---
license: apache-2.0
language:
- en
tags:
- diffusion model
- stable diffusion
- SCEdit
- Scepter
- Scepter studio
- Controllable
- ControlNet
- Lora
---
<p align="center"><h2 align="center">🪄SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing</h2> </p>
<p align="center">
    <strong>Zeyinzi Jiang</strong>
    ·
    <strong>Chaojie Mao</strong>
    ·
    <strong>Yulin Pan</strong>
    ·
    <strong>Zhen Han</strong>
    ·
    <strong>Jingfeng Zhang</strong>
</p>

<div align="center">
  
[**Paper (ArXiv)**](https://arxiv.org/abs/2312.11392) **|** [**Project Page**](https://scedit.github.io/) **|** [**Code**](https://github.com/modelscope/scepter)**|** [**Swift**](https://github.com/modelscope/swift)
</div>

<p align="center">
    <b>Alibaba Group</b>
</p>

<p>
  <table align="center">
    <tr>
    <td>
      <img src="assets/figures/show.jpg">
    </td>
    </tr>
  </table>
</p>



SCEdit is an efficient generative fine-tuning framework proposed by Alibaba TongYi Vision Intelligence Lab. This framework enhances the fine-tuning capabilities for text-to-image generation downstream tasks and enables quick adaptation to specific generative scenarios, **saving 30%-50% of training memory costs compared to LoRA**. Furthermore, it can be directly extended to controllable image generation tasks, **requiring only 7.9% of the parameters that ControlNet needs for conditional generation and saving 30% of memory usage**. It supports various conditional generation tasks including edge maps, depth maps, segmentation maps, poses, color maps, and image completion.
#### Use Models
```shell
pip install scepter
python -m scepter.tools.webui
```

## BibTeX

```bibtex
@article{jiang2023scedit,
    title = {SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing},
    author = {Jiang, Zeyinzi and Mao, Chaojie and Pan, Yulin and Han, Zhen and Zhang, Jingfeng},
    year = {2023},
    journal = {arXiv preprint arXiv:2312.11392}  
}
```