From af3a55b21d8efa03307153c66d07842bca211391 Mon Sep 17 00:00:00 2001
From: woctordho <woctordho@outlook.com>
Date: Fri, 20 Feb 2026 12:16:43 +0800
Subject: [PATCH 1/5] Add min_orig_resolution and max_orig_resolution

---
 library/config_util.py |  10 +-
 library/train_util.py  | 209 ++++++++++++++++++++++++++++++++++++-----
 train_network.py       |   4 +
 3 files changed, 198 insertions(+), 25 deletions(-)

diff --git a/library/config_util.py b/library/config_util.py
index 53727f25..d41e6166 100644
--- a/library/config_util.py
+++ b/library/config_util.py
@@ -108,6 +108,8 @@ class BaseDatasetParams:
     validation_seed: Optional[int] = None
     validation_split: float = 0.0
     resize_interpolation: Optional[str] = None
+    min_orig_resolution: float = 0.0
+    max_orig_resolution: float = float("inf")
 
 @dataclass
 class DreamBoothDatasetParams(BaseDatasetParams):
@@ -118,7 +120,7 @@ class DreamBoothDatasetParams(BaseDatasetParams):
     bucket_reso_steps: int = 64
     bucket_no_upscale: bool = False
     prior_loss_weight: float = 1.0
-    
+
 @dataclass
 class FineTuningDatasetParams(BaseDatasetParams):
     batch_size: int = 1
@@ -244,6 +246,8 @@ class ConfigSanitizer:
         "resolution": functools.partial(__validate_and_convert_scalar_or_twodim.__func__, int),
         "network_multiplier": float,
         "resize_interpolation": str,
+        "min_orig_resolution": Any(float, int),
+        "max_orig_resolution": Any(float, int),
     }
 
     # options handled by argparse but not handled by user config
@@ -256,6 +260,8 @@ class ConfigSanitizer:
     ARGPARSE_NULLABLE_OPTNAMES = [
         "face_crop_aug_range",
         "resolution",
+        "min_orig_resolution",
+        "max_orig_resolution",
     ]
     # prepare map because option name may differ among argparse and user config
     ARGPARSE_OPTNAME_TO_CONFIG_OPTNAME = {
@@ -528,6 +534,8 @@ def generate_dataset_group_by_blueprint(dataset_group_blueprint: DatasetGroupBlu
                 [{dataset_type} {i}]
                   batch_size: {dataset.batch_size}
                   resolution: {(dataset.width, dataset.height)}
+                  min_orig_resolution: {dataset.min_orig_resolution}
+                  max_orig_resolution: {dataset.max_orig_resolution}
                   resize_interpolation: {dataset.resize_interpolation}
                   enable_bucket: {dataset.enable_bucket}
             """)
diff --git a/library/train_util.py b/library/train_util.py
index d8577b9d..e5645cea 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -687,6 +687,8 @@ class BaseDataset(torch.utils.data.Dataset):
         network_multiplier: float,
         debug_dataset: bool,
         resize_interpolation: Optional[str] = None,
+        min_orig_resolution: float = 0.0,
+        max_orig_resolution: float = float("inf"),
     ) -> None:
         super().__init__()
 
@@ -727,6 +729,12 @@ class BaseDataset(torch.utils.data.Dataset):
             ), f'Resize interpolation "{resize_interpolation}" is not a valid interpolation'
         self.resize_interpolation = resize_interpolation
 
+        assert (
+            min_orig_resolution <= max_orig_resolution
+        ), f"min_orig_resolution {min_orig_resolution} cannot be larger than max_orig_resolution {max_orig_resolution}"
+        self.min_orig_resolution = min_orig_resolution
+        self.max_orig_resolution = max_orig_resolution
+
         self.image_data: Dict[str, ImageInfo] = {}
         self.image_to_subset: Dict[str, Union[DreamBoothSubset, FineTuningSubset]] = {}
 
@@ -772,6 +780,50 @@ class BaseDataset(torch.utils.data.Dataset):
 
         return min_bucket_reso, max_bucket_reso
 
+    def check_orig_resolution(self, image_size: Tuple[int, int]) -> bool:
+        orig_resolution = math.sqrt(image_size[0] * image_size[1])
+        # min_orig_resolution is exclusive, max_orig_resolution is inclusive
+        return self.min_orig_resolution < orig_resolution <= self.max_orig_resolution
+
+    def has_orig_resolution_filter(self) -> bool:
+        return not (self.min_orig_resolution == 0.0 and self.max_orig_resolution == float("inf"))
+
+    def update_dataset_image_counts(self):
+        for subset in self.subsets:
+            subset.img_count = 0
+
+        num_train_images = 0
+        num_reg_images = 0
+        for image_key, image_info in self.image_data.items():
+            subset = self.image_to_subset[image_key]
+            subset.img_count += 1
+
+            if image_info.is_reg:
+                num_reg_images += image_info.num_repeats
+            else:
+                num_train_images += image_info.num_repeats
+
+        self.num_train_images = num_train_images
+        self.num_reg_images = num_reg_images
+
+    def filter_registered_images_by_orig_resolution(self) -> int:
+        if not self.has_orig_resolution_filter():
+            return 0
+
+        filtered_count = 0
+        for image_key, image_info in list(self.image_data.items()):
+            if self.check_orig_resolution(image_info.image_size):
+                continue
+
+            del self.image_data[image_key]
+            del self.image_to_subset[image_key]
+            filtered_count += 1
+
+        if filtered_count > 0:
+            self.update_dataset_image_counts()
+
+        return filtered_count
+
     def set_seed(self, seed):
         self.seed = seed
 
