# training with captions import argparse import copy import math import os from multiprocessing import Value from typing import List import toml from tqdm import tqdm import torch from library.device_utils import init_ipex, clean_memory_on_device init_ipex() from accelerate.utils import set_seed from library import deepspeed_utils, flux_train_utils, flux_utils, strategy_base, strategy_flux from library.sd3_train_utils import load_prompts, FlowMatchEulerDiscreteScheduler import library.train_util as train_util from library.utils import setup_logging, add_logging_arguments setup_logging() import logging logger = logging.getLogger(__name__) import library.config_util as config_util # import library.sdxl_train_util as sdxl_train_util from library.config_util import ( ConfigSanitizer, BlueprintGenerator, ) from library.custom_train_functions import apply_masked_loss, add_custom_train_arguments def train(args): train_util.verify_training_args(args) train_util.prepare_dataset_args(args, True) # sdxl_train_util.verify_sdxl_training_args(args) deepspeed_utils.prepare_deepspeed_args(args) setup_logging(args, reset=True) # assert ( # not args.weighted_captions # ), "weighted_captions is not supported currently / weighted_captionsは現在サポートされていません" if args.cache_text_encoder_outputs_to_disk and not args.cache_text_encoder_outputs: logger.warning( "cache_text_encoder_outputs_to_disk is enabled, so cache_text_encoder_outputs is also enabled / cache_text_encoder_outputs_to_diskが有効になっているため、cache_text_encoder_outputsも有効になります" ) args.cache_text_encoder_outputs = True cache_latents = args.cache_latents use_dreambooth_method = args.in_json is None if args.seed is not None: set_seed(args.seed) # 乱数系列を初期化する # prepare caching strategy: this must be set before preparing dataset. because dataset may use this strategy for initialization. if args.cache_latents: latents_caching_strategy = strategy_flux.FluxLatentsCachingStrategy( args.cache_latents_to_disk, args.vae_batch_size, args.skip_latents_validity_check ) strategy_base.LatentsCachingStrategy.set_strategy(latents_caching_strategy) # データセットを準備する if args.dataset_class is None: blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, args.masked_loss, True)) if args.dataset_config is not None: logger.info(f"Load dataset config from {args.dataset_config}") user_config = config_util.load_user_config(args.dataset_config) ignored = ["train_data_dir", "in_json"] if any(getattr(args, attr) is not None for attr in ignored): logger.warning( "ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format( ", ".join(ignored) ) ) else: if use_dreambooth_method: logger.info("Using DreamBooth method.") user_config = { "datasets": [ { "subsets": config_util.generate_dreambooth_subsets_config_by_subdirs( args.train_data_dir, args.reg_data_dir ) } ] } else: logger.info("Training with captions.") user_config = { "datasets": [ { "subsets": [ { "image_dir": args.train_data_dir, "metadata_file": args.in_json, } ] } ] } blueprint = blueprint_generator.generate(user_config, args) train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group) else: train_dataset_group = train_util.load_arbitrary_dataset(args) current_epoch = Value("i", 0) current_step = Value("i", 0) ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None collator = train_util.collator_class(current_epoch, current_step, ds_for_collator) train_dataset_group.verify_bucket_reso_steps(16) # TODO これでいいか確認 if args.debug_dataset: if args.cache_text_encoder_outputs: strategy_base.TextEncoderOutputsCachingStrategy.set_strategy( strategy_flux.FluxTextEncoderOutputsCachingStrategy( args.cache_text_encoder_outputs_to_disk, args.text_encoder_batch_size, False, False ) ) train_dataset_group.set_current_strategies() train_util.debug_dataset(train_dataset_group, True) return if len(train_dataset_group) == 0: logger.error( "No data found. Please verify the metadata file and train_data_dir option. / 画像がありません。