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Lumina-Image 2.0: A Unified and Efficient Image Generative Framework

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arxiv 2503.21758 v1 pith:3CZKOB7F submitted 2025-03-27 cs.CV

Qi Qin , Le Zhuo , Yi Xin , Ruoyi Du , Zhen Li , Bin Fu , Yiting Lu , Jiakang Yuan
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Xinyue Li Dongyang Liu Xiangyang Zhu Manyuan Zhang Will Beddow Erwann Millon Victor Perez Wenhai Wang Conghui He Bo Zhang Xiaohong Liu Hongsheng Li Yu Qiao Chang Xu Peng Gao
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classification cs.CV
keywords unifiedlumina-imageefficiencyimageintroducetrainingframeworkgeneration
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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