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

Abstract

We introduce Lumina-Image 2.0, an advanced text-to-image (T2I) model that surpasses previous state-of-the-art methods across multiple benchmarks. Lumina-Image 2.0 is characterized by two key features: (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), which can generate detailed and accurate multilingual captions for our model. This not only accelerates model convergence, but also enhances prompt adherence, multi-granularity prompt handling, and task expansion with customized prompt templates. (2)Efficiency - to improve the efficiency of our proposed model, we develop multi-stage progressive training strategies to optimize our model, alongside inference-time acceleration strategies without compromising image quality. We evaluate our model on academic benchmarks and T2I arenas, with results confirming that it matches or exceeds existing state-of-the-art models across various metrics, highlighting the effectiveness of our methods. We have released our training details, code, and models at https://github.com/Alpha-VLLM/Lumina-Image-2.0.