Merge pull request #1936 from rockerBOO/resize-interpolation

Add resize interpolation parameter

finetune/tag_images_by_wd14_tagger.py

@@ -11,7 +11,7 @@ from PIL import Image
 from tqdm import tqdm
 
 import library.train_util as train_util
-from library.utils import setup_logging, pil_resize
+from library.utils import setup_logging, resize_image
 
 setup_logging()
 import logging
@@ -42,10 +42,7 @@ def preprocess_image(image):
     pad_t = pad_y // 2
     image = np.pad(image, ((pad_t, pad_y - pad_t), (pad_l, pad_x - pad_l), (0, 0)), mode="constant", constant_values=255)
 
-    if size > IMAGE_SIZE:
-        image = cv2.resize(image, (IMAGE_SIZE, IMAGE_SIZE), cv2.INTER_AREA)
-    else:
-        image = pil_resize(image, (IMAGE_SIZE, IMAGE_SIZE))
+    image = resize_image(image, image.shape[0], image.shape[1], IMAGE_SIZE, IMAGE_SIZE)
 
     image = image.astype(np.float32)
     return image

library/config_util.py

@@ -75,6 +75,7 @@ class BaseSubsetParams:
     custom_attributes: Optional[Dict[str, Any]] = None
     validation_seed: int = 0
     validation_split: float = 0.0
+    resize_interpolation: Optional[str] = None
 
 
 @dataclass
@@ -106,7 +107,7 @@ class BaseDatasetParams:
     debug_dataset: bool = False
     validation_seed: Optional[int] = None
     validation_split: float = 0.0
-
+    resize_interpolation: Optional[str] = None
 
 @dataclass
 class DreamBoothDatasetParams(BaseDatasetParams):
@@ -196,6 +197,7 @@ class ConfigSanitizer:
         "caption_prefix": str,
         "caption_suffix": str,
         "custom_attributes": dict,
+        "resize_interpolation": str,
     }
     # DO means DropOut
     DO_SUBSET_ASCENDABLE_SCHEMA = {
@@ -241,6 +243,7 @@ class ConfigSanitizer:
         "validation_split": float,
         "resolution": functools.partial(__validate_and_convert_scalar_or_twodim.__func__, int),
         "network_multiplier": float,
+        "resize_interpolation": str,
     }
 
     # options handled by argparse but not handled by user config
@@ -525,6 +528,7 @@ def generate_dataset_group_by_blueprint(dataset_group_blueprint: DatasetGroupBlu
                 [{dataset_type} {i}]
                   batch_size: {dataset.batch_size}
                   resolution: {(dataset.width, dataset.height)}
+                  resize_interpolation: {dataset.resize_interpolation}
                   enable_bucket: {dataset.enable_bucket}
             """)
 
@@ -558,6 +562,7 @@ def generate_dataset_group_by_blueprint(dataset_group_blueprint: DatasetGroupBlu
                     token_warmup_min: {subset.token_warmup_min},
                     token_warmup_step: {subset.token_warmup_step},
                     alpha_mask: {subset.alpha_mask}
+                    resize_interpolation: {subset.resize_interpolation}
                     custom_attributes: {subset.custom_attributes}
                 """), "  ")
 

library/train_util.py

@@ -74,7 +74,7 @@ import library.model_util as model_util
 import library.huggingface_util as huggingface_util
 import library.sai_model_spec as sai_model_spec
 import library.deepspeed_utils as deepspeed_utils
-from library.utils import setup_logging, pil_resize
+from library.utils import setup_logging, resize_image
 
 setup_logging()
 import logging
@@ -205,6 +205,7 @@ class ImageInfo:
         self.text_encoder_pool2: Optional[torch.Tensor] = None
 
         self.alpha_mask: Optional[torch.Tensor] = None  # alpha mask can be flipped in runtime
+        self.resize_interpolation: Optional[str] = None
 
 
 class BucketManager:
@@ -429,6 +430,7 @@ class BaseSubset:
         custom_attributes: Optional[Dict[str, Any]] = None,
         validation_seed: Optional[int] = None,
         validation_split: Optional[float] = 0.0,
+        resize_interpolation: Optional[str] = None,
     ) -> None:
         self.image_dir = image_dir
         self.alpha_mask = alpha_mask if alpha_mask is not None else False
@@ -459,6 +461,8 @@ class BaseSubset:
         self.validation_seed = validation_seed
         self.validation_split = validation_split
 
