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

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

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. • inference and training code
2. • pre-trained weights

Getting Started

Environment Setup

Our code is built on the diffusers version of Stable Diffusion v1-5. We use SPIGA and facelib to draw face structural images.

git clone https://github.com/Xiaojiu-z/Stable-Makeup.git
cd Stable-Makeup

Pretrained Models

Google Drive. 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 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).

bash train.sh

Gradio demo

We provide a simple gr demo for more flexible use.

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}
}