index.mdx

🧨 Diffusers

🤗 Diffusers provides pretrained vision diffusion models, and serves as a modular toolbox for inference and training.

More precisely, 🤗 Diffusers offers:

• State-of-the-art diffusion pipelines that can be run in inference with just a couple of lines of code (see Using Diffusers) or have a look at Pipelines to get an overview of all supported pipelines and their corresponding papers.
• Various noise schedulers that can be used interchangeably for the preferred speed vs. quality trade-off in inference. For more information see Schedulers.
• Multiple types of models, such as UNet, can be used as building blocks in an end-to-end diffusion system. See Models for more details
• Training examples to show how to train the most popular diffusion model tasks. For more information see Training.

🧨 Diffusers Pipelines

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
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_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
stable_diffusion	Stable Diffusion	Image-to-Image Text-Guided Generation
stable_diffusion	Stable Diffusion	Text-Guided Image Inpainting
stochastic_karras_ve	Elucidating the Design Space of Diffusion-Based Generative Models	Unconditional Image Generation

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