@@ -994,6 +1046,10 @@ class BaseDataset(torch.utils.data.Dataset):
             if info.image_size is None:
                 info.image_size = self.get_image_size(info.absolute_path)
 
+        filtered_count = self.filter_registered_images_by_orig_resolution()
+        if filtered_count > 0:
+            logger.info(f"filtered {filtered_count} images by original resolution")
+
         # # run in parallel
         # max_workers = min(os.cpu_count(), len(self.image_data))  # TODO consider multi-gpu (processes)
         # with ThreadPoolExecutor(max_workers) as executor:
@@ -1895,6 +1951,57 @@ class BaseDataset(torch.utils.data.Dataset):
 class DreamBoothDataset(BaseDataset):
     IMAGE_INFO_CACHE_FILE = "metadata_cache.json"
 
+    def register_regularization_images(
+        self, reg_infos: Sequence[Tuple[ImageInfo, DreamBoothSubset]], num_train_images: int
+    ) -> None:
+        if len(reg_infos) == 0 or num_train_images <= 0:
+            return
+
+        n = 0
+        first_loop = True
+        while n < num_train_images:
+            for info, subset in reg_infos:
+                if first_loop:
+                    self.register_image(info, subset)
+                    n += info.num_repeats
+                else:
+                    info.num_repeats += 1
+                    n += 1
+                if n >= num_train_images:
+                    break
+            first_loop = False
+
+    def rebalance_regularization_images(self):
+        if not self.is_training_dataset:
+            return
+
+        reg_infos = []
+        for image_key, image_info in list(self.image_data.items()):
+            if not image_info.is_reg:
+                continue
+
+            reg_infos.append((image_info, self.image_to_subset[image_key]))
+            del self.image_data[image_key]
+            del self.image_to_subset[image_key]
+
+        num_train_images = sum(info.num_repeats for info in self.image_data.values())
+        if len(reg_infos) == 0:
+            return
+
+        for info, subset in reg_infos:
+            info.num_repeats = subset.num_repeats
+
+        self.register_regularization_images(reg_infos, num_train_images)
+
+    def filter_registered_images_by_orig_resolution(self) -> int:
+        filtered_count = super().filter_registered_images_by_orig_resolution()
+
+        if filtered_count > 0 and self.is_training_dataset:
+            self.rebalance_regularization_images()
+            self.update_dataset_image_counts()
+
+        return filtered_count
+
     # The is_training_dataset defines the type of dataset, training or validation
     # if is_training_dataset is True -> training dataset
     # if is_training_dataset is False -> validation dataset
@@ -1915,8 +2022,17 @@ class DreamBoothDataset(BaseDataset):
         validation_split: float,
         validation_seed: Optional[int],
         resize_interpolation: Optional[str],
+        min_orig_resolution: Optional[float] = None,
+        max_orig_resolution: Optional[float] = None,
     ) -> None:
-        super().__init__(resolution, network_multiplier, debug_dataset, resize_interpolation)
+        super().__init__(
+            resolution,
+            network_multiplier,
+            debug_dataset,
+            resize_interpolation,
+            min_orig_resolution,
+            max_orig_resolution,
+        )
 
         assert resolution is not None, f"resolution is required / resolution（解像度）指定は必須です"
 
@@ -2059,7 +2175,7 @@ class DreamBoothDataset(BaseDataset):
             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()]
+                captions = [metas[img_path]["caption"] for img_path in img_paths]
                 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
@@ -2166,20 +2282,7 @@ class DreamBoothDataset(BaseDataset):
         if num_reg_images == 0:
             logger.warning("no regularization images / 正則化画像が見つかりませんでした")
         else:
-            # num_repeatsを計算する：どうせ大した数ではないのでループで処理する
-            n = 0
-            first_loop = True
-            while n < num_train_images:
-                for info, subset in reg_infos:
-                    if first_loop:
-                        self.register_image(info, subset)
-                        n += info.num_repeats
-                    else:
-                        info.num_repeats += 1  # rewrite registered info
-                        n += 1
-                    if n >= num_train_images:
-                        break
-                first_loop = False
+            self.register_regularization_images(reg_infos, num_train_images)
 
         self.num_reg_images = num_reg_images
 
@@ -2200,8 +2303,17 @@ class FineTuningDataset(BaseDataset):
         validation_seed: int,
         validation_split: float,
         resize_interpolation: Optional[str],
+        min_orig_resolution: Optional[float] = None,
+        max_orig_resolution: Optional[float] = None,
     ) -> None:
-        super().__init__(resolution, network_multiplier, debug_dataset, resize_interpolation)
+        super().__init__(
+            resolution,
+            network_multiplier,
+            debug_dataset,
+            resize_interpolation,
+            min_orig_resolution,
+            max_orig_resolution,
+        )
 
         self.batch_size = batch_size
         self.size = min(self.width, self.height)  # 短いほう
@@ -2387,8 +2499,17 @@ class ControlNetDataset(BaseDataset):
         validation_split: float,
         validation_seed: Optional[int],
         resize_interpolation: Optional[str] = None,
+        min_orig_resolution: float = 0.0,
+        max_orig_resolution: float = float("inf"),
     ) -> None:
-        super().__init__(resolution, network_multiplier, debug_dataset, resize_interpolation)
+        super().__init__(
+            resolution,
+            network_multiplier,
+            debug_dataset,
+            resize_interpolation,
+            min_orig_resolution,
+            max_orig_resolution,
+        )
 
         db_subsets = []
         for subset in subsets:
@@ -2440,6 +2561,8 @@ class ControlNetDataset(BaseDataset):
             validation_split,
             validation_seed,
             resize_interpolation,
+            min_orig_resolution,
+            max_orig_resolution,
         )
 
