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🧨 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 Open In Colab
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 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
stochatic_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.