implemented for all pipelines (downloading, saving, running on a particular device, etc.). The pipeline also inherits the following loading methods: load_textual_inversion() for loading textual inversion embeddings load_lora_weights() for loading LoRA weights save_lora_weights() for saving LoRA weights from_single_file() for loading .ckpt files load_ip_adapter() for loading IP Adapters  __call__  < source > ( prompt: Union = None image: Union = None num_inference_steps: int = 4 strength: float = 0.8 original_inference_steps: int = None timesteps: List = None guidance_scale: float = 8.5 num_images_per_prompt: Optional = 1 generator: Union = None latents: Optional = None prompt_embeds: Optional = None ip_adapter_image: Union = None output_type: Optional = 'pil' return_dict: bool = True cross_attention_kwargs: Optional = None clip_skip: Optional = None callback_on_step_end: Optional = None callback_on_step_end_tensor_inputs: List = ['latents'] **kwargs  ) → StableDiffusionPipelineOutput or tuple  Parameters   prompt (str or List[str], optional) —