         # config_util等から参照される値をいれておく（若干微妙なのでなんとかしたい）
@@ -2484,12 +2607,25 @@ class ControlNetDataset(BaseDataset):
             conditioning_img_paths = [os.path.abspath(p) for p in conditioning_img_paths]  # normalize path
             extra_imgs.extend([p for p in conditioning_img_paths if os.path.splitext(p)[0] not in cond_imgs_with_pair])
 
-        assert (
-            len(missing_imgs) == 0
-        ), f"missing conditioning data for {len(missing_imgs)} images / 制御用画像が見つかりませんでした: {missing_imgs}"
-        assert (
-            len(extra_imgs) == 0
-        ), f"extra conditioning data for {len(extra_imgs)} images / 余分な制御用画像があります: {extra_imgs}"
+        if self.has_orig_resolution_filter():
+            if len(missing_imgs) > 0:
+                logger.warning(
+                    f"ignore {len(missing_imgs)} missing conditioning images because original-resolution filtering is enabled"
+                    + f" / 元画像解像度フィルタが有効なため、{len(missing_imgs)}枚の不足した制御用画像を無視します"
+                )
+            if len(extra_imgs) > 0:
+                logger.warning(
+                    f"ignore {len(extra_imgs)} extra conditioning images because original-resolution filtering is enabled"
+                    + f" / 元画像解像度フィルタが有効なため、{len(extra_imgs)}枚の余分な制御用画像を無視します"
+                )
+            # Later in `make_buckets` we assert `len(missing_imgs) == 0` but still ignore `extra_imgs`
+        else:
+            assert (
+                len(missing_imgs) == 0
+            ), f"missing conditioning data for {len(missing_imgs)} images / 制御用画像が見つかりませんでした: {missing_imgs}"
+            assert (
+                len(extra_imgs) == 0
+            ), f"extra conditioning data for {len(extra_imgs)} images / 余分な制御用画像があります: {extra_imgs}"
 
         self.conditioning_image_transforms = IMAGE_TRANSFORMS
 
@@ -2498,8 +2634,19 @@ class ControlNetDataset(BaseDataset):
 
     def make_buckets(self):
         self.dreambooth_dataset_delegate.make_buckets()
+
+        missing_imgs = []
+        for info in self.dreambooth_dataset_delegate.image_data.values():
+            if info.cond_img_path is None:
+                missing_imgs.append(os.path.splitext(os.path.basename(info.absolute_path))[0])
+        assert (
+            len(missing_imgs) == 0
+        ), f"missing conditioning data for {len(missing_imgs)} images / 制御用画像が見つかりませんでした: {missing_imgs}"
+
         self.bucket_manager = self.dreambooth_dataset_delegate.bucket_manager
         self.buckets_indices = self.dreambooth_dataset_delegate.buckets_indices
+        self.num_train_images = self.dreambooth_dataset_delegate.num_train_images
+        self.num_reg_images = self.dreambooth_dataset_delegate.num_reg_images
 
     def cache_latents(self, vae, vae_batch_size=1, cache_to_disk=False, is_main_process=True):
         return self.dreambooth_dataset_delegate.cache_latents(vae, vae_batch_size, cache_to_disk, is_main_process)
@@ -4601,6 +4748,20 @@ def add_dataset_arguments(
         help="maximum resolution for buckets, must be divisible by bucket_reso_steps "
         " / bucketの最大解像度、bucket_reso_stepsで割り切れる必要があります",
     )
+    parser.add_argument(
+        "--min_orig_resolution",
+        type=float,
+        default=0.0,
+        help="minimum original resolution for images (exclusive), defined by sqrt(width * height) before scaling"
+        " / 画像の元解像度の下限（排他的）。リサイズ前のsqrt(width * height)で判定します",
+    )
+    parser.add_argument(
+        "--max_orig_resolution",
+        type=float,
+        default=float("inf"),
+        help="maximum original resolution for images (inclusive), defined by sqrt(width * height) before scaling"
+        " / 画像の元解像度の上限（包含的）。リサイズ前のsqrt(width * height)で判定します",
+    )
     parser.add_argument(
         "--bucket_reso_steps",
         type=int,
diff --git a/train_network.py b/train_network.py
index 2f8797d2..af48200d 100644
--- a/train_network.py
+++ b/train_network.py
@@ -1085,6 +1085,8 @@ class NetworkTrainer:
                     "enable_bucket": bool(dataset.enable_bucket),
                     "min_bucket_reso": dataset.min_bucket_reso,
                     "max_bucket_reso": dataset.max_bucket_reso,
+                    "min_orig_resolution": dataset.min_orig_resolution,
+                    "max_orig_resolution": dataset.max_orig_resolution,
                     "tag_frequency": dataset.tag_frequency,
                     "bucket_info": dataset.bucket_info,
                     "resize_interpolation": dataset.resize_interpolation,
@@ -1191,6 +1193,8 @@ class NetworkTrainer:
                     "ss_bucket_no_upscale": bool(dataset.bucket_no_upscale),
                     "ss_min_bucket_reso": dataset.min_bucket_reso,
                     "ss_max_bucket_reso": dataset.max_bucket_reso,
+                    "ss_min_orig_resolution": dataset.min_orig_resolution,
+                    "ss_max_orig_resolution": dataset.max_orig_resolution,
                     "ss_keep_tokens": args.keep_tokens,
                     "ss_dataset_dirs": json.dumps(dataset_dirs_info),
                     "ss_reg_dataset_dirs": json.dumps(reg_dataset_dirs_info),