+        self.resize_interpolation = resize_interpolation
+
 
 class DreamBoothSubset(BaseSubset):
     def __init__(
@@ -490,6 +494,7 @@ class DreamBoothSubset(BaseSubset):
         custom_attributes: Optional[Dict[str, Any]] = None,
         validation_seed: Optional[int] = None,
         validation_split: Optional[float] = 0.0,
+        resize_interpolation: Optional[str] = None,
     ) -> None:
         assert image_dir is not None, "image_dir must be specified / image_dirは指定が必須です"
 
@@ -517,6 +522,7 @@ class DreamBoothSubset(BaseSubset):
             custom_attributes=custom_attributes,
             validation_seed=validation_seed,
             validation_split=validation_split,
+            resize_interpolation=resize_interpolation,
         )
 
         self.is_reg = is_reg
@@ -559,6 +565,7 @@ class FineTuningSubset(BaseSubset):
         custom_attributes: Optional[Dict[str, Any]] = None,
         validation_seed: Optional[int] = None,
         validation_split: Optional[float] = 0.0,
+        resize_interpolation: Optional[str] = None,
     ) -> None:
         assert metadata_file is not None, "metadata_file must be specified / metadata_fileは指定が必須です"
 
@@ -586,6 +593,7 @@ class FineTuningSubset(BaseSubset):
             custom_attributes=custom_attributes,
             validation_seed=validation_seed,
             validation_split=validation_split,
+            resize_interpolation=resize_interpolation,
         )
 
         self.metadata_file = metadata_file
@@ -624,6 +632,7 @@ class ControlNetSubset(BaseSubset):
         custom_attributes: Optional[Dict[str, Any]] = None,
         validation_seed: Optional[int] = None,
         validation_split: Optional[float] = 0.0,
+        resize_interpolation: Optional[str] = None,
     ) -> None:
         assert image_dir is not None, "image_dir must be specified / image_dirは指定が必須です"
 
@@ -651,6 +660,7 @@ class ControlNetSubset(BaseSubset):
             custom_attributes=custom_attributes,
             validation_seed=validation_seed,
             validation_split=validation_split,
+            resize_interpolation=resize_interpolation,
         )
 
         self.conditioning_data_dir = conditioning_data_dir
@@ -671,6 +681,7 @@ class BaseDataset(torch.utils.data.Dataset):
         resolution: Optional[Tuple[int, int]],
         network_multiplier: float,
         debug_dataset: bool,
+        resize_interpolation: Optional[str] = None
     ) -> None:
         super().__init__()
 
@@ -705,6 +716,10 @@ class BaseDataset(torch.utils.data.Dataset):
 
         self.image_transforms = IMAGE_TRANSFORMS
 
+        if resize_interpolation is not None:
+            assert validate_interpolation_fn(resize_interpolation), f"Resize interpolation \"{resize_interpolation}\" is not a valid interpolation"
+        self.resize_interpolation = resize_interpolation
+
         self.image_data: Dict[str, ImageInfo] = {}
         self.image_to_subset: Dict[str, Union[DreamBoothSubset, FineTuningSubset]] = {}
 
