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.
Figures & tables
Figure 1: (a) Existing Token-Level Verification (e.g., SJD): Tokens are verified individually, often causing rejection of locally ambiguous tokens despite their contextual coherence, which breaks visual semantics.(b) Our Phrase-Level Verification (SJD-PV) :Contiguous tokens are grouped against a pre-constructed phrase library and verified jointly as coherent phrases This preserves coherent visual semantics to resolve local ambiguity, improving acceptance and accelerating AR image generation.
Figure 2: A toy example of the token phrase’s visual semantic. While the isolated patch for Token #6302 (top) presents an ambiguous texture, placing it within its contiguous sequence (bottom) exhibits a distinct zebra stripe structure. This validates that visual semantics are fundamentally defined by the collective behavior of neighboring tokens rather than stored independently.
Figure 3: Short-range token co-occurrence statistics. We compute token co-occurrence over consecutive positions in tokenized image sequences, revealing strong local correlations among specific visual tokens.
Configuration
Latency (↓)
NFE (↓)
Acceleration (↑)
FID (↓)
CLIP-Score (↑)
Latency
NFE
Parti-prompt
Lumina-mGPT ( Liu et al., 2024 )
79.37s
2392
1.00×
1.00×
–
32.091
Jacobi Decoding ( Song et al., 2021a )
82.21s
2300.0
0.97x
1.04×
–
32.091
SJD ( Teng et al., 2024 )
36.07s
1035.3
2.15x
2.31×
–
32.090
SJD + Ours
34.58s
941.4
2.29x
2.54x
–
32.098
Table 1: Quantitative evaluation on the MS-COCO 2017 and Parti-prompt.
Figure 4: Qualitative experiment. Our method on SJD and GSD shows on significant NFE acceleration while maintaining image quality.
Setting
Latency ↓
NFE ↓
CLIP ↑
w/o
35.96s
998.3
32.090
SJD-PV (Ours)
34.58s
941.4
32.098
Table 2: Ablation of Adaptive Neighborhood strategy
Setting
Latency ↓
NFE ↓
CLIP ↑
M=4k
37.02s
1005.6
32.072
M=8k
34.58 s
941.4
32.098
M=16k
34.60s
942.1
31.790
Table 3: Ablation of iterations M
Threshold ( τ )
Latency
NFE ↓
CLIP-Score ↑
τ=0.005
35.12s
965.2
32.095
τ=0.01
34.58s
941.4
32.098
τ=0.02
34.20s
928.5
32.012
τ=0.05
33.85s
915.0
31.650
Table 4: Ablation study on the neighborhood threshold τ .