AIBullisharXiv – CS AI · 6h ago6/10
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Variable-Length Tokenization via Learnable Global Merging for Diffusion Transformers
Researchers propose a novel variable-length tokenizer using learnable global merging to improve the quality-compute trade-off in latent diffusion models. Unlike conventional truncation-based approaches, the merging method maintains representational alignment across different compression levels, enabling diffusion transformers to operate more effectively with adaptive token counts.