README.md

---
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)


![Tracing the Evolution of Unified Generation Foundation Models](./Evolution.png)


## 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}
}
```