Diffusers documentation

AutoPipeline

Join the Model Database community

and get access to the augmented documentation experience

Collaborate on models, datasets and Spaces

Faster examples with accelerated inference

Switch between documentation themes

to get started

AutoPipeline

🤗 Diffusers is able to complete many different tasks, and you can often reuse the same pretrained weights for multiple tasks such as text-to-image, image-to-image, and inpainting. If you’re new to the library and diffusion models though, it may be difficult to know which pipeline to use for a task. For example, if you’re using the runwayml/stable-diffusion-v1-5 checkpoint for text-to-image, you might not know that you could also use it for image-to-image and inpainting by loading the checkpoint with the StableDiffusionImg2ImgPipeline and StableDiffusionInpaintPipeline classes respectively.

The AutoPipeline class is designed to simplify the variety of pipelines in 🤗 Diffusers. It is a generic, task-first pipeline that lets you focus on the task. The AutoPipeline automatically detects the correct pipeline class to use, which makes it easier to load a checkpoint for a task without knowing the specific pipeline class name.

Take a look at the AutoPipeline reference to see which tasks are supported. Currently, it supports text-to-image, image-to-image, and inpainting.

This tutorial shows you how to use an AutoPipeline to automatically infer the pipeline class to load for a specific task, given the pretrained weights.

Choose an AutoPipeline for your task

Start by picking a checkpoint. For example, if you’re interested in text-to-image with the runwayml/stable-diffusion-v1-5 checkpoint, use AutoPipelineForText2Image:

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 = "peasant and dragon combat, wood cutting style, viking era, bevel with rune"

image = pipeline(prompt, num_inference_steps=25).images[0]

generated image of peasant fighting dragon in wood cutting style

Under the hood, AutoPipelineForText2Image:

automatically detects a "stable-diffusion" class from the model_index.json file
loads the corresponding text-to-image StableDiffusionPipline based on the "stable-diffusion" class name

Likewise, for image-to-image, AutoPipelineForImage2Image detects a "stable-diffusion" checkpoint from the model_index.json file and it’ll load the corresponding StableDiffusionImg2ImgPipeline behind the scenes. You can also pass any additional arguments specific to the pipeline class such as strength, which determines the amount of noise or variation added to an input image:

from diffusers import AutoPipelineForImage2Image

pipeline = AutoPipelineForImage2Image.from_pretrained(
    "runwayml/stable-diffusion-v1-5",
    torch_dtype=torch.float16,
    use_safetensors=True,
).to("cuda")
prompt = "a portrait of a dog wearing a pearl earring"

url = "https://upload.wikimedia.org/wikipedia/commons/thumb/0/0f/1665_Girl_with_a_Pearl_Earring.jpg/800px-1665_Girl_with_a_Pearl_Earring.jpg"

response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")
image.thumbnail((768, 768))

image = pipeline(prompt, image, num_inference_steps=200, strength=0.75, guidance_scale=10.5).images[0]

generated image of a vermeer portrait of a dog wearing a pearl earring

And if you want to do inpainting, then AutoPipelineForInpainting loads the underlying StableDiffusionInpaintPipeline class in the same way:

from diffusers import AutoPipelineForInpainting
from diffusers.utils import load_image

pipeline = AutoPipelineForInpainting.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True
).to("cuda")

img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"

init_image = load_image(img_url).convert("RGB")
mask_image = load_image(mask_url).convert("RGB")

prompt = "A majestic tiger sitting on a bench"
image = pipeline(prompt, image=init_image, mask_image=mask_image, num_inference_steps=50, strength=0.80).images[0]

generated image of a tiger sitting on a bench

If you try to load an unsupported checkpoint, it’ll throw an error:

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"

Use multiple pipelines

For some workflows or if you’re loading many pipelines, it is more memory-efficient to reuse the same components from a checkpoint instead of reloading them which would unnecessarily consume additional memory. For example, if you’re using a checkpoint for text-to-image and you want to use it again for image-to-image, use the from_pipe() method. This method creates a new pipeline from the components of a previously loaded pipeline at no additional memory cost.

The from_pipe() method detects the original pipeline class and maps it to the new pipeline class corresponding to the task you want to do. For example, if you load a "stable-diffusion" class pipeline for text-to-image:

from diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image

pipeline_text2img = AutoPipelineForText2Image.from_pretrained(
    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
)
print(type(pipeline_text2img))
"<class 'diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline'>"

Then from_pipe() maps the original "stable-diffusion" pipeline class to StableDiffusionImg2ImgPipeline:

pipeline_img2img = AutoPipelineForImage2Image.from_pipe(pipeline_text2img)
print(type(pipeline_img2img))
"<class 'diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline'>"

If you passed an optional argument - like disabling the safety checker - to the original pipeline, this argument is also passed on to the new pipeline:

from diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image

pipeline_text2img = AutoPipelineForText2Image.from_pretrained(
    "runwayml/stable-diffusion-v1-5",
    torch_dtype=torch.float16,
    use_safetensors=True,
    requires_safety_checker=False,
).to("cuda")

pipeline_img2img = AutoPipelineForImage2Image.from_pipe(pipeline_text2img)
print(pipe.config.requires_safety_checker)
"False"

You can overwrite any of the arguments and even configuration from the original pipeline if you want to change the behavior of the new pipeline. For example, to turn the safety checker back on and add the strength argument:

pipeline_img2img = AutoPipelineForImage2Image.from_pipe(pipeline_text2img, requires_safety_checker=True, strength=0.3)

←Understanding models and schedulers Train a diffusion model→