# latentsのdiskへの事前キャッシュを行う / cache latents to disk 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 latents arg assert args.cache_latents_to_disk, "cache_latents_to_disk must be True / cache_latents_to_diskはTrueである必要があります" 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) # datasetのcache_latentsを呼ばなければ、生の画像が返る 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) vae_dtype = torch.float32 if args.no_half_vae else weight_dtype # モデルを読み込む logger.info("load model") if args.sdxl: (_, _, _, vae, _, _, _) = sdxl_train_util.load_target_model(args, accelerator, "sdxl", weight_dtype) else: _, vae, _, _ = train_util.load_target_model(args, weight_dtype, accelerator) if torch.__version__ >= "2.0.0": # PyTorch 2.0.0 以上対応のxformersなら以下が使える vae.set_use_memory_efficient_attention_xformers(args.xformers) vae.to(accelerator.device, dtype=vae_dtype) vae.requires_grad_(False) vae.eval() # dataloaderを準備する train_dataset_group.set_caching_mode("latents") # 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): b_size = len(batch["images"]) vae_batch_size = b_size if args.vae_batch_size is None else args.vae_batch_size flip_aug = batch["flip_aug"] random_crop = batch["random_crop"] bucket_reso = batch["bucket_reso"] # バッチを分割して処理する for i in range(0, b_size, vae_batch_size): images = batch["images"][i : i + vae_batch_size] absolute_paths = batch["absolute_paths"][i : i + vae_batch_size] resized_sizes = batch["resized_sizes"][i : i + vae_batch_size] image_infos = [] for i, (image, absolute_path, resized_size) in enumerate(zip(images, absolute_paths, resized_sizes)): image_info = train_util.ImageInfo(absolute_path, 1, "dummy", False, absolute_path) image_info.image = image image_info.bucket_reso = bucket_reso image_info.resized_size = resized_size image_info.latents_npz = os.path.splitext(absolute_path)[0] + ".npz" if args.skip_existing: if train_util.is_disk_cached_latents_is_expected(image_info.bucket_reso, image_info.latents_npz, flip_aug): logger.warning(f"Skipping {image_info.latents_npz} because it already exists.") continue image_infos.append(image_info) if len(image_infos) > 0: train_util.cache_batch_latents(vae, True, image_infos, flip_aug, random_crop) 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) parser.add_argument("--sdxl", action="store_true", help="Use SDXL model / SDXLモデルを使用する") parser.add_argument( "--no_half_vae", action="store_true", help="do not use fp16/bf16 VAE in mixed precision (use float VAE) / mixed precisionでも fp16/bf16 VAEを使わずfloat VAEを使う", ) 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)