import argparse import os import json from tqdm import tqdm import numpy as np from PIL import Image import cv2 import torch from torchvision import transforms import library.model_util as model_util import library.train_util as train_util DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu') IMAGE_TRANSFORMS = transforms.Compose( [ transforms.ToTensor(), transforms.Normalize([0.5], [0.5]), ] ) def collate_fn_remove_corrupted(batch): """Collate function that allows to remove corrupted examples in the dataloader. It expects that the dataloader returns 'None' when that occurs. The 'None's in the batch are removed. """ # Filter out all the Nones (corrupted examples) batch = list(filter(lambda x: x is not None, batch)) return batch def get_latents(vae, images, weight_dtype): img_tensors = [IMAGE_TRANSFORMS(image) for image in images] img_tensors = torch.stack(img_tensors) img_tensors = img_tensors.to(DEVICE, weight_dtype) with torch.no_grad(): latents = vae.encode(img_tensors).latent_dist.sample().float().to("cpu").numpy() return latents def get_npz_filename_wo_ext(data_dir, image_key, is_full_path, flip): if is_full_path: base_name = os.path.splitext(os.path.basename(image_key))[0] else: base_name = image_key if flip: base_name += '_flip' return os.path.join(data_dir, base_name) def main(args): # assert args.bucket_reso_steps % 8 == 0, f"bucket_reso_steps must be divisible by 8 / bucket_reso_stepは8で割り切れる必要があります" if args.bucket_reso_steps % 8 > 0: print(f"resolution of buckets in training time is a multiple of 8 / 学習時の各bucketの解像度は8単位になります") image_paths = train_util.glob_images(args.train_data_dir) print(f"found {len(image_paths)} images.") if os.path.exists(args.in_json): print(f"loading existing metadata: {args.in_json}") with open(args.in_json, "rt", encoding='utf-8') as f: metadata = json.load(f) else: print(f"no metadata / メタデータファイルがありません: {args.in_json}") return weight_dtype = torch.float32 if args.mixed_precision == "fp16": weight_dtype = torch.float16 elif args.mixed_precision == "bf16": weight_dtype = torch.bfloat16 vae = model_util.load_vae(args.model_name_or_path, weight_dtype) vae.eval() vae.to(DEVICE, dtype=weight_dtype) # bucketのサイズを計算する max_reso = tuple([int(t) for t in args.max_resolution.split(',')]) assert len(max_reso) == 2, f"illegal resolution (not 'width,height') / 画像サイズに誤りがあります。'幅,高さ'で指定してください: {args.max_resolution}" bucket_manager = train_util.BucketManager(args.bucket_no_upscale, max_reso, args.min_bucket_reso, args.max_bucket_reso, args.bucket_reso_steps) if not args.bucket_no_upscale: bucket_manager.make_buckets() else: print("min_bucket_reso and max_bucket_reso are ignored if bucket_no_upscale is set, because bucket reso is defined by image size automatically / bucket_no_upscaleが指定された場合は、bucketの解像度は画像サイズから自動計算されるため、min_bucket_resoとmax_bucket_resoは無視されます") # 画像をひとつずつ適切なbucketに割り当てながらlatentを計算する img_ar_errors = [] def process_batch(is_last): for bucket in bucket_manager.buckets: if (is_last and len(bucket) > 0) or len(bucket) >= args.batch_size: latents = get_latents(vae, [img for _, img in bucket], weight_dtype) assert latents.shape[2] == bucket[0][1].shape[0] // 8 and latents.shape[3] == bucket[0][1].shape[1] // 8, \ f"latent shape {latents.shape}, {bucket[0][1].shape}" for (image_key, _), latent in zip(bucket, latents): npz_file_name = get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, False) np.savez(npz_file_name, latent) # flip if args.flip_aug: latents = get_latents(vae, [img[:, ::-1].copy() for _, img in bucket], weight_dtype) # copyがないとTensor変換できない for (image_key, _), latent in zip(bucket, latents): npz_file_name = get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, True) np.savez(npz_file_name, latent) else: # remove existing flipped npz for image_key, _ in bucket: npz_file_name = get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, True) + ".npz" if os.path.isfile(npz_file_name): print(f"remove existing flipped npz / 既存のflipされたnpzファイルを削除します: {npz_file_name}") os.remove(npz_file_name) bucket.clear() # 読み込みの高速化のためにDataLoaderを使うオプション if args.max_data_loader_n_workers is not None: dataset = train_util.ImageLoadingDataset(image_paths) data = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, num_workers=args.max_data_loader_n_workers, collate_fn=collate_fn_remove_corrupted, drop_last=False) else: data = [[(None, ip)] for ip in image_paths] bucket_counts = {} for data_entry in tqdm(data, smoothing=0.0): if data_entry[0] is None: continue img_tensor, image_path = data_entry[0] if img_tensor is not None: image = transforms.functional.to_pil_image(img_tensor) else: try: image = Image.open(image_path) if image.mode != 'RGB': image = image.convert("RGB") except Exception as e: print(f"Could not load image path / 画像を読み込めません: {image_path}, error: {e}") continue image_key = image_path if args.full_path else