メタデータおよびtrain_data_dirオプションを確認してください。" ) return if cache_latents: assert ( train_dataset_group.is_latent_cacheable() ), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません" if args.cache_text_encoder_outputs: assert ( train_dataset_group.is_text_encoder_output_cacheable() ), "when caching text encoder output, either caption_dropout_rate, shuffle_caption, token_warmup_step or caption_tag_dropout_rate cannot be used / text encoderの出力をキャッシュするときはcaption_dropout_rate, shuffle_caption, token_warmup_step, caption_tag_dropout_rateは使えません" # acceleratorを準備する logger.info("prepare accelerator") accelerator = train_util.prepare_accelerator(args) # mixed precisionに対応した型を用意しておき適宜castする weight_dtype, save_dtype = train_util.prepare_dtype(args) # モデルを読み込む name = "schnell" if "schnell" in args.pretrained_model_name_or_path else "dev" # load VAE for caching latents ae = None if cache_latents: ae = flux_utils.load_ae(name, args.ae, weight_dtype, "cpu") ae.to(accelerator.device, dtype=weight_dtype) ae.requires_grad_(False) ae.eval() train_dataset_group.new_cache_latents(ae, accelerator.is_main_process) ae.to("cpu") # if no sampling, vae can be deleted clean_memory_on_device(accelerator.device) accelerator.wait_for_everyone() # prepare tokenize strategy if args.t5xxl_max_token_length is None: if name == "schnell": t5xxl_max_token_length = 256 else: t5xxl_max_token_length = 512 else: t5xxl_max_token_length = args.t5xxl_max_token_length flux_tokenize_strategy = strategy_flux.FluxTokenizeStrategy(t5xxl_max_token_length) strategy_base.TokenizeStrategy.set_strategy(flux_tokenize_strategy) # load clip_l, t5xxl for caching text encoder outputs clip_l = flux_utils.load_clip_l(args.clip_l, weight_dtype, "cpu") t5xxl = flux_utils.load_t5xxl(args.t5xxl, weight_dtype, "cpu") clip_l.eval() t5xxl.eval() clip_l.requires_grad_(False) t5xxl.requires_grad_(False) text_encoding_strategy = strategy_flux.FluxTextEncodingStrategy(args.apply_t5_attn_mask) strategy_base.TextEncodingStrategy.set_strategy(text_encoding_strategy) # cache text encoder outputs sample_prompts_te_outputs = None if args.cache_text_encoder_outputs: # Text Encodes are eval and no grad here clip_l.to(accelerator.device) t5xxl.to(accelerator.device) text_encoder_caching_strategy = strategy_flux.FluxTextEncoderOutputsCachingStrategy( args.cache_text_encoder_outputs_to_disk, args.text_encoder_batch_size, False, False, args.apply_t5_attn_mask ) strategy_base.TextEncoderOutputsCachingStrategy.set_strategy(text_encoder_caching_strategy) with accelerator.autocast(): train_dataset_group.new_cache_text_encoder_outputs([clip_l, t5xxl], accelerator.is_main_process) # cache sample prompt's embeddings to free text encoder's memory if args.sample_prompts is not None: logger.info(f"cache Text Encoder outputs for sample prompt: {args.sample_prompts}") tokenize_strategy: strategy_flux.FluxTokenizeStrategy = strategy_base.TokenizeStrategy.get_strategy() text_encoding_strategy: strategy_flux.FluxTextEncodingStrategy = strategy_base.TextEncodingStrategy.get_strategy() prompts = load_prompts(args.sample_prompts) sample_prompts_te_outputs = {} # key: prompt, value: text encoder outputs with accelerator.autocast(), torch.no_grad(): for prompt_dict in prompts: for p in [prompt_dict.get("prompt", ""), prompt_dict.get("negative_prompt", "")]: if p not in sample_prompts_te_outputs: logger.info(f"cache Text Encoder outputs for prompt: {p}") tokens_and_masks = tokenize_strategy.tokenize(p) sample_prompts_te_outputs[p] = text_encoding_strategy.encode_tokens( tokenize_strategy, [clip_l, t5xxl], tokens_and_masks, args.apply_t5_attn_mask ) accelerator.wait_for_everyone() # now we can delete Text Encoders to free memory clip_l = None t5xxl = None # load FLUX # if we load to cpu, flux.to(fp8) takes a long time flux = flux_utils.load_flow_model(name, args.pretrained_model_name_or_path, weight_dtype, "cpu") if args.gradient_checkpointing: flux.enable_gradient_checkpointing() flux.requires_grad_(True) if not cache_latents: # load VAE here if not cached ae = flux_utils.load_ae(name, args.ae, weight_dtype, "cpu") ae.requires_grad_(False) ae.eval() ae.to(accelerator.device, dtype=weight_dtype) training_models = [] params_to_optimize = [] training_models.append(flux) params_to_optimize.append({"params": list(flux.parameters()), "lr": args.learning_rate}) # calculate number of trainable parameters n_params = 0 for group in params_to_optimize: for p in group["params"]: n_params += p.numel() accelerator.print(f"number of trainable parameters: {n_params}") # 学習に必要なクラスを準備する accelerator.print("prepare optimizer, data loader etc.") if args.fused_optimizer_groups: # fused backward pass: https://pytorch.org/tutorials/intermediate/optimizer_step_in_backward_tutorial.html # Instead of creating an optimizer for all parameters as in the tutorial, we create an optimizer for each group of parameters. # This balances memory usage and management complexity. # calculate total number of parameters n_total_params = sum(len(params["params"]) for params in params_to_optimize) params_per_group = math.ceil(n_total_params / args.fused_optimizer_groups) # split params into groups, keeping the learning rate the same for all params in a group # this will increase the number of groups if the learning rate is different for different params (e.g. U-Net and text encoders) grouped_params = [] param_group = [] param_group_lr = -1 for group in params_to_optimize: lr = group["lr"] for p in group["params"]: # if the learning rate is different for different params, start a new group if lr != param_group_lr: if param_group: grouped_params.append({"params": param_group, "lr": param_group_lr}) param_group = [] param_group_lr = lr param_group.append(p) # if the group has enough parameters, start a new group if len(param_group) == params_per_group: grouped_params.append({"params": param_group, "lr": param_group_lr}) param_group = [] param_group_lr = -1 if param_group: grouped_params.append({"params": param_group, "lr": param_group_lr}) # prepare optimizers for each group optimizers = [] for group in grouped_params: _, _, optimizer = train_util.get_optimizer(args, trainable_params=[group]) optimizers.append(optimizer) optimizer = optimizers[0] # avoid error in the following code logger.info(f"using {len(optimizers)} optimizers for fused optimizer groups") else: _, _, optimizer = train_util.get_optimizer(args, trainable_params=params_to_optimize) # prepare dataloader # strategies are set here because they cannot be referenced in another process. Copy them with the dataset # some strategies can be None train_dataset_group.set_current_strategies() # DataLoaderのプロセス数：0 は persistent_workers が使えないので注意 n_workers = min(args.max_data_loader_n_workers, os.cpu_count()) # cpu_count or max_data_loader_n_workers train_dataloader = torch.utils.data.DataLoader( train_dataset_group, batch_size=1, shuffle=True, collate_fn=collator, num_workers=n_workers, persistent_workers=args.persistent_data_loader_workers, ) # 学習ステップ数を計算する if args.max_train_epochs is not None: args.max_train_steps = args.max_train_epochs * math.ceil( len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps ) accelerator.print( f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}" ) # データセット側にも学習ステップを送信 train_dataset_group.set_max_train_steps(args.max_train_steps) # lr schedulerを用意する if args.fused_optimizer_groups: # prepare lr schedulers for each optimizer lr_schedulers = [train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes) for optimizer in optimizers] lr_scheduler = lr_schedulers[0] # avoid error in the following code else: lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes) # 実験的機能：勾配も含めたfp16/bf16学習を行う　モデル全体をfp16/bf16にする if args.full_fp16: assert ( args.mixed_precision == "fp16" ), "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。" accelerator.print("enable full fp16 training.") flux.to(weight_dtype) if clip_l is not None: clip_l.to(weight_dtype) t5xxl.to(weight_dtype) # TODO check works with fp16 or not elif args.full_bf16: assert ( args.mixed_precision == "bf16" ), "full_bf16 requires mixed precision='bf16' / full_bf16を使う場合はmixed_precision='bf16'を指定してください。" accelerator.print("enable full bf16 