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
Published: 2025-03-27 17:57:07

Abstract: 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.