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[JMLR] MarkDiffusion: An Open-Source Toolkit for Generative Watermarking of Latent Diffusion Models
| Date | Stars |
|---|---|
| 2026-07-31 | 324 |
| 2026-08-01 | 324 |
| 2026-08-06 | 324 |
Today
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<img src="img/markdiffusion-color-1.jpg" style="width: 65%;"/>
# An Open-Source Toolkit for Generative Watermarking of Latent Diffusion Models
[](https://generative-watermark.github.io/)
[](https://arxiv.org/abs/2509.10569)
[](https://huggingface.co/Generative-Watermark-Toolkits)
[](https://colab.research.google.com/drive/1N1C9elDAB5zwF4FxKKYMCqR3eSpCSqAW?usp=sharing)
[](https://markdiffusion.readthedocs.io)
[](https://pypi.org/project/markdiffusion)
[](https://github.com/conda-forge/markdiffusion-feedstock)
**Language Versions:** [English](README.md) | [中文](README_zh.md) | [Français](README_fr.md) | [Español](README_es.md)
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> 🔥 **As a new released project, We welcome PRs!** If you have implemented a LDM watermarking algorithm or are interested in contributing one, we'd love to include it in MarkDiffusion. Join our community and help make generative watermarking more accessible to everyone!
## Contents
- [Updates](#-updates)
- [Introduction to MarkDiffusion](#-introduction-to-markdiffusion)
- [Overview](#-overview)
- [Key Features](#-key-features)
- [Implemented Algorithms](#-implemented-algorithms)
- [Evaluation Module](#-evaluation-module)
- [Quick Start](#-quick-start)
- [Google Colab Demo](#google-colab-demo)
- [Installation](#installation)
- [How to Use the Toolkit](#how-to-use-the-toolkit)
- [Test Modules](#-test-modules)
- [Citation](#citation)
## 🔥 Updates
🎉 **(2026.07.11)** MarkDiffusion is accepted by JMLR!
🛠 **(2026.05.15)** Expanded the test suite to 672 unit tests with 94.73% code coverage (full GPU + CPU regression on the new `markdiffusion-test` env).
🏗️ **(2026.05.10)** Restructured the repo into a proper `markdiffusion/` Python package so `pip install -e .` and PyPI installs share the same import paths (`from markdiffusion.watermark import AutoWatermark`). Editable installs and CI now run from a single source layout.
🎯 **(2026.05.10)** Add *DiffusionPurification* and *NeuralCodecCompression* regeneration attacks; *CrSc* (Crop & Scale) gains `position="random"` and explicit offset support — thanks contributors!
🛠 **(2025.12.19)** Add a complete test suite for all functionality with 658 test cases.
🛠 **(2025.12.10)** Add a continuous integration testing system using github actions.
🎯 **(2025.10.10)** Add *Mask, Overlay, AdaptiveNoiseInjection* image attack tools, thanks Zheyu Fu for his PR!
🎯 **(2025.10.09)** Add *FrameRateAdapter, FrameInterpolationAttack* video attack tools, thanks Luyang Si for his PR!
🎯 **(2025.10.08)** Add *SSIM, BRISQUE, VIF, FSIM* image quality analyzer, thanks Huan Wang for her PR!
✨ **(2025.10.07)** Add [SFW](https://arxiv.org/pdf/2509.07647) watermarking method, thanks Huan Wang for her PR!
✨ **(2025.10.07)** Add [VideoMark](https://arxiv.org/abs/2504.16359) watermarking method, thanks Hanqian Li for his PR!
✨ **(2025.9.29)** Add [GaussMarker](https://arxiv.org/abs/2506.11444) watermarking method, thanks Luyang Si for his PR!
## 🔓 Introduction to MarkDiffusion
### 👀 Overview
MarkDiffusion is an open-source Python toolkit for generative watermarking of latent diffusion models. As the use of diffusion-based generative models expands, ensuring the authenticityExcerpt of 19,997 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2309863285865180, desc:latent diffusion