A Training-Free Plug-and-Play Watermark Framework for Stable Diffusion
Guokai Zhang Lanjun Wang Yuting Su An-An Liu
Nowadays, the family of Stable Diffusion (SD) models has gained prominence
for its high quality outputs and scalability. This has also raised security
concerns on social media, as malicious users can create and disseminate harmful
content. Existing approaches involve training components or entire SDs to embed
a watermark in generated images for traceability and responsibility
attribution. However, in the era of AI-generated content (AIGC), the rapid
iteration of SDs renders retraining with watermark models costly. To address
this, we propose a training-free plug-and-play watermark framework for SDs.
Without modifying any components of SDs, we embed diverse watermarks in the
latent space, adapting to the denoising process. Our experimental findings
reveal that our method effectively harmonizes image quality and watermark
invisibility. Furthermore, it performs robustly under various attacks. We also
have validated that our method is generalized to multiple versions of SDs, even
without retraining the watermark model.