Recent diffusion model research focuses on generating identity-consistent images from a reference photo, but they struggle to accurately control age while preserving identity, and fine-tuning such models often requires costly paired images across ages. In this paper, we propose AgeBooth, a novel age-specific finetuning approach that can effectively enhance the age control capability of adapterbased identity personalization models without the need for expensive age-varied datasets. To reduce dependence on a large amount of age-labeled data, we exploit the linear nature of aging by introducing age-conditioned prompt blending and an age-specific LoRA fusion strategy that leverages SVDMix, a matrix fusion technique. These techniques enable high-quality generation of intermediate-age portraits. Our AgeBooth produces realistic and identity-consistent face images across different ages from a single reference image. Experiments show that AgeBooth achieves superior age control and visual quality compared to previous state-of-the-art editing-based methods.
@article{arxiv.2510.05715,
title = {AgeBooth: Controllable Facial Aging and Rejuvenation via Diffusion Models},
author = {Shihao Zhu and Bohan Cao and Ziheng Ouyang and Zhen Li and Peng-Tao Jiang and Qibin Hou},
journal= {arXiv preprint arXiv:2510.05715},
year = {2025}
}