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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 | |