SJD-SV: Speculative Jacobi Decoding with Semantics Verification for Autoregressive Image Generation
Speculative Jacobi Decoding (SJD) is an important approach for accelerating autoregressive image generation. Although SJD has shown superior performance, recent studies point out that it usually suffers from a token ambiguity issue during token verification but its reason can not be well explained. To figure out this reason, in this paper, we conduct a visualization analysis on vision token and find that different from text tokens, vision tokens generally corresponds to some local, small, and unclear vision details, which means only using single token is difficult to accurately express a certain semantic, thereby causing token ambiguity issue. To this end, we propose a novel Speculative J...
| Authors | Baoquan Zhang, Bingqi Shan, Shihao Fang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 September 2026 |
| Last updated at source | 4 September 2026 |
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