@@ -1494,7 +1509,7 @@ class BaseDataset(torch.utils.data.Dataset):
         nh = int(height * scale + 0.5)
         nw = int(width * scale + 0.5)
         assert nh >= self.height and nw >= self.width, f"internal error. small scale {scale}, {width}*{height}"
-        image = cv2.resize(image, (nw, nh), interpolation=cv2.INTER_AREA)
+        image = resize_image(image, width, height, nw, nh, subset.resize_interpolation)
         face_cx = int(face_cx * scale + 0.5)
         face_cy = int(face_cy * scale + 0.5)
         height, width = nh, nw
@@ -1591,7 +1606,7 @@ class BaseDataset(torch.utils.data.Dataset):
 
                 if self.enable_bucket:
                     img, original_size, crop_ltrb = trim_and_resize_if_required(
-                        subset.random_crop, img, image_info.bucket_reso, image_info.resized_size
+                        subset.random_crop, img, image_info.bucket_reso, image_info.resized_size, resize_interpolation=image_info.resize_interpolation
                     )
                 else:
                     if face_cx > 0:  # 顔位置情報あり
@@ -1852,8 +1867,9 @@ class DreamBoothDataset(BaseDataset):
         debug_dataset: bool,
         validation_split: float,
         validation_seed: Optional[int],
+        resize_interpolation: Optional[str],
     ) -> None:
-        super().__init__(resolution, network_multiplier, debug_dataset)
+        super().__init__(resolution, network_multiplier, debug_dataset, resize_interpolation)
 
         assert resolution is not None, f"resolution is required / resolution（解像度）指定は必須です"
 
@@ -2078,6 +2094,7 @@ class DreamBoothDataset(BaseDataset):
 
             for img_path, caption, size in zip(img_paths, captions, sizes):
                 info = ImageInfo(img_path, num_repeats, caption, subset.is_reg, img_path)
+                info.resize_interpolation = subset.resize_interpolation if subset.resize_interpolation is not None else self.resize_interpolation
                 if size is not None:
                     info.image_size = size
                 if subset.is_reg:
@@ -2360,9 +2377,10 @@ class ControlNetDataset(BaseDataset):
         bucket_no_upscale: bool,
         debug_dataset: bool,
         validation_split: float,
-        validation_seed: Optional[int],
+        validation_seed: Optional[int],        
+        resize_interpolation: Optional[str] = None,
     ) -> None:
-        super().__init__(resolution, network_multiplier, debug_dataset)
+        super().__init__(resolution, network_multiplier, debug_dataset, resize_interpolation)
 
         db_subsets = []
         for subset in subsets:
@@ -2394,6 +2412,7 @@ class ControlNetDataset(BaseDataset):
                 subset.caption_suffix,
                 subset.token_warmup_min,
                 subset.token_warmup_step,
+                resize_interpolation=subset.resize_interpolation,
             )
             db_subsets.append(db_subset)
 
@@ -2412,6 +2431,7 @@ class ControlNetDataset(BaseDataset):
             debug_dataset,
             validation_split,
             validation_seed,
+            resize_interpolation,
         )
 
         # 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）
@@ -2420,7 +2440,8 @@ class ControlNetDataset(BaseDataset):
         self.num_train_images = self.dreambooth_dataset_delegate.num_train_images
         self.num_reg_images = self.dreambooth_dataset_delegate.num_reg_images
         self.validation_split = validation_split
-        self.validation_seed = validation_seed
+        self.validation_seed = validation_seed 
+        self.resize_interpolation = resize_interpolation
 
         # assert all conditioning data exists
         missing_imgs = []
@@ -2508,9 +2529,8 @@ class ControlNetDataset(BaseDataset):
                 assert (
                     cond_img.shape[0] == original_size_hw[0] and cond_img.shape[1] == original_size_hw[1]
                 ), f"size of conditioning image is not match / 画像サイズが合いません: {image_info.absolute_path}"
-                cond_img = cv2.resize(
-                    cond_img, image_info.resized_size, interpolation=cv2.INTER_AREA
-                )  # INTER_AREAでやりたいのでcv2でリサイズ
+
+                cond_img = resize_image(cond_img, original_size_hw[1], original_size_hw[0], target_size_hw[1], target_size_hw[0], self.resize_interpolation)
 
