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Update train_util.py
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@@ -1511,17 +1511,6 @@ class DreamBoothDataset(BaseDataset):
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logger.warning(f"not directory: {subset.image_dir}")
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logger.warning(f"not directory: {subset.image_dir}")
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return [], []
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return [], []
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img_paths = glob_images(subset.image_dir, "*")
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if self.validation_split > 0.0:
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img_paths = split_train_val(img_paths, self.is_train, self.validation_split, self.validation_seed)
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logger.info(f"found directory {subset.image_dir} contains {len(img_paths)} image files")
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# 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
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captions = []
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missing_captions = []
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for img_path in img_paths:
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cap_for_img = read_caption(img_path, subset.caption_extension)
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if cap_for_img is None and subset.class_tokens is None:
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info_cache_file = os.path.join(subset.image_dir, self.IMAGE_INFO_CACHE_FILE)
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info_cache_file = os.path.join(subset.image_dir, self.IMAGE_INFO_CACHE_FILE)
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use_cached_info_for_subset = subset.cache_info
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use_cached_info_for_subset = subset.cache_info
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if use_cached_info_for_subset:
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if use_cached_info_for_subset:
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@@ -1545,6 +1534,8 @@ class DreamBoothDataset(BaseDataset):
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# we may need to check image size and existence of image files, but it takes time, so user should check it before training
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# we may need to check image size and existence of image files, but it takes time, so user should check it before training
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else:
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else:
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img_paths = glob_images(subset.image_dir, "*")
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img_paths = glob_images(subset.image_dir, "*")
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if self.validation_split > 0.0:
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img_paths = split_train_val(img_paths, self.is_train, self.validation_split, self.validation_seed)
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sizes = [None] * len(img_paths)
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sizes = [None] * len(img_paths)
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logger.info(f"found directory {subset.image_dir} contains {len(img_paths)} image files")
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logger.info(f"found directory {subset.image_dir} contains {len(img_paths)} image files")
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