Chinese Media Report

• MaskGIT: Masked Generative Image Transformer [CVPR 2022]

• Muse: Text-To-Image Generation via Masked Generative Transformers [ICML 2023]

• [🌟]Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis [ICLR 2025]

• Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer

• Di[𝙼]O: Distilling Masked Diffusion Models into One-step Generator [ICCV 2025]

• [🌟]Muddit: Liberating Generation Beyond Text-to-Image with a Unified Discrete Diffusion Model [ICLR 2026]

• DC-AR: Efficient Masked Autoregressive Image Generation with Deep Compression Hybrid Tokenizer [ICCV 2025]

• MDNS: Masked Diffusion Neural Sampler via Stochastic Optimal Control

• Lavida-O: Elastic Large Masked Diffusion Models for Unified Multimodal Understanding and Generation

• [🌟]Lumina-DiMOO: An Omni Diffusion Large Language Model for Multi-Modal Generation and Understanding

• Token Painter: Training-Free Text-Guided Image Inpainting via Mask Autoregressive Models

• TR2-D2: Tree Search Guided Trajectory-Aware Fine-Tuning for Discrete Diffusion

• OneFlow: Concurrent Mixed-Modal and Interleaved Generation with Edit Flows

• Diffuse Everything: Multimodal Diffusion Models on Arbitrary State Spaces [ICML 2025]

• Towards Better & Faster Autoregressive Image Generation: From the Perspective of Entropy [NeurIPS 2025]

• [🌟]From Masks to Worlds: A Hitchhiker's Guide to World Models

• Soft-Di[M]O: Improving One-Step Discrete Image Generation with Soft Embeddings

• Accelerating Inference of Masked Image Generators via Reinforcement Learning

• Sparse-LaViDa: Sparse Multimodal Discrete Diffusion Language Models

• Reinforcement Learning Meets Masked Generative Models: Mask-GRPO for Text-to-Image Generation

• MaskFocus: Focusing Policy Optimization on Critical Steps for Masked Image Generation

• Co-GRPO: Co-Optimized Group Relative Policy Optimization for Masked Diffusion Model

• Synergistic Multimodal Diffusion Transformer: Unifying and Enhancing Multimodal Generation via Adaptive Discrete Diffusion

• [🌟]Prism: Efficient Test-Time Scaling via Hierarchical Search and Self-Verification for Discrete Diffusion Language Models

• More papers are coming soon! See MeissonFlow Research (Organization Card) for more about our vision.

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.

Please refer to https://huggingface.co/spaces/MeissonFlow/muddit/blob/main/app.py

To train Muddit, follow these steps:

1. Install dependencies:

   pip install -r requirements.txt

2. Prepare your own dataset and dataset class following the format in dataset_utils.py and train_meissonic.py

   • Modify train.sh with your dataset path

3. Start training:

   bash train/train_unified.sh

Note: For custom datasets, you'll likely need to implement your own dataset class.

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}
}

Made with ❤️ by the MeissonFlow Research

See MeissonFlow Research (Organization Card) for more about our vision.