Diffusers is the go-to library for diffusion-based generative AI developed by Hugging Face.
It provides:
- Turn-key pipelines for tasks like text-to-image, image-to-image, inpainting, depth-to-image, video generation, and audio synthesis, runnable in just a few lines of code.
- Interchangeable schedulers that let you trade off generation speed and output quality, making benchmarking and research easier.
- Modular model components (UNets, auto-encoders, Transformers, VAE decoders, etc.) that can be mixed-and-matched or fine-tuned for custom applications.
- Training utilities & examples covering LoRA/PEFT fine-tuning, DreamBooth-style personalization, and full-from-scratch model training with PyTorch or Flax/JAX back-ends.
- Seamless Hub integration, so you can push or pull checkpoints, collaborate via Spaces, and leverage the wider Hugging Face ecosystem (Accelerate, Optimum, PEFT, etc.).
Designed with readability over raw performance in mind, Diffusers lowers the barrier to extending or prototyping diffusion research while remaining production-ready under the permissive Apache-2.0 license.
