Lumina-Image 2.0: A Unified and Efficient Image Generative Framework
Authors: name: Qi Qin, name: Le Zhuo, name: Yi Xin, name: Ruoyi Du, name: Zhen Li, name: Bin Fu, name: Yiting Lu, name: Jiakang Yuan, name: Xinyue Li, name: Dongyang Liu, name: Xiangyang Zhu, name: Manyuan Zhang, name: Will Beddow, name: Erwann Millon, name: Victor Perez, name: Wenhai Wang, name: Conghui He, name: Bo Zhang, name: Xiaohong Liu, name: Hongsheng Li, name: Yu Qiao, name: Chang Xu, name: Peng Gao

We introduce Lumina-Image 2.0, an advanced text-to-image generation framework
that achieves significant progress compared to previous work, Lumina-Next.
Lumina-Image 2.0 is built upon two key principles: (1) Unification - it adopts
a unified architecture (Unified Next-DiT) that treats text and image tokens as
a joint sequence, enabling natural cross-modal interactions and allowing
seamless task expansion. Besides, since high-quality captioners can provide
semantically well-aligned text-image training pairs, we introduce a unified
captioning system, Unified Captioner (UniCap), specifically designed for T2I
generation tasks. UniCap excels at generating comprehensive and accurate
captions, accelerating convergence and enhancing prompt adherence. (2)
Efficiency - to improve the efficiency of our proposed model, we develop
multi-stage progressive training strategies and introduce inference
acceleration techniques without compromising image quality. Extensive
evaluations on academic benchmarks and public text-to-image arenas show that
Lumina-Image 2.0 delivers strong performances even with only 2.6B parameters,
highlighting its scalability and design efficiency. We have released our
training details, code, and models at
https://github.com/Alpha-VLLM/Lumina-Image-2.0.