training.") flux.to(weight_dtype) if clip_l is not None: clip_l.to(weight_dtype) t5xxl.to(weight_dtype) # if we don't cache text encoder outputs, move them to device if not args.cache_text_encoder_outputs: clip_l.to(accelerator.device) t5xxl.to(accelerator.device) clean_memory_on_device(accelerator.device) if args.deepspeed: ds_model = deepspeed_utils.prepare_deepspeed_model(args, mmdit=flux) # most of ZeRO stage uses optimizer partitioning, so we have to prepare optimizer and ds_model at the same time. # pull/1139#issuecomment-1986790007 ds_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( ds_model, optimizer, train_dataloader, lr_scheduler ) training_models = [ds_model] else: # acceleratorがなんかよろしくやってくれるらしい flux = accelerator.prepare(flux) optimizer, train_dataloader, lr_scheduler = accelerator.prepare(optimizer, train_dataloader, lr_scheduler) # 実験的機能：勾配も含めたfp16学習を行う　PyTorchにパッチを当ててfp16でのgrad scaleを有効にする if args.full_fp16: # During deepseed training, accelerate not handles fp16/bf16|mixed precision directly via scaler. Let deepspeed engine do. # -> But we think it's ok to patch accelerator even if deepspeed is enabled. train_util.patch_accelerator_for_fp16_training(accelerator) # resumeする train_util.resume_from_local_or_hf_if_specified(accelerator, args) if args.fused_backward_pass: # use fused optimizer for backward pass: other optimizers will be supported in the future import library.adafactor_fused library.adafactor_fused.patch_adafactor_fused(optimizer) for param_group in optimizer.param_groups: for parameter in param_group["params"]: if parameter.requires_grad: def __grad_hook(tensor: torch.Tensor, param_group=param_group): if accelerator.sync_gradients and args.max_grad_norm != 0.0: accelerator.clip_grad_norm_(tensor, args.max_grad_norm) optimizer.step_param(tensor, param_group) tensor.grad = None parameter.register_post_accumulate_grad_hook(__grad_hook) elif args.fused_optimizer_groups: # prepare for additional optimizers and lr schedulers for i in range(1, len(optimizers)): optimizers[i] = accelerator.prepare(optimizers[i]) lr_schedulers[i] = accelerator.prepare(lr_schedulers[i]) # counters are used to determine when to step the optimizer global optimizer_hooked_count global num_parameters_per_group global parameter_optimizer_map optimizer_hooked_count = {} num_parameters_per_group = [0] * len(optimizers) parameter_optimizer_map = {} for opt_idx, optimizer in enumerate(optimizers): for param_group in optimizer.param_groups: for parameter in param_group["params"]: if parameter.requires_grad: def optimizer_hook(parameter: torch.Tensor): if accelerator.sync_gradients and args.max_grad_norm != 0.0: accelerator.clip_grad_norm_(parameter, args.max_grad_norm) i = parameter_optimizer_map[parameter] optimizer_hooked_count[i] += 1 if optimizer_hooked_count[i] == num_parameters_per_group[i]: optimizers[i].step() optimizers[i].zero_grad(set_to_none=True) parameter.register_post_accumulate_grad_hook(optimizer_hook) parameter_optimizer_map[parameter] = opt_idx num_parameters_per_group[opt_idx] += 1 # epoch数を計算する num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0): args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1 # 学習する # total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps accelerator.print("running training / 学習開始") accelerator.print(f" num examples / サンプル数: {train_dataset_group.num_train_images}") accelerator.print(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}") accelerator.print(f" num epochs / epoch数: {num_train_epochs}") accelerator.print( f" batch size per device / バッチサイズ: {', '.join([str(d.batch_size) for d in train_dataset_group.datasets])}" ) # accelerator.print( # f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}" # ) accelerator.print(f" gradient accumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}") accelerator.print(f" total optimization steps / 学習ステップ数: {args.max_train_steps}") progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps") global_step = 0 noise_scheduler = FlowMatchEulerDiscreteScheduler(num_train_timesteps=1000, shift=args.discrete_flow_shift) noise_scheduler_copy = copy.deepcopy(noise_scheduler) if accelerator.is_main_process: init_kwargs = {} if args.wandb_run_name: init_kwargs["wandb"] = {"name": args.wandb_run_name} if args.log_tracker_config is not None: init_kwargs = toml.load(args.log_tracker_config) accelerator.init_trackers( "finetuning" if args.log_tracker_name is None else args.log_tracker_name, config=train_util.get_sanitized_config_or_none(args), init_kwargs=init_kwargs, ) # For --sample_at_first flux_train_utils.sample_images(accelerator, args, 0, global_step, flux, ae, [clip_l, t5xxl], sample_prompts_te_outputs) loss_recorder = train_util.LossRecorder() epoch = 0 # avoid error when max_train_steps is 0 for epoch in range(num_train_epochs): accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}") current_epoch.value = epoch + 1 for m in training_models: m.train() for step, batch in enumerate(train_dataloader): current_step.value = global_step if args.fused_optimizer_groups: optimizer_hooked_count = {i: 0 for i in range(len(optimizers))} # reset counter for each step with accelerator.accumulate(*training_models): if "latents" in batch and batch["latents"] is not None: latents = batch["latents"].to(accelerator.device, dtype=weight_dtype) else: with torch.no_grad(): # encode images to latents. images are [-1, 1] latents = ae.encode(batch["images"]) # NaNが含まれていれば警告を表示し0に置き換える if torch.any(torch.isnan(latents)): accelerator.print("NaN found in latents, replacing with zeros") latents = torch.nan_to_num(latents, 0, out=latents) text_encoder_outputs_list = batch.get("text_encoder_outputs_list", None) if text_encoder_outputs_list is not None: text_encoder_conds = text_encoder_outputs_list else: # not cached or training, so get from text encoders tokens_and_masks = batch["input_ids_list"] with torch.no_grad(): input_ids = [ids.to(accelerator.device) for ids in batch["input_ids_list"]] text_encoder_conds = text_encoding_strategy.encode_tokens( tokenize_strategy, [clip_l, t5xxl], input_ids, args.apply_t5_attn_mask ) if args.full_fp16: text_encoder_conds = [c.to(weight_dtype) for c in text_encoder_conds] # TODO support some features for noise implemented in get_noise_noisy_latents_and_timesteps # Sample noise that we'll add to the latents noise = torch.randn_like(latents) bsz = latents.shape[0] # get noisy model input and timesteps noisy_model_input, timesteps, sigmas = flux_train_utils.get_noisy_model_input_and_timesteps( args, noise_scheduler, latents, noise, accelerator.device, weight_dtype ) # pack latents and get img_ids packed_noisy_model_input = flux_utils.pack_latents(noisy_model_input) # b, c, h*2, w*2 -> b, h*w, c*4 packed_latent_height, packed_latent_width = noisy_model_input.shape[2] // 2, noisy_model_input.shape[3] // 2 img_ids = flux_utils.prepare_img_ids(bsz, packed_latent_height, packed_latent_width).to(device=accelerator.device) # get guidance guidance_vec = torch.full((bsz,), args.guidance_scale, device=accelerator.device) # call model l_pooled, t5_out, txt_ids = text_encoder_conds with accelerator.autocast(): # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transformer model (we should not keep it but I want to keep the inputs same for the model for testing) model_pred = flux( img=packed_noisy_model_input, img_ids=img_ids, txt=t5_out, txt_ids=txt_ids, y=l_pooled, timesteps=timesteps / 1000, guidance=guidance_vec, ) # unpack latents model_pred = flux_utils.unpack_latents(model_pred, packed_latent_height, packed_latent_width) # apply model prediction type model_pred, weighting = flux_train_utils.apply_model_prediction_type(args, model_pred, noisy_model_input, sigmas) # flow matching loss: this is different from SD3 target = noise - latents # calculate loss loss = train_util.conditional_loss( model_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=None ) if weighting is not None: loss = loss * weighting if args.masked_loss or ("alpha_masks" in batch and batch["alpha_masks"] is not None): loss = apply_masked_loss(loss, batch) loss = loss.mean([1, 2, 3]) loss_weights = batch["loss_weights"] # 各sampleごとのweight loss = loss * loss_weights loss = loss.mean() # backward accelerator.backward(loss) if not (args.fused_backward_pass or args.fused_optimizer_groups): if accelerator.sync_gradients and args.max_grad_norm != 0.0: params_to_clip = [] for m in training_models: params_to_clip.extend(m.parameters()) accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) optimizer.step() lr_scheduler.step() optimizer.zero_grad(set_to_none=True) else: # optimizer.step() and optimizer.zero_grad() are called in the optimizer hook lr_scheduler.step() if args.fused_optimizer_groups: for i in range(1, len(optimizers)): lr_schedulers[i].step() # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: progress_bar.update(1) global_step += 1 flux_train_utils.sample_images( accelerator, args, epoch, global_step, flux, ae, [clip_l, t5xxl], sample_prompts_te_outputs ) # 指定ステップごとにモデルを保存 if args.save_every_n_steps is not None and global_step % args.save_every_n_steps == 0: accelerator.wait_for_everyone() if accelerator.is_main_process: flux_train_utils.save_flux_model_on_epoch_end_or_stepwise( args, False, accelerator, save_dtype, epoch, num_train_epochs, global_step, accelerator.unwrap_model(flux), ) current_loss = loss.detach().item() # 平均なのでbatch sizeは関係ないはず if args.logging_dir is not None: logs = {"loss": current_loss} train_util.append_lr_to_logs(logs, lr_scheduler, args.optimizer_type, including_unet=True) accelerator.log(logs, step=global_step) loss_recorder.add(epoch=epoch, step=step, loss=current_loss) avr_loss: float = loss_recorder.moving_average logs = {"avr_loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]} progress_bar.set_postfix(**logs) if global_step >= args.max_train_steps: break if args.logging_dir is not None: logs = {"loss/epoch": loss_recorder.moving_average} accelerator.log(logs, step=epoch + 1) accelerator.wait_for_everyone() if args.save_every_n_epochs is not None: if accelerator.is_main_process: flux_train_utils.save_flux_model_on_epoch_end_or_stepwise( args, True, accelerator, save_dtype, epoch, num_train_epochs, global_step, accelerator.unwrap_model(flux), ) flux_train_utils.sample_images( accelerator, args, epoch + 1, global_step, flux, ae, [clip_l, t5xxl], sample_prompts_te_outputs ) is_main_process = accelerator.is_main_process # if is_main_process: flux = accelerator.unwrap_model(flux) clip_l = accelerator.unwrap_model(clip_l) clip_g = accelerator.unwrap_model(clip_g) if t5xxl is not None: t5xxl = accelerator.unwrap_model(t5xxl) accelerator.end_training() if args.save_state or args.save_state_on_train_end: train_util.save_state_on_train_end(args, accelerator) del accelerator # この後メモリを使うのでこれは消す if is_main_process: flux_train_utils.save_flux_model_on_train_end(args, save_dtype, epoch, global_step, flux, ae) logger.info("model saved.") def setup_parser() -> argparse.ArgumentParser: parser = argparse.ArgumentParser() add_logging_arguments(parser) train_util.add_sd_models_arguments(parser) # TODO split this train_util.add_dataset_arguments(parser, True, True, True) train_util.add_training_arguments(parser, False) train_util.add_masked_loss_arguments(parser) deepspeed_utils.add_deepspeed_arguments(parser) train_util.add_sd_saving_arguments(parser) train_util.add_optimizer_arguments(parser) config_util.add_config_arguments(parser) add_custom_train_arguments(parser) # TODO remove this from here flux_train_utils.add_flux_train_arguments(parser) parser.add_argument( "--fused_optimizer_groups", type=int, default=None, help="number of optimizers for fused backward pass and optimizer step / fused backward passとoptimizer stepのためのoptimizer数", ) parser.add_argument( "--skip_latents_validity_check", action="store_true", help="skip latents validity check / latentsの正当性チェックをスキップする", ) return parser if __name__ == "__main__": parser = setup_parser() args = parser.parse_args() train_util.verify_command_line_training_args(args) args = train_util.read_config_from_file(args, parser) train(args)