🤗 Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or training your own diffusion models, 🤗 Diffusers is a modular toolbox that supports both. Our library is designed with a focus on usability over performance, simple over easy, and customizability over abstractions.

🤗 Diffusers offers three core components:

• State-of-the-art diffusion pipelines that can be run in inference with just a few lines of code.
• Interchangeable noise schedulers for different diffusion speeds and output quality.
• Pretrained models that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems.

Installation

We recommend installing 🤗 Diffusers in a virtual environment from PyPi or Conda. For more details about installing PyTorch and Flax, please refer to their official documentation.

PyTorch

With pip (official package):

pip install --upgrade diffusers[torch]

With conda (maintained by the community):

conda install -c conda-forge diffusers

Flax

With pip (official package):

pip install --upgrade diffusers[flax]

Apple Silicon (M1/M2) support

Please refer to the How to use Stable Diffusion in Apple Silicon guide.

Quickstart

Generating outputs is super easy with 🤗 Diffusers. To generate an image from text, use the from_pretrained method to load any pretrained diffusion model (browse the Hub for 4000+ checkpoints):

from diffusers import DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
pipeline.to("cuda")
pipeline("An image of a squirrel in Picasso style").images[0]

You can also dig into the models and schedulers toolbox to build your own diffusion system:

from diffusers import DDPMScheduler, UNet2DModel
from PIL import Image
import torch
import numpy as np

scheduler = DDPMScheduler.from_pretrained("google/ddpm-cat-256")
model = UNet2DModel.from_pretrained("google/ddpm-cat-256").to("cuda")
scheduler.set_timesteps(50)

sample_size = model.config.sample_size
noise = torch.randn((1, 3, sample_size, sample_size)).to("cuda")
input = noise

for t in scheduler.timesteps:
    with torch.no_grad():
        noisy_residual = model(input, t).sample
        prev_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample
        input = prev_noisy_sample

image = (input / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()[0]
image = Image.fromarray((image * 255).round().astype("uint8"))
image

Check out the Quickstart to launch your diffusion journey today!

How to navigate the documentation

Documentation	What can I learn?
Tutorial	A basic crash course for learning how to use the library's most important features like using models and schedulers to build your own diffusion system, and training your own diffusion model.
Loading	Guides for how to load and configure all the components (pipelines, models, and schedulers) of the library, as well as how to use different schedulers.
Pipelines for inference	Guides for how to use pipelines for different inference tasks, batched generation, controlling generated outputs and randomness, and how to contribute a pipeline to the library.
Optimization	Guides for how to optimize your diffusion model to run faster and consume less memory.
Training	Guides for how to train a diffusion model for different tasks with different training techniques.

Supported pipelines

Pipeline	Paper	Tasks
alt_diffusion	AltDiffusion	Image-to-Image Text-Guided Generation
audio_diffusion	Audio Diffusion	Unconditional Audio Generation
controlnet	ControlNet with Stable Diffusion	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
paint_by_example	Paint by Example: Exemplar-based Image Editing with Diffusion Models	Image-Guided Image Inpainting
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
semantic_stable_diffusion	Semantic Guidance	Text-Guided Generation
stable_diffusion_text2img	Stable Diffusion	Text-to-Image Generation
stable_diffusion_img2img	Stable Diffusion	Image-to-Image Text-Guided Generation
stable_diffusion_inpaint	Stable Diffusion	Text-Guided Image Inpainting
stable_diffusion_panorama	MultiDiffusion	Text-to-Panorama Generation
stable_diffusion_pix2pix	InstructPix2Pix	Text-Guided Image Editing
stable_diffusion_pix2pix_zero	Zero-shot Image-to-Image Translation	Text-Guided Image Editing
stable_diffusion_attend_and_excite	Attend and Excite for Stable Diffusion	Text-to-Image Generation
stable_diffusion_self_attention_guidance	Self-Attention Guidance	Text-to-Image Generation
stable_diffusion_image_variation	Stable Diffusion Image Variations	Image-to-Image Generation
stable_diffusion_latent_upscale	Stable Diffusion Latent Upscaler	Text-Guided Super Resolution Image-to-Image
stable_diffusion_2	Stable Diffusion 2	Text-to-Image Generation
stable_diffusion_2	Stable Diffusion 2	Text-Guided Image Inpainting
stable_diffusion_2	Depth-Conditional Stable Diffusion	Depth-to-Image Generation
stable_diffusion_2	Stable Diffusion 2	Text-Guided Super Resolution Image-to-Image
stable_diffusion_safe	Safe Stable Diffusion	Text-Guided Generation
stable_unclip	Stable unCLIP	Text-to-Image Generation
stable_unclip	Stable unCLIP	Image-to-Image Text-Guided Generation
stochastic_karras_ve	Elucidating the Design Space of Diffusion-Based Generative Models	Unconditional Image Generation
unclip	Hierarchical Text-Conditional Image Generation with CLIP Latents	Text-to-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

Credits

This library concretizes previous work by many different authors and would not have been possible without their great research and implementations. We'd like to thank, in particular, the following implementations which have helped us in our development and without which the API could not have been as polished today:

• @CompVis' latent diffusion models library, available here
• @hojonathanho original DDPM implementation, available here as well as the extremely useful translation into PyTorch by @pesser, available here
• @ermongroup's DDIM implementation, available here
• @yang-song's Score-VE and Score-VP implementations, available here

We also want to thank @heejkoo for the very helpful overview of papers, code and resources on diffusion models, available here as well as @crowsonkb and @rromb for useful discussions and insights.

Citation

@misc{von-platen-etal-2022-diffusers,
  author = {Patrick von Platen and Suraj Patil and Anton Lozhkov and Pedro Cuenca and Nathan Lambert and Kashif Rasul and Mishig Davaadorj and Thomas Wolf},
  title = {Diffusers: State-of-the-art diffusion models},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/huggingface/diffusers}}
}