From 47afa8b2bd4e49465eb4fedcf7e8dd26a0d63401 Mon Sep 17 00:00:00 2001
From: woctordho <woctordho@outlook.com>
Date: Fri, 20 Feb 2026 22:59:40 +0800
Subject: [PATCH 2/5] Rename min_orig_resolution to skip_image_resolution;
 remove max_orig_resolution

---
 library/config_util.py | 12 ++++------
 library/train_util.py  | 54 +++++++++++++-----------------------------
 train_network.py       |  6 ++---
 3 files changed, 22 insertions(+), 50 deletions(-)

diff --git a/library/config_util.py b/library/config_util.py
index d41e6166..7197662e 100644
--- a/library/config_util.py
+++ b/library/config_util.py
@@ -108,8 +108,7 @@ class BaseDatasetParams:
     validation_seed: Optional[int] = None
     validation_split: float = 0.0
     resize_interpolation: Optional[str] = None
-    min_orig_resolution: float = 0.0
-    max_orig_resolution: float = float("inf")
+    skip_image_resolution: float = 0.0
 
 @dataclass
 class DreamBoothDatasetParams(BaseDatasetParams):
@@ -246,8 +245,7 @@ class ConfigSanitizer:
         "resolution": functools.partial(__validate_and_convert_scalar_or_twodim.__func__, int),
         "network_multiplier": float,
         "resize_interpolation": str,
-        "min_orig_resolution": Any(float, int),
-        "max_orig_resolution": Any(float, int),
+        "skip_image_resolution": Any(float, int),
     }
 
     # options handled by argparse but not handled by user config
@@ -260,8 +258,7 @@ class ConfigSanitizer:
     ARGPARSE_NULLABLE_OPTNAMES = [
         "face_crop_aug_range",
         "resolution",
-        "min_orig_resolution",
-        "max_orig_resolution",
+        "skip_image_resolution",
     ]
     # prepare map because option name may differ among argparse and user config
     ARGPARSE_OPTNAME_TO_CONFIG_OPTNAME = {
@@ -534,8 +531,7 @@ def generate_dataset_group_by_blueprint(dataset_group_blueprint: DatasetGroupBlu
                 [{dataset_type} {i}]
                   batch_size: {dataset.batch_size}
                   resolution: {(dataset.width, dataset.height)}
-                  min_orig_resolution: {dataset.min_orig_resolution}
-                  max_orig_resolution: {dataset.max_orig_resolution}
+                  skip_image_resolution: {dataset.skip_image_resolution}
                   resize_interpolation: {dataset.resize_interpolation}
                   enable_bucket: {dataset.enable_bucket}
             """)
diff --git a/library/train_util.py b/library/train_util.py
index e5645cea..5db837d4 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -687,8 +687,7 @@ class BaseDataset(torch.utils.data.Dataset):
         network_multiplier: float,
         debug_dataset: bool,
         resize_interpolation: Optional[str] = None,
-        min_orig_resolution: float = 0.0,
-        max_orig_resolution: float = float("inf"),
+        skip_image_resolution: float = 0.0,
     ) -> None:
         super().__init__()
 
@@ -729,11 +728,7 @@ class BaseDataset(torch.utils.data.Dataset):
             ), f'Resize interpolation "{resize_interpolation}" is not a valid interpolation'
         self.resize_interpolation = resize_interpolation
 
-        assert (
-            min_orig_resolution <= max_orig_resolution
-        ), f"min_orig_resolution {min_orig_resolution} cannot be larger than max_orig_resolution {max_orig_resolution}"
-        self.min_orig_resolution = min_orig_resolution
-        self.max_orig_resolution = max_orig_resolution
+        self.skip_image_resolution = skip_image_resolution
 
         self.image_data: Dict[str, ImageInfo] = {}
         self.image_to_subset: Dict[str, Union[DreamBoothSubset, FineTuningSubset]] = {}
@@ -782,11 +777,8 @@ class BaseDataset(torch.utils.data.Dataset):
 
     def check_orig_resolution(self, image_size: Tuple[int, int]) -> bool:
         orig_resolution = math.sqrt(image_size[0] * image_size[1])
-        # min_orig_resolution is exclusive, max_orig_resolution is inclusive
-        return self.min_orig_resolution < orig_resolution <= self.max_orig_resolution
-
-    def has_orig_resolution_filter(self) -> bool:
-        return not (self.min_orig_resolution == 0.0 and self.max_orig_resolution == float("inf"))
+        # skip_image_resolution is exclusive
+        return self.skip_image_resolution < orig_resolution
 
     def update_dataset_image_counts(self):
         for subset in self.subsets:
@@ -807,7 +799,7 @@ class BaseDataset(torch.utils.data.Dataset):
         self.num_reg_images = num_reg_images
 
     def filter_registered_images_by_orig_resolution(self) -> int:
-        if not self.has_orig_resolution_filter():
+        if self.skip_image_resolution == 0:
             return 0
 
         filtered_count = 0
@@ -2022,16 +2014,14 @@ class DreamBoothDataset(BaseDataset):
         validation_split: float,
         validation_seed: Optional[int],
         resize_interpolation: Optional[str],
-        min_orig_resolution: Optional[float] = None,
-        max_orig_resolution: Optional[float] = None,
+        skip_image_resolution: Optional[float] = None,
     ) -> None:
         super().__init__(
             resolution,
             network_multiplier,
             debug_dataset,
             resize_interpolation,
-            min_orig_resolution,
-            max_orig_resolution,
+            skip_image_resolution,
         )
 
