# text encoder出力のdiskへの事前キャッシュを行う / cache text encoder outputs to disk in advance import argparse import math from multiprocessing import Value import os from accelerate.utils import set_seed import torch from tqdm import tqdm from library import config_util from library import train_util from library import sdxl_train_util from library.config_util import ( ConfigSanitizer, BlueprintGenerator, ) from library.utils import setup_logging setup_logging() import logging logger = logging.getLogger(__name__) def cache_to_disk(args: argparse.Namespace) -> None: train_util.prepare_dataset_args(args, True) # check cache arg assert ( args.cache_text_encoder_outputs_to_disk ), "cache_text_encoder_outputs_to_disk must be True / cache_text_encoder_outputs_to_diskはTrueである必要があります" # できるだけ準備はしておくが今のところSDXLのみしか動かない assert ( args.sdxl ), "cache_text_encoder_outputs_to_disk is only available for SDXL / cache_text_encoder_outputs_to_diskはSDXLのみ利用可能です" 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 # tokenizerを準備する：datasetを動かすために必要 if args.sdxl: tokenizer1, tokenizer2 = sdxl_train_util.load_tokenizers(args) tokenizers = [tokenizer1, tokenizer2] else: tokenizer = train_util.load_tokenizer(args) tokenizers = [tokenizer] # データセットを準備する if args.dataset_class is None: blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, False, 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, tokenizer=tokenizers) train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group) else: train_dataset_group = train_util.load_arbitrary_dataset(args, tokenizers) 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) # 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, _ = train_util.prepare_dtype(args) # モデルを読み込む logger.info("load model") if args.sdxl: (_, text_encoder1, text_encoder2, _, _, _, _) = sdxl_train_util.load_target_model(args, accelerator, "sdxl", weight_dtype) text_encoders = [text_encoder1, text_encoder2] else: text_encoder1, _, _, _ = train_util.load_target_model(args, weight_dtype, accelerator) text_encoders = [text_encoder1] for text_encoder in text_encoders: text_encoder.to(accelerator.device, dtype=weight_dtype) text_encoder.requires_grad_(False) text_encoder.eval() # dataloaderを準備する train_dataset_group.set_caching_mode("text") # 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, ) # acceleratorを使ってモデルを準備する：マルチGPUで使えるようになるはず train_dataloader = accelerator.prepare(train_dataloader) # データ取得のためのループ for batch in tqdm(train_dataloader): absolute_paths = batch["absolute_paths"] input_ids1_list = batch["input_ids1_list"] input_ids2_list = batch["input_ids2_list"] image_infos = [] for absolute_path, input_ids1, input_ids2 in zip(absolute_paths, input_ids1_list, input_ids2_list): image_info = train_util.ImageInfo(absolute_path, 1, "dummy", False, absolute_path) image_info.text_encoder_outputs_npz = os.path.splitext(absolute_path)[0] + train_util.TEXT_ENCODER_OUTPUTS_CACHE_SUFFIX image_info if args.skip_existing: if os.path.exists(image_info.text_encoder_outputs_npz): logger.warning(f"Skipping {image_info.text_encoder_outputs_npz} because it already exists.") continue image_info.input_ids1 = input_ids1 image_info.input_ids2 = input_ids2 image_infos.append(image_info) if len(image_infos) > 0: b_input_ids1 = torch.stack([image_info.input_ids1 for image_info in image_infos]) b_input_ids2 = torch.stack([image_info.input_ids2 for image_info in image_infos]) train_util.cache_batch_text_encoder_outputs( image_infos, tokenizers, text_encoders, args.max_token_length, True, b_input_ids1, b_input_ids2, weight_dtype ) accelerator.wait_for_everyone() accelerator.print(f"Finished caching latents for {len(train_dataset_group)} batches.") def setup_parser() -> argparse.ArgumentParser: parser = argparse.ArgumentParser() train_util.add_sd_models_arguments(parser) train_util.add_training_arguments(parser, True) train_util.add_dataset_arguments(parser, True, True, True) config_util.add_config_arguments(parser) sdxl_train_util.add_sdxl_training_arguments(parser) parser.add_argument("--sdxl", action="store_true", help="Use SDXL model / SDXLモデルを使用する") parser.add_argument( "--skip_existing", action="store_true", help="skip images if npz already exists (both normal and flipped exists if flip_aug is enabled) / npzが既に存在する画像をスキップする（flip_aug有効時は通常、反転の両方が存在する画像をスキップ）", ) return parser if __name__ == "__main__": parser = setup_parser() args = parser.parse_args() args = train_util.read_config_from_file(args, parser) cache_to_disk(args)