#
🧨 Diffusers

🤗 Diffusers provides pretrained vision and audio 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
--- | --- | --- | ---
alt_diffusion | AltDiffusion | Image-to-Image Text-Guided Generation |
audio_diffusion | Audio Diffusion | Unconditional Audio 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 |
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 |
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 |
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 |

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