🤗 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 want to train your own diffusion model, 🤗 Diffusers is a modular toolbox that supports both. Our library is designed with a focus on [usability over performance](conceptual/philosophy#usability-over-performance), [simple over easy](conceptual/philosophy#simple-over-easy), and [customizability over abstractions](conceptual/philosophy#tweakable-contributorfriendly-over-abstraction).
Diffusers is a library of state-of-the-art pretrained diffusion models for generating videos, images, and audio.
The library has three main components:
The library revolves around the [`DiffusionPipeline`], an API designed for:
- State-of-the-art diffusion pipelines for inference with just a few lines of code. There are many pipelines in 🤗 Diffusers, check out the table in the pipeline [overview](api/pipelines/overview) for a complete list of available pipelines and the task they solve.
- easy inference with only a few lines of code
- Interchangeable [noise schedulers](api/schedulers/overview) for balancing trade-offs between generation speed and quality.
- flexibility to mix-and-match pipeline components (models, schedulers)
- Pretrained [models](api/models) that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems.
- loading and using adapters like LoRA
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Diffusers also comes with optimizations - such as offloading and quantization - to ensure even the largest models are accessible on memory-constrained devices. If memory is not an issue, Diffusers supports torch.compile to boost inference speed.
<pclass="text-gray-700">Learn the fundamental skills you need to start generating outputs, build your own diffusion system, and train a diffusion model. We recommend starting here if you're using 🤗 Diffusers for the first time!</p>
If you're a beginner, we recommend starting with the [Hugging Face Diffusion Models Course](https://huggingface.co/learn/diffusion-course/unit0/1). You'll learn the theory behind diffusion models, and learn how to use the Diffusers library to generate images, fine-tune your own models, and more.
<pclass="text-gray-700">Practical guides for helping you load pipelines, models, and schedulers. You'll also learn how to use pipelines for specific tasks, control how outputs are generated, optimize for inference speed, and different training techniques.</p>
<pclass="text-gray-700">Understand why the library was designed the way it was, and learn more about the ethical guidelines and safety implementations for using the library.</p>
Sayak Paul <spsayakpaul@gmail.com>