         assert resolution is not None, f"resolution is required / resolution（解像度）指定は必須です"
@@ -2303,16 +2293,14 @@ class FineTuningDataset(BaseDataset):
         validation_seed: int,
         validation_split: float,
         resize_interpolation: Optional[str],
-        min_orig_resolution: Optional[float] = None,
-        max_orig_resolution: Optional[float] = None,
+        skip_image_resolution: Optional[float] = None,
     ) -> None:
         super().__init__(
             resolution,
             network_multiplier,
             debug_dataset,
             resize_interpolation,
-            min_orig_resolution,
-            max_orig_resolution,
+            skip_image_resolution,
         )
 
         self.batch_size = batch_size
@@ -2499,16 +2487,14 @@ class ControlNetDataset(BaseDataset):
         validation_split: float,
         validation_seed: Optional[int],
         resize_interpolation: Optional[str] = None,
-        min_orig_resolution: float = 0.0,
-        max_orig_resolution: float = float("inf"),
+        skip_image_resolution: float = 0.0,
     ) -> None:
         super().__init__(
             resolution,
             network_multiplier,
             debug_dataset,
             resize_interpolation,
-            min_orig_resolution,
-            max_orig_resolution,
+            skip_image_resolution,
         )
 
         db_subsets = []
@@ -2561,8 +2547,7 @@ class ControlNetDataset(BaseDataset):
             validation_split,
             validation_seed,
             resize_interpolation,
-            min_orig_resolution,
-            max_orig_resolution,
+            skip_image_resolution,
         )
 
         # config_util等から参照される値をいれておく（若干微妙なのでなんとかしたい）
@@ -2607,7 +2592,7 @@ class ControlNetDataset(BaseDataset):
             conditioning_img_paths = [os.path.abspath(p) for p in conditioning_img_paths]  # normalize path
             extra_imgs.extend([p for p in conditioning_img_paths if os.path.splitext(p)[0] not in cond_imgs_with_pair])
 
-        if self.has_orig_resolution_filter():
+        if self.skip_image_resolution != 0:
             if len(missing_imgs) > 0:
                 logger.warning(
                     f"ignore {len(missing_imgs)} missing conditioning images because original-resolution filtering is enabled"
@@ -4749,18 +4734,11 @@ def add_dataset_arguments(
         " / bucketの最大解像度、bucket_reso_stepsで割り切れる必要があります",
     )
     parser.add_argument(
-        "--min_orig_resolution",
+        "--skip_image_resolution",
         type=float,
         default=0.0,
-        help="minimum original resolution for images (exclusive), defined by sqrt(width * height) before scaling"
-        " / 画像の元解像度の下限（排他的）。リサイズ前のsqrt(width * height)で判定します",
-    )
-    parser.add_argument(
-        "--max_orig_resolution",
-        type=float,
-        default=float("inf"),
-        help="maximum original resolution for images (inclusive), defined by sqrt(width * height) before scaling"
-        " / 画像の元解像度の上限（包含的）。リサイズ前のsqrt(width * height)で判定します",
+        help="images not larger than this resolution will be skipped, defined by sqrt(width * height) before scaling"
+        " / この解像度以下の画像はスキップされます。リサイズ前のsqrt(width * height)で判定します",
     )
     parser.add_argument(
         "--bucket_reso_steps",
diff --git a/train_network.py b/train_network.py
index af48200d..2ee671e9 100644
--- a/train_network.py
+++ b/train_network.py
@@ -1085,8 +1085,7 @@ class NetworkTrainer:
                     "enable_bucket": bool(dataset.enable_bucket),
                     "min_bucket_reso": dataset.min_bucket_reso,
                     "max_bucket_reso": dataset.max_bucket_reso,
-                    "min_orig_resolution": dataset.min_orig_resolution,
-                    "max_orig_resolution": dataset.max_orig_resolution,
+                    "skip_image_resolution": dataset.skip_image_resolution,
                     "tag_frequency": dataset.tag_frequency,
                     "bucket_info": dataset.bucket_info,
                     "resize_interpolation": dataset.resize_interpolation,
@@ -1193,8 +1192,7 @@ class NetworkTrainer:
                     "ss_bucket_no_upscale": bool(dataset.bucket_no_upscale),
                     "ss_min_bucket_reso": dataset.min_bucket_reso,
                     "ss_max_bucket_reso": dataset.max_bucket_reso,
-                    "ss_min_orig_resolution": dataset.min_orig_resolution,
-                    "ss_max_orig_resolution": dataset.max_orig_resolution,
+                    "ss_skip_image_resolution": dataset.skip_image_resolution,
                     "ss_keep_tokens": args.keep_tokens,
                     "ss_dataset_dirs": json.dumps(dataset_dirs_info),
                     "ss_reg_dataset_dirs": json.dumps(reg_dataset_dirs_info),

From 3cdd62bbbf5026e942fbdc0a2f1b7357cbf381fd Mon Sep 17 00:00:00 2001
From: woctordho <woctordho@outlook.com>
Date: Mon, 23 Feb 2026 17:05:49 +0800
Subject: [PATCH 3/5] Change skip_image_resolution to tuple

---
 library/config_util.py |  4 ++--
 library/train_util.py  | 31 +++++++++++++++++++------------
 2 files changed, 21 insertions(+), 14 deletions(-)

diff --git a/library/config_util.py b/library/config_util.py
index 7197662e..b31f9665 100644
--- a/library/config_util.py
+++ b/library/config_util.py
@@ -108,7 +108,7 @@ class BaseDatasetParams:
     validation_seed: Optional[int] = None
     validation_split: float = 0.0
     resize_interpolation: Optional[str] = None
-    skip_image_resolution: float = 0.0
+    skip_image_resolution: Optional[Tuple[int, int]] = None
 
