Merge pull request #1206 from kohya-ss/dataset-cache

Add metadata caching for DreamBooth dataset

Author: kohya-ss

docs/config_README-en.md

@@ -177,6 +177,7 @@ Options related to the configuration of DreamBooth subsets.
 | `image_dir` | `'C:\hoge'` | - | - | o (required) |
 | `caption_extension` | `".txt"` | o | o | o |
 | `class_tokens` | `"sks girl"` | - | - | o |
+| `cache_info` | `false` | o | o | o |
 | `is_reg` | `false` | - | - | o |
 
 Firstly, note that for `image_dir`, the path to the image files must be specified as being directly in the directory. Unlike the previous DreamBooth method, where images had to be placed in subdirectories, this is not compatible with that specification. Also, even if you name the folder something like "5_cat", the number of repeats of the image and the class name will not be reflected. If you want to set these individually, you will need to explicitly specify them using `num_repeats` and `class_tokens`.
@@ -187,6 +188,9 @@ Firstly, note that for `image_dir`, the path to the image files must be specifie
 * `class_tokens`
     * Sets the class tokens.
     * Only used during training when a corresponding caption file does not exist. The determination of whether or not to use it is made on a per-image basis. If `class_tokens` is not specified and a caption file is not found, an error will occur.
+* `cache_info`
+    * Specifies whether to cache the image size and caption. If not specified, it is set to `false`. The cache is saved in `metadata_cache.json` in `image_dir`.
+    * Caching speeds up the loading of the dataset after the first time. It is effective when dealing with thousands of images or more.
 * `is_reg`
     * Specifies whether the subset images are for normalization. If not specified, it is set to `false`, meaning that the images are not for normalization.
 

docs/config_README-ja.md

@@ -173,6 +173,7 @@ DreamBooth 方式のサブセットの設定に関わるオプションです。
 | `image_dir` | `‘C:\hoge’` | - | - | o（必須） |
 | `caption_extension` | `".txt"` | o | o | o |
 | `class_tokens` | `“sks girl”` | - | - | o |
+| `cache_info` | `false` | o | o | o | 
 | `is_reg` | `false` | - | - | o |
 
 まず注意点として、 `image_dir` には画像ファイルが直下に置かれているパスを指定する必要があります。従来の DreamBooth の手法ではサブディレクトリに画像を置く必要がありましたが、そちらとは仕様に互換性がありません。また、`5_cat` のようなフォルダ名にしても、画像の繰り返し回数とクラス名は反映されません。これらを個別に設定したい場合、`num_repeats` と `class_tokens` で明示的に指定する必要があることに注意してください。
@@ -183,6 +184,9 @@ DreamBooth 方式のサブセットの設定に関わるオプションです。
 * `class_tokens`
     * クラストークンを設定します。
     * 画像に対応する caption ファイルが存在しない場合にのみ学習時に利用されます。利用するかどうかの判定は画像ごとに行います。`class_tokens` を指定しなかった場合に caption ファイルも見つからなかった場合にはエラーになります。
+* `cache_info`
+    * 画像サイズ、キャプションをキャッシュするかどうかを指定します。指定しなかった場合は `false` になります。キャッシュは `image_dir` に `metadata_cache.json` というファイル名で保存されます。
+    * キャッシュを行うと、二回目以降のデータセット読み込みが高速化されます。数千枚以上の画像を扱う場合には有効です。
 * `is_reg`
     * サブセットの画像が正規化用かどうかを指定します。指定しなかった場合は `false` として、つまり正規化画像ではないとして扱います。
 

library/config_util.py

@@ -85,6 +85,7 @@ class DreamBoothSubsetParams(BaseSubsetParams):
     is_reg: bool = False
     class_tokens: Optional[str] = None
     caption_extension: str = ".caption"
+    cache_info: bool = False
 
 
 @dataclass
@@ -96,6 +97,7 @@ class FineTuningSubsetParams(BaseSubsetParams):
 class ControlNetSubsetParams(BaseSubsetParams):
     conditioning_data_dir: str = None
     caption_extension: str = ".caption"
+    cache_info: bool = False
 
 
 @dataclass
@@ -205,6 +207,7 @@ def __validate_and_convert_scalar_or_twodim(klass, value: Union[float, Sequence]
     DB_SUBSET_ASCENDABLE_SCHEMA = {
         "caption_extension": str,
         "class_tokens": str,
+        "cache_info": bool,
     }
     DB_SUBSET_DISTINCT_SCHEMA = {
         Required("image_dir"): str,
@@ -217,6 +220,7 @@ def __validate_and_convert_scalar_or_twodim(klass, value: Union[float, Sequence]
     }
     CN_SUBSET_ASCENDABLE_SCHEMA = {
         "caption_extension": str,
+        "cache_info": bool,
     }
     CN_SUBSET_DISTINCT_SCHEMA = {
         Required("image_dir"): str,

