SJD-PV: Speculative Jacobi Decoding with Phrase Verification for Autoregressive Image Generation

• URL: http://arxiv.org/abs/2603.06666v1
• Date: Mon, 02 Mar 2026 13:09:15 GMT
• Title: SJD-PV: Speculative Jacobi Decoding with Phrase Verification for Autoregressive Image Generation
• Abstract summary: Autoregressive (AR) image models have recently demonstrated remarkable generative capability, but their sequential nature results in significant inference latency.<n>We introduce a novel training-free acceleration framework that performs phrase-level speculative verification.<n>Our method significantly reduces the number of function evaluations and achieves up to 30% faster decoding without compromising visual fidelity.
• Score: 20.808533388639468
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Autoregressive (AR) image models have recently demonstrated remarkable generative capability, but their sequential nature results in significant inference latency. Existing training-free acceleration methods typically verify tokens independently, overlooking the strong co-occurrence patterns between adjacent visual tokens. This independence assumption often leads to contextual inconsistency and limits decoding efficiency. In this work, we introduce a novel training-free acceleration framework that performs phrase-level speculative verification, enabling the model to jointly validate multiple correlated tokens within each decoding window. To construct such phrase units, we analyze token co-occurrence statistics from the training corpus and group frequently co-occurring tokens into semantically coherent visual phrases. During inference, the proposed phrase-level verification evaluates aggregated likelihood ratios over each phrase, allowing simultaneous acceptance of multiple tokens while preserving generation quality. Extensive experiments on autoregressive text-to-image generation show that our method significantly reduces the number of function evaluations (NFE) and achieves up to 30% faster decoding without compromising visual fidelity. Our findings reveal that modeling short-range token co-occurrence provides an effective and general principle for accelerating autoregressive inference.

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