_decay_steps,
            **lr_scheduler_kwargs,
        )

    def resume_from_local_or_hf_if_specified(self, accelerator: Accelerator, args: argparse.Namespace) -> bool:
        if not args.resume:
            return False

        if not args.resume_from_huggingface:
            logger.info(f"resume training from local state: {args.resume}")
            accelerator.load_state(args.resume)
            return True

        logger.info(f"resume training from huggingface state: {args.resume}")
        repo_id = args.resume.split("/")[0] + "/" + args.resume.split("/")[1]
        path_in_repo = "/".join(args.resume.split("/")[2:])
        revision = None
        repo_type = None
        if ":" in path_in_repo:
            divided = path_in_repo.split(":")
            if len(divided) == 2:
                path_in_repo, revision = divided
                repo_type = "model"
            else:
                path_in_repo, revision, repo_type = divided
        logger.info(f"Downloading state from huggingface: {repo_id}/{path_in_repo}@{revision}")

        list_files = huggingface_utils.list_dir(
            repo_id=repo_id,
            subfolder=path_in_repo,
            revision=revision,
            token=args.huggingface_token,
            repo_type=repo_type,
        )

        async def download(filename) -> str:
            def task():
                return huggingface_hub.hf_hub_download(
                    repo_id=repo_id,
                    filename=filename,
                    revision=revision,
                    repo_type=repo_type,
                    token=args.huggingface_token,
                )

            return await asyncio.get_event_loop().run_in_executor(None, task)

        loop = asyncio.get_event_loop()
        results = loop.run_until_complete(asyncio.gather(*[download(filename=filename.rfilename) for filename in list_files]))
        if len(results) == 0:
            raise ValueError(
                "No files found in the specified repo id/path/revision / 指定されたリポジトリID/パス/リビジョンにファイルが見つかりませんでした"
            )
        dirname = os.path.dirname(results[0])
        accelerator.load_state(dirname)

        return True

    def get_noisy_model_input_and_timesteps(
        self,
        args: argparse.Namespace,
        noise: torch.Tensor,
        latents: torch.Tensor,
        noise_scheduler: FlowMatchDiscreteScheduler,
        device: torch.device,
        dtype: torch.dtype,
    ):
        batch_size = noise.shape[0]

        if args.timestep_sampling == "uniform" or args.timestep_sampling == "sigmoid" or args.timestep_sampling == "shift":
            if args.timestep_sampling == "uniform" or args.timestep_sampling == "sigmoid":
                # Simple random t-based noise sampling
                if args.timestep_sampling == "sigmoid":
                    t = torch.sigmoid(args.sigmoid_scale * torch.randn((batch_size,), device=device))
                else:
                    t = torch.rand((batch_size,), device=device)

            elif args.timestep_sampling == "shift":
                shift = args.discrete_flow_shift
                logits_norm = torch.randn(batch_size, device=device)
                logits_norm = logits_norm * args.sigmoid_scale  # larger scale for more uniform sampling
                t = logits_norm.sigmoid()
                t = (t * shift) / (1 + (shift - 1) * t)

            t_min = args.min_timestep if args.min_timestep is not None else 0
            t_max = args.max_timestep if args.max_timestep is not None else 1000.0
            t_min /= 1000.0
            t_max /= 1000.0
            t = t * (t_max - t_min) + t_min  # scale to [t_min, t_max], default [0, 1]

            timesteps = t * 1000.0
            t = t.view(-1, 1, 1, 1, 1)
            noisy_model_input = (1 - t) * latents + t * noise

            timesteps += 1  # 1 to 1000
        else:
            # Sample a random timestep for each image
            # for weighting schemes where we sample timesteps non-uniformly
            u = compute_density_for_timestep_sampling(
                weighting_scheme=args.weighting_scheme,
                batch_size=batch_size,
                logit_mean=args.logit_mean,
                logit_std=args.logit_std,
                mode_scale=args.mode_scale,
            )
            # indices = (u * noise_scheduler.config.num_train_timesteps).long()
            t_min = args.min_timestep if args.min_timestep is not None else 0
            t_max = args.max_timestep if args.max_timestep is not None else 1000
            indices = (u * (t_max - t_min) + t_min).long()

            timesteps = noise_scheduler.timesteps[indices].to(device=device)  # 1 to 1000

            # Add noise according to flow matching.
            sigmas = get_sigmas(noise_scheduler, timesteps, device, n_dim=latents.ndim, dtype=dtype)
            noisy_model_input = sigmas * noise + (1.0 - sigmas) * latents

        return noisy_model_input, timesteps

    def show_timesteps(self, args: argparse.Namespace):
        N_TRY = 100000
        BATCH_SIZE = 1000
        CONSOLE_WIDTH = 64
        N_TIMESTEPS_PER_LINE = 25

        noise_scheduler = FlowMatchDiscreteScheduler(shift=args.discrete_flow_shift, reverse=True, solver="euler")
        # print(f"Noise scheduler timesteps: {noise_scheduler.timesteps}")

        latents = torch.zeros(BATCH_SIZE, 1, 1, 1, 1, dtype=torch.float16)
        noise = torch.ones_like(latents)

        # sample timesteps
        sampled_timesteps = [0] * noise_scheduler.config.num_train_timesteps
        for i in tqdm(range(N_TRY // BATCH_SIZE)):
            # we use noise=1, so retured noisy_model_input is same as timestep, because `noisy_model_input = (1 - t) * latents + t * noise`
            actual_timesteps, _ = self.get_noisy_model_input_and_timesteps(
                args, noise, latents, noise_scheduler, "cpu", torch.float16
            )
            actual_timesteps = actual_timesteps[:, 0, 0, 0, 0] * 1000
            for t in actual_timesteps:
                t = int(t.item())
                sampled_timesteps[t] += 1

        # sample weighting
        sampled_weighting = [0] * noise_scheduler.config.num_train_timesteps
        for i in tqdm(range(len(sampled_weighting))):
            timesteps = torch.tensor([i + 1], device="cpu")
            weighting = compute_loss_weighting_for_sd3(args.weighting_scheme, noise_scheduler, timesteps, "cpu", torch.float16)
            if weighting is None:
                weighting = torch.tensor(1.0, device="cpu")
            elif torch.isinf(weighting).any():
                weighting = torch.tensor(1.0, device="cpu")
            sampled_weighting[i] = weighting.item()

        # show results
        if args.show_timesteps == "image":
            # show timesteps with matplotlib
            import matplotlib.pyplot as plt

            plt.figure(figsize=(10, 5))
            plt.subplot(1, 2, 1)
            plt.bar(range(len(sampled_timesteps)), sampled_timesteps, width=1.0)
            plt.title("Sampled timesteps")
            plt.xlabel("Timestep")
            plt.ylabel("Count")

            plt.subplot(1, 2, 2)
            plt.bar(range(len(sampled_weighting)), sampled_weighting, width=1.0)
            plt.title("Sampled loss weighting")
            plt.xlabel("Timestep")
            plt.ylabel("Weighting")

            plt.tight_layout()
            plt.show()

        else:
            sampled_timesteps = np.array(sampled_timesteps)
            sampled_weighting = np.array(sampled_weighting)

