Muddit

Introduction

Welcome to the official repository of Muddit — a next-generation foundation model in the Meissonic family, built upon discrete diffusion for unified and efficient multimodal generation.

Unlike traditional autoregressive methods, Muddit leverages discrete diffusion (a.k.a. MaskGIT-style masking) as its core mechanism — enabling fast, parallel decoding across modalities.

While most unified models are still rooted in language priors, Muddit is developed from a visual-first perspective for scalable and flexible generation.

Muddit (512) and Muddit Plus (1024) aim to handle diverse tasks across modalities -- such as text generation, image generation, and vision-language reasoning -- within a single architecture and decoding paradigm.

Usage

Please refer to github link.

Citation

If you find this work helpful, please consider citing:

@article{shi2025muddit,
  title={Muddit: Liberating generation beyond text-to-image with a unified discrete diffusion model},
  author={Shi, Qingyu and Bai, Jinbin and Zhao, Zhuoran and Chai, Wenhao and Yu, Kaidong and Wu, Jianzong and Song, Shuangyong and Tong, Yunhai and Li, Xiangtai and Li, Xuelong and others},
  journal={arXiv preprint arXiv:2505.23606},
  year={2025}
}

--- pipeline_tag: image-to-text license: apache-2.0 tags: - Non-Autoregressive - Masked-Generative-Transformer - Discrete-Diffusion - Unified-Model language: - en --- # Muddit: Liberating Generation Beyond Text-to-Image with a Unified Discrete Diffusion Model [Paper](https://arxiv.org/abs/2505.23606) | [Model](https://huggingface.co/MeissonFlow/Muddit) | [Code](https://github.com/M-E-AGI-Lab/Muddit) | [Demo](https://huggingface.co/spaces/MeissonFlow/muddit)  ## Introduction Welcome to the official repository of **Muddit** — a next-generation foundation model in the Meissonic family, built upon discrete diffusion for unified and efficient multimodal generation. Unlike traditional autoregressive methods, **Muddit** leverages discrete diffusion (a.k.a. MaskGIT-style masking) as its core mechanism — enabling fast, parallel decoding across modalities. While most unified models are still rooted in language priors, **Muddit** is developed from a visual-first perspective for scalable and flexible generation. Muddit (512) and Muddit Plus (1024) aim to handle diverse tasks across modalities -- such as text generation, image generation, and vision-language reasoning -- within a single architecture and decoding paradigm. ## Usage Please refer to [github link](https://github.com/M-E-AGI-Lab/Muddit). ## Citation If you find this work helpful, please consider citing: ```bibtex @article{shi2025muddit, title={Muddit: Liberating generation beyond text-to-image with a unified discrete diffusion model}, author={Shi, Qingyu and Bai, Jinbin and Zhao, Zhuoran and Chai, Wenhao and Yu, Kaidong and Wu, Jianzong and Song, Shuangyong and Tong, Yunhai and Li, Xiangtai and Li, Xuelong and others}, journal={arXiv preprint arXiv:2505.23606}, year={2025} } ```