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UniWeTok: An Unified Binary Tokenizer with Codebook Size $\mathit{2^{128}}$ for Unified Multimodal Large Language Model
arXiv – CS AI|Shaobin Zhuang, Yuang Ai, Jiaming Han, Weijia Mao, Xiaohui Li, Fangyikang Wang, Xiao Wang, Yan Li, Shanchuan Lin, Kun Xu, Zhenheng Yang, Huaibo Huang, Xiangyu Yue, Hao Chen, Yali Wang||3 views
🤖AI Summary
Researchers introduce UniWeTok, a unified binary tokenizer with a massive 2^128 codebook for multimodal large language models. The system achieves state-of-the-art image generation performance on ImageNet while requiring significantly less training compute than existing solutions.
Key Takeaways
- →UniWeTok uses a massive binary codebook of 2^128 tokens to handle visual representation for multimodal AI models.
- →The system achieves superior ImageNet performance (FID: 1.38) while using 8x less training compute than REPA (33B vs 262B tokens).
- →UniWeTok outperforms FLUX.1 on image generation tasks with a DPG score of 86.63 vs 83.84.
- →The researchers introduce Pre-Post Distillation and SigLu activation function to optimize semantic extraction.
- →Code and models are being released open-source to facilitate community development of unified tokenizers.
#multimodal-ai#tokenizer#image-generation#llm#computer-vision#open-source#research#arxiv#machine-learning
Read Original →via arXiv – CS AI
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