            # average per line
            sampled_timesteps = sampled_timesteps.reshape(-1, N_TIMESTEPS_PER_LINE).mean(axis=1)
            sampled_weighting = sampled_weighting.reshape(-1, N_TIMESTEPS_PER_LINE).mean(axis=1)

            max_count = max(sampled_timesteps)
            print(f"Sampled timesteps: max count={max_count}")
            for i, t in enumerate(sampled_timesteps):
                line = f"{(i)*N_TIMESTEPS_PER_LINE:4d}-{(i+1)*N_TIMESTEPS_PER_LINE-1:4d}: "
                line += "#" * int(t / max_count * CONSOLE_WIDTH)
                print(line)

            max_weighting = max(sampled_weighting)
            print(f"Sampled loss weighting: max weighting={max_weighting}")
            for i, w in enumerate(sampled_weighting):
                line = f"{i*N_TIMESTEPS_PER_LINE:4d}-{(i+1)*N_TIMESTEPS_PER_LINE-1:4d}: {w:8.2f} "
                line += "#" * int(w / max_weighting * CONSOLE_WIDTH)
                print(line)

    def train(self, args):
        # check required arguments
        if args.dataset_config is None:
            raise ValueError("dataset_config is required / dataset_configが必要です")
        if args.dit is None:
            raise ValueError("path to DiT model is required / DiTモデルのパスが必要です")

        # show timesteps for debugging
        if args.show_timesteps:
            self.show_timesteps(args)
            return

        session_id = random.randint(0, 2**32)
        training_started_at = time.time()
        # setup_logging(args, reset=True)

        if args.seed is None:
            args.seed = random.randint(0, 2**32)
        set_seed(args.seed)

        # Load dataset config
        blueprint_generator = BlueprintGenerator(ConfigSanitizer())
        logger.info(f"Load dataset config from {args.dataset_config}")
        user_config = config_utils.load_user_config(args.dataset_config)
        blueprint = blueprint_generator.generate(user_config, args)
        train_dataset_group = config_utils.generate_dataset_group_by_blueprint(blueprint.dataset_group, training=True)

        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 = collator_class(current_epoch, current_step, ds_for_collator)

        # prepare accelerator
        logger.info("preparing accelerator")
        accelerator = prepare_accelerator(args)
        is_main_process = accelerator.is_main_process

        # prepare dtype
        weight_dtype = torch.float32
        if args.mixed_precision == "fp16":
            weight_dtype = torch.float16
        elif args.mixed_precision == "bf16":
            weight_dtype = torch.bfloat16

        # HunyuanVideo specific
        dit_dtype = torch.bfloat16 if args.dit_dtype is None else model_utils.str_to_dtype(args.dit_dtype)
        dit_weight_dtype = torch.float8_e4m3fn if args.fp8_base else dit_dtype
        logger.info(f"DiT precision: {dit_dtype}, weight precision: {dit_weight_dtype}")
        vae_dtype = torch.float16 if args.vae_dtype is None else model_utils.str_to_dtype(args.vae_dtype)

        # get embedding for sampling images
        sample_parameters = vae = None
        if args.sample_prompts:
            sample_parameters = self.process_sample_prompts(
                args, accelerator, args.sample_prompts, args.text_encoder1, args.text_encoder2, args.fp8_llm
            )

            # Load VAE model for sampling images: VAE is loaded to cpu to save gpu memory
            vae, _, s_ratio, t_ratio = load_vae(vae_dtype=vae_dtype, device="cpu", vae_path=args.vae)
            vae.requires_grad_(False)
            vae.eval()

            if args.vae_chunk_size is not None:
                vae.set_chunk_size_for_causal_conv_3d(args.vae_chunk_size)
                logger.info(f"Set chunk_size to {args.vae_chunk_size} for CausalConv3d in VAE")
            if args.vae_spatial_tile_sample_min_size is not None:
                vae.enable_spatial_tiling(True)
                vae.tile_sample_min_size = args.vae_spatial_tile_sample_min_size
                vae.tile_latent_min_size = args.vae_spatial_tile_sample_min_size // 8
            elif args.vae_tiling:
                vae.enable_spatial_tiling(True)

        # load DiT model
        blocks_to_swap = args.blocks_to_swap if args.blocks_to_swap else 0
        loading_device = "cpu" if blocks_to_swap > 0 else accelerator.device

        logger.info(f"Loading DiT model from {args.dit}")
        if args.sdpa:
            attn_mode = "torch"
        elif args.flash_attn:
            attn_mode = "flash"
        elif args.sage_attn:
            attn_mode = "sageattn"
        elif args.xformers:
            attn_mode = "xformers"
        else:
            raise ValueError(
                f"either --sdpa, --flash-attn, --sage-attn or --xformers must be specified / --sdpa, --flash-attn, --sage-attn, --xformersのいずれかを指定してください"
            )
        transformer = load_transformer(args.dit, attn_mode, args.split_attn, loading_device, dit_weight_dtype, args.dit_in_channels)
        transformer.eval()
        transformer.requires_grad_(False)

        i2v_training = args.dit_in_channels == 32  # may be changed in the future
        if i2v_training:
            logger.info("I2V training mode")

        if blocks_to_swap > 0:
            logger.info(f"enable swap {blocks_to_swap} blocks to CPU from device: {accelerator.device}")
            transformer.enable_block_swap(blocks_to_swap, accelerator.device, supports_backward=True)
            transformer.move_to_device_except_swap_blocks(accelerator.device)
        if args.img_in_txt_in_offloading:
            logger.info("Enable offloading img_in and txt_in to CPU")
            transformer.enable_img_in_txt_in_offloading()

        # load network model for differential training
        sys.path.append(os.path.dirname(__file__))
        accelerator.print("import network module:", args.network_module)
        network_module: lora_module = importlib.import_module(args.network_module)  # actual module may be different

        if args.base_weights is not None:
            # if base_weights is specified, merge the weights to DiT model
            for i, weight_path in enumerate(args.base_weights):
                if args.base_weights_multiplier is None or len(args.base_weights_multiplier) <= i:
                    multiplier = 1.0
                else:
                    multiplier = args.base_weights_multiplier[i]

                accelerator.print(f"merging module: {weight_path} with multiplier {multiplier}")

                weights_sd = load_file(weight_path)
                module = network_module.create_network_from_weights_hunyuan_video(
                    multiplier, weights_sd, unet=transformer, for_inference=True
                )
                module.merge_to(None, transformer, weights_sd, weight_dtype, "cpu")

            accelerator.print(f"all weights merged: {', '.join(args.base_weights)}")

