UniCode$^2$: Cascaded Large-scale Codebooks for Unified Multimodal Understanding and Generation

• URL: http://arxiv.org/abs/2506.20214v2
• Date: Tue, 08 Jul 2025 07:46:39 GMT
• Title: UniCode$^2$: Cascaded Large-scale Codebooks for Unified Multimodal Understanding and Generation
• Authors: Yanzhe Chen, Huasong Zhong, Yan Li, Zhenheng Yang,
• Abstract summary: We propose UniCode$2$, a cascaded codebook framework enabling large-scale, semantically aligned, and stable visual tokenization.<n>By clustering millions of SigLIP sequence embeddings, we build a 500K-entry codebook that preserves vision-language alignment while expanding capacity.<n>UniCode2 delivers strong performance across diverse benchmarks, demonstrating the viability of scaling visual token spaces without sacrificing stability, semantics, or modularity.
• Score: 5.286124283204446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unified multimodal large language models (MLLMs) have shown promise in jointly advancing multimodal understanding and generation, with visual codebooks discretizing images into tokens for autoregressive modeling. Existing codebook-based methods either rely on small vocabularies (~16K entries) that lack fine-grained semantics or naively scale up, resulting in low token utilization and unstable training. We propose UniCode$^2$, a cascaded codebook framework enabling large-scale, semantically aligned, and stable visual tokenization. By clustering millions of SigLIP sequence embeddings, we build a 500K-entry codebook that preserves vision-language alignment while expanding capacity. Stability is ensured via a cascaded design: a frozen codebook anchors the embedding space, and a trainable codebook refines task-specific semantics. This decoupling promotes high utilization and robust learning. Moreover, the alignment of our visual tokens with textual semantics enables seamless integration with pretrained diffusion decoders, supporting high-quality visual synthesis with minimal adaptation. UniCode^2 delivers strong performance across diverse benchmarks, demonstrating the viability of scaling visual token spaces without sacrificing stability, semantics, or modularity.

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