dev(narugo): add upscale

    return ort, scale_h

_CDC_INPUT_UNIT = 16

def upscale_with_cdc(image: ImageTyping, model: str = 'HGSR-MHR-anime-aug_X4_320',

                     tile_size: int = 512, tile_overlap: int = 64, batch_size: int = 1,

                     silent: bool = False) -> Image.Image:

    ort, scale = _open_cdc_upscaler_model(model)

    def _method(ix):

        ox, = ort.run(['output'], {'input': ix.astype(np.float32)})

        ix = ix.astype(np.float32)

        batch, channels, height, width = ix.shape

        p_height = 0 if height % _CDC_INPUT_UNIT == 0 else _CDC_INPUT_UNIT - (height % _CDC_INPUT_UNIT)

        p_width = 0 if width % _CDC_INPUT_UNIT == 0 else _CDC_INPUT_UNIT - (width % _CDC_INPUT_UNIT)

        if p_height > 0 or p_width > 0:  # align to 16

            ix = np.pad(ix, ((0, 0), (0, 0), (0, p_height), (0, p_width)), mode='reflect')

        actual_height, actual_width = height, width

        ox, = ort.run(['output'], {'input': ix})

        batch, channels, scale_, height, scale_, width = ox.shape

        return ox.reshape((batch, channels, scale_ * height, scale_ * width))

        ox = ox.reshape((batch, channels, scale_ * height, scale_ * width))

        ox = ox[..., :scale_ * actual_height, :scale_ * actual_width]  # crop back

        return ox

    output_ = area_batch_run(

        input_, _method,

    tile = min(tile_size, height, width)

    stride = tile - tile_overlap

    h_idx_list = list(range(0, height - tile, stride)) + [height - tile]

    w_idx_list = list(range(0, width - tile, stride)) + [width - tile]

    h_idx_list = sorted(set(list(range(0, height - tile, stride)) + [height - tile]))

    w_idx_list = sorted(set(list(range(0, width - tile, stride)) + [width - tile]))

    sum_ = np.zeros((batch, output_channels, height * scale, width * scale), dtype=origin_input.dtype)

    weight = np.zeros_like(sum_, dtype=origin_input.dtype)