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.

在保护 AI 生成图像的版权方面，水印技术至关重要。我们提出了 WMAdapter，这是一个扩散模型水印插件，可以在扩散生成过程中进行无缝水印印制。WMAdapter 高效、稳健，着重于高品质图像生成。为了实现这一点，我们进行了两个关键设计：（1）我们开发了一个轻量级的上下文适配器结构，实现了从强大预训练后期水印模型的有效知识转移。 （2）我们引入了额外的微调步骤，并设计了混合微调策略，以进一步提高图像质量并消除微小的瑕疵。实证结果表明，WMAdapter 具有较强的灵活性、异常的图像生成质量和竞争性的水印鲁棒性。