StageVAR: Stage-Aware Acceleration for Visual Autoregressive Models
Visual Autoregressive (VAR) modeling departs from the next-token prediction paradigm of traditional Autoregressive (AR) models through next-scale prediction, enabling high-quality image generation. However, the VAR paradigm suffers from sharply increased computational complexity and running time at large-scale steps. Although existing acceleration methods reduce runtime for large-scale steps, but rely on manual step selection and overlook the varying importance of different stages in the generation process. To address this challenge, we present StageVAR, a systematic study and stage-aware acceleration framework for VAR models. Our analysis shows that early steps are critical for preserving semantic and structural consistency and should remain intact, while later steps mainly refine details and can be pruned or approximated for acceleration. Building on these insights, StageVAR introduces a plug-and-play acceleration strategy that exploits semantic irrelevance and low-rank properties in late-stage computations, without requiring additional training. Our proposed StageVAR achieves up to 3.4x speedup with only a 0.01 drop on GenEval and a 0.26 decrease on DPG, consistently outperforming existing acceleration baselines. These results highlight stage-aware design as a powerful principle for efficient visual autoregressive image generation.
6948c3ee34f46eaf46cbb31d
Senmao Li
6948c3ee34f46eaf46cbb31e
Kai Wang
6948c3ee34f46eaf46cbb31f
Salman Khan
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Fahad Shahbaz Khan
6948c3ee34f46eaf46cbb321
Jian Yang
6948c3ee34f46eaf46cbb322
Yaxing Wang
StageVAR: Stage-Aware Acceleration for Visual Autoregressive Models
Visual Autoregressive (VAR) modeling departs from the next-token prediction paradigm of traditional Autoregressive (AR) models through next-scale prediction, enabling high-quality image generation. However, the VAR paradigm suffers from sharply increased computational complexity and running time at large-scale steps. Although existing acceleration methods reduce runtime for large-scale steps, but rely on manual step selection and overlook the varying importance of different stages in the generation process. To address this challenge, we present StageVAR, a systematic study and stage-aware acceleration framework for VAR models. Our analysis shows that early steps are critical for preserving semantic and structural consistency and should remain intact, while later steps mainly refine details and can be pruned or approximated for acceleration. Building on these insights, StageVAR introduces a plug-and-play acceleration strategy that exploits semantic irrelevance and low-rank properties in late-stage computations, without requiring additional training. Our proposed StageVAR achieves up to 3.4x speedup with only a 0.01 drop on GenEval and a 0.26 decrease on DPG, consistently outperforming existing acceleration baselines. These results highlight stage-aware design as a powerful principle for efficient visual autoregressive image generation.
StageVAR accelerates visual autoregressive models by selectively pruning or approximating less critical later stages, achieving significant speedup with minimal quality loss.
Visual Autoregressive (VAR) modeling next-token prediction next-scale prediction computational complexity stage-aware acceleration semantic consistency structural consistency plug-and-play acceleration semantic irrelevance low-rank properties speedup GenEval DPG efficient visual autoregressive image generation