Overview
Diffusers is a modular library from Hugging Face that provides state-of-the-art pretrained diffusion models and pipelines for image, audio and 3D generation. It focuses on usability and customizability, offering easy inference APIs as well as tools for training and experimenting with schedulers, models and pipelines.
Key Features
- Ready-to-use pipelines for text-to-image, image-to-image, inpainting and more.
- Interchangeable schedulers and modular model components to tune sampling quality and speed.
- Large collection of pretrained checkpoints on the Hugging Face Hub and compatibility with popular backends (PyTorch, optimized runtimes).
- Active community, extensive documentation and frequent releases.
Use Cases
- Rapid prototyping of generative models for research and creative applications.
- Production inference pipelines for image and media generation.
- Training and fine-tuning diffusion models with custom schedulers and components.
Technical Characteristics
- Python-first library with strong PyTorch integration and optional optimizations for different hardware.
- Modular design: pipelines, schedulers, models and utilities are composable and extendable.
- Large ecosystem integration with the Hugging Face Hub for model discovery and distribution.