library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)  __call__  < source > ( prompt: typing.Union[typing.List[str], str, NoneType] = None source_embeds: Tensor = None target_embeds: Tensor = None height: typing.Optional[int] = None width: typing.Optional[int] = None num_inference_steps: int = 50 guidance_scale: float = 7.5 negative_prompt: typing.Union[typing.List[str], str, NoneType] = None num_images_per_prompt: typing.Optional[int] = 1 eta: float = 0.0 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.FloatTensor] = None prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None cross_attention_guidance_amount: float = 0.1 output_type: typing.Optional[str] = 'pil' return_dict: bool = True callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None callback_steps: typing.Optional[int] = 1 cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None clip_skip: typing.Optional[int] = None  ) → StableDiffusionPipelineOutput or tuple  Parameters   prompt (str or List[str], optional) —