You are viewing v0.9.0 version. A newer version v0.14.0 is available.
🤗 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.
Note: Pipelines are simple examples of how to play around with the diffusion systems as described in the corresponding papers.