Method postprocess

src/diffusers/image_processor.py:596–649

Postprocess the image output from tensor to `output_type`. Args: image (`torch.Tensor`): The image input, should be a pytorch tensor with shape `B x C x H x W`. output_type (`str`, *optional*, defaults to `pil`): The output ty

(
        self,
        image: torch.Tensor,
        output_type: str = "pil",
        do_denormalize: Optional[List[bool]] = None,
    )

        return image

    def postprocess(
        self,
        image: torch.Tensor,
        output_type: str = "pil",
        do_denormalize: Optional[List[bool]] = None,
    ) -> Union[PIL.Image.Image, np.ndarray, torch.Tensor]:
        """
        Postprocess the image output from tensor to `output_type`.

        Args:
            image (`torch.Tensor`):
                The image input, should be a pytorch tensor with shape `B x C x H x W`.
            output_type (`str`, *optional*, defaults to `pil`):
                The output type of the image, can be one of `pil`, `np`, `pt`, `latent`.
            do_denormalize (`List[bool]`, *optional*, defaults to `None`):
                Whether to denormalize the image to [0,1]. If `None`, will use the value of `do_normalize` in the
                `VaeImageProcessor` config.

        Returns:
            `PIL.Image.Image`, `np.ndarray` or `torch.Tensor`:
                The postprocessed image.
        """
        if not isinstance(image, torch.Tensor):
            raise ValueError(
                f"Input for postprocessing is in incorrect format: {type(image)}. We only support pytorch tensor"
            )
        if output_type not in ["latent", "pt", "np", "pil"]:
            deprecation_message = (
                f"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
                "`pil`, `np`, `pt`, `latent`"
            )
            deprecate("Unsupported output_type", "1.0.0", deprecation_message, standard_warn=False)
            output_type = "np"

        if output_type == "latent":
            return image

        if do_denormalize is None:
            do_denormalize = [self.config.do_normalize] * image.shape[0]

        image = torch.stack(
            [self.denormalize(image[i]) if do_denormalize[i] else image[i] for i in range(image.shape[0])]
        )

        if output_type == "pt":
            return image

        image = self.pt_to_numpy(image)

        if output_type == "np":
            return image

        if output_type == "pil":
            return self.numpy_to_pil(image)

    def apply_overlay(
        self,