                 # TODO support random crop
                 # 現在サポートしているcropはrandomではなく中央のみ
@@ -2524,7 +2544,7 @@ class ControlNetDataset(BaseDataset):
                 # ), f"image size is small / 画像サイズが小さいようです: {image_info.absolute_path}"
                 # resize to target
                 if cond_img.shape[0] != target_size_hw[0] or cond_img.shape[1] != target_size_hw[1]:
-                    cond_img = pil_resize(cond_img, (int(target_size_hw[1]), int(target_size_hw[0])))
+                    cond_img = resize_image(cond_img, cond_img.shape[0], cond_img.shape[1], target_size_hw[1], target_size_hw[0], self.resize_interpolation)
 
             if flipped:
                 cond_img = cond_img[:, ::-1, :].copy()  # copy to avoid negative stride
@@ -2921,17 +2941,13 @@ def load_image(image_path, alpha=False):
 
 # 画像を読み込む。戻り値はnumpy.ndarray,(original width, original height),(crop left, crop top, crop right, crop bottom)
 def trim_and_resize_if_required(
-    random_crop: bool, image: np.ndarray, reso, resized_size: Tuple[int, int]
+    random_crop: bool, image: np.ndarray, reso, resized_size: Tuple[int, int], resize_interpolation: Optional[str] = None
 ) -> Tuple[np.ndarray, Tuple[int, int], Tuple[int, int, int, int]]:
     image_height, image_width = image.shape[0:2]
     original_size = (image_width, image_height)  # size before resize
 
     if image_width != resized_size[0] or image_height != resized_size[1]:
-        # リサイズする
-        if image_width > resized_size[0] and image_height > resized_size[1]:
-            image = cv2.resize(image, resized_size, interpolation=cv2.INTER_AREA)  # INTER_AREAでやりたいのでcv2でリサイズ
-        else:
-            image = pil_resize(image, resized_size)
+        image = resize_image(image, image_width, image_height, resized_size[0], resized_size[1], resize_interpolation)
 
     image_height, image_width = image.shape[0:2]
 
@@ -2976,7 +2992,7 @@ def load_images_and_masks_for_caching(
     for info in image_infos:
         image = load_image(info.absolute_path, use_alpha_mask) if info.image is None else np.array(info.image, np.uint8)
         # TODO 画像のメタデータが壊れていて、メタデータから割り当てたbucketと実際の画像サイズが一致しない場合があるのでチェック追加要
-        image, original_size, crop_ltrb = trim_and_resize_if_required(random_crop, image, info.bucket_reso, info.resized_size)
+        image, original_size, crop_ltrb = trim_and_resize_if_required(random_crop, image, info.bucket_reso, info.resized_size, resize_interpolation=info.resize_interpolation)
 
         original_sizes.append(original_size)
         crop_ltrbs.append(crop_ltrb)
@@ -3017,7 +3033,7 @@ def cache_batch_latents(
     for info in image_infos:
         image = load_image(info.absolute_path, use_alpha_mask) if info.image is None else np.array(info.image, np.uint8)
         # TODO 画像のメタデータが壊れていて、メタデータから割り当てたbucketと実際の画像サイズが一致しない場合があるのでチェック追加要
-        image, original_size, crop_ltrb = trim_and_resize_if_required(random_crop, image, info.bucket_reso, info.resized_size)
+        image, original_size, crop_ltrb = trim_and_resize_if_required(random_crop, image, info.bucket_reso, info.resized_size, resize_interpolation=info.resize_interpolation)
 
         info.latents_original_size = original_size
         info.latents_crop_ltrb = crop_ltrb
@@ -4495,7 +4511,13 @@ def add_dataset_arguments(
         action="store_true",
         help="make bucket for each image without upscaling / 画像を拡大せずbucketを作成します",
     )
-
+    parser.add_argument(
+        "--resize_interpolation",
+        type=str,
+        default=None,
+        choices=["lanczos", "nearest", "bilinear", "linear", "bicubic", "cubic", "area"],
+        help="Resize interpolation when required. Default: area Options: lanczos, nearest, bilinear, bicubic, area / 必要に応じてサイズ補間を変更します。デフォルト: area オプション: lanczos, nearest, bilinear, bicubic, area",
+    )
     parser.add_argument(
         "--token_warmup_min",
         type=int,
@@ -6533,3 +6555,4 @@ class LossRecorder:
         if losses == 0:
             return 0
         return self.loss_total / losses
+

library/utils.py

@@ -16,7 +16,6 @@ from PIL import Image
 import numpy as np
 from safetensors.torch import load_file
 
