MergeTok: Unified Continuous and Discrete Visual Tokenization via Token Merging 事件

MergeTok: Unified Continuous and Discrete Visual Tokenization via Token Merging arXiv:2605.30904v1 Announce Type: new Abstract: Most visual tokenizers for image generation are bifurcated into two families with complementary limitations: continuous VAEs offer high-fidelity reconstruction but suffer from dense, entangled latents that are poorly suited for semantic control, whereas discrete VQ-based models enable autoregressive generation yet struggle with gradient sparsity, unstable training, and