🤗 Diffusers is a PyTorch and Flax library for state-of-the-art diffusion models, enabling image, video, and audio generation. It targets researchers and developers seeking a modular toolbox for both inference and training, prioritizing usability, simplicity, and customizability.

How It Works

The library provides three core components: pre-trained diffusion pipelines for easy inference, interchangeable noise schedulers for controlling generation speed and quality, and modular pre-trained models that can be combined to build custom diffusion systems. This modular design allows users to leverage existing components or swap them out for custom implementations.

Quick Start & Requirements

• Install with pip install --upgrade diffusers[torch] or pip install --upgrade diffusers[flax].
• Requires PyTorch or Flax. GPU with CUDA is recommended for performance.
• Official Quickstart: https://huggingface.co/docs/diffusers/quicktour

Highlighted Details

• Supports over 30,000 checkpoints from the Hugging Face Hub.
• Offers pipelines for various tasks including text-to-image, image-to-image, inpainting, and super-resolution.
• Includes optimization guides for faster inference and reduced memory usage.
• Active community with contributions and issue tracking for new models and schedulers.

Maintenance & Community

• Actively maintained by Hugging Face.
• Community contributions are encouraged via a dedicated contribution guide and issue labels.
• Discord channel available for discussions and support.

Licensing & Compatibility

• Licensed under Apache 2.0.
• Compatible with commercial use and closed-source linking.

Limitations & Caveats

While the library prioritizes usability, performance optimization (e.g., FP16, MPS for Apple Silicon) is available but may require specific configurations. The vast number of models and schedulers can present a learning curve for beginners.