Shift-and-Sum Quantization for Visual Autoregressive Models

Abstract:Post-training quantization (PTQ) enables efficient deployment of deep networks using a small set of data. Its application to visual autoregressive models (VAR), however, remains relatively unexplored. We identify two key challenges for applying PTQ to VAR: (i) large reconstruction errors in attention-value products, especially at coarse scales where high attention scores occur more frequently; and (ii) a discrepancy between the sampling frequencies of codebook entries and their predicted probabilities due to limited calibration data. To address these challenges, we propose a PTQ framework tailored for VAR. First, we introduce a shift-and-sum quantization method that reduces reconstruction errors by aggregating quantized results from symmetrically shifted duplicates of value tokens. Second, we present a resampling strategy for calibration data that aligns sampling frequencies of codebook entries with their predicted probabilities. Experiments on class-conditional image generation, inpainting, outpainting, and class-conditional editing show consistent improvements across