 @dataclass
 class DreamBoothDatasetParams(BaseDatasetParams):
@@ -245,7 +245,7 @@ class ConfigSanitizer:
         "resolution": functools.partial(__validate_and_convert_scalar_or_twodim.__func__, int),
         "network_multiplier": float,
         "resize_interpolation": str,
-        "skip_image_resolution": Any(float, int),
+        "skip_image_resolution": functools.partial(__validate_and_convert_scalar_or_twodim.__func__, int),
     }
 
     # options handled by argparse but not handled by user config
diff --git a/library/train_util.py b/library/train_util.py
index 5db837d4..5b9d0615 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -687,7 +687,7 @@ class BaseDataset(torch.utils.data.Dataset):
         network_multiplier: float,
         debug_dataset: bool,
         resize_interpolation: Optional[str] = None,
-        skip_image_resolution: float = 0.0,
+        skip_image_resolution: Optional[Tuple[int, int]] = None,
     ) -> None:
         super().__init__()
 
@@ -776,9 +776,8 @@ class BaseDataset(torch.utils.data.Dataset):
         return min_bucket_reso, max_bucket_reso
 
     def check_orig_resolution(self, image_size: Tuple[int, int]) -> bool:
-        orig_resolution = math.sqrt(image_size[0] * image_size[1])
         # skip_image_resolution is exclusive
-        return self.skip_image_resolution < orig_resolution
+        return self.skip_image_resolution[0] * self.skip_image_resolution[1] < image_size[0] * image_size[1]
 
     def update_dataset_image_counts(self):
         for subset in self.subsets:
@@ -799,7 +798,7 @@ class BaseDataset(torch.utils.data.Dataset):
         self.num_reg_images = num_reg_images
 
     def filter_registered_images_by_orig_resolution(self) -> int:
-        if self.skip_image_resolution == 0:
+        if self.skip_image_resolution is None:
             return 0
 
         filtered_count = 0
@@ -2014,7 +2013,7 @@ class DreamBoothDataset(BaseDataset):
         validation_split: float,
         validation_seed: Optional[int],
         resize_interpolation: Optional[str],
-        skip_image_resolution: Optional[float] = None,
+        skip_image_resolution: Optional[Tuple[int, int]] = None,
     ) -> None:
         super().__init__(
             resolution,
@@ -2293,7 +2292,7 @@ class FineTuningDataset(BaseDataset):
         validation_seed: int,
         validation_split: float,
         resize_interpolation: Optional[str],
-        skip_image_resolution: Optional[float] = None,
+        skip_image_resolution: Optional[Tuple[int, int]] = None,
     ) -> None:
         super().__init__(
             resolution,
@@ -2487,7 +2486,7 @@ class ControlNetDataset(BaseDataset):
         validation_split: float,
         validation_seed: Optional[int],
         resize_interpolation: Optional[str] = None,
-        skip_image_resolution: float = 0.0,
+        skip_image_resolution: Optional[Tuple[int, int]] = None,
     ) -> None:
         super().__init__(
             resolution,
@@ -2592,7 +2591,7 @@ class ControlNetDataset(BaseDataset):
             conditioning_img_paths = [os.path.abspath(p) for p in conditioning_img_paths]  # normalize path
             extra_imgs.extend([p for p in conditioning_img_paths if os.path.splitext(p)[0] not in cond_imgs_with_pair])
 
-        if self.skip_image_resolution != 0:
+        if self.skip_image_resolution is not None:
             if len(missing_imgs) > 0:
                 logger.warning(
                     f"ignore {len(missing_imgs)} missing conditioning images because original-resolution filtering is enabled"
@@ -4735,10 +4734,10 @@ def add_dataset_arguments(
     )
     parser.add_argument(
         "--skip_image_resolution",
-        type=float,
-        default=0.0,
-        help="images not larger than this resolution will be skipped, defined by sqrt(width * height) before scaling"
-        " / この解像度以下の画像はスキップされます。リサイズ前のsqrt(width * height)で判定します",
+        type=str,
+        default=None,
+        help="images not larger than this resolution will be skipped ('size' or 'width,height')"
+        " / この解像度以下の画像はスキップされます（'サイズ'指定、または'幅,高さ'指定）",
     )
     parser.add_argument(
         "--bucket_reso_steps",
@@ -5553,6 +5552,14 @@ def prepare_dataset_args(args: argparse.Namespace, support_metadata: bool):
             len(args.resolution) == 2
         ), f"resolution must be 'size' or 'width,height' / resolution（解像度）は'サイズ'または'幅','高さ'で指定してください: {args.resolution}"
 
+    if args.skip_image_resolution is not None:
+        args.skip_image_resolution = tuple([int(r) for r in args.skip_image_resolution.split(",")])
+        if len(args.skip_image_resolution) == 1:
+            args.skip_image_resolution = (args.skip_image_resolution[0], args.skip_image_resolution[0])
+        assert (
+            len(args.skip_image_resolution) == 2
+        ), f"skip_image_resolution must be 'size' or 'width,height' / skip_image_resolutionは'サイズ'または'幅','高さ'で指定してください: {args.skip_image_resolution}"
+
     if args.face_crop_aug_range is not None:
         args.face_crop_aug_range = tuple([float(r) for r in args.face_crop_aug_range.split(",")])
         assert (

From 5af418025de26ce028beae6e9d191ce591d77fb8 Mon Sep 17 00:00:00 2001
From: woctordho <woctordho@outlook.com>
Date: Mon, 23 Feb 2026 17:28:04 +0800
Subject: [PATCH 4/5] Move filtering to __init__

