Pipelines

Pipelines provide a simple way to run state-of-the-art diffusion models in inference. Most diffusion systems consist of multiple independently-trained models and highly adaptable scheduler components - all of which are needed to have a functioning end-to-end diffusion system.

As an example, Stable Diffusion has three independently trained models:

To that end, we strive to offer all open-sourced, state-of-the-art diffusion system under a unified API. More specifically, we strive to provide pipelines that

Note that pipelines do not (and should not) offer any training functionality. If you are looking for official training examples, please have a look at examples.

🧨 Diffusers Summary

The following table summarizes all officially supported pipelines, their corresponding paper, and if available a colab notebook to directly try them out.

Pipeline Paper Tasks Colab
alt_diffusion AltDiffusion Image-to-Image Text-Guided Generation -
cycle_diffusion Cycle Diffusion Image-to-Image Text-Guided Generation
dance_diffusion Dance Diffusion Unconditional Audio Generation
ddpm Denoising Diffusion Probabilistic Models Unconditional Image Generation
ddim Denoising Diffusion Implicit Models Unconditional Image Generation
latent_diffusion High-Resolution Image Synthesis with Latent Diffusion Models Text-to-Image Generation
latent_diffusion High-Resolution Image Synthesis with Latent Diffusion Models Super Resolution Image-to-Image
latent_diffusion_uncond High-Resolution Image Synthesis with Latent Diffusion Models Unconditional Image Generation
pndm Pseudo Numerical Methods for Diffusion Models on Manifolds Unconditional Image Generation
score_sde_ve Score-Based Generative Modeling through Stochastic Differential Equations Unconditional Image Generation
score_sde_vp Score-Based Generative Modeling through Stochastic Differential Equations Unconditional Image Generation
stable_diffusion Stable Diffusion Text-to-Image Generation Open In Colab
stable_diffusion Stable Diffusion Image-to-Image Text-Guided Generation Open In Colab
stable_diffusion Stable Diffusion Text-Guided Image Inpainting Open In Colab
stable_diffusion_2 Stable Diffusion 2 Text-to-Image Generation
stable_diffusion_2 Stable Diffusion 2 Text-Guided Image Inpainting
stable_diffusion_2 Stable Diffusion 2 Text-Guided Super Resolution Image-to-Image
stable_diffusion_safe Safe Stable Diffusion Text-Guided Generation Open In Colab
stochastic_karras_ve Elucidating the Design Space of Diffusion-Based Generative Models Unconditional Image Generation
versatile_diffusion Versatile Diffusion: Text, Images and Variations All in One Diffusion Model Text-to-Image Generation
versatile_diffusion Versatile Diffusion: Text, Images and Variations All in One Diffusion Model Image Variations Generation
versatile_diffusion Versatile Diffusion: Text, Images and Variations All in One Diffusion Model Dual Image and Text Guided Generation
vq_diffusion Vector Quantized Diffusion Model for Text-to-Image Synthesis Text-to-Image Generation

Note: Pipelines are simple examples of how to play around with the diffusion systems as described in the corresponding papers.

However, most of them can be adapted to use different scheduler components or even different model components. Some pipeline examples are shown in the Examples below.

Pipelines API

Diffusion models often consist of multiple independently-trained models or other previously existing components.

Each model has been trained independently on a different task and the scheduler can easily be swapped out and replaced with a different one. During inference, we however want to be able to easily load all components and use them in inference - even if one component, e.g. CLIP’s text encoder, originates from a different library, such as Transformers. To that end, all pipelines provide the following functionality:

Note: All pipelines have PyTorch’s autograd disabled by decorating the __call__ method with a torch.no_grad decorator because pipelines should not be used for training. If you want to store the gradients during the forward pass, we recommend writing your own pipeline, see also our community-examples

Contribution

We are more than happy about any contribution to the officially supported pipelines 🤗. We aspire all of our pipelines to be self-contained, easy-to-tweak, beginner-friendly and for one-purpose-only.

Examples

Text-to-Image generation with Stable Diffusion

# make sure you're logged in with `huggingface-cli login`
from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler

pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
pipe = pipe.to("cuda")

prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]

image.save("astronaut_rides_horse.png")

Image-to-Image text-guided generation with Stable Diffusion

The StableDiffusionImg2ImgPipeline lets you pass a text prompt and an initial image to condition the generation of new images.

import requests
from PIL import Image
from io import BytesIO

from diffusers import StableDiffusionImg2ImgPipeline

# load the pipeline
device = "cuda"
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5", revision="fp16", torch_dtype=torch.float16
).to(device)

# let's download an initial image
url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"

response = requests.get(url)
init_image = Image.open(BytesIO(response.content)).convert("RGB")
init_image = init_image.resize((768, 512))

prompt = "A fantasy landscape, trending on artstation"

images = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images

images[0].save("fantasy_landscape.png")

You can also run this example on colab Open In Colab

Tweak prompts reusing seeds and latents

You can generate your own latents to reproduce results, or tweak your prompt on a specific result you liked. This notebook shows how to do it step by step. You can also run it in Google Colab Open In Colab.

In-painting using Stable Diffusion

The StableDiffusionInpaintPipeline lets you edit specific parts of an image by providing a mask and text prompt.

import PIL
import requests
import torch
from io import BytesIO

from diffusers import StableDiffusionInpaintPipeline


def download_image(url):
    response = requests.get(url)
    return PIL.Image.open(BytesIO(response.content)).convert("RGB")


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 = download_image(img_url).resize((512, 512))
mask_image = download_image(mask_url).resize((512, 512))

pipe = StableDiffusionInpaintPipeline.from_pretrained(
    "runwayml/stable-diffusion-inpainting",
    revision="fp16",
    torch_dtype=torch.float16,
)
pipe = pipe.to("cuda")

prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]

You can also run this example on colab Open In Colab