        # prepare network
        net_kwargs = {}
        if args.network_args is not None:
            for net_arg in args.network_args:
                key, value = net_arg.split("=")
                net_kwargs[key] = value

        if args.dim_from_weights:
            logger.info(f"Loading network from weights: {args.dim_from_weights}")
            weights_sd = load_file(args.dim_from_weights)
            network, _ = network_module.create_network_from_weights_hunyuan_video(1, weights_sd, unet=transformer)
        else:
            if hasattr(network_module, "create_network_hunyuan_video"):
                network = network_module.create_network_hunyuan_video(
                    1.0,
                    args.network_dim,
                    args.network_alpha,
                    vae,
                    None,
                    transformer,
                    neuron_dropout=args.network_dropout,
                    **net_kwargs,
                )
            else:
                network = network_module.create_network(
                    1.0,
                    args.network_dim,
                    args.network_alpha,
                    vae,
                    None,
                    transformer,
                    **net_kwargs,
                )
        if network is None:
            return

        if hasattr(network_module, "prepare_network"):
            network.prepare_network(args)

        # apply network to DiT
        network.apply_to(None, transformer, apply_text_encoder=False, apply_unet=True)

        if args.network_weights is not None:
            # FIXME consider alpha of weights: this assumes that the alpha is not changed
            info = network.load_weights(args.network_weights)
            accelerator.print(f"load network weights from {args.network_weights}: {info}")

        if args.gradient_checkpointing:
            transformer.enable_gradient_checkpointing()
            network.enable_gradient_checkpointing()  # may have no effect

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

        trainable_params, lr_descriptions = network.prepare_optimizer_params(unet_lr=args.learning_rate)
        optimizer_name, optimizer_args, optimizer, optimizer_train_fn, optimizer_eval_fn = self.get_optimizer(
            args, trainable_params
        )

        # prepare dataloader

        # num workers for data loader: if 0, persistent_workers is not available
        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,
        )

        # calculate max_train_steps
        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}"
            )

        # send max_train_steps to train_dataset_group
        train_dataset_group.set_max_train_steps(args.max_train_steps)

        # prepare lr_scheduler
        lr_scheduler = self.get_scheduler(args, optimizer, accelerator.num_processes)

        # prepare training model. accelerator does some magic here

        # experimental feature: train the model with gradients in fp16/bf16
        network_dtype = torch.float32
        args.full_fp16 = args.full_bf16 = False  # temporary disabled because stochastic rounding is not supported yet
        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.")
            network_dtype = weight_dtype
            network.to(network_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.")
            network_dtype = weight_dtype
            network.to(network_dtype)

        if dit_weight_dtype != dit_dtype:
            logger.info(f"casting model to {dit_weight_dtype}")
            transformer.to(dit_weight_dtype)

        if blocks_to_swap > 0:
            transformer = accelerator.prepare(transformer, device_placement=[not blocks_to_swap > 0])
            accelerator.unwrap_model(transformer).move_to_device_except_swap_blocks(accelerator.device)  # reduce peak memory usage
            accelerator.unwrap_model(transformer).prepare_block_swap_before_forward()
        else:
            transformer = accelerator.prepare(transformer)

        network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(network, optimizer, train_dataloader, lr_scheduler)
        training_model = network

        if args.gradient_checkpointing:
            transformer.train()
        else:
            transformer.eval()

        accelerator.unwrap_model(network).prepare_grad_etc(transformer)

        if args.full_fp16:
            # patch accelerator for fp16 training
            # def patch_accelerator_for_fp16_training(accelerator):
            org_unscale_grads = accelerator.scaler._unscale_grads_

            def _unscale_grads_replacer(optimizer, inv_scale, found_inf, allow_fp16):
                return org_unscale_grads(optimizer, inv_scale, found_inf, True)

            accelerator.scaler._unscale_grads_ = _unscale_grads_replacer

        # before resuming make hook for saving/loading to save/load the network weights only
        def save_model_hook(models, weights, output_dir):
            # pop weights of other models than network to save only network weights
            # only main process or deepspeed https://github.com/huggingface/diffusers/issues/2606
            if accelerator.is_main_process:  # or args.deepspeed:
                remove_indices = []
                for i, model in enumerate(models):
                    if not isinstance(model, type(accelerator.unwrap_model(network))):
                        remove_indices.append(i)
                for i in reversed(remove_indices):
                    if len(weights) > i:
                        weights.pop(i)
                # print(f"save model hook: {len(weights)} weights will be saved")

        def load_model_hook(models, input_dir):
            # remove models except network
            remove_indices = []
            for i, model in enumerate(models):
                if not isinstance(model, type(accelerator.unwrap_model(network))):
                    remove_indices.append(i)
            for i in reversed(remove_indices):
                models.pop(i)
            # print(f"load model hook: {len(models)} models will be loaded")

        accelerator.register_save_state_pre_hook(save_model_hook)
        accelerator.register_load_state_pre_hook(load_model_hook)

        # resume from local or huggingface. accelerator.step is set
        self.resume_from_local_or_hf_if_specified(accelerator, args)  # 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

        accelerator.print("running training / 学習開始")
        accelerator.print(f"  num train items / 学習画像、動画数: {train_dataset_group.num_train_items}")
        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}")

        # TODO refactor metadata creation and move to util
        metadata = {
            "ss_" "ss_session_id": session_id,  # random integer indicating which group of epochs the model came from
            "ss_training_started_at": training_started_at,  # unix timestamp
            "ss_output_name": args.output_name,
            "ss_learning_rate": args.learning_rate,
            "ss_num_train_items": train_dataset_group.num_train_items,
            "ss_num_batches_per_epoch": len(train_dataloader),
            "ss_num_epochs": num_train_epochs,
            "ss_gradient_checkpointing": args.gradient_checkpointing,
            "ss_gradient_accumulation_steps": args.gradient_accumulation_steps,
            "ss_max_train_steps": args.max_train_steps,
            "ss_lr_warmup_steps": args.lr_warmup_steps,
            "ss_lr_scheduler": args.lr_scheduler,
            SS_METADATA_KEY_BASE_MODEL_VERSION: BASE_MODEL_VERSION_HUNYUAN_VIDEO,
            # "ss_network_module": args.network_module,
            # "ss_network_dim": args.network_dim,  # None means default because another network than LoRA may have another default dim
            # "ss_network_alpha": args.network_alpha,  # some networks may not have alpha
            SS_METADATA_KEY_NETWORK_MODULE: args.network_module,
            SS_METADATA_KEY_NETWORK_DIM: args.network_dim,
            SS_METADATA_KEY_NETWORK_ALPHA: args.network_alpha,
            "ss_network_dropout": args.network_dropout,  # some networks may not have dropout
            "ss_mixed_precision": args.mixed_precision,
            "ss_seed": args.seed,
            "ss_training_comment": args.training_comment,  # will not be updated after training
            # "ss_sd_scripts_commit_hash": train_util.get_git_revision_hash(),
            "ss_optimizer": optimizer_name + (f"({optimizer_args})" if len(optimizer_args) > 0 else ""),
            "ss_max_grad_norm": args.max_grad_norm,
            "ss_fp8_base": bool(args.fp8_base),
            "ss_fp8_llm": bool(args.fp8_llm),
            "ss_full_fp16": bool(args.full_fp16),
            "ss_full_bf16": bool(args.full_bf16),
            "ss_weighting_scheme": args.weighting_scheme,
            "ss_logit_mean": args.logit_mean,
            "ss_logit_std": args.logit_std,
            "ss_mode_scale": args.mode_scale,
            "ss_guidance_scale": args.guidance_scale,
            "ss_timestep_sampling": args.timestep_sampling,
            "ss_sigmoid_scale": args.sigmoid_scale,
            "ss_discrete_flow_shift": args.discrete_flow_shift,
        }

        datasets_metadata = []
        # tag_frequency = {}  # merge tag frequency for metadata editor # TODO support tag frequency
        for dataset in train_dataset_group.datasets:
            dataset_metadata = dataset.get_metadata()
            datasets_metadata.append(dataset_metadata)

        metadata["ss_datasets"] = json.dumps(datasets_metadata)

