Rename min_orig_resolution to skip_image_resolution; remove max_orig_resolution

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}
             """)

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",

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),