# AutoPipeline `AutoPipeline` is designed to: 1. make it easy for you to load a checkpoint for a task without knowing the specific pipeline class to use 2. use multiple pipelines in your workflow Based on the task, the `AutoPipeline` class automatically retrieves the relevant pipeline given the name or path to the pretrained weights with the `from_pretrained()` method. To seamlessly switch between tasks with the same checkpoint without reallocating additional memory, use the `from_pipe()` method to transfer the components from the original pipeline to the new one. ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained( "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True ).to("cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipeline(prompt, num_inference_steps=25).images[0] ``` Check out the [AutoPipeline](../../tutorials/autopipeline) tutorial to learn how to use this API! `AutoPipeline` supports text-to-image, image-to-image, and inpainting for the following diffusion models: - [Stable Diffusion](./stable_diffusion/overview) - [ControlNet](./controlnet) - [Stable Diffusion XL (SDXL)](./stable_diffusion/stable_diffusion_xl) - [DeepFloyd IF](./deepfloyd_if) - [Kandinsky 2.1](./kandinsky) - [Kandinsky 2.2](./kandinsky_v22) ## AutoPipelineForText2Image [[autodoc]] AutoPipelineForText2Image - all - from_pretrained - from_pipe ## AutoPipelineForImage2Image [[autodoc]] AutoPipelineForImage2Image - all - from_pretrained - from_pipe ## AutoPipelineForInpainting [[autodoc]] AutoPipelineForInpainting - all - from_pretrained - from_pipe