library/train_util.py

@@ -63,6 +63,7 @@
 from huggingface_hub import hf_hub_download
 import numpy as np
 from PIL import Image
+import imagesize
 import cv2
 import safetensors.torch
 from library.lpw_stable_diffusion import StableDiffusionLongPromptWeightingPipeline
@@ -410,6 +411,7 @@ def __init__(
         is_reg: bool,
         class_tokens: Optional[str],
         caption_extension: str,
+        cache_info: bool,
         num_repeats,
         shuffle_caption,
         caption_separator: str,
@@ -458,6 +460,7 @@ def __init__(
         self.caption_extension = caption_extension
         if self.caption_extension and not self.caption_extension.startswith("."):
             self.caption_extension = "." + self.caption_extension
+        self.cache_info = cache_info
 
     def __eq__(self, other) -> bool:
         if not isinstance(other, DreamBoothSubset):
@@ -527,6 +530,7 @@ def __init__(
         image_dir: str,
         conditioning_data_dir: str,
         caption_extension: str,
+        cache_info: bool,
         num_repeats,
         shuffle_caption,
         caption_separator,
@@ -574,6 +578,7 @@ def __init__(
         self.caption_extension = caption_extension
         if self.caption_extension and not self.caption_extension.startswith("."):
             self.caption_extension = "." + self.caption_extension
+        self.cache_info = cache_info
 
     def __eq__(self, other) -> bool:
         if not isinstance(other, ControlNetSubset):
@@ -1081,8 +1086,7 @@ def cache_text_encoder_outputs(
             )
 
     def get_image_size(self, image_path):
-        image = Image.open(image_path)
-        return image.size
+        return imagesize.get(image_path)
 
     def load_image_with_face_info(self, subset: BaseSubset, image_path: str):
         img = load_image(image_path)
@@ -1411,6 +1415,8 @@ def get_item_for_caching(self, bucket, bucket_batch_size, image_index):
 
 
 class DreamBoothDataset(BaseDataset):
+    IMAGE_INFO_CACHE_FILE = "metadata_cache.json"
+
     def __init__(
         self,
         subsets: Sequence[DreamBoothSubset],
@@ -1485,26 +1491,54 @@ def load_dreambooth_dir(subset: DreamBoothSubset):
                 logger.warning(f"not directory: {subset.image_dir}")
                 return [], []
 
-            img_paths = glob_images(subset.image_dir, "*")
-            logger.info(f"found directory {subset.image_dir} contains {len(img_paths)} image files")
-
-            # 画像ファイルごとにプロンプトを読み込み、もしあればそちらを使う
-            captions = []
-            missing_captions = []
-            for img_path in img_paths:
-                cap_for_img = read_caption(img_path, subset.caption_extension, subset.enable_wildcard)
-                if cap_for_img is None and subset.class_tokens is None:
+            info_cache_file = os.path.join(subset.image_dir, self.IMAGE_INFO_CACHE_FILE)
+            use_cached_info_for_subset = subset.cache_info
+            if use_cached_info_for_subset:
+                logger.info(
+                    f"using cached image info for this subset / このサブセットで、キャッシュされた画像情報を使います: {info_cache_file}"
+                )
+                if not os.path.isfile(info_cache_file):
                     logger.warning(
-                        f"neither caption file nor class tokens are found. use empty caption for {img_path} / キャプションファイルもclass tokenも見つかりませんでした。空のキャプションを使用します: {img_path}"
+                        f"image info file not found. You can ignore this warning if this is the first time to use this subset"
+                        + " / キャッシュファイルが見つかりませんでした。初回実行時はこの警告を無視してください: {metadata_file}"
                     )
-                    captions.append("")
-                    missing_captions.append(img_path)
-                else:
-                    if cap_for_img is None:
-                        captions.append(subset.class_tokens)
+                    use_cached_info_for_subset = False
+
+            if use_cached_info_for_subset:
+                # json: {`img_path`:{"caption": "caption...", "resolution": [width, height]}, ...}
+                with open(info_cache_file, "r", encoding="utf-8") as f:
+                    metas = json.load(f)
+                img_paths = list(metas.keys())
+                sizes = [meta["resolution"] for meta in metas.values()]
+
+                # we may need to check image size and existence of image files, but it takes time, so user should check it before training
+            else:
+                img_paths = glob_images(subset.image_dir, "*")
+                sizes = [None] * len(img_paths)
+
+            logger.info(f"found directory {subset.image_dir} contains {len(img_paths)} image files")
+
+            if use_cached_info_for_subset:
+                captions = [meta["caption"] for meta in metas.values()]
+                missing_captions = [img_path for img_path, caption in zip(img_paths, captions) if caption is None or caption == ""]
+            else:
+                # 画像ファイルごとにプロンプトを読み込み、もしあればそちらを使う
+                captions = []
+                missing_captions = []
+                for img_path in img_paths:
+                    cap_for_img = read_caption(img_path, subset.caption_extension, subset.enable_wildcard)
+                    if cap_for_img is None and subset.class_tokens is None:
+                        logger.warning(
+                            f"neither caption file nor class tokens are found. use empty caption for {img_path} / キャプションファイルもclass tokenも見つかりませんでした。空のキャプションを使用します: {img_path}"
+                        )
+                        captions.append("")
                         missing_captions.append(img_path)
                     else:
-                        captions.append(cap_for_img)
+                        if cap_for_img is None:
+                            captions.append(subset.class_tokens)
+                            missing_captions.append(img_path)
+                        else:
+                            captions.append(cap_for_img)
 
