Watermarking is crucial for protecting the copyright of AI-generated images. We propose WMAdapter, a diffusion model watermark plugin that takes user-specified watermark information and allows for seamless watermark imprinting during the diffusion generation process. WMAdapter is efficient and robust, with a strong emphasis on high generation quality. To achieve this, we make two key designs: (1) We develop a contextual adapter structure that is lightweight and enables effective knowledge transfer from heavily pretrained post-hoc watermarking models. (2) We introduce an extra finetuning step and design a hybrid finetuning strategy to further improve image quality and eliminate tiny artifacts. Empirical results demonstrate that WMAdapter offers strong flexibility, exceptional image generation quality and competitive watermark robustness.
@article{arxiv.2406.08337,
title = {WMAdapter: Adding WaterMark Control to Latent Diffusion Models},
author = {Hai Ci and Yiren Song and Pei Yang and Jinheng Xie and Mike Zheng Shou},
journal= {arXiv preprint arXiv:2406.08337},
year = {2024}
}