def __call__(
        self,
        prompt_ids: jnp.array,
        params: Union[Dict, FrozenDict],
        prng_seed: jax.random.PRNGKey,
        num_inference_steps: int = 50,
        height: int = 512,
        width: int = 512,
        guidance_scale: float = 7.5,
        latents: jnp.array = None,
        return_dict: bool = True,
        jit: bool = False,
        debug: bool = False,
        **kwargs,
    ):
        r"""
        Function invoked when calling the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`):
                The prompt or prompts to guide the image generation.
            height (`int`, *optional*, defaults to 512):
                The height in pixels of the generated image.
            width (`int`, *optional*, defaults to 512):
                The width in pixels of the generated image.
            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.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            generator (`torch.Generator`, *optional*):
                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
                deterministic.
            latents (`jnp.array`, *optional*):
                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                tensor will ge generated by sampling using the supplied random `generator`.
            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`.
            jit (`bool`, defaults to `False`):
                Whether to run `pmap` versions of the generation and safety scoring functions. NOTE: This argument
                exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a future release.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] instead of
                a plain tuple.

        Returns:
            [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] or `tuple`:
            [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a
            `tuple. When returning a tuple, the first element is a list with the generated images, and the second
            element is a list of `bool`s denoting whether the corresponding generated image likely represents
            "not-safe-for-work" (nsfw) content, according to the `safety_checker`.
        """
        if jit:
            images = _p_generate(
                self, prompt_ids, params, prng_seed, num_inference_steps, height, width, guidance_scale, latents, debug
            )
        else:
            images = self._generate(
                prompt_ids, params, prng_seed, num_inference_steps, height, width, guidance_scale, latents, debug
            )

        if self.safety_checker is not None:
            safety_params = params["safety_checker"]
            images_uint8_casted = (images * 255).round().astype("uint8")
            num_devices, batch_size = images.shape[:2]

            images_uint8_casted = np.asarray(images_uint8_casted).reshape(num_devices * batch_size, height, width, 3)
            images_uint8_casted, has_nsfw_concept = self._run_safety_checker(images_uint8_casted, safety_params, jit)
            images = np.asarray(images)

            # block images
            if any(has_nsfw_concept):
                for i, is_nsfw in enumerate(has_nsfw_concept):
                    if is_nsfw:
                        images[i] = np.asarray(images_uint8_casted[i])

            images = images.reshape(num_devices, batch_size, height, width, 3)
        else:
            has_nsfw_concept = False

        if not return_dict:
            return (images, has_nsfw_concept)

        return FlaxStableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept)

        Function invoked when calling the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`):
                The prompt or prompts to guide the image generation.
            height (`int`, *optional*, defaults to 512):
                The height in pixels of the generated image.
            width (`int`, *optional*, defaults to 512):
                The width in pixels of the generated image.
            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.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            generator (`torch.Generator`, *optional*):
                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
                deterministic.
            latents (`jnp.array`, *optional*):
                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                tensor will ge generated by sampling using the supplied random `generator`.
            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`.
            jit (`bool`, defaults to `False`):
                Whether to run `pmap` versions of the generation and safety scoring functions. NOTE: This argument
                exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a future release.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] instead of
                a plain tuple.

        Returns:
            [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] or `tuple`:
            [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a
            `tuple. When returning a tuple, the first element is a list with the generated images, and the second
            element is a list of `bool`s denoting whether the corresponding generated image likely represents
            "not-safe-for-work" (nsfw) content, according to the `safety_checker`.
        
promptist/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py