Loading imgutils/upscale/cdc.py +34 −1 Original line number Diff line number Diff line Loading @@ -12,6 +12,15 @@ from ..utils import open_onnx_model, area_batch_run @lru_cache() def _open_cdc_upscaler_model(model: str) -> Tuple[Any, int]: """ Opens and initializes the CDC upscaler model. :param model: The name of the model to use. :type model: str :return: Tuple of the ONNX model and the scale factor. :rtype: Tuple[Any, int] """ ort = open_onnx_model(hf_hub_download( f'deepghs/cdc_anime_onnx', f'{model}.onnx' Loading @@ -34,6 +43,30 @@ _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: """ Upscale the input image using the CDC upscaler model. :param image: The input image. :type image: ImageTyping :param model: The name of the model to use. (default: 'HGSR-MHR-anime-aug_X4_320') :type model: str :param tile_size: The size of each tile. (default: 512) :type tile_size: int :param tile_overlap: The overlap between tiles. (default: 64) :type tile_overlap: int :param batch_size: The batch size. (default: 1) :type batch_size: int :param silent: Whether to suppress progress messages. (default: False) :type silent: bool :return: The upscaled image. :rtype: Image.Image """ image, alpha_mask = _rgba_preprocess(image) image = load_image(image, mode='RGB', force_background='white') input_ = np.array(image).astype(np.float32) / 255.0 Loading Loading @@ -62,5 +95,5 @@ def upscale_with_cdc(image: ImageTyping, model: str = 'HGSR-MHR-anime-aug_X4_320 scale=scale, silent=silent, process_title='CDC Upscale', ) output_ = np.clip(output_, a_min=0.0, a_max=1.0) ret_image = Image.fromarray((output_[0].transpose((1, 2, 0)) * 255).astype(np.int8), 'RGB') ret_image = Image.fromarray((output_[0].transpose((1, 2, 0)) * 255).astype(np.uint8), 'RGB') return _rgba_postprocess(ret_image, alpha_mask) Loading
imgutils/upscale/cdc.py +34 −1 Original line number Diff line number Diff line Loading @@ -12,6 +12,15 @@ from ..utils import open_onnx_model, area_batch_run @lru_cache() def _open_cdc_upscaler_model(model: str) -> Tuple[Any, int]: """ Opens and initializes the CDC upscaler model. :param model: The name of the model to use. :type model: str :return: Tuple of the ONNX model and the scale factor. :rtype: Tuple[Any, int] """ ort = open_onnx_model(hf_hub_download( f'deepghs/cdc_anime_onnx', f'{model}.onnx' Loading @@ -34,6 +43,30 @@ _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: """ Upscale the input image using the CDC upscaler model. :param image: The input image. :type image: ImageTyping :param model: The name of the model to use. (default: 'HGSR-MHR-anime-aug_X4_320') :type model: str :param tile_size: The size of each tile. (default: 512) :type tile_size: int :param tile_overlap: The overlap between tiles. (default: 64) :type tile_overlap: int :param batch_size: The batch size. (default: 1) :type batch_size: int :param silent: Whether to suppress progress messages. (default: False) :type silent: bool :return: The upscaled image. :rtype: Image.Image """ image, alpha_mask = _rgba_preprocess(image) image = load_image(image, mode='RGB', force_background='white') input_ = np.array(image).astype(np.float32) / 255.0 Loading Loading @@ -62,5 +95,5 @@ def upscale_with_cdc(image: ImageTyping, model: str = 'HGSR-MHR-anime-aug_X4_320 scale=scale, silent=silent, process_title='CDC Upscale', ) output_ = np.clip(output_, a_min=0.0, a_max=1.0) ret_image = Image.fromarray((output_[0].transpose((1, 2, 0)) * 255).astype(np.int8), 'RGB') ret_image = Image.fromarray((output_[0].transpose((1, 2, 0)) * 255).astype(np.uint8), 'RGB') return _rgba_postprocess(ret_image, alpha_mask)
