# 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, flux_train_utils, flux_utils, strategy_base, strategy_flux, strategy_sd, strategy_sdxl from library import train_util from library import sdxl_train_util import library.sai_model_spec as sai_model_spec from library.config_util import ( ConfigSanitizer, BlueprintGenerator, ) from library.utils import setup_logging, add_logging_arguments setup_logging() import logging logger = logging.getLogger(__name__) def set_tokenize_strategy(is_sd: bool, is_sdxl: bool, is_flux: bool, args: argparse.Namespace) -> None: if is_flux: _, is_schnell, _ = flux_utils.check_flux_state_dict_diffusers_schnell(args.pretrained_model_name_or_path) else: is_schnell = False if is_sd: tokenize_strategy = strategy_sd.SdTokenizeStrategy(args.v2, args.max_token_length, args.tokenizer_cache_dir) elif is_sdxl: tokenize_strategy = strategy_sdxl.SdxlTokenizeStrategy(args.max_token_length, args.tokenizer_cache_dir) else: if args.t5xxl_max_token_length is None: if is_schnell: t5xxl_max_token_length = 256 else: t5xxl_max_token_length = 512 else: t5xxl_max_token_length = args.t5xxl_max_token_length logger.info(f"t5xxl_max_token_length: {t5xxl_max_token_length}") tokenize_strategy = strategy_flux.FluxTokenizeStrategy(t5xxl_max_token_length, args.tokenizer_cache_dir) strategy_base.TokenizeStrategy.set_strategy(tokenize_strategy) def cache_to_disk(args: argparse.Namespace) -> None: setup_logging(args, reset=True) train_util.prepare_dataset_args(args, True) train_util.enable_high_vram(args) # 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" args.cache_latents = True args.cache_latents_to_disk = True 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 is_sd = not args.sdxl and not args.flux is_sdxl = args.sdxl is_flux = args.flux set_tokenize_strategy(is_sd, is_sdxl, is_flux, args) if is_sd or is_sdxl: latents_caching_strategy = strategy_sd.SdSdxlLatentsCachingStrategy(is_sd, True, args.vae_batch_size, args.skip_cache_check) else: latents_caching_strategy = strategy_flux.FluxLatentsCachingStrategy(True, args.vae_batch_size, args.skip_cache_check) strategy_base.LatentsCachingStrategy.set_strategy(latents_caching_strategy) # データセットを準備する use_user_config = args.dataset_config is not None if args.dataset_class is None: blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, args.masked_loss, True)) if use_user_config: logger.info(f"Loading dataset config from {args.dataset_config}") user_config = config_util.load_user_config(args.dataset_config) ignored = ["train_data_dir", "reg_data_dir", "in_json"] if any(getattr(args, attr) is not None for attr in ignored): logger.warning( "ignoring the 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, val_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group) else: # use arbitrary dataset class train_dataset_group = train_util.load_arbitrary_dataset(args) val_dataset_group = None # acceleratorを準備する logger.info("prepare accelerator") args.deepspeed = False 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 is_sd: _, vae, _, _ = train_util.load_target_model(args, weight_dtype, accelerator) elif is_sdxl: (_, _, _, vae, _, _, _) = sdxl_train_util.load_target_model(args, accelerator, "sdxl", weight_dtype) else: vae = flux_utils.load_ae(args.ae, weight_dtype, "cpu", disable_mmap=args.disable_mmap_load_safetensors) if is_sd or is_sdxl: 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() # cache latents with dataset # TODO use DataLoader to speed up train_dataset_group.new_cache_latents(vae, accelerator) accelerator.wait_for_everyone() accelerator.print(f"Finished caching latents to disk.") def setup_parser() -> argparse.ArgumentParser: parser = argparse.ArgumentParser() add_logging_arguments(parser) train_util.add_sd_models_arguments(parser) sai_model_spec.add_model_spec_arguments(parser) train_util.add_training_arguments(parser, True) train_util.add_dataset_arguments(parser, True, True, True) train_util.add_masked_loss_arguments(parser) config_util.add_config_arguments(parser) train_util.add_dit_training_arguments(parser) flux_train_utils.add_flux_train_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("--flux", action="store_true", help="Use FLUX model / FLUX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="[Deprecated] This option does not work. Existing .npz files are always checked. Use `--skip_cache_check` to skip the check." " / [非推奨] このオプションは機能しません。既存の .npz は常に検証されます。`--skip_cache_check` で検証をスキップできます。", ) 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)