Download
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. 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. 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.
Features
- Apple Silicon (M1/M2) support
- 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
- Schedulers, for creating your own end-to-end diffusion systems
- To generate an image from text, use the from_pretrained method to load any pretrained diffusion model
Project Samples
