# AutoPipeline Diffusers provides many pipelines for basic tasks like generating images, videos, audio, and inpainting. On top of these, there are specialized pipelines for adapters and features like upscaling, super-resolution, and more. Different pipeline classes can even use the same checkpoint because they share the same pretrained model! With so many different pipelines, it can be overwhelming to know which pipeline class to use. The [AutoPipeline](../api/pipelines/auto_pipeline) class is designed to simplify the variety of pipelines in Diffusers. It is a generic *task-first* pipeline that lets you focus on a task ([`AutoPipelineForText2Image`], [`AutoPipelineForImage2Image`], and [`AutoPipelineForInpainting`]) without needing to know the specific pipeline class. The [AutoPipeline](../api/pipelines/auto_pipeline) automatically detects the correct pipeline class to use. For example, let's use the [dreamlike-art/dreamlike-photoreal-2.0](https://hf.co/dreamlike-art/dreamlike-photoreal-2.0) checkpoint. Under the hood, [AutoPipeline](../api/pipelines/auto_pipeline): 1. Detects a `"stable-diffusion"` class from the [model_index.json](https://hf.co/dreamlike-art/dreamlike-photoreal-2.0/blob/main/model_index.json) file. 2. Depending on the task you're interested in, it loads the [`StableDiffusionPipeline`], [`StableDiffusionImg2ImgPipeline`], or [`StableDiffusionInpaintPipeline`]. Any parameter (`strength`, `num_inference_steps`, etc.) you would pass to these specific pipelines can also be passed to the [AutoPipeline](../api/pipelines/auto_pipeline). ```py from diffusers import AutoPipelineForText2Image import torch pipe_txt2img = AutoPipelineForText2Image.from_pretrained( "dreamlike-art/dreamlike-photoreal-2.0", torch_dtype=torch.float16, use_safetensors=True ).to("cuda") prompt = "cinematic photo of Godzilla eating sushi with a cat in a izakaya, 35mm photograph, film, professional, 4k, highly detailed" generator = torch.Generator(device="cpu").manual_seed(37) image = pipe_txt2img(prompt, generator=generator).images[0] image ```

```py from diffusers import AutoPipelineForImage2Image from diffusers.utils import load_image import torch pipe_img2img = AutoPipelineForImage2Image.from_pretrained( "dreamlike-art/dreamlike-photoreal-2.0", torch_dtype=torch.float16, use_safetensors=True ).to("cuda") init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/autopipeline-text2img.png") prompt = "cinematic photo of Godzilla eating burgers with a cat in a fast food restaurant, 35mm photograph, film, professional, 4k, highly detailed" generator = torch.Generator(device="cpu").manual_seed(53) image = pipe_img2img(prompt, image=init_image, generator=generator).images[0] image ``` Notice how the [dreamlike-art/dreamlike-photoreal-2.0](https://hf.co/dreamlike-art/dreamlike-photoreal-2.0) checkpoint is used for both text-to-image and image-to-image tasks? To save memory and avoid loading the checkpoint twice, use the [`~DiffusionPipeline.from_pipe`] method. ```py pipe_img2img = AutoPipelineForImage2Image.from_pipe(pipe_txt2img).to("cuda") image = pipeline(prompt, image=init_image, generator=generator).images[0] image ``` You can learn more about the [`~DiffusionPipeline.from_pipe`] method in the [Reuse a pipeline](../using-diffusers/loading#reuse-a-pipeline) guide.

```py from diffusers import AutoPipelineForInpainting from diffusers.utils import load_image import torch pipeline = AutoPipelineForInpainting.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True ).to("cuda") init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/autopipeline-img2img.png") mask_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/autopipeline-mask.png") prompt = "cinematic photo of a owl, 35mm photograph, film, professional, 4k, highly detailed" generator = torch.Generator(device="cpu").manual_seed(38) image = pipeline(prompt, image=init_image, mask_image=mask_image, generator=generator, strength=0.4).images[0] image ```

## Unsupported checkpoints The [AutoPipeline](../api/pipelines/auto_pipeline) supports [Stable Diffusion](../api/pipelines/stable_diffusion/overview), [Stable Diffusion XL](../api/pipelines/stable_diffusion/stable_diffusion_xl), [ControlNet](../api/pipelines/controlnet), [Kandinsky 2.1](../api/pipelines/kandinsky.md), [Kandinsky 2.2](../api/pipelines/kandinsky_v22), and [DeepFloyd IF](../api/pipelines/deepfloyd_if) checkpoints. If you try to load an unsupported checkpoint, you'll get an error. ```py from diffusers import AutoPipelineForImage2Image import torch pipeline = AutoPipelineForImage2Image.from_pretrained( "openai/shap-e-img2img", torch_dtype=torch.float16, use_safetensors=True ) "ValueError: AutoPipeline can't find a pipeline linked to ShapEImg2ImgPipeline for None" ```