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
Organization: Alpha-VLLM

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.