# Diffusers Diffusers provides pretrained diffusion models and the building blocks for custom image, video, and audio workflows. It has two main paths. - [`DiffusionPipeline`] supports few-line inference with pretrained checkpoints, plus adapters like LoRA. This is the easy path for generation. - [Modular Diffusers](./modular_diffusers/overview) enables composable blocks and [`ModularPipeline`] for custom pipelines when you need more control. Optimizations such as offloading and quantization keep large models runnable on memory-constrained devices. If memory is not an issue, Diffusers also supports `torch.compile` for faster inference. Browse trending Diffusers models on the [Hub](https://huggingface.co/models?library=diffusers&sort=trending) now. ## Learn If you're a beginner, start with the [Hugging Face Diffusion Models Course](https://huggingface.co/learn/diffusion-course/unit0/1). It covers diffusion theory and how to generate images, fine-tune models, and more with Diffusers. The [Quickstart](./quicktour) also includes a copyable agent setup prompt for inference. ## Where next - [Inference](./using-diffusers/loading) — load pipelines and run generation - [Optimize and scale](./stable_diffusion) — memory, speed, quantization, and serving - [Modular Diffusers](./modular_diffusers/overview) — build custom pipelines from blocks - [Train and fine-tune](./training/overview) — train diffusion models and adapters - [CLI](./using-diffusers/cli) - run and package pipelines from the command line