---
title: scepter_scedit
canonical_url: "https://www.modelscope.cn/models/iic/scepter_scedit"
md_url: "https://www.modelscope.cn/models/iic/scepter_scedit.md"
repository: iic/scepter_scedit
last_updated: 2024-04-18
license: "Apache License 2.0"
pipeline_tag: efficient-diffusion-tuning
tasks:
  - efficient-diffusion-tuning
library_name:
  - pytorch
frameworks:
  - Pytorch
downloads: 1430
stars: 3
---

# scepter_scedit

> scepter_scedit - iic 在 ModelScope 开源的模型。🪄SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing Zeyinzi Jiang · Chaojie Mao · Yulin Pan · Zhen Han · Jingfeng Zhang Alibaba Group

iic/scepter_scedit 是 ModelScope 魔搭社区上的efficient-diffusion-tuning模型，采用 Apache License 2.0 许可。

- **Repository**: iic/scepter_scedit
- **License**: Apache License 2.0
- **Tasks**: efficient-diffusion-tuning
- **Downloads**: 1430
- **Stars**: 3
- **Last updated**: 2024-04-18

Source: https://www.modelscope.cn/models/iic/scepter_scedit

---

<p align="center">

  <h2 align="center">🪄SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing</h2>
  <p align="center">
    <strong>Zeyinzi Jiang</strong>
    ·
    <strong>Chaojie Mao</strong>
    ·
    <strong>Yulin Pan</strong>
    ·
    <strong>Zhen Han</strong>
    ·
    <strong>Jingfeng Zhang</strong>
    <br>
    <br>
        <a href="https://arxiv.org/abs/2312.11392"><img src='https://img.shields.io/badge/arXiv-SCEdit-red' alt='Paper PDF'></a>
        <a href='https://scedit.github.io/'><img src='https://img.shields.io/badge/Project_Page-SCEdit-green' alt='Project Page'></a>
        <a href='https://github.com/modelscope/scepter'><img src='https://img.shields.io/badge/scepter-SCEdit-yellow'></a>
        <a href='https://github.com/modelscope/swift'><img src='https://img.shields.io/badge/swift-SCEdit-blue'></a>
    <br>
    <b>Alibaba Group</b>
  </p>
  
  <table align="center">
    <tr>
    <td>
      <img src="assets/figures/show.jpg">
    </td>
    </tr>
  </table>

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.
#### Code Example
```shell
git clone https://github.com/modelscope/scepter.git
cd scepter
PYTHONPATH=. python scepter/tools/run_train.py --cfg scepter/methods/SCEdit/t2i_sdxl_1024_sce.yaml
```

To prepare  the training dataset.

```python
# pip install modelscope
from modelscope.msdatasets import MsDataset
ms_train_dataset = MsDataset.load('style_custom_dataset', namespace='damo', subset_name='3D', split='train_short')
print(next(iter(ms_train_dataset)))
```

## 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}  
}
```