Lumina-DiMOO: An Omni Diffusion Large Language Model for Multi-Modal
  Generation and Understanding
Authors: name: Yi Xin; name: Qi Qin; name: Siqi Luo; name: Kaiwen Zhu; name: Juncheng Yan; name: Yan Tai; name: Jiayi Lei; name: Yuewen Cao; name: Keqi Wang; name: Yibin Wang; name: Jinbin Bai; name: Qian Yu; name: Dengyang Jiang; name: Yuandong Pu; name: Haoxing Chen; name: Le Zhuo; name: Junjun He; name: Gen Luo; name: Tianbin Li; name: Ming Hu; name: Jin Ye; name: Shenglong Ye; name: Bo Zhang; name: Chang Xu; name: Wenhai Wang; name: Hongsheng Li; name: Guangtao Zhai; name: Tianfan Xue; name: Bin Fu; name: Xiaohong Liu; name: Yu Qiao; name: Yihao Liu
Published: 2025-10-07 17:59:20

Abstract: We introduce Lumina-DiMOO, an open-source foundational model for seamless
multi-modal generation and understanding. Lumina-DiMOO sets itself apart from
prior unified models by utilizing a fully discrete diffusion modeling to handle
inputs and outputs across various modalities. This innovative approach allows
Lumina-DiMOO to achieve higher sampling efficiency compared to previous
autoregressive (AR) or hybrid AR-Diffusion paradigms and adeptly support a
broad spectrum of multi-modal tasks, including text-to-image generation,
image-to-image generation (e.g., image editing, subject-driven generation, and
image inpainting, etc.), as well as image understanding. Lumina-DiMOO achieves
state-of-the-art performance on multiple benchmarks, surpassing existing
open-source unified multi-modal models. To foster further advancements in
multi-modal and discrete diffusion model research, we release our code and
checkpoints to the community. Project Page:
https://synbol.github.io/Lumina-DiMOO.