JMLR

MarkDiffusion: An Open-Source Toolkit for Generative Watermarking of Latent Diffusion Models

Authors
Leyi Pan Sheng Guan Zheyu Fu Luyang Si Huan Wang Zian Wang Hanqian Li Xuming Hu Irwin King Philip S. Yu Aiwei Liu Lijie Wen
Paper Information
  • Journal:
    Journal of Machine Learning Research
  • Added to Tracker:
    Sep 09, 2026
Abstract

We introduce MarkDiffusion, an open-source Python toolkit for generative watermarking of latent diffusion models. It comprises three key components: a unified implementation framework for streamlined watermarking algorithm integration and user-friendly interfaces; a mechanism visualization suite that intuitively presents embedded and extracted watermark patterns to aid public understanding; and a comprehensive evaluation module offering standard implementations of 24 tools for assessing detectability, robustness, and output quality, plus 8 automated evaluation pipelines. Counts reflect the initial release; see the repository for the latest version. Through MarkDiffusion, we seek to assist researchers, enhance public awareness of and engagement with generative watermarking, help build consensus, and advance research and applications. Code is available at https://github.com/THU-BPM/MarkDiffusion.

Author Details
Leyi Pan
Author
Sheng Guan
Author
Zheyu Fu
Author
Luyang Si
Author
Huan Wang
Author
Zian Wang
Author
Hanqian Li
Author
Xuming Hu
Author
Irwin King
Author
Philip S. Yu
Author
Aiwei Liu
Author
Lijie Wen
Author
Citation Information
APA Format
Leyi Pan , Sheng Guan , Zheyu Fu , Luyang Si , Huan Wang , Zian Wang , Hanqian Li , Xuming Hu , Irwin King , Philip S. Yu , Aiwei Liu & Lijie Wen . MarkDiffusion: An Open-Source Toolkit for Generative Watermarking of Latent Diffusion Models. Journal of Machine Learning Research .
BibTeX Format
@article{paper1658,
  title = { MarkDiffusion: An Open-Source Toolkit for Generative Watermarking of Latent Diffusion Models },
  author = { Leyi Pan and Sheng Guan and Zheyu Fu and Luyang Si and Huan Wang and Zian Wang and Hanqian Li and Xuming Hu and Irwin King and Philip S. Yu and Aiwei Liu and Lijie Wen },
  journal = { Journal of Machine Learning Research },
  url = { https://www.jmlr.org/papers/v27/25-2553.html }
}