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# 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)
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