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| # Diffusers | |
| Diffusers is a library of state-of-the-art pretrained diffusion models for generating videos, images, and audio. | |
| The library revolves around the [DiffusionPipeline](/docs/diffusers/pr_11739/en/api/pipelines/overview#diffusers.DiffusionPipeline), an API designed for: | |
| - easy inference with only a few lines of code | |
| - flexibility to mix-and-match pipeline components (models, schedulers) | |
| - loading and using adapters like LoRA | |
| 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. | |
| Get started right away with a Diffusers model on the [Hub](https://huggingface.co/models?library=diffusers&sort=trending) today! | |
| ## Learn | |
| 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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