        # add extra args
        if args.network_args:
            # metadata["ss_network_args"] = json.dumps(net_kwargs)
            metadata[SS_METADATA_KEY_NETWORK_ARGS] = json.dumps(net_kwargs)

        # model name and hash
        if args.dit is not None:
            logger.info(f"calculate hash for DiT model: {args.dit}")
            sd_model_name = args.dit
            if os.path.exists(sd_model_name):
                # metadata["ss_sd_model_hash"] = model_utils.model_hash(sd_model_name)
                # metadata["ss_new_sd_model_hash"] = model_utils.calculate_sha256(sd_model_name)
                sd_model_name = os.path.basename(sd_model_name)
            metadata["ss_sd_model_name"] = sd_model_name

        if args.vae is not None:
            logger.info(f"calculate hash for VAE model: {args.vae}")
            vae_name = args.vae
            if os.path.exists(vae_name):
                # metadata["ss_vae_hash"] = model_utils.model_hash(vae_name)
                # metadata["ss_new_vae_hash"] = model_utils.calculate_sha256(vae_name)
                vae_name = os.path.basename(vae_name)
            metadata["ss_vae_name"] = vae_name

        metadata = {k: str(v) for k, v in metadata.items()}

        # make minimum metadata for filtering
        minimum_metadata = {}
        for key in SS_METADATA_MINIMUM_KEYS:
            if key in metadata:
                minimum_metadata[key] = metadata[key]

        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(
                "network_train" if args.log_tracker_name is None else args.log_tracker_name,
                config=train_utils.get_sanitized_config_or_none(args),
                init_kwargs=init_kwargs,
            )

        # TODO skip until initial step
        progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")

        epoch_to_start = 0
        global_step = 0
        noise_scheduler = FlowMatchDiscreteScheduler(shift=args.discrete_flow_shift, reverse=True, solver="euler")

        loss_recorder = train_utils.LossRecorder()
        del train_dataset_group

        # function for saving/removing
        save_dtype = dit_dtype

        def save_model(ckpt_name: str, 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}")
            metadata["ss_training_finished_at"] = str(time.time())
            metadata["ss_steps"] = str(steps)
            metadata["ss_epoch"] = str(epoch_no)

            metadata_to_save = minimum_metadata if args.no_metadata else metadata

            title = args.metadata_title if args.metadata_title is not None else args.output_name
            if args.min_timestep is not None or args.max_timestep is not None:
                min_time_step = args.min_timestep if args.min_timestep is not None else 0
                max_time_step = args.max_timestep if args.max_timestep is not None else 1000
                md_timesteps = (min_time_step, max_time_step)
            else:
                md_timesteps = None

            sai_metadata = sai_model_spec.build_metadata(
                None,
                time.time(),
                title,
                None,
                args.metadata_author,
                args.metadata_description,
                args.metadata_license,
                args.metadata_tags,
                timesteps=md_timesteps,
            )

            metadata_to_save.update(sai_metadata)

            unwrapped_nw.save_weights(ckpt_file, save_dtype, metadata_to_save)
            if args.huggingface_repo_id is not None:
                huggingface_utils.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
        if should_sample_images(args, global_step, epoch=0):
            optimizer_eval_fn()
            sample_images(accelerator, args, 0, global_step, vae, transformer, sample_parameters, dit_dtype)
            optimizer_train_fn()
        if len(accelerator.trackers) > 0:
            # log empty object to commit the sample images to wandb
            accelerator.log({}, step=0)

        # training loop

        # log device and dtype for each model
        logger.info(f"DiT dtype: {transformer.dtype}, device: {transformer.device}")

        clean_memory_on_device(accelerator.device)

        pos_embed_cache = {}

        for epoch in range(epoch_to_start, num_train_epochs):
            accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}")
            current_epoch.value = epoch + 1

            metadata["ss_epoch"] = str(epoch + 1)

            accelerator.unwrap_model(network).on_epoch_start(transformer)

            for step, batch in enumerate(train_dataloader):
                latents, llm_embeds, llm_mask, clip_embeds = batch
                bsz = latents.shape[0]
                current_step.value = global_step

                with accelerator.accumulate(training_model):
                    accelerator.unwrap_model(network).on_step_start()

                    latents = latents * vae_module.SCALING_FACTOR

                    # Sample noise that we'll add to the latents
                    noise = torch.randn_like(latents)

                    # calculate model input and timesteps
                    noisy_model_input, timesteps = self.get_noisy_model_input_and_timesteps(
                        args, noise, latents, noise_scheduler, accelerator.device, dit_dtype
                    )

                    # I2V training
                    if i2v_training:
                        image_latents = torch.zeros_like(latents)
                        image_latents[:, :, :1, :, :] = latents[:, :, :1, :, :]
                        noisy_model_input = torch.cat([noisy_model_input, image_latents], dim=1)  # concat along channel dim

                    weighting = compute_loss_weighting_for_sd3(
                        args.weighting_scheme, noise_scheduler, timesteps, accelerator.device, dit_dtype
                    )

                    # ensure guidance_scale in args is float
                    guidance_vec = torch.full((bsz,), float(args.guidance_scale), device=accelerator.device)  # , dtype=dit_dtype)

                    # ensure the hidden state will require grad
                    if args.gradient_checkpointing:
                        noisy_model_input.requires_grad_(True)
                        guidance_vec.requires_grad_(True)

                    pos_emb_shape = latents.shape[1:]
                    if pos_emb_shape not in pos_embed_cache:
                        freqs_cos, freqs_sin = get_rotary_pos_embed_by_shape(transformer, latents.shape[2:])
                        # freqs_cos = freqs_cos.to(device=accelerator.device, dtype=dit_dtype)
                        # freqs_sin = freqs_sin.to(device=accelerator.device, dtype=dit_dtype)
                        pos_embed_cache[pos_emb_shape] = (freqs_cos, freqs_sin)
                    else:
                        freqs_cos, freqs_sin = pos_embed_cache[pos_emb_shape]

