SJD-PAC: Accelerating Speculative Jacobi Decoding via Proactive Drafting and Adaptive Continuation

Speculative Jacobi Decoding (SJD) provides a compelling, draft-model-free approach to accelerating autoregressive text-to-image synthesis. However, the high-entropy nature of image generation leads to low acceptance rates of draft tokens in high-complexity image regions, where rejections occur frequently. This bottleneck restricts SJD’s practical efficiency and limits overall throughput. To address the bottleneck, we introduce SJD++, an enhanced speculative Jacobi decoding framework. First, SJD++ integrates a well-designed multi-drafting strategy to improve local acceptance rates when generating these challenging regions. Furthermore, we propose an adaptive rejection mechanism that enhances sequence stability by continuing validation instead of reverting to full resampling after an initial mismatch. These key optimizations work in tandem to significantly increase the average accepted token length, boosting overall inference speed while strictly preserving the integrity of the target output distribution. Experiments on text-to-image benchmarks demonstrate that SJD++ achieves 3.8× acceleration with lossless image quality.