WMAdapter: Adding WaterMark Control to Latent Diffusion Models
<|reference_start|>WMAdapter: Adding WaterMark Control to Latent Diffusion Models: 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.<|reference_end|>
@article{ci2024wmadapter:,
    title={WMAdapter: Adding WaterMark Control to Latent Diffusion Models},
    author={Hai Ci, Yiren Song, Pei Yang, Jinheng Xie, Mike Zheng Shou},
    journal={arXiv preprint arXiv:2406.08337},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08337},
    primaryClass={cs.CV eess.IV}
}