Method postprocess

src/diffusers/image_processor.py:937–964

Postprocess the image, optionally apply mask overlay

(
        self,
        image: torch.Tensor,
        output_type: str = "pil",
        original_image: PIL.Image.Image | None = None,
        original_mask: PIL.Image.Image | None = None,
        crops_coords: tuple[int, int, int, int] | None = None,
    )

        return processed_image, processed_mask, postprocessing_kwargs

    def postprocess(
        self,
        image: torch.Tensor,
        output_type: str = "pil",
        original_image: PIL.Image.Image | None = None,
        original_mask: PIL.Image.Image | None = None,
        crops_coords: tuple[int, int, int, int] | None = None,
    ) -> tuple[PIL.Image.Image, PIL.Image.Image]:
        """
        Postprocess the image, optionally apply mask overlay
        """
        image = self._image_processor.postprocess(
            image,
            output_type=output_type,
        )
        # optionally apply the mask overlay
        if crops_coords is not None and (original_image is None or original_mask is None):
            raise ValueError("original_image and original_mask must be provided if crops_coords is provided")

        elif crops_coords is not None and output_type != "pil":
            raise ValueError("output_type must be 'pil' if crops_coords is provided")

        elif crops_coords is not None:
            image = [
                self._image_processor.apply_overlay(original_mask, original_image, i, crops_coords) for i in image
            ]

        return image


class VaeImageProcessorLDM3D(VaeImageProcessor):