# Face Aging via Diffusion-based Editing Source: [Face Aging via Diffusion-based Editing]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:26:19.104Z Authors: Xiangyi Chen, St\\'ephane Lathuili\\\`ere Published: 2023-09-20T13:47:10.000Z ## Abstract In this paper, we address the problem of face aging: generating past or future facial images by incorporating age-related changes to the given face. Previous aging methods rely solely on human facial image datasets and are thus constrained by their inherent scale and bias. This restricts their application to a limited generatable age range and the inability to handle large age gaps. We propose FADING, a novel approach to address Face Aging via DIffusion-based editiNG. We go beyond existing methods by leveraging the rich prior of large-scale language-image diffusion models. First, we specialize a pre-trained diffusion model for the task of face age editing by using an age-aware fine-tuning scheme. Next, we invert the input image to latent noise and obtain optimized null text embeddings. Finally, we perform text-guided local age editing via attention control. The quantitative and qualitative analyses demonstrate that our method outperforms existing approaches with respect to aging accuracy, attribute preservation, and aging quality. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Face Restoration and Age Editing on Cross-age dataset](): Face Restoration and Age Editing; Cross-age dataset; PSNR: 22.28. - [Face Re-aging on FFHQ (1,000 unseen images)](): Face Re-aging; FFHQ (1,000 unseen images); Age Error (10): 10.2. ## Original sources and implementation [Original paper]() [PDF]()