def __call__(
        self,
        batch_size: int = 1,
        num_inference_steps: int = 50,
        generator: Optional[torch.Generator] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        **kwargs,
    ) -> Union[ImagePipelineOutput, Tuple]:
        r"""
        Args:
            batch_size (`int`, `optional`, defaults to 1): The number of images to generate.
            num_inference_steps (`int`, `optional`, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            generator (`torch.Generator`, `optional`): A [torch
                generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
                deterministic.
            output_type (`str`, `optional`, defaults to `"pil"`): The output format of the generate image. Choose
                between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
            return_dict (`bool`, `optional`, defaults to `True`): Whether or not to return a
                [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.

        Returns:
            [`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
            `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
            generated images.
        """
        # For more information on the sampling method you can take a look at Algorithm 2 of
        # the official paper: https://arxiv.org/pdf/2202.09778.pdf

        # Sample gaussian noise to begin loop
        image = torch.randn(
            (batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
            generator=generator,
        )
        image = image.to(self.device)

        self.scheduler.set_timesteps(num_inference_steps)
        for t in self.progress_bar(self.scheduler.timesteps):
            model_output = self.unet(image, t).sample

            image = self.scheduler.step(model_output, t, image).prev_sample

        image = (image / 2 + 0.5).clamp(0, 1)
        image = image.cpu().permute(0, 2, 3, 1).numpy()
        if output_type == "pil":
            image = self.numpy_to_pil(image)

        if not return_dict:
            return (image,)

        return ImagePipelineOutput(images=image)

        Args:
            batch_size (`int`, `optional`, defaults to 1): The number of images to generate.
            num_inference_steps (`int`, `optional`, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            generator (`torch.Generator`, `optional`): A [torch
                generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
                deterministic.
            output_type (`str`, `optional`, defaults to `"pil"`): The output format of the generate image. Choose
                between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
            return_dict (`bool`, `optional`, defaults to `True`): Whether or not to return a
                [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.

        Returns:
            [`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
            `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
            generated images.
        
promptist/diffusers/src/diffusers/pipelines/pndm/pipeline_pndm.py