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# 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]
```
<Tip>
Check out the [AutoPipeline](../../tutorials/autopipeline) tutorial to learn how to use this API!
</Tip>
`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
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