---
 library/train_util.py | 175 ++++++++++++------------------------------
 1 file changed, 50 insertions(+), 125 deletions(-)

diff --git a/library/train_util.py b/library/train_util.py
index 5b9d0615..c96e0b85 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -775,46 +775,6 @@ class BaseDataset(torch.utils.data.Dataset):
 
         return min_bucket_reso, max_bucket_reso
 
-    def check_orig_resolution(self, image_size: Tuple[int, int]) -> bool:
-        # skip_image_resolution is exclusive
-        return self.skip_image_resolution[0] * self.skip_image_resolution[1] < image_size[0] * image_size[1]
-
-    def update_dataset_image_counts(self):
-        for subset in self.subsets:
-            subset.img_count = 0
-
-        num_train_images = 0
-        num_reg_images = 0
-        for image_key, image_info in self.image_data.items():
-            subset = self.image_to_subset[image_key]
-            subset.img_count += 1
-
-            if image_info.is_reg:
-                num_reg_images += image_info.num_repeats
-            else:
-                num_train_images += image_info.num_repeats
-
-        self.num_train_images = num_train_images
-        self.num_reg_images = num_reg_images
-
-    def filter_registered_images_by_orig_resolution(self) -> int:
-        if self.skip_image_resolution is None:
-            return 0
-
-        filtered_count = 0
-        for image_key, image_info in list(self.image_data.items()):
-            if self.check_orig_resolution(image_info.image_size):
-                continue
-
-            del self.image_data[image_key]
-            del self.image_to_subset[image_key]
-            filtered_count += 1
-
-        if filtered_count > 0:
-            self.update_dataset_image_counts()
-
-        return filtered_count
-
     def set_seed(self, seed):
         self.seed = seed
 
@@ -1037,10 +997,6 @@ class BaseDataset(torch.utils.data.Dataset):
             if info.image_size is None:
                 info.image_size = self.get_image_size(info.absolute_path)
 
-        filtered_count = self.filter_registered_images_by_orig_resolution()
-        if filtered_count > 0:
-            logger.info(f"filtered {filtered_count} images by original resolution")
-
         # # run in parallel
         # max_workers = min(os.cpu_count(), len(self.image_data))  # TODO consider multi-gpu (processes)
         # with ThreadPoolExecutor(max_workers) as executor:
@@ -1942,57 +1898,6 @@ class BaseDataset(torch.utils.data.Dataset):
 class DreamBoothDataset(BaseDataset):
     IMAGE_INFO_CACHE_FILE = "metadata_cache.json"
 
-    def register_regularization_images(
-        self, reg_infos: Sequence[Tuple[ImageInfo, DreamBoothSubset]], num_train_images: int
-    ) -> None:
-        if len(reg_infos) == 0 or num_train_images <= 0:
-            return
-
-        n = 0
-        first_loop = True
-        while n < num_train_images:
-            for info, subset in reg_infos:
-                if first_loop:
-                    self.register_image(info, subset)
-                    n += info.num_repeats
-                else:
-                    info.num_repeats += 1
-                    n += 1
-                if n >= num_train_images:
-                    break
-            first_loop = False
-
-    def rebalance_regularization_images(self):
-        if not self.is_training_dataset:
-            return
-
-        reg_infos = []
-        for image_key, image_info in list(self.image_data.items()):
-            if not image_info.is_reg:
-                continue
-
-            reg_infos.append((image_info, self.image_to_subset[image_key]))
-            del self.image_data[image_key]
-            del self.image_to_subset[image_key]
-
-        num_train_images = sum(info.num_repeats for info in self.image_data.values())
-        if len(reg_infos) == 0:
-            return
-
-        for info, subset in reg_infos:
-            info.num_repeats = subset.num_repeats
-
-        self.register_regularization_images(reg_infos, num_train_images)
-
-    def filter_registered_images_by_orig_resolution(self) -> int:
-        filtered_count = super().filter_registered_images_by_orig_resolution()
-
-        if filtered_count > 0 and self.is_training_dataset:
-            self.rebalance_regularization_images()
-            self.update_dataset_image_counts()
-
-        return filtered_count
-
     # The is_training_dataset defines the type of dataset, training or validation
     # if is_training_dataset is True -> training dataset
     # if is_training_dataset is False -> validation dataset
@@ -2139,6 +2044,22 @@ class DreamBoothDataset(BaseDataset):
                             size_set_count += 1
                     logger.info(f"set image size from cache files: {size_set_count}/{len(img_paths)}")
 
+            if self.skip_image_resolution is not None:
+                filtered_img_paths = []
+                filtered_sizes = []
+                skip_image_area = self.skip_image_resolution[0] * self.skip_image_resolution[1]
+                for img_path, size in zip(img_paths, sizes):
+                    if size is None:  # no latents cache file, get image size by reading image file (slow)
+                        size = self.get_image_size(img_path)
+                    if size[0] * size[1] <= skip_image_area:
+                        continue
+                    filtered_img_paths.append(img_path)
+                    filtered_sizes.append(size)
+                if len(filtered_img_paths) < len(img_paths):
+                    logger.info(f"filtered {len(img_paths) - len(filtered_img_paths)} images by original resolution from {subset.image_dir}")
+                img_paths = filtered_img_paths
+                sizes = filtered_sizes
+
             # We want to create a training and validation split. This should be improved in the future
             # to allow a clearer distinction between training and validation. This can be seen as a
             # short-term solution to limit what is necessary to implement validation datasets
@@ -2271,7 +2192,20 @@ class DreamBoothDataset(BaseDataset):
         if num_reg_images == 0:
             logger.warning("no regularization images / 正則化画像が見つかりませんでした")
         else:
-            self.register_regularization_images(reg_infos, num_train_images)
+            # num_repeatsを計算する：どうせ大した数ではないのでループで処理する
+            n = 0
+            first_loop = True
+            while n < num_train_images:
+                for info, subset in reg_infos:
+                    if first_loop:
+                        self.register_image(info, subset)
+                        n += info.num_repeats
+                    else:
+                        info.num_repeats += 1  # rewrite registered info
+                        n += 1
+                    if n >= num_train_images:
+                        break
+                first_loop = False
 
         self.num_reg_images = num_reg_images
 
@@ -2396,6 +2330,7 @@ class FineTuningDataset(BaseDataset):
             tags_list = []
             size_set_from_metadata = 0
             size_set_from_cache_filename = 0
+            num_filtered = 0
             for image_key in image_keys_sorted_by_length_desc:
                 img_md = metadata[image_key]
                 caption = img_md.get("caption")
@@ -2454,6 +2389,16 @@ class FineTuningDataset(BaseDataset):
                     image_info.image_size = (w, h)
                     size_set_from_cache_filename += 1
 