-
 def fire_in_thread(f, *args, **kwargs):
     threading.Thread(target=f, args=args, kwargs=kwargs).start()
 
@@ -89,6 +88,8 @@ def setup_logging(args=None, log_level=None, reset=False):
         logger = logging.getLogger(__name__)
         logger.info(msg_init)
 
+setup_logging()
+logger = logging.getLogger(__name__)
 
 # endregion
 
@@ -378,7 +379,7 @@ def load_safetensors(
 # region Image utils
 
 
-def pil_resize(image, size, interpolation=Image.LANCZOS):
+def pil_resize(image, size, interpolation):
     has_alpha = image.shape[2] == 4 if len(image.shape) == 3 else False
 
     if has_alpha:
@@ -386,7 +387,7 @@ def pil_resize(image, size, interpolation=Image.LANCZOS):
     else:
         pil_image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
 
-    resized_pil = pil_image.resize(size, interpolation)
+    resized_pil = pil_image.resize(size, resample=interpolation)
 
     # Convert back to cv2 format
     if has_alpha:
@@ -397,6 +398,100 @@ def pil_resize(image, size, interpolation=Image.LANCZOS):
     return resized_cv2
 
 
+def resize_image(image: np.ndarray, width: int, height: int, resized_width: int, resized_height: int, resize_interpolation: Optional[str] = None):
+    """
+    Resize image with resize interpolation. Default interpolation to AREA if image is smaller, else LANCZOS
+
+    Args:
+        image: numpy.ndarray
+        width: int Original image width
+        height: int Original image height
+        resized_width: int Resized image width
+        resized_height: int Resized image height
+        resize_interpolation: Optional[str] Resize interpolation method "lanczos", "area", "bilinear", "bicubic", "nearest", "box"
+
+    Returns:
+        image
+    """
+    interpolation = get_cv2_interpolation(resize_interpolation)
+    resized_size = (resized_width, resized_height)
+    if width > resized_width and height > resized_width:
+        image = cv2.resize(image, resized_size, interpolation=interpolation if interpolation is not None else cv2.INTER_AREA)  # INTER_AREAでやりたいのでcv2でリサイズ
+        logger.debug(f"resize image using {resize_interpolation}")
+    else:
+        image = cv2.resize(image, resized_size, interpolation=interpolation if interpolation is not None else cv2.INTER_LANCZOS4)  # INTER_AREAでやりたいのでcv2でリサイズ
+        logger.debug(f"resize image using {resize_interpolation}")
+
+    return image
+
+
+def get_cv2_interpolation(interpolation: Optional[str]) -> Optional[int]:
+    """
+    Convert interpolation value to cv2 interpolation integer
+
+    https://docs.opencv.org/3.4/da/d54/group__imgproc__transform.html#ga5bb5a1fea74ea38e1a5445ca803ff121
+    """
+    if interpolation is None:
+        return None 
+
+    if interpolation == "lanczos" or interpolation == "lanczos4":
+        # Lanczos interpolation over 8x8 neighborhood 
+        return cv2.INTER_LANCZOS4
+    elif interpolation == "nearest":
+        # Bit exact nearest neighbor interpolation. This will produce same results as the nearest neighbor method in PIL, scikit-image or Matlab. 
+        return cv2.INTER_NEAREST_EXACT
+    elif interpolation == "bilinear" or interpolation == "linear":
+        # bilinear interpolation
+        return cv2.INTER_LINEAR
+    elif interpolation == "bicubic" or interpolation == "cubic":
+        # bicubic interpolation 
+        return cv2.INTER_CUBIC
+    elif interpolation == "area":
+        # resampling using pixel area relation. It may be a preferred method for image decimation, as it gives moire'-free results. But when the image is zoomed, it is similar to the INTER_NEAREST method. 
+        return cv2.INTER_AREA
+    elif interpolation == "box":
+        # resampling using pixel area relation. It may be a preferred method for image decimation, as it gives moire'-free results. But when the image is zoomed, it is similar to the INTER_NEAREST method. 
+        return cv2.INTER_AREA
+    else:
+        return None
+
+def get_pil_interpolation(interpolation: Optional[str]) -> Optional[Image.Resampling]:
+    """
+    Convert interpolation value to PIL interpolation
+
+    https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-filters
+    """
+    if interpolation is None:
+        return None 
+
+    if interpolation == "lanczos":
+        return Image.Resampling.LANCZOS
+    elif interpolation == "nearest":
+        # Pick one nearest pixel from the input image. Ignore all other input pixels.
+        return Image.Resampling.NEAREST
+    elif interpolation == "bilinear" or interpolation == "linear":
+        # For resize calculate the output pixel value using linear interpolation on all pixels that may contribute to the output value. For other transformations linear interpolation over a 2x2 environment in the input image is used.
+        return Image.Resampling.BILINEAR
+    elif interpolation == "bicubic" or interpolation == "cubic":
+        # For resize calculate the output pixel value using cubic interpolation on all pixels that may contribute to the output value. For other transformations cubic interpolation over a 4x4 environment in the input image is used.
+        return Image.Resampling.BICUBIC
+    elif interpolation == "area":
+        # Image.Resampling.BOX may be more appropriate if upscaling 
+        # Area interpolation is related to cv2.INTER_AREA
+        # Produces a sharper image than Resampling.BILINEAR, doesn’t have dislocations on local level like with Resampling.BOX.
+        return Image.Resampling.HAMMING
+    elif interpolation == "box":
+        # Each pixel of source image contributes to one pixel of the destination image with identical weights. For upscaling is equivalent of Resampling.NEAREST.
+        return Image.Resampling.BOX
+    else:
+        return None
+
+def validate_interpolation_fn(interpolation_str: str) -> bool:
+    """
+    Check if a interpolation function is supported
+    """
+    return interpolation_str in ["lanczos", "nearest", "bilinear", "linear", "bicubic", "cubic", "area", "box"]
+
 # endregion
 