             self.set_tag_frequency(os.path.basename(subset.image_dir), captions)  # タグ頻度を記録
 
@@ -1521,7 +1555,19 @@ def load_dreambooth_dir(subset: DreamBoothSubset):
                         logger.warning(missing_caption + f"... and {remaining_missing_captions} more")
                         break
                     logger.warning(missing_caption)
-            return img_paths, captions
+
+            if not use_cached_info_for_subset and subset.cache_info:
+                logger.info(f"cache image info for / 画像情報をキャッシュします : {info_cache_file}")
+                sizes = [self.get_image_size(img_path) for img_path in tqdm(img_paths, desc="get image size")]
+                matas = {}
+                for img_path, caption, size in zip(img_paths, captions, sizes):
+                    matas[img_path] = {"caption": caption, "resolution": list(size)}
+                with open(info_cache_file, "w", encoding="utf-8") as f:
+                    json.dump(matas, f, ensure_ascii=False, indent=2)
+                logger.info(f"cache image info done for / 画像情報を出力しました : {info_cache_file}")
+
+            # if sizes are not set, image size will be read in make_buckets
+            return img_paths, captions, sizes
 
         logger.info("prepare images.")
         num_train_images = 0
@@ -1540,7 +1586,7 @@ def load_dreambooth_dir(subset: DreamBoothSubset):
                 )
                 continue
 
-            img_paths, captions = load_dreambooth_dir(subset)
+            img_paths, captions, sizes = load_dreambooth_dir(subset)
             if len(img_paths) < 1:
                 logger.warning(
                     f"ignore subset with image_dir='{subset.image_dir}': no images found / 画像が見つからないためサブセットを無視します"
@@ -1552,8 +1598,10 @@ def load_dreambooth_dir(subset: DreamBoothSubset):
             else:
                 num_train_images += subset.num_repeats * len(img_paths)
 
-            for img_path, caption in zip(img_paths, captions):
+            for img_path, caption, size in zip(img_paths, captions, sizes):
                 info = ImageInfo(img_path, subset.num_repeats, caption, subset.is_reg, img_path)
+                if size is not None:
+                    info.image_size = size
                 if subset.is_reg:
                     reg_infos.append((info, subset))
                 else:
@@ -1842,7 +1890,8 @@ def __init__(
                 subset.image_dir,
                 False,
                 None,
-                subset.caption_extension,
+                subset.caption_extension, 
+                subset.cache_info,
                 subset.num_repeats,
                 subset.shuffle_caption,
                 subset.caption_separator,
@@ -3384,6 +3433,12 @@ def add_dataset_arguments(
     parser.add_argument(
         "--train_data_dir", type=str, default=None, help="directory for train images / 学習画像データのディレクトリ"
     )
+    parser.add_argument(
+        "--cache_info",
+        action="store_true",
+        help="cache meta information (caption and image size) for faster dataset loading. only available for DreamBooth"
+        + " / メタ情報（キャプションとサイズ）をキャッシュしてデータセット読み込みを高速化する。DreamBooth方式のみ有効",
+    )
     parser.add_argument(
         "--shuffle_caption", action="store_true", help="shuffle separated caption / 区切られたcaptionの各要素をshuffleする"
     )

requirements.txt

@@ -17,6 +17,8 @@ easygui==0.98.3
 toml==0.10.2
 voluptuous==0.13.1
 huggingface-hub==0.20.1
+# for Image utils
+imagesize==1.4.1
 # for BLIP captioning
 # requests==2.28.2
 # timm==0.6.12

train_network.py

@@ -14,19 +14,14 @@
 import torch
 from library.device_utils import init_ipex, clean_memory_on_device
 
-
 init_ipex()
 
-from torch.nn.parallel import DistributedDataParallel as DDP
-
 from accelerate.utils import set_seed
 from diffusers import DDPMScheduler
 from library import deepspeed_utils, model_util
 
 import library.train_util as train_util
-from library.train_util import (
-    DreamBoothDataset,
-)
+from library.train_util import DreamBoothDataset
 import library.config_util as config_util
 from library.config_util import (
     ConfigSanitizer,