def preprocess(
        self,
        rgb: Union[torch.Tensor, PIL.Image.Image, np.ndarray],
        depth: Union[torch.Tensor, PIL.Image.Image, np.ndarray],
        height: Optional[int] = None,
        width: Optional[int] = None,
        target_res: Optional[int] = None,
    ) -> torch.Tensor:
        r"""
        Preprocess the image input. Accepted formats are PIL images, NumPy arrays, or PyTorch tensors.

        Args:
            rgb (`Union[torch.Tensor, PIL.Image.Image, np.ndarray]`):
                The RGB input image, which can be a single image or a batch.
            depth (`Union[torch.Tensor, PIL.Image.Image, np.ndarray]`):
                The depth input image, which can be a single image or a batch.
            height (`Optional[int]`, *optional*, defaults to `None`):
                The desired height of the processed image. If `None`, defaults to the height of the input image.
            width (`Optional[int]`, *optional*, defaults to `None`):
                The desired width of the processed image. If `None`, defaults to the width of the input image.
            target_res (`Optional[int]`, *optional*, defaults to `None`):
                Target resolution for resizing the images. If specified, overrides height and width.

        Returns:
            `Tuple[torch.Tensor, torch.Tensor]`:
                A tuple containing the processed RGB and depth images as PyTorch tensors.
        """
        supported_formats = (PIL.Image.Image, np.ndarray, torch.Tensor)

        # Expand the missing dimension for 3-dimensional pytorch tensor or numpy array that represents grayscale image
        if self.config.do_convert_grayscale and isinstance(rgb, (torch.Tensor, np.ndarray)) and rgb.ndim == 3:
            raise Exception("This is not yet supported")

        if isinstance(rgb, supported_formats):
            rgb = [rgb]
            depth = [depth]
        elif not (isinstance(rgb, list) and all(isinstance(i, supported_formats) for i in rgb)):
            raise ValueError(
                f"Input is in incorrect format: {[type(i) for i in rgb]}. Currently, we only support {', '.join(supported_formats)}"
            )

        if isinstance(rgb[0], PIL.Image.Image):
            if self.config.do_convert_rgb:
                raise Exception("This is not yet supported")
                # rgb = [self.convert_to_rgb(i) for i in rgb]
                # depth = [self.convert_to_depth(i) for i in depth]  #TODO define convert_to_depth
            if self.config.do_resize or target_res:
                height, width = self.get_default_height_width(rgb[0], height, width) if not target_res else target_res
                rgb = [self.resize(i, height, width) for i in rgb]
                depth = [self.resize(i, height, width) for i in depth]
            rgb = self.pil_to_numpy(rgb)  # to np
            rgb = self.numpy_to_pt(rgb)  # to pt

            depth = self.depth_pil_to_numpy(depth)  # to np
            depth = self.numpy_to_pt(depth)  # to pt

        elif isinstance(rgb[0], np.ndarray):
            rgb = np.concatenate(rgb, axis=0) if rgb[0].ndim == 4 else np.stack(rgb, axis=0)
            rgb = self.numpy_to_pt(rgb)
            height, width = self.get_default_height_width(rgb, height, width)
            if self.config.do_resize:
                rgb = self.resize(rgb, height, width)

            depth = np.concatenate(depth, axis=0) if rgb[0].ndim == 4 else np.stack(depth, axis=0)
            depth = self.numpy_to_pt(depth)
            height, width = self.get_default_height_width(depth, height, width)
            if self.config.do_resize:
                depth = self.resize(depth, height, width)

        elif isinstance(rgb[0], torch.Tensor):
            raise Exception("This is not yet supported")
            # rgb = torch.cat(rgb, axis=0) if rgb[0].ndim == 4 else torch.stack(rgb, axis=0)

            # if self.config.do_convert_grayscale and rgb.ndim == 3:
            #     rgb = rgb.unsqueeze(1)

            # channel = rgb.shape[1]

            # height, width = self.get_default_height_width(rgb, height, width)
            # if self.config.do_resize:
            #     rgb = self.resize(rgb, height, width)

            # depth = torch.cat(depth, axis=0) if depth[0].ndim == 4 else torch.stack(depth, axis=0)

            # if self.config.do_convert_grayscale and depth.ndim == 3:
            #     depth = depth.unsqueeze(1)

            # channel = depth.shape[1]
            # # don't need any preprocess if the image is latents
            # if depth == 4:
            #     return rgb, depth

            # height, width = self.get_default_height_width(depth, height, width)
            # if self.config.do_resize:
            #     depth = self.resize(depth, height, width)
        # expected range [0,1], normalize to [-1,1]
        do_normalize = self.config.do_normalize
        if rgb.min() < 0 and do_normalize:
            warnings.warn(
                "Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
                f"when passing as pytorch tensor or numpy Array. You passed `image` with value range [{rgb.min()},{rgb.max()}]",
                FutureWarning,
            )
            do_normalize = False

        if do_normalize:
            rgb = self.normalize(rgb)
            depth = self.normalize(depth)

        if self.config.do_binarize:
            rgb = self.binarize(rgb)
            depth = self.binarize(depth)

        return rgb, depth

        Preprocess the image input. Accepted formats are PIL images, NumPy arrays, or PyTorch tensors.

        Args:
            rgb (`Union[torch.Tensor, PIL.Image.Image, np.ndarray]`):
                The RGB input image, which can be a single image or a batch.
            depth (`Union[torch.Tensor, PIL.Image.Image, np.ndarray]`):
                The depth input image, which can be a single image or a batch.
            height (`Optional[int]`, *optional*, defaults to `None`):
                The desired height of the processed image. If `None`, defaults to the height of the input image.
            width (`Optional[int]`, *optional*, defaults to `None`):
                The desired width of the processed image. If `None`, defaults to the width of the input image.
            target_res (`Optional[int]`, *optional*, defaults to `None`):
                Target resolution for resizing the images. If specified, overrides height and width.

        Returns:
            `Tuple[torch.Tensor, torch.Tensor]`:
                A tuple containing the processed RGB and depth images as PyTorch tensors.
        
src/diffusers/image_processor.py