Lumina-Image 2.0：统一高效的图像生成框架

摘要

我们推出Lumina-Image 2.0，这是一个先进的文本到图像生成框架，相较于前作Lumina-Next取得了显著进展。Lumina-Image 2.0基于两大核心原则构建：(1) 统一性——采用统一架构（Unified Next-DiT），将文本与图像标记视为联合序列处理，促进了跨模态的自然交互，并支持任务的无缝扩展。此外，鉴于高质量描述器能提供语义高度对齐的文本-图像训练对，我们引入了专为文本到图像生成任务设计的统一描述系统——Unified Captioner (UniCap)。UniCap擅长生成全面且准确的描述，加速模型收敛并增强对提示的遵循度。(2) 效率——为提升模型效率，我们开发了多阶段渐进式训练策略，并引入了不影响图像质量的推理加速技术。在学术基准和公开文本到图像平台上的广泛评估表明，Lumina-Image 2.0即使仅拥有26亿参数，也能展现出强劲性能，凸显了其可扩展性和设计效率。我们已在https://github.com/Alpha-VLLM/Lumina-Image-2.0公开了训练细节、代码及模型。

English

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.

Lumina-Image 2.0：统一高效的图像生成框架

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

摘要

Summary

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