Artificial Intelligence #variable-length tokenization#learnable global merging
New Tokenization Method Merges Tokens to Improve Diffusion Transformer Efficiency
A research paper introduces a variable-length tokenizer that merges tokens instead of truncating them, enabling adaptive compression for diffusion transformers. The method, called learnable global merging, addresses representational alignment issues across token lengths and achieves a superior trade-off between image quality (gFID) and computational cost.
Jun 20, 2026 1 source