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| <p align="center"> |
| <br> |
| <img src="https://raw.githubusercontent.com/huggingface/diffusers/77aadfee6a891ab9fcfb780f87c693f7a5beeb8e/docs/source/imgs/diffusers_library.jpg" width="400" style="border: none;"/> |
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| </p> |
| |
| # Diffusers |
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| Diffusers is a library of state-of-the-art pretrained diffusion models for generating videos, images, and audio. |
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| The library revolves around the [`DiffusionPipeline`], an API designed for: |
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| - easy inference with only a few lines of code |
| - flexibility to mix-and-match pipeline components (models, schedulers) |
| - 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. |
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| Get started right away with a Diffusers model on the [Hub](https://huggingface.co/models?library=diffusers&sort=trending) today! |
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| ## Learn |
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| 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. |
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