SJD-VP: Speculative Jacobi Decoding with Verification Prediction for Autoregressive Image Generation

Speculative Jacobi Decoding (SJD) has emerged as a promising method for accelerating autoregressive image generation. Despite its potential, existing SJD often suffers from the low acceptance rate issue of speculative tokens due to token selection ambiguity. Recent works attempt to mitigate this issue primarily from the perspective of revising token verification, but fail to fully exploit the dynamics of Jacobi decoding. In this paper, we conduct an in-depth analysis and find that tokens whose probabilities exhibit an increasing trend in the Jacobi dynamic are more likely to match the verification-accepted and correct token. Based on this, we propose a novel Speculative Jacobi Decoding with Verification Prediction (SJD-VP). The key idea is to leverage the change in token probabilities across Jacobi dynamics to guide the token drafting process, favoring tokens whose probabilities increase. This effectively predicts which tokens are likely to pass subsequent verification, boosting the acceptance rate. In particular, our SJD-VP is plug-and-play and can be seamlessly integrated into existing SJD variants. Extensive experiments on standard benchmarks demonstrate that our SJD-VP method consistently accelerates autoregressive decoding while maintaining image generation quality.