# Stable-Makeup

**Repository Path**: sinkids/Stable-Makeup

## Basic Information

- **Project Name**: Stable-Makeup
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-12-22
- **Last Updated**: 2025-12-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Stable-Makeup: When Real-World Makeup Transfer Meets Diffusion Model

<a href="https://arxiv.org/abs/2403.07764"><img src="https://img.shields.io/badge/arXiv-2403.07764-b31b1b.svg" height=22.5></a>
![teaser](assets/sm_teaser.jpg)
Our proposed framework, Stable-Makeup, is a novel diffusion-based method for makeup transfer that can robustly transfer a diverse range of real-world makeup styles, from light to extremely heavy makeup.

## Method Details
![method](https://github.com/Xiaojiu-z/Stable-Makeup/blob/main/assets/sm_method.jpg)
Given a source image $\mathit{I_s}$ , a reference makeup image $\mathit{I_m}$ and an obtained facial structure control image $\mathit{I_c}$ , Stable-Makeup utilizes D-P makeup encoder to encode $\mathit{I_m}$. Content and structural encoders are used to encode $\mathit{I_s}$ and $\mathit{I_c}$ respectively. With the aid of the makeup cross-attention layers, Stable-Makeup aligns the facial regions of $\mathit{I_s}$ and $\mathit{I_m}$ , enabling successful transfers the intricate makeup details. After content-structure decoupling training, Stable-Makeup further maintains content and structure of $\mathit{I_s}$ .

## Todo List
1. - [x] inference and training code
2. - [x] pre-trained weights

## Getting Started
### Environment Setup
Our code is built on the [diffusers](https://github.com/huggingface/diffusers/) version of Stable Diffusion v1-5. We use [SPIGA](https://github.com/andresprados/SPIGA) and [facelib](https://github.com/sajjjadayobi/FaceLib) to draw face structural images. 
```shell
git clone https://github.com/Xiaojiu-z/Stable-Makeup.git
cd Stable-Makeup
```
### Pretrained Models
[Google Drive](https://drive.google.com/drive/folders/1397t27GrUyLPnj17qVpKWGwg93EcaFfg?usp=sharing).
Download them and save them to the directory `models/stablemakeup`. One deviation from the original paper is randomly dropping out inputs into the structural encoder during training, resulting in improved semantic alignment. Enjoy it!

### Inference

```python
python infer_kps.py
```

### Training
You can prepare datasets following our paper and make a jsonl file (each line with 4 key-value pairs, including original id, edited id, augmented id, face structural image of edited id) or you can implement a dataset and a dataloader class by yourself (Probably faster than organizing into my data form).

```python
bash train.sh
```

### Gradio demo
We provide a simple gr demo for more flexible use.
```python
python gradio_demo_kps.py
```

## Citation
```
@article{zhang2024stable,
  title={Stable-Makeup: When Real-World Makeup Transfer Meets Diffusion Model},
  author={Zhang, Yuxuan and Wei, Lifu and Zhang, Qing and Song, Yiren and Liu, Jiaming and Li, Huaxia and Tang, Xu and Hu, Yao and Zhao, Haibo},
  journal={arXiv preprint arXiv:2403.07764},
  year={2024}
}
```