Omni-Diffusion: Unified Multimodal Understanding and Generation with Masked Discrete Diffusion

Omni-Diffusion is the first any-to-any multimodal language model built entirely on a mask-based discrete diffusion model. It unifies understanding and generation across text, speech, and images by modeling a joint distribution over discrete multimodal tokens.

• Paper: Omni-Diffusion: Unified Multimodal Understanding and Generation with Masked Discrete Diffusion
• Project Page: https://omni-diffusion.github.io
• Repository: https://github.com/VITA-MLLM/Omni-Diffusion

Usage

As the model uses a custom architecture, it can be loaded using the transformers library with trust_remote_code=True:

from transformers import AutoModel

model = AutoModel.from_pretrained("lijiang/Omni-Diffusion", trust_remote_code=True)

For detailed inference instructions and environment setup (including required image and audio tokenizers), please refer to the official GitHub repository.

Citation

If you find this work helpful for your research, please consider citing:

@article{li2026omni,
  title={Omni-Diffusion: Unified Multimodal Understanding and Generation with Masked Discrete Diffusion},
  author={Li, Lijiang and Long, Zuwei and Shen, Yunhang and Gao, Heting and Cao, Haoyu and Sun, Xing and Shan, Caifeng and He, Ran and Fu, Chaoyou},
  journal={arXiv preprint arXiv:2603.06577},
  year={2026}
}