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] 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 (