 # TODO make inf_utils.py

tools/detect_face_rotate.py

@@ -15,7 +15,7 @@ import os
 from anime_face_detector import create_detector
 from tqdm import tqdm
 import numpy as np
-from library.utils import setup_logging, pil_resize
+from library.utils import setup_logging, resize_image
 setup_logging()
 import logging
 logger = logging.getLogger(__name__)
@@ -170,12 +170,9 @@ def process(args):
             scale = max(cur_crop_width / w, cur_crop_height / h)
 
         if scale != 1.0:
-          w = int(w * scale + .5)
-          h = int(h * scale + .5)
-          if scale < 1.0:
-            face_img = cv2.resize(face_img, (w, h), interpolation=cv2.INTER_AREA)
-          else:
-            face_img = pil_resize(face_img, (w, h))
+          rw = int(w * scale + .5)
+          rh = int(h * scale + .5)
+          face_img = resize_image(face_img, w, h, rw, rh)
           cx = int(cx * scale + .5)
           cy = int(cy * scale + .5)
           fw = int(fw * scale + .5)

tools/resize_images_to_resolution.py

@@ -6,7 +6,7 @@ import shutil
 import math
 from PIL import Image
 import numpy as np
-from library.utils import setup_logging, pil_resize
+from library.utils import setup_logging, resize_image
 setup_logging()
 import logging
 logger = logging.getLogger(__name__)
@@ -22,14 +22,6 @@ def resize_images(src_img_folder, dst_img_folder, max_resolution="512x512", divi
   if not os.path.exists(dst_img_folder):
     os.makedirs(dst_img_folder)
 