                    # call DiT
                    latents = latents.to(device=accelerator.device, dtype=network_dtype)
                    noisy_model_input = noisy_model_input.to(device=accelerator.device, dtype=network_dtype)
                    # timesteps = timesteps.to(device=accelerator.device, dtype=dit_dtype)
                    # llm_embeds = llm_embeds.to(device=accelerator.device, dtype=dit_dtype)
                    # llm_mask = llm_mask.to(device=accelerator.device)
                    # clip_embeds = clip_embeds.to(device=accelerator.device, dtype=dit_dtype)
                    with accelerator.autocast():
                        model_pred = transformer(
                            noisy_model_input,
                            timesteps,
                            text_states=llm_embeds,
                            text_mask=llm_mask,
                            text_states_2=clip_embeds,
                            freqs_cos=freqs_cos,
                            freqs_sin=freqs_sin,
                            guidance=guidance_vec,
                            return_dict=False,
                        )

                    # flow matching loss
                    target = noise - latents

                    loss = torch.nn.functional.mse_loss(model_pred.to(network_dtype), target, reduction="none")

                    if weighting is not None:
                        loss = loss * weighting
                    # loss = loss.mean([1, 2, 3])
                    # # min snr gamma, scale v pred loss like noise pred, v pred like loss, debiased estimation etc.
                    # loss = self.post_process_loss(loss, args, 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:
                        # self.all_reduce_network(accelerator, network)  # sync DDP grad manually
                        state = accelerate.PartialState()
                        if state.distributed_type != accelerate.DistributedType.NO:
                            for param in network.parameters():
                                if param.grad is not None:
                                    param.grad = accelerator.reduce(param.grad, reduction="mean")

                        if args.max_grad_norm != 0.0:
                            params_to_clip = accelerator.unwrap_model(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)

                if args.scale_weight_norms:
                    keys_scaled, mean_norm, maximum_norm = accelerator.unwrap_model(network).apply_max_norm_regularization(
                        args.scale_weight_norms, accelerator.device
                    )
                    max_mean_logs = {"Keys Scaled": keys_scaled, "Average key norm": mean_norm}
                else:
                    keys_scaled, mean_norm, maximum_norm = None, None, None

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

                    # to avoid calling optimizer_eval_fn() too frequently, we call it only when we need to sample images or save the model
                    should_sampling = should_sample_images(args, global_step, epoch=None)
                    should_saving = args.save_every_n_steps is not None and global_step % args.save_every_n_steps == 0

                    if should_sampling or should_saving:
                        optimizer_eval_fn()
                        if should_sampling:
                            sample_images(accelerator, args, None, global_step, vae, transformer, sample_parameters, dit_dtype)

                        if should_saving:
                            accelerator.wait_for_everyone()
                            if accelerator.is_main_process:
                                ckpt_name = train_utils.get_step_ckpt_name(args.output_name, global_step)
                                save_model(ckpt_name, accelerator.unwrap_model(network), global_step, epoch)

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

                                remove_step_no = train_utils.get_remove_step_no(args, global_step)
                                if remove_step_no is not None:
                                    remove_ckpt_name = train_utils.get_step_ckpt_name(args.output_name, remove_step_no)
                                    remove_model(remove_ckpt_name)
                        optimizer_train_fn()

                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.scale_weight_norms:
                    progress_bar.set_postfix(**{**max_mean_logs, **logs})

                if len(accelerator.trackers) > 0:
                    logs = self.generate_step_logs(
                        args, current_loss, avr_loss, lr_scheduler, lr_descriptions, optimizer, keys_scaled, mean_norm, maximum_norm
                    )
                    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()

            # save model at the end of epoch if needed
            optimizer_eval_fn()
            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_utils.get_epoch_ckpt_name(args.output_name, epoch + 1)
                    save_model(ckpt_name, accelerator.unwrap_model(network), global_step, epoch + 1)

                    remove_epoch_no = train_utils.get_remove_epoch_no(args, epoch + 1)
                    if remove_epoch_no is not None:
                        remove_ckpt_name = train_utils.get_epoch_ckpt_name(args.output_name, remove_epoch_no)
                        remove_model(remove_ckpt_name)

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

            sample_images(accelerator, args, epoch + 1, global_step, vae, transformer, sample_parameters, dit_dtype)
            optimizer_train_fn()

            # end of epoch

        # metadata["ss_epoch"] = str(num_train_epochs)
        metadata["ss_training_finished_at"] = str(time.time())

        if is_main_process:
            network = accelerator.unwrap_model(network)

        accelerator.end_training()
        optimizer_eval_fn()

        if is_main_process and (args.save_state or args.save_state_on_train_end):
            train_utils.save_state_on_train_end(args, accelerator)

        if is_main_process:
            ckpt_name = train_utils.get_last_ckpt_name(args.output_name)
            save_model(ckpt_name, network, global_step, num_train_epochs, force_sync_upload=True)

            logger.info("model saved.")


def setup_parser() -> argparse.ArgumentParser:
    def int_or_float(value):
        if value.endswith("%"):
            try:
                return float(value[:-1]) / 100.0
            except ValueError:
                raise argparse.ArgumentTypeError(f"Value '{value}' is not a valid percentage")
        try:
            float_value = float(value)
            if float_value >= 1 and float_value.is_integer():
                return int(value)
            return float(value)
        except ValueError:
            raise argparse.ArgumentTypeError(f"'{value}' is not an int or float")

    parser = argparse.ArgumentParser()

    # general settings
    parser.add_argument(
        "--config_file",
        type=str,
        default=None,
        help="using .toml instead of args to pass hyperparameter / ハイパーパラメータを引数ではなく.tomlファイルで渡す",
    )
    parser.add_argument(
        "--dataset_config",
        type=pathlib.Path,
        default=None,
        help="config file for dataset / データセットの設定ファイル",
    )

    # training settings
    parser.add_argument(
        "--sdpa",
        action="store_true",
        help="use sdpa for CrossAttention (requires PyTorch 2.0) / CrossAttentionにsdpaを使う（PyTorch 2.0が必要）",
    )
    parser.add_argument(
        "--flash_attn",
        action="store_true",
        help="use FlashAttention for CrossAttention, requires FlashAttention / CrossAttentionにFlashAttentionを使う、FlashAttentionが必要",
    )
    parser.add_argument(
        "--sage_attn",
        action="store_true",
        help="use SageAttention. requires SageAttention / SageAttentionを使う。SageAttentionが必要",
    )
    parser.add_argument(
        "--xformers",
        action="store_true",
        help="use xformers for CrossAttention, requires xformers / CrossAttentionにxformersを使う、xformersが必要",
    )
    parser.add_argument(
        "--split_attn",
        action="store_true",
        help="use split attention for attention calculation (split batch size=1, affects memory usage and speed)"
        " / attentionを分割して計算する（バッチサイズ=1に分割、メモリ使用量と速度に影響）",
    )

