make_image_grid([pt_to_pil(stage_1_output)[0], pt_to_pil(stage_2_output)[0]], rows=1, rows=2)  Available Pipelines: Pipeline Tasks Colab pipeline_if.py Text-to-Image Generation - pipeline_if_superresolution.py Text-to-Image Generation - pipeline_if_img2img.py Image-to-Image Generation - pipeline_if_img2img_superresolution.py Image-to-Image Generation - pipeline_if_inpainting.py Image-to-Image Generation - pipeline_if_inpainting_superresolution.py Image-to-Image Generation -  IFPipeline  class diffusers.IFPipeline  < source > ( tokenizer: T5Tokenizer text_encoder: T5EncoderModel unet: UNet2DConditionModel scheduler: DDPMScheduler safety_checker: Optional feature_extractor: Optional watermarker: Optional requires_safety_checker: bool = True  )     __call__  < source > ( prompt: Union = None num_inference_steps: int = 100 timesteps: List = None guidance_scale: float = 7.0 negative_prompt: Union = None num_images_per_prompt: Optional = 1 height: Optional = None width: Optional = None eta: float = 0.0 generator: Union = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None output_type: Optional = 'pil' return_dict: bool = True callback: Optional = None callback_steps: int = 1 clean_caption: bool = True cross_attention_kwargs: Optional = None  ) → ~pipelines.stable_diffusion.IFPipelineOutput or tuple  Parameters   prompt (str or List[str], optional) —