title: Lumina-Image 2.0: A Unified and Efficient Image Generative Framework
summary: 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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paper_title: Lumina-Image 2.0: A Unified and Efficient Image Generative Framework
paper_summary: 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.
paper_ai_summary: Lumina-Image 2.0, a text-to-image framework, achieves significant advancements through a unified architecture and efficient training, enabling high-quality image generation with reduced parameters.
paper_ai_keywords: Unified Next-DiT text-to-image cross-modal interactions Unified Captioner (UniCap) multi-stage progressive training inference acceleration