+                if self.skip_image_resolution is not None:
+                    size = image_info.image_size
+                    if size is None:  # no image size in metadata or latents cache file, get image size by reading image file (slow)
+                        size = self.get_image_size(abs_path)
+                        image_info.image_size = size
+                    skip_image_area = self.skip_image_resolution[0] * self.skip_image_resolution[1]
+                    if size[0] * size[1] <= skip_image_area:
+                        num_filtered += 1
+                        continue
+
                 self.register_image(image_info, subset)
 
             if size_set_from_cache_filename > 0:
@@ -2462,6 +2407,8 @@ class FineTuningDataset(BaseDataset):
                 )
             if size_set_from_metadata > 0:
                 logger.info(f"set image size from metadata: {size_set_from_metadata}/{len(image_keys_sorted_by_length_desc)}")
+            if num_filtered > 0:
+                logger.info(f"filtered {num_filtered} images by original resolution from {subset.metadata_file}")
             self.num_train_images += len(metadata) * subset.num_repeats
 
             # TODO do not record tag freq when no tag
@@ -2591,25 +2538,13 @@ class ControlNetDataset(BaseDataset):
             conditioning_img_paths = [os.path.abspath(p) for p in conditioning_img_paths]  # normalize path
             extra_imgs.extend([p for p in conditioning_img_paths if os.path.splitext(p)[0] not in cond_imgs_with_pair])
 
-        if self.skip_image_resolution is not None:
-            if len(missing_imgs) > 0:
-                logger.warning(
-                    f"ignore {len(missing_imgs)} missing conditioning images because original-resolution filtering is enabled"
-                    + f" / 元画像解像度フィルタが有効なため、{len(missing_imgs)}枚の不足した制御用画像を無視します"
-                )
-            if len(extra_imgs) > 0:
-                logger.warning(
-                    f"ignore {len(extra_imgs)} extra conditioning images because original-resolution filtering is enabled"
-                    + f" / 元画像解像度フィルタが有効なため、{len(extra_imgs)}枚の余分な制御用画像を無視します"
-                )
-            # Later in `make_buckets` we assert `len(missing_imgs) == 0` but still ignore `extra_imgs`
-        else:
-            assert (
-                len(missing_imgs) == 0
-            ), f"missing conditioning data for {len(missing_imgs)} images / 制御用画像が見つかりませんでした: {missing_imgs}"
-            assert (
-                len(extra_imgs) == 0
-            ), f"extra conditioning data for {len(extra_imgs)} images / 余分な制御用画像があります: {extra_imgs}"
+        assert (
+            len(missing_imgs) == 0
+        ), f"missing conditioning data for {len(missing_imgs)} images / 制御用画像が見つかりませんでした: {missing_imgs}"
+        if len(extra_imgs) > 0:
+            logger.warning(
+                f"extra conditioning data for {len(extra_imgs)} images / 余分な制御用画像があります: {extra_imgs}"
+            )
 
         self.conditioning_image_transforms = IMAGE_TRANSFORMS
 
@@ -2619,18 +2554,8 @@ class ControlNetDataset(BaseDataset):
     def make_buckets(self):
         self.dreambooth_dataset_delegate.make_buckets()
 
-        missing_imgs = []
-        for info in self.dreambooth_dataset_delegate.image_data.values():
-            if info.cond_img_path is None:
-                missing_imgs.append(os.path.splitext(os.path.basename(info.absolute_path))[0])
-        assert (
-            len(missing_imgs) == 0
-        ), f"missing conditioning data for {len(missing_imgs)} images / 制御用画像が見つかりませんでした: {missing_imgs}"
-
         self.bucket_manager = self.dreambooth_dataset_delegate.bucket_manager
         self.buckets_indices = self.dreambooth_dataset_delegate.buckets_indices
-        self.num_train_images = self.dreambooth_dataset_delegate.num_train_images
-        self.num_reg_images = self.dreambooth_dataset_delegate.num_reg_images
 
     def cache_latents(self, vae, vae_batch_size=1, cache_to_disk=False, is_main_process=True):
         return self.dreambooth_dataset_delegate.cache_latents(vae, vae_batch_size, cache_to_disk, is_main_process)

From 1e4f55cc53b551fc1d2946a60401fcac378cbeb1 Mon Sep 17 00:00:00 2001
From: woctordho <woctordho@outlook.com>
Date: Mon, 23 Feb 2026 17:36:46 +0800
Subject: [PATCH 5/5] Minor fix

---
 library/train_util.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/library/train_util.py b/library/train_util.py
index c96e0b85..b65f06b9 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -2553,7 +2553,6 @@ class ControlNetDataset(BaseDataset):
 
     def make_buckets(self):
         self.dreambooth_dataset_delegate.make_buckets()
-
         self.bucket_manager = self.dreambooth_dataset_delegate.bucket_manager
         self.buckets_indices = self.dreambooth_dataset_delegate.buckets_indices