    parser.add_argument("--max_train_steps", type=int, default=1600, help="training steps / 学習ステップ数")
    parser.add_argument(
        "--max_train_epochs",
        type=int,
        default=None,
        help="training epochs (overrides max_train_steps) / 学習エポック数（max_train_stepsを上書きします）",
    )
    parser.add_argument(
        "--max_data_loader_n_workers",
        type=int,
        default=8,
        help="max num workers for DataLoader (lower is less main RAM usage, faster epoch start and slower data loading) / DataLoaderの最大プロセス数（小さい値ではメインメモリの使用量が減りエポック間の待ち時間が減りますが、データ読み込みは遅くなります）",
    )
    parser.add_argument(
        "--persistent_data_loader_workers",
        action="store_true",
        help="persistent DataLoader workers (useful for reduce time gap between epoch, but may use more memory) / DataLoader のワーカーを持続させる (エポック間の時間差を少なくするのに有効だが、より多くのメモリを消費する可能性がある)",
    )
    parser.add_argument("--seed", type=int, default=None, help="random seed for training / 学習時の乱数のseed")
    parser.add_argument(
        "--gradient_checkpointing", action="store_true", help="enable gradient checkpointing / gradient checkpointingを有効にする"
    )
    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass / 学習時に逆伝播をする前に勾配を合計するステップ数",
    )
    parser.add_argument(
        "--mixed_precision",
        type=str,
        default="no",
        choices=["no", "fp16", "bf16"],
        help="use mixed precision / 混合精度を使う場合、その精度",
    )

    parser.add_argument(
        "--logging_dir",
        type=str,
        default=None,
        help="enable logging and output TensorBoard log to this directory / ログ出力を有効にしてこのディレクトリにTensorBoard用のログを出力する",
    )
    parser.add_argument(
        "--log_with",
        type=str,
        default=None,
        choices=["tensorboard", "wandb", "all"],
        help="what logging tool(s) to use (if 'all', TensorBoard and WandB are both used) / ログ出力に使用するツール (allを指定するとTensorBoardとWandBの両方が使用される)",
    )
    parser.add_argument(
        "--log_prefix", type=str, default=None, help="add prefix for each log directory / ログディレクトリ名の先頭に追加する文字列"
    )
    parser.add_argument(
        "--log_tracker_name",
        type=str,
        default=None,
        help="name of tracker to use for logging, default is script-specific default name / ログ出力に使用するtrackerの名前、省略時はスクリプトごとのデフォルト名",
    )
    parser.add_argument(
        "--wandb_run_name",
        type=str,
        default=None,
        help="The name of the specific wandb session / wandb ログに表示される特定の実行の名前",
    )
    parser.add_argument(
        "--log_tracker_config",
        type=str,
        default=None,
        help="path to tracker config file to use for logging / ログ出力に使用するtrackerの設定ファイルのパス",
    )
    parser.add_argument(
        "--wandb_api_key",
        type=str,
        default=None,
        help="specify WandB API key to log in before starting training (optional). / WandB APIキーを指定して学習開始前にログインする（オプション）",
    )
    parser.add_argument("--log_config", action="store_true", help="log training configuration / 学習設定をログに出力する")

    parser.add_argument(
        "--ddp_timeout",
        type=int,
        default=None,
        help="DDP timeout (min, None for default of accelerate) / DDPのタイムアウト（分、Noneでaccelerateのデフォルト）",
    )
    parser.add_argument(
        "--ddp_gradient_as_bucket_view",
        action="store_true",
        help="enable gradient_as_bucket_view for DDP / DDPでgradient_as_bucket_viewを有効にする",
    )
    parser.add_argument(
        "--ddp_static_graph",
        action="store_true",
        help="enable static_graph for DDP / DDPでstatic_graphを有効にする",
    )

    parser.add_argument(
        "--sample_every_n_steps",
        type=int,
        default=None,
        help="generate sample images every N steps / 学習中のモデルで指定ステップごとにサンプル出力する",
    )
    parser.add_argument(
        "--sample_at_first", action="store_true", help="generate sample images before training / 学習前にサンプル出力する"
    )
    parser.add_argument(
        "--sample_every_n_epochs",
        type=int,
        default=None,
        help="generate sample images every N epochs (overwrites n_steps) / 学習中のモデルで指定エポックごとにサンプル出力する（ステップ数指定を上書きします）",
    )
    parser.add_argument(
        "--sample_prompts",
        type=str,
        default=None,
        help="file for prompts to generate sample images / 学習中モデルのサンプル出力用プロンプトのファイル",
    )

    # optimizer and lr scheduler settings
    parser.add_argument(
        "--optimizer_type",
        type=str,
        default="",
        help="Optimizer to use / オプティマイザの種類: AdamW (default), AdamW8bit, AdaFactor. "
        "Also, you can use any optimizer by specifying the full path to the class, like 'torch.optim.AdamW', 'bitsandbytes.optim.AdEMAMix8bit' or 'bitsandbytes.optim.PagedAdEMAMix8bit' etc. / ",
    )
    parser.add_argument(
        "--optimizer_args",
        type=str,
        default=None,
        nargs="*",
        help='additional arguments for optimizer (like "weight_decay=0.01 betas=0.9,0.999 ...") / オプティマイザの追加引数（例： "weight_decay=0.01 betas=0.9,0.999 ..."）',
    )
    parser.add_argument("--learning_rate", type=float, default=2.0e-6, help="learning rate / 学習率")
    parser.add_argument(
        "--max_grad_norm",
        default=1.0,
        type=float,
        help="Max gradient norm, 0 for no clipping / 勾配正規化の最大norm、0でclippingを行わない",
    )

    parser.add_argument(
        "--lr_scheduler",
        type=str,
        default="constant",
        help="scheduler to use for learning rate / 学習率のスケジューラ: linear, cosine, cosine_with_restarts, polynomial, constant (default), constant_with_warmup, adafactor",
    )
    parser.add_argument(
        "--lr_warmup_steps",
        type=int_or_float,
        default=0,
        help="Int number of steps for the warmup in the lr scheduler (default is 0) or float with ratio of train steps"
        " / 学習率のスケジューラをウォームアップするステップ数（デフォルト0）、または学習ステップの比率（1未満のfloat値の場合）",
    )
    parser.add_argument(
        "--lr_decay_steps",
        type=int_or_float,
        default=0,
        help="Int number of steps for the decay in the lr scheduler (default is 0) or float (<1) with ratio of train steps"
        " / 学習率のスケジューラを減衰させるステップ数（デフォルト0）、または学習ステップの比率（1未満のfloat値の場合）",
    )
    parser.add_argument(
        "--lr_scheduler_num_cycles",
        type=int,
        default=1,
        help="Number of restarts for cosine scheduler with restarts / cosine with restartsスケジューラでのリスタート回数",
    )
    parser.add_argument(
        "--lr_scheduler_power",
        type=float,
        default=1,
        help="Polynomial power for polynomial scheduler / polynomialスケジューラでのpolynomial power",
    )
    parser.add_argument(
        "--lr_scheduler_timescale",
        type=int,
        default=None,
        help="Inverse sqrt timescale for inverse sqrt scheduler,defaults to `num_warmup_steps`"
        + " / 逆平方根スケジューラのタイムスケール、デフォルトは`num_warmup_steps`",
    )
    parser.add_argument(
        "--lr_scheduler_min_lr_ratio",
        type=float,
        default=None,
        help="The minimum learning rate as a ratio of the initial learning rate for cosine with min lr scheduler and warmup decay scheduler"
        + " / 初期学習率の比率としての最小学習率を指定する、cosine with min lr と warmup decay スケジューラ で有効",
    )
    parser.add_argument("--lr_scheduler_type", type=str, default="", help="custom scheduler module / 使用するスケジューラ")
    parser.add_argument(
        "--lr_scheduler_args",
        type=str,
        default=None,
        nargs="*",
        help='additional arguments for scheduler (like "T_max=100") / スケジューラの追加引数（例： "T_max100"）',
    )

