[Experimental] Add cache mechanism for dataset groups to avoid long waiting time for initilization

cache_dataset_meta.py

@@ -0,0 +1,103 @@
+import argparse
+import random
+
+from accelerate.utils import set_seed
+
+import library.train_util as train_util
+import library.config_util as config_util
+from library.config_util import (
+    ConfigSanitizer,
+    BlueprintGenerator,
+)
+import library.custom_train_functions as custom_train_functions
+from library.utils import setup_logging, add_logging_arguments
+
+setup_logging()
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+def make_dataset(args):
+    train_util.prepare_dataset_args(args, True)
+    setup_logging(args, reset=True)
+
+    use_dreambooth_method = args.in_json is None
+    use_user_config = args.dataset_config is not None
+
+    if args.seed is None:
+        args.seed = random.randint(0, 2**32)
+    set_seed(args.seed)
+
+    # 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 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, tokenizer=None)
+        train_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, tokenizer=None)
+    return train_dataset_group
+
+
+def setup_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser()
+    add_logging_arguments(parser)
+    train_util.add_dataset_arguments(parser, True, True, True)
+    train_util.add_training_arguments(parser, True)
+    config_util.add_config_arguments(parser)
+    custom_train_functions.add_custom_train_arguments(parser)
+    return parser
+
+
+if __name__ == "__main__":
+    parser = setup_parser()
+
+    args, unknown = parser.parse_known_args()
+    args = train_util.read_config_from_file(args, parser)
+    if args.max_token_length is None:
+        args.max_token_length = 75
+    args.cache_meta = True
+
+    dataset_group = make_dataset(args)

library/config_util.py

@@ -111,6 +111,8 @@ class DreamBoothDatasetParams(BaseDatasetParams):
     bucket_reso_steps: int = 64
     bucket_no_upscale: bool = False
     prior_loss_weight: float = 1.0
+    cache_meta: bool = False
+    use_cached_meta: bool = False
 
 
 @dataclass
@@ -228,6 +230,8 @@ def __validate_and_convert_scalar_or_twodim(klass, value: Union[float, Sequence]
         "min_bucket_reso": int,
         "resolution": functools.partial(__validate_and_convert_scalar_or_twodim.__func__, int),
         "network_multiplier": float,
+        "cache_meta": bool,
+        "use_cached_meta": bool,
     }
 
     # options handled by argparse but not handled by user config

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
@@ -1080,8 +1081,7 @@ 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)
@@ -1425,6 +1425,8 @@ def __init__(
         bucket_no_upscale: bool,
         prior_loss_weight: float,
         debug_dataset: bool,
+        cache_meta: bool,
+        use_cached_meta: bool,
     ) -> None:
         super().__init__(tokenizer, max_token_length, resolution, network_multiplier, debug_dataset)
 
@@ -1484,26 +1486,43 @@ def load_dreambooth_dir(subset: DreamBoothSubset):
                 logger.warning(f"not directory: {subset.image_dir}")
                 return [], []
 
-            img_paths = glob_images(subset.image_dir, "*")
+            sizes = None
+            if use_cached_meta:
+                logger.info(f"using cached metadata: {subset.image_dir}/dataset.txt")
+                # [img_path, caption, resolution]
+                with open(f"{subset.image_dir}/dataset.txt", "r", encoding="utf-8") as f:
+                    metas = f.readlines()
+                metas = [x.strip().split("<|##|>") for x in metas]
+                sizes = [tuple(int(res) for res in x[2].split(" ")) for x in metas]
+
+            if use_cached_meta:
+                img_paths = [x[0] for x in metas]
+            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")
 
-            # 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:
-                    if cap_for_img is None:
-                        captions.append(subset.class_tokens)
+            if use_cached_meta:
+                captions = [x[1] for x in metas]
+                missing_captions = [x[0] for x in metas if x[1] == ""]
+            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
 
@@ -1520,7 +1539,21 @@ 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 cache_meta:
+                logger.info(f"cache metadata for {subset.image_dir}")
+                if sizes is None or sizes[0] is None:
+                    sizes = [self.get_image_size(img_path) for img_path in img_paths]
+                # [img_path, caption, resolution]
+                data = [
+                    (img_path, caption, " ".join(str(x) for x in size))
+                    for img_path, caption, size in zip(img_paths, captions, sizes)
+                ]
+                with open(f"{subset.image_dir}/dataset.txt", "w", encoding="utf-8") as f:
+                    f.write("\n".join(["<|##|>".join(x) for x in data]))
+                logger.info(f"cache metadata done for {subset.image_dir}")
+
+            return img_paths, captions, sizes
 
         logger.info("prepare images.")
         num_train_images = 0
@@ -1539,7 +1572,7 @@ 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"
@@ -1551,8 +1584,10 @@ 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:
@@ -3355,6 +3390,12 @@ def add_dataset_arguments(
     parser: argparse.ArgumentParser, support_dreambooth: bool, support_caption: bool, support_caption_dropout: bool
 ):
     # dataset common
+    parser.add_argument(
+        "--cache_meta", action="store_true"
+    )
+    parser.add_argument(
+        "--use_cached_meta", action="store_true"
+    )
     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"
     )

requirements.txt

@@ -15,6 +15,8 @@ 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

@@ -6,6 +6,7 @@
 import random
 import time
 import json
+import pickle
 from multiprocessing import Value
 import toml
 
@@ -23,7 +24,7 @@
 
 import library.train_util as train_util
 from library.train_util import (
-    DreamBoothDataset,
+    DreamBoothDataset, DatasetGroup
 )
 import library.config_util as config_util
 from library.config_util import (