diffusers/docs/source/en/api/pipelines/auto_pipeline.md

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.

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 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
• ControlNet
• Stable Diffusion XL (SDXL)
• DeepFloyd IF
• Kandinsky 2.1
• Kandinsky 2.2

AutoPipelineForText2Image

[[autodoc]] AutoPipelineForText2Image - all - from_pretrained - from_pipe

AutoPipelineForImage2Image

[[autodoc]] AutoPipelineForImage2Image - all - from_pretrained - from_pipe

AutoPipelineForInpainting

[[autodoc]] AutoPipelineForInpainting - all - from_pretrained - from_pipe