os.path.splitext(os.path.basename(image_path))[0] if image_key not in metadata: metadata[image_key] = {} # 本当はこのあとの部分もDataSetに持っていけば高速化できるがいろいろ大変 reso, resized_size, ar_error = bucket_manager.select_bucket(image.width, image.height) img_ar_errors.append(abs(ar_error)) bucket_counts[reso] = bucket_counts.get(reso, 0) + 1 # メタデータに記録する解像度はlatent単位とするので、8単位で切り捨て metadata[image_key]['train_resolution'] = (reso[0] - reso[0] % 8, reso[1] - reso[1] % 8) if not args.bucket_no_upscale: # upscaleを行わないときには、resize後のサイズは、bucketのサイズと、縦横どちらかが同じであることを確認する assert resized_size[0] == reso[0] or resized_size[1] == reso[ 1], f"internal error, resized size not match: {reso}, {resized_size}, {image.width}, {image.height}" assert resized_size[0] >= reso[0] and resized_size[1] >= reso[ 1], f"internal error, resized size too small: {reso}, {resized_size}, {image.width}, {image.height}" assert resized_size[0] >= reso[0] and resized_size[1] >= reso[ 1], f"internal error resized size is small: {resized_size}, {reso}" # 既に存在するファイルがあればshapeを確認して同じならskipする if args.skip_existing: npz_files = [get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, False) + ".npz"] if args.flip_aug: npz_files.append(get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, True) + ".npz") found = True for npz_file in npz_files: if not os.path.exists(npz_file): found = False break dat = np.load(npz_file)['arr_0'] if dat.shape[1] != reso[1] // 8 or dat.shape[2] != reso[0] // 8: # latentsのshapeを確認 found = False break if found: continue # 画像をリサイズしてトリミングする # PILにinter_areaがないのでcv2で…… image = np.array(image) if resized_size[0] != image.shape[1] or resized_size[1] != image.shape[0]: # リサイズ処理が必要？ image = cv2.resize(image, resized_size, interpolation=cv2.INTER_AREA) if resized_size[0] > reso[0]: trim_size = resized_size[0] - reso[0] image = image[:, trim_size//2:trim_size//2 + reso[0]] if resized_size[1] > reso[1]: trim_size = resized_size[1] - reso[1] image = image[trim_size//2:trim_size//2 + reso[1]] assert image.shape[0] == reso[1] and image.shape[1] == reso[0], f"internal error, illegal trimmed size: {image.shape}, {reso}" # # debug # cv2.imwrite(f"r:\\test\\img_{len(img_ar_errors)}.jpg", image[:, :, ::-1]) # バッチへ追加 bucket_manager.add_image(reso, (image_key, image)) # バッチを推論するか判定して推論する process_batch(False) # 残りを処理する process_batch(True) bucket_manager.sort() for i, reso in enumerate(bucket_manager.resos): count = bucket_counts.get(reso, 0) if count > 0: print(f"bucket {i} {reso}: {count}") img_ar_errors = np.array(img_ar_errors) print(f"mean ar error: {np.mean(img_ar_errors)}") # metadataを書き出して終わり print(f"writing metadata: {args.out_json}") with open(args.out_json, "wt", encoding='utf-8') as f: json.dump(metadata, f, indent=2) print("done!") def setup_parser() -> argparse.ArgumentParser: parser = argparse.ArgumentParser() parser.add_argument("train_data_dir", type=str, help="directory for train images / 学習画像データのディレクトリ") parser.add_argument("in_json", type=str, help="metadata file to input / 読み込むメタデータファイル") parser.add_argument("out_json", type=str, help="metadata file to output / メタデータファイル書き出し先") parser.add_argument("model_name_or_path", type=str, help="model name or path to encode latents / latentを取得するためのモデル") parser.add_argument("--v2", action='store_true', help='not used (for backward compatibility) / 使用されません（互換性のため残してあります）') parser.add_argument("--batch_size", type=int, default=1, help="batch size in inference / 推論時のバッチサイズ") parser.add_argument("--max_data_loader_n_workers", type=int, default=None, help="enable image reading by DataLoader with this number of workers (faster) / DataLoaderによる画像読み込みを有効にしてこのワーカー数を適用する（読み込みを高速化）") parser.add_argument("--max_resolution", type=str, default="512,512", help="max resolution in fine tuning (width,height) / fine tuning時の最大画像サイズ 「幅,高さ」（使用メモリ量に関係します）") parser.add_argument("--min_bucket_reso", type=int, default=256, help="minimum resolution for buckets / bucketの最小解像度") parser.add_argument("--max_bucket_reso", type=int, default=1024, help="maximum resolution for buckets / bucketの最小解像度") parser.add_argument("--bucket_reso_steps", type=int, default=64, help="steps of resolution for buckets, divisible by 8 is recommended / bucketの解像度の単位、8で割り切れる値を推奨します") parser.add_argument("--bucket_no_upscale", action="store_true", help="make bucket for each image without upscaling / 画像を拡大せずbucketを作成します") parser.add_argument("--mixed_precision", type=str, default="no", choices=["no", "fp16", "bf16"], help="use mixed precision / 混合精度を使う場合、その精度") parser.add_argument("--full_path", action="store_true", help="use full path as image-key in metadata (supports multiple directories) / メタデータで画像キーをフルパスにする（複数の学習画像ディレクトリに対応）") parser.add_argument("--flip_aug", action="store_true", help="flip augmentation, save latents for flipped images / 左右反転した画像もlatentを取得、保存する") 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() main(args)