Intern Lumina U2: A Multi-Codebook Diffusion Large Language Model for Omni-Visual Understanding and Image Generation

Key points

• Unified multimodal generation covers discrete, continuous, and hybrid paradigms.1
• Discrete approaches with single-codebook visual tokenizers align naturally with text modeling but have limited representational capacity.1
• Continuous and hybrid approaches retain richer visual representations, but they model text and images in heterogeneous spaces with different training objectives, potentially introducing optimization conflicts between modalities with misaligned representation spaces.1
• These choices expose a central question: how to efficiently model high-capacity visual representations alongside textual content in a shared optimization space within a single backbone.1

Sources (2)

• [1]Intern Lumina U2: A Multi-Codebook Diffusion Large Language Model for Omni-Visual Understanding and Image Generation
  Hugging Face Daily Papers · Oct 14, 12:00 AM

  We present Intern Lumina U2, to our knowledge the first diffusion language model to represent multi-codebook visual tokens in a fully discrete space.

  Unified multimodal generation covers discrete, continuous, and hybrid paradigms.

• [2]Intern Lumina U2: A Multi-Codebook Diffusion Large Language Model for Omni-Visual Understanding and Image Generation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 07:05 PM · same content

Extractive summary: sentences quoted from the sources.