> For the complete documentation index, see [llms.txt](https://aisuko.gitbook.io/wiki/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://aisuko.gitbook.io/wiki/ai-techniques/diffusers.md).

# diffusers

## Diffusers

### Overview

Huggingce Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. It can be used to:

* The simple inference solution
* Training customize diffusion model

It focus on:

* Usability over performance
* Simple over easy
* Customizability over abstractions

### The main components

It has three main componenets:

* State-of-the-art `diffusion pipelines` for interence with code
* Interchangeable `noise schedulers` for balancing trade-offs between generation speed and quality
* Pretrained `models` that can be used as building blocks, and combined with schedulers, for creating end-to-end diffusion systems

## Reference

[Huggingface diffusers](https://huggingface.co/docs/diffusers/index) [Diffusers intro](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb)