huggingface/diffusers

🤗 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. 🤗 Diffusers offers three core components: - 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. ## Installation We recommend installing 🤗 Diffusers in a virtual environment from PyPI or Conda. For more details about installing PyTorch, please refer to their official documentation. ### PyTorch With `pip` (official package): pip install --upgrade diffusers[torch] With `conda` (maintained by the community):