Awesome

<p align="center"> <h2 align="center">SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing</h2> <h3 align="center">(CVPR 2024 Highlight)</h3> <p align="center"> <strong>Zeyinzi Jiang</strong> · <strong>Chaojie Mao</strong> · <strong>Yulin Pan</strong> · <strong>Zhen Han</strong> · <strong>Jingfeng Zhang</strong> <br> <b>Alibaba Group</b> <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> </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.

News

[2023.12.29] Official implementations: code and models have been made public on 🪄SCEPTER library.
[2023.12.28] Integrate into the SWIFT(Scalable lightWeight Infrastructure for Fine-Tuning) library.
[2023.12.18] We propose SCEdit, an efficient and controllable generation framework.

Usage

For a complete implementation, please refer to 🪄SCEPTER.

Installation

git clone https://github.com/modelscope/scepter.git

Text-to-Image Generation

# SD v1.5
python scepter/tools/run_train.py --cfg scepter/methods/scedit/t2i/sd15_512_sce_t2i.yaml
# SD v2.1
python scepter/tools/run_train.py --cfg scepter/methods/scedit/t2i/sd21_768_sce_t2i.yaml
# SD XL
python scepter/tools/run_train.py --cfg scepter/methods/scedit/t2i/sdxl_1024_sce_t2i.yaml

Controllable Image Synthesis

# SD v1.5 + hed
python scepter/tools/run_train.py --cfg scepter/methods/scedit/ctr/sd15_512_sce_ctr_hed.yaml
# SD v2.1 + canny
python scepter/tools/run_train.py --cfg scepter/methods/scedit/ctr/sd21_768_sce_ctr_canny.yaml
# SD XL + depth
python scepter/tools/run_train.py --cfg scepter/methods/scedit/ctr/sdxl_1024_sce_ctr_depth.yaml

Gradio

python -m scepter.tools.webui

We release an independent implementation of SCEdit under SCEPTER, which aims to provide developers with greater flexibility when working with SCEdit. Additionally, for rapid integration, kindly refer to our implementation of SCEdit in SWIFT, specifically within the scetuning_components.

Installation

pip install ms_swift

Training

from swift import Swift, SCETuningConfig
model = ...
tuner_config = SCETuningConfig(
    dims=[1280, 1280, 1280, 1280, 1280, 640, 640, 640, 320, 320, 320, 320],
    target_modules=r'^unet\.up_blocks\.\d+\.resnets\.\d+$') # for diffusers library
model = Swift.prepare_model(model, tuner_config)
print(model.get_trainable_parameters())

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}  
}