Class ACEPlusImageProcessor

inference/utils.py:24–132

    return Image.composite(new_edit_image, edit_image, edit_mask)

class ACEPlusImageProcessor():
    def __init__(self, max_aspect_ratio=4, d=16, max_seq_len=2048):
        self.max_aspect_ratio = max_aspect_ratio
        self.d = d
        self.max_seq_len = max_seq_len
        self.transforms = T.Compose([
            T.ToTensor(),
            T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
        ])

    def image_check(self, image):
        if image is None:
            return image
        # preprocess
        W, H = image.size
        if H / W > self.max_aspect_ratio:
            image = T.CenterCrop([int(self.max_aspect_ratio * W), W])(image)
        elif W / H > self.max_aspect_ratio:
            image = T.CenterCrop([H, int(self.max_aspect_ratio * H)])(image)
        return self.transforms(image)


    def preprocess(self,
                   reference_image=None,
                   edit_image=None,
                   edit_mask=None,
                   height=1024,
                   width=1024,
                   repainting_scale = 1.0,
                   keep_pixels = False,
                   keep_pixels_rate = 0.8,
                   use_change = False):
        reference_image = self.image_check(reference_image)
        edit_image = self.image_check(edit_image)
        # for reference generation
        if edit_image is None:
            edit_image = torch.zeros([3, height, width])
            edit_mask = torch.ones([1, height, width])
        else:
            if edit_mask is None:
                _, eH, eW = edit_image.shape
                edit_mask = np.ones((eH, eW))
            else:
                edit_mask = np.asarray(edit_mask)
                edit_mask = np.where(edit_mask > 128, 1, 0)
            edit_mask = edit_mask.astype(
                np.float32) if np.any(edit_mask) else np.ones_like(edit_mask).astype(
                np.float32)
            edit_mask = torch.tensor(edit_mask).unsqueeze(0)

        edit_image = edit_image * (1 - edit_mask * repainting_scale)


        out_h, out_w = edit_image.shape[-2:]

        assert edit_mask is not None
        if reference_image is not None:
            _, H, W = reference_image.shape