From f83dd5c984bc9898e01bc46fd43e0f8455604adb Mon Sep 17 00:00:00 2001 From: Steven Liu <59462357+stevhliu@users.noreply.github.com> Date: Wed, 30 Jul 2025 08:31:01 -0700 Subject: [PATCH] [docs] Update index (#12020) initial Co-authored-by: Sayak Paul --- docs/source/en/index.md | 39 +++++++++++++-------------------------- 1 file changed, 13 insertions(+), 26 deletions(-) diff --git a/docs/source/en/index.md b/docs/source/en/index.md index 04e907a542..0aca1d22c1 100644 --- a/docs/source/en/index.md +++ b/docs/source/en/index.md @@ -12,37 +12,24 @@ specific language governing permissions and limitations under the License.


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# Diffusers -🤗 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. -- Interchangeable [noise schedulers](api/schedulers/overview) for balancing trade-offs between generation speed and quality. -- Pretrained [models](api/models) that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems. +- 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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Tutorials
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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!

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How-to guides
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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.

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Conceptual guides
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Understand why the library was designed the way it was, and learn more about the ethical guidelines and safety implementations for using the library.

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Reference
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Technical descriptions of how 🤗 Diffusers classes and methods work.

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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. + +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.