FLUX-Makeup

FLUX-Makeup: High-Fidelity, Identity-Consistent, and Robust Makeup Transfer via Diffusion Transformer

We propose FLUX-Makeup, a high-fidelity, identityconsistent, and robust makeup transfer framework that eliminates the need for any auxiliary face-control components.

💪 Highlight Feature

• Core Strengths: FLUX-Makeup delivers high-fidelity, consistent, and robust makeup transfer using only a 'source + reference' input—the most natural interaction format—without the need for any additional facial control modules.
• Data Engine & HQMT Dataset: We developed an extensible, filterable, and quality-controlled data generation pipeline, and curated a high-quality paired makeup dataset containing over 50,000 samples, named HQMT.
• Architecture: Decoupled Feature Injection: Through RefLoRAInjector, we define two sets of low-rank projections to achieve precise extraction of makeup-related information while effectively preventing identity collapse and background distortion.

💡 Github

FLUX-Makeup

🧩 Environment Setup

pip install -r requirements.txt

📂 Preparation of Pretrained Models

We provide pretrained weights for evaluation and deployment. Please download the checkpoint.pt files from here and place them in the model directory. Also, please download the 79999_iter.pth files from here and place them in the model directory. In addition, download the pretrained weights of Flux-Kontext.dev from here to serve as the backbone model.

📂 ComfyUI

You can use ComfyUI within Flux_Makeup_ComfyUI, where the model folders need to be placed in:

Flux_Makeup_ComfyUI/models/stable_makeup

⏳ Inference Pipeline

Here we provide the inference code for our FLUX-Makeup.

sh eval.sh

🌸 Acknowledgement

This code is mainly built upon Diffusers, Flux, Stable-Makeup and ComfyUI_Stable_Makeup repositories. Thanks so much for their solid work!

If you find this repository useful, please consider citing our paper:

@article{zhu2025flux,
  title={FLUX-Makeup: High-Fidelity, Identity-Consistent, and Robust Makeup Transfer via Diffusion Transformer},
  author={Zhu, Jian and Liu, Shanyuan and Li, Liuzhuozheng and Gong, Yue and Wang, He and Cheng, Bo and Ma, Yuhang and Wu, Liebucha and Wu, Xiaoyu and Leng, Dawei and others},
  journal={arXiv preprint arXiv:2508.05069},
  year={2025}
}