    # model settings
    parser.add_argument("--dit", type=str, help="DiT checkpoint path / DiTのチェックポイントのパス")
    parser.add_argument("--dit_dtype", type=str, default=None, help="data type for DiT, default is bfloat16")
    parser.add_argument("--dit_in_channels", type=int, default=16, help="input channels for DiT, default is 16, skyreels I2V is 32")
    parser.add_argument("--vae", type=str, help="VAE checkpoint path / VAEのチェックポイントのパス")
    parser.add_argument("--vae_dtype", type=str, default=None, help="data type for VAE, default is float16")
    parser.add_argument(
        "--vae_tiling",
        action="store_true",
        help="enable spatial tiling for VAE, default is False. If vae_spatial_tile_sample_min_size is set, this is automatically enabled."
        " / VAEの空間タイリングを有効にする、デフォルトはFalse。vae_spatial_tile_sample_min_sizeが設定されている場合、自動的に有効になります。",
    )
    parser.add_argument("--vae_chunk_size", type=int, default=None, help="chunk size for CausalConv3d in VAE")
    parser.add_argument(
        "--vae_spatial_tile_sample_min_size", type=int, default=None, help="spatial tile sample min size for VAE, default 256"
    )
    parser.add_argument("--text_encoder1", type=str, help="Text Encoder 1 directory / テキストエンコーダ1のディレクトリ")
    parser.add_argument("--text_encoder2", type=str, help="Text Encoder 2 directory / テキストエンコーダ2のディレクトリ")
    parser.add_argument("--text_encoder_dtype", type=str, default=None, help="data type for Text Encoder, default is float16")
    parser.add_argument("--fp8_llm", action="store_true", help="use fp8 for LLM / LLMにfp8を使う")
    parser.add_argument("--fp8_base", action="store_true", help="use fp8 for base model / base modelにfp8を使う")
    # parser.add_argument("--full_fp16", action="store_true", help="fp16 training including gradients / 勾配も含めてfp16で学習する")
    # parser.add_argument("--full_bf16", action="store_true", help="bf16 training including gradients / 勾配も含めてbf16で学習する")

    parser.add_argument(
        "--blocks_to_swap",
        type=int,
        default=None,
        help="number of blocks to swap in the model, max XXX / モデル内のブロックの数、最大XXX",
    )
    parser.add_argument(
        "--img_in_txt_in_offloading",
        action="store_true",
        help="offload img_in and txt_in to cpu / img_inとtxt_inをCPUにオフロードする",
    )

    # parser.add_argument("--flow_shift", type=float, default=7.0, help="Shift factor for flow matching schedulers")
    parser.add_argument("--guidance_scale", type=float, default=1.0, help="Embeded classifier free guidance scale.")
    parser.add_argument(
        "--timestep_sampling",
        choices=["sigma", "uniform", "sigmoid", "shift"],
        default="sigma",
        help="Method to sample timesteps: sigma-based, uniform random, sigmoid of random normal and shift of sigmoid."
        " / タイムステップをサンプリングする方法：sigma、random uniform、random normalのsigmoid、sigmoidのシフト。",
    )
    parser.add_argument(
        "--discrete_flow_shift",
        type=float,
        default=1.0,
        help="Discrete flow shift for the Euler Discrete Scheduler, default is 1.0. / Euler Discrete Schedulerの離散フローシフト、デフォルトは1.0。",
    )
    parser.add_argument(
        "--sigmoid_scale",
        type=float,
        default=1.0,
        help='Scale factor for sigmoid timestep sampling (only used when timestep-sampling is "sigmoid" or "shift"). / sigmoidタイムステップサンプリングの倍率（timestep-samplingが"sigmoid"または"shift"の場合のみ有効）。',
    )
    parser.add_argument(
        "--weighting_scheme",
        type=str,
        default="none",
        choices=["logit_normal", "mode", "cosmap", "sigma_sqrt", "none"],
        help="weighting scheme for timestep distribution. Default is none"
        " / タイムステップ分布の重み付けスキーム、デフォルトはnone",
    )
    parser.add_argument(
        "--logit_mean",
        type=float,
        default=0.0,
        help="mean to use when using the `'logit_normal'` weighting scheme / `'logit_normal'`重み付けスキームを使用する場合の平均",
    )
    parser.add_argument(
        "--logit_std",
        type=float,
        default=1.0,
        help="std to use when using the `'logit_normal'` weighting scheme / `'logit_normal'`重み付けスキームを使用する場合のstd",
    )
    parser.add_argument(
        "--mode_scale",
        type=float,
        default=1.29,
        help="Scale of mode weighting scheme. Only effective when using the `'mode'` as the `weighting_scheme` / モード重み付けスキームのスケール",
    )
    parser.add_argument(
        "--min_timestep",
        type=int,
        default=None,
        help="set minimum time step for training (0~999, default is 0) / 学習時のtime stepの最小値を設定する（0~999で指定、省略時はデフォルト値(0)） ",
    )
    parser.add_argument(
        "--max_timestep",
        type=int,
        default=None,
        help="set maximum time step for training (1~1000, default is 1000) / 学習時のtime stepの最大値を設定する（1~1000で指定、省略時はデフォルト値(1000)）",
    )

    parser.add_argument(
        "--show_timesteps",
        type=str,
        default=None,
        choices=["image", "console"],
        help="show timesteps in image or console, and return to console / タイムステップを画像またはコンソールに表示し、コンソールに戻る",
    )

    # network settings
    parser.add_argument(
        "--no_metadata", action="store_true", help="do not save metadata in output model / メタデータを出力先モデルに保存しない"
    )
    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_module", type=str, default=None, help="network module to train / 学習対象のネットワークのモジュール"
    )
    parser.add_argument(
        "--network_dim",
        type=int,
        default=None,
        help="network dimensions (depends on each network) / モジュールの次元数（ネットワークにより定義は異なります）",
    )
    parser.add_argument(
        "--network_alpha",
        type=float,
        default=1,
        help="alpha for LoRA weight scaling, default 1 (same as network_dim for same behavior as old version) / LoRaの重み調整のalpha値、デフォルト1（旧バージョンと同じ動作をするにはnetwork_dimと同じ値を指定）",
    )
    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(
        "--network_args",
        type=str,
        default=None,
        nargs="*",
        help="additional arguments for network (key=value) / ネットワークへの追加の引数",
    )
    parser.add_argument(
        "--training_comment",
        type=str,
        default=None,
        help="arbitrary comment string stored in metadata / メタデータに記録する任意のコメント文字列",
    )
    parser.add_argument(
        "--dim_from_weights",
        action="store_true",
        help="automatically determine dim (rank) from network_weights / dim (rank)をnetwork_weightsで指定した重みから自動で決定する",
    )
    parser.add_argument(
        "--scale_weight_norms",
        type=float,
        default=None,
        help="Scale the weight of each key pair to help prevent overtraing via exploding gradients. (1 is a good starting point) / 重みの値をスケーリングして勾配爆発を防ぐ（1が初期値としては適当）",
    )
    parser.add_argument(
        "--base_weights",
        type=str,
        default=None,
        nargs="*",
        help="network weights to merge into the model before training / 学習前にあらかじめモデルにマージするnetworkの重みファイル",
    )
    parser.add_argument(
        "--base_weights_multiplier",
        type=float,
        default=None,
        nargs="*",
        help="multiplier for network weights to merge into the model before training / 学習前にあらかじめモデルにマージするnetworkの重みの倍率",
    )

