diff --git a/imgutils/upscale/__init__.py b/imgutils/upscale/__init__.py
deleted file mode 100644
index c9e8fe991b7..00000000000
--- a/imgutils/upscale/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .waifu2x import upscale_image_by_waifu2x
diff --git a/imgutils/upscale/waifu2x.py b/imgutils/upscale/waifu2x.py
deleted file mode 100644
index d0ec4a73658..00000000000
--- a/imgutils/upscale/waifu2x.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import os
-import re
-from functools import lru_cache
-from typing import Optional, Mapping, Tuple
-
-import numpy as np
-from PIL import Image
-from huggingface_hub import hf_hub_download, HfFileSystem
-
-from ..data import ImageTyping, load_image
-from ..utils import open_onnx_model, area_batch_run
-
-_hf_fs = HfFileSystem()
-
-_REPOSITORY = 'deepghs/waifu2x_onnx'
-_FILENAME_PATTERN = re.compile(r'^(noise(?P<noise>\d+)_?)?(scale(?P<scale>\d+)x)?\.onnx$')
-
-
-@lru_cache()
-def _load_available_version() -> Mapping[Tuple[str, str, str], Mapping[Tuple[Optional[int], int], str]]:
-    """
-    Load available versions of the waifu2x model from the Hugging Face Hub.
-
-    :return: A mapping of available model versions.
-    :rtype: Mapping
-    """
-    records = {}
-    for file in _hf_fs.glob(f'{_REPOSITORY}/*/onnx_models/*/*/*.onnx'):
-        segments = os.path.relpath(file, _REPOSITORY).split('/')
-        assert len(segments) == 5 and segments[1] == 'onnx_models'
-        version, model, type_ = segments[0], segments[2], segments[3]
-        filename = segments[4]
-
-        key = (version, model, type_)
-        if key not in records:
-            records[key] = []
-        records[key].append(filename)
-
-    retval = {}
-    for key, filenames in records.items():
-        retval[key] = {}
-        for filename in filenames:
-            matching = _FILENAME_PATTERN.fullmatch(filename)
-            assert matching, f'Not matched, {filename!r}, key: {key!r}'
-            noise = int(matching.group('noise')) if matching.group('noise') else None
-            scale = int(matching.group('scale')) if matching.group('scale') else 1
-            retval[key][(noise, scale)] = filename
-
-    return retval
-
-
-@lru_cache()
-def _open_waifu2x_onnx_model(version: str, model: str, type_: str, noise: Optional[int], scale: int):
-    """
-    Open a specific version of the waifu2x ONNX model.
-
-    :param version: The model version.
-    :type version: str
-    :param model: The model type.
-    :type model: str
-    :param type_: The model usage type (e.g., 'art').
-    :type type_: str
-    :param noise: The noise level (None for no noise).
-    :type noise: Optional[int]
-    :param scale: The scaling factor.
-    :type scale: int
-    :return: The ONNX model.
-    """
-    _all_versions = _load_available_version()
-    if (version, model, type_) in _all_versions:
-        _all_k = _all_versions[(version, model, type_)]
-        if (noise, scale) in _all_k:
-            filename = _all_k[(noise, scale)]
-            return open_onnx_model(hf_hub_download(
-                f'deepghs/waifu2x_onnx',
-                f'{version}/onnx_models/{model}/{type_}/{filename}',
-            ))
-        else:
-            raise ValueError(f'Noise {noise!r} or scale {scale!r} may be invalid.')
-    else:
-        raise ValueError(f"Version {version!r} or model {model!r} or type_ {type_!r} may be invalid.")
-
-
-def _single_upscale_by_waifu2x(x, version: str = '20230504', model: str = 'swin_unet',
-                               type_: str = 'art', noise: Optional[int] = None, scale: int = 2):
-    ort = _open_waifu2x_onnx_model(version, model, type_, noise, scale)
-    # noinspection PyTypeChecker
-    x = np.pad(x, ((0, 0), (0, 0), (8, 8), (8, 8)), mode='reflect')
-    y, = ort.run(['y'], {'x': x})
-    return y
-
-
-def upscale_image_by_waifu2x(image: ImageTyping, scale: int = 2, noise: Optional[int] = None,
-                             version: str = '20230504', model: str = 'swin_unet', type_: str = 'art',
-                             tile_size: int = 64, tile_overlap: int = 8, silent: bool = False) -> Image.Image:
-    """
-    Upscale an image using the waifu2x model.
-
-    :param image: The input image.
-    :type image: ImageTyping
-    :param scale: The scaling factor. Default is 2.
-    :type scale: int
-    :param noise: The noise level. Default is None.
-    :type noise: Optional[int]
-    :param version: The model version. Default is '20230504'.
-    :type version: str
-    :param model: The model type. Default is 'swin_unet'.
-    :type model: str
-    :param type_: The model usage type (e.g., 'art'). Default is 'art'.
-    :type type_: str
-    :param tile_size: The size of processing tiles. Default is 64.
-    :type tile_size: int
-    :param tile_overlap: The overlap between tiles. Default is 8.
-    :type tile_overlap: int
-    :param silent: If True, the progress will not be displayed. Default is False.
-    :type silent: bool
-    :return: The upscaled image.
-    :rtype: Image.Image
-    """
-    image = load_image(image, mode='RGB', force_background='white')
-    input_ = np.array(image).astype(np.float32) / 255.0
-    input_ = input_.transpose((2, 0, 1))[None, ...]
-
-    def _method(ix):
-        return _single_upscale_by_waifu2x(ix, version, model, type_, noise, scale)
-
-    output_ = area_batch_run(
-        input_, _method,
-        scale=scale, tile_size=tile_size, tile_overlap=tile_overlap, silent=silent,
-        process_title='Waifu2x Upscale',
-    )
-    output_ = np.clip(output_, a_min=0.0, a_max=1.0)
-    return Image.fromarray((output_[0].transpose((1, 2, 0)) * 255).astype(np.int8), 'RGB')