AutoPipeline 🤗 Diffusers is able to complete many different tasks, and you can often reuse the same pretrained weights for multiple tasks such as text-to-image, image-to-image, and inpainting. If you’re new to the library and diffusion models though, it may be difficult to know which pipeline to use for a task. For example, if you’re using the runwayml/stable-diffusion-v1-5 checkpoint for text-to-image, you might not know that you could also use it for image-to-image and inpainting by loading the checkpoint with the StableDiffusionImg2ImgPipeline and StableDiffusionInpaintPipeline classes respectively. The AutoPipeline class is designed to simplify the variety of pipelines in 🤗 Diffusers. It is a generic, task-first pipeline that lets you focus on the task. The AutoPipeline automatically detects the correct pipeline class to use, which makes it easier to load a checkpoint for a task without knowing the specific pipeline class name. Take a look at the AutoPipeline reference to see which tasks are supported. Currently, it supports text-to-image, image-to-image, and inpainting. This tutorial shows you how to use an AutoPipeline to automatically infer the pipeline class to load for a specific task, given the pretrained weights.  Choose an AutoPipeline for your task Start by picking a checkpoint. For example, if you’re interested in text-to-image with the runwayml/stable-diffusion-v1-5 checkpoint, use AutoPipelineForText2Image:   Copied from diffusers import AutoPipelineForText2Image