    # save and load settings
    parser.add_argument(
        "--output_dir", type=str, default=None, help="directory to output trained model / 学習後のモデル出力先ディレクトリ"
    )
    parser.add_argument(
        "--output_name",
        type=str,
        default=None,
        help="base name of trained model file / 学習後のモデルの拡張子を除くファイル名",
    )
    parser.add_argument("--resume", type=str, default=None, help="saved state to resume training / 学習再開するモデルのstate")

    parser.add_argument(
        "--save_every_n_epochs",
        type=int,
        default=None,
        help="save checkpoint every N epochs / 学習中のモデルを指定エポックごとに保存する",
    )
    parser.add_argument(
        "--save_every_n_steps",
        type=int,
        default=None,
        help="save checkpoint every N steps / 学習中のモデルを指定ステップごとに保存する",
    )
    parser.add_argument(
        "--save_last_n_epochs",
        type=int,
        default=None,
        help="save last N checkpoints when saving every N epochs (remove older checkpoints) / 指定エポックごとにモデルを保存するとき最大Nエポック保存する（古いチェックポイントは削除する）",
    )
    parser.add_argument(
        "--save_last_n_epochs_state",
        type=int,
        default=None,
        help="save last N checkpoints of state (overrides the value of --save_last_n_epochs)/ 最大Nエポックstateを保存する（--save_last_n_epochsの指定を上書きする）",
    )
    parser.add_argument(
        "--save_last_n_steps",
        type=int,
        default=None,
        help="save checkpoints until N steps elapsed (remove older checkpoints if N steps elapsed) / 指定ステップごとにモデルを保存するとき、このステップ数経過するまで保存する（このステップ数経過したら削除する）",
    )
    parser.add_argument(
        "--save_last_n_steps_state",
        type=int,
        default=None,
        help="save states until N steps elapsed (remove older states if N steps elapsed, overrides --save_last_n_steps) / 指定ステップごとにstateを保存するとき、このステップ数経過するまで保存する（このステップ数経過したら削除する。--save_last_n_stepsを上書きする）",
    )
    parser.add_argument(
        "--save_state",
        action="store_true",
        help="save training state additionally (including optimizer states etc.) when saving model / optimizerなど学習状態も含めたstateをモデル保存時に追加で保存する",
    )
    parser.add_argument(
        "--save_state_on_train_end",
        action="store_true",
        help="save training state (including optimizer states etc.) on train end even if --save_state is not specified"
        " / --save_stateが未指定時にもoptimizerなど学習状態も含めたstateを学習終了時に保存する",
    )

    # SAI Model spec
    parser.add_argument(
        "--metadata_title",
        type=str,
        default=None,
        help="title for model metadata (default is output_name) / メタデータに書き込まれるモデルタイトル、省略時はoutput_name",
    )
    parser.add_argument(
        "--metadata_author",
        type=str,
        default=None,
        help="author name for model metadata / メタデータに書き込まれるモデル作者名",
    )
    parser.add_argument(
        "--metadata_description",
        type=str,
        default=None,
        help="description for model metadata / メタデータに書き込まれるモデル説明",
    )
    parser.add_argument(
        "--metadata_license",
        type=str,
        default=None,
        help="license for model metadata / メタデータに書き込まれるモデルライセンス",
    )
    parser.add_argument(
        "--metadata_tags",
        type=str,
        default=None,
        help="tags for model metadata, separated by comma / メタデータに書き込まれるモデルタグ、カンマ区切り",
    )

    # huggingface settings
    parser.add_argument(
        "--huggingface_repo_id",
        type=str,
        default=None,
        help="huggingface repo name to upload / huggingfaceにアップロードするリポジトリ名",
    )
    parser.add_argument(
        "--huggingface_repo_type",
        type=str,
        default=None,
        help="huggingface repo type to upload / huggingfaceにアップロードするリポジトリの種類",
    )
    parser.add_argument(
        "--huggingface_path_in_repo",
        type=str,
        default=None,
        help="huggingface model path to upload files / huggingfaceにアップロードするファイルのパス",
    )
    parser.add_argument("--huggingface_token", type=str, default=None, help="huggingface token / huggingfaceのトークン")
    parser.add_argument(
        "--huggingface_repo_visibility",
        type=str,
        default=None,
        help="huggingface repository visibility ('public' for public, 'private' or None for private) / huggingfaceにアップロードするリポジトリの公開設定（'public'で公開、'private'またはNoneで非公開）",
    )
    parser.add_argument(
        "--save_state_to_huggingface", action="store_true", help="save state to huggingface / huggingfaceにstateを保存する"
    )
    parser.add_argument(
        "--resume_from_huggingface",
        action="store_true",
        help="resume from huggingface (ex: --resume {repo_id}/{path_in_repo}:{revision}:{repo_type}) / huggingfaceから学習を再開する(例: --resume {repo_id}/{path_in_repo}:{revision}:{repo_type})",
    )
    parser.add_argument(
        "--async_upload",
        action="store_true",
        help="upload to huggingface asynchronously / huggingfaceに非同期でアップロードする",
    )

    return parser


def read_config_from_file(args: argparse.Namespace, parser: argparse.ArgumentParser):
    if not args.config_file:
        return args

    config_path = args.config_file + ".toml" if not args.config_file.endswith(".toml") else args.config_file

    if not os.path.exists(config_path):
        logger.info(f"{config_path} not found.")
        exit(1)

    logger.info(f"Loading settings from {config_path}...")
    with open(config_path, "r", encoding="utf-8") as f:
        config_dict = toml.load(f)

    # combine all sections into one
    ignore_nesting_dict = {}
    for section_name, section_dict in config_dict.items():
        # if value is not dict, save key and value as is
        if not isinstance(section_dict, dict):
            ignore_nesting_dict[section_name] = section_dict
            continue

        # if value is dict, save all key and value into one dict
        for key, value in section_dict.items():
            ignore_nesting_dict[key] = value

    config_args = argparse.Namespace(**ignore_nesting_dict)
    args = parser.parse_args(namespace=config_args)
    args.config_file = os.path.splitext(args.config_file)[0]
    logger.info(args.config_file)

    return args


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

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

    trainer = NetworkTrainer()
    trainer.train(args)