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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 |
|
|