-  # Select interpolation method
-  if interpolation == 'lanczos4':
-    pil_interpolation = Image.LANCZOS
-  elif interpolation == 'cubic':
-    pil_interpolation = Image.BICUBIC
-  else:
-    cv2_interpolation = cv2.INTER_AREA
-
   # Iterate through all files in src_img_folder
   img_exts = (".png", ".jpg", ".jpeg", ".webp", ".bmp")                   # copy from train_util.py
   for filename in os.listdir(src_img_folder):
@@ -63,11 +55,7 @@ def resize_images(src_img_folder, dst_img_folder, max_resolution="512x512", divi
         new_height = int(img.shape[0] * math.sqrt(scale_factor))
         new_width = int(img.shape[1] * math.sqrt(scale_factor))
 
-        # Resize image
-        if cv2_interpolation:
-          img = cv2.resize(img, (new_width, new_height), interpolation=cv2_interpolation)
-        else:
-          img = pil_resize(img, (new_width, new_height), interpolation=pil_interpolation)
+        img = resize_image(img,  img.shape[0], img.shape[1], new_height, new_width, interpolation)
       else:
         new_height, new_width = img.shape[0:2]
 
@@ -113,8 +101,8 @@ def setup_parser() -> argparse.ArgumentParser:
                       help='Maximum resolution(s) in the format "512x512,384x384, etc, etc" / 最大画像サイズをカンマ区切りで指定 ("512x512,384x384, etc, etc" など)', default="512x512,384x384,256x256,128x128")
   parser.add_argument('--divisible_by', type=int,
                       help='Ensure new dimensions are divisible by this value / リサイズ後の画像のサイズをこの値で割り切れるようにします', default=1)
-  parser.add_argument('--interpolation', type=str, choices=['area', 'cubic', 'lanczos4'],
-                      default='area', help='Interpolation method for resizing / リサイズ時の補完方法')
+  parser.add_argument('--interpolation', type=str, choices=['area', 'cubic', 'lanczos4', 'nearest', 'linear', 'box'],
+                      default=None, help='Interpolation method for resizing. Default to area if smaller, lanczos if larger / サイズ変更の補間方法。小さい場合はデフォルトでエリア、大きい場合はランチョスになります。')
   parser.add_argument('--save_as_png', action='store_true', help='Save as png format / png形式で保存')
   parser.add_argument('--copy_associated_files', action='store_true',
                       help='Copy files with same base name to images (captions etc) / 画像と同じファイル名（拡張子を除く）のファイルもコピーする')

train_network.py

@@ -1012,11 +1012,12 @@ class NetworkTrainer:
             "ss_huber_c": args.huber_c,
             "ss_fp8_base": bool(args.fp8_base),
             "ss_fp8_base_unet": bool(args.fp8_base_unet),
-            "ss_validation_seed": args.validation_seed,
-            "ss_validation_split": args.validation_split,
-            "ss_max_validation_steps": args.max_validation_steps,
-            "ss_validate_every_n_epochs": args.validate_every_n_epochs,
-            "ss_validate_every_n_steps": args.validate_every_n_steps,
+            "ss_validation_seed": args.validation_seed, 
+            "ss_validation_split": args.validation_split, 
+            "ss_max_validation_steps": args.max_validation_steps, 
+            "ss_validate_every_n_epochs": args.validate_every_n_epochs, 
+            "ss_validate_every_n_steps": args.validate_every_n_steps, 
+            "ss_resize_interpolation": args.resize_interpolation
         }
 
         self.update_metadata(metadata, args)  # architecture specific metadata
@@ -1042,6 +1043,7 @@ class NetworkTrainer:
                     "max_bucket_reso": dataset.max_bucket_reso,
                     "tag_frequency": dataset.tag_frequency,
                     "bucket_info": dataset.bucket_info,
+                    "resize_interpolation": dataset.resize_interpolation,
                 }
 
                 subsets_metadata = []
@@ -1059,6 +1061,7 @@ class NetworkTrainer:
                         "enable_wildcard": bool(subset.enable_wildcard),
                         "caption_prefix": subset.caption_prefix,
                         "caption_suffix": subset.caption_suffix,
+                        "resize_interpolation": subset.resize_interpolation,
                     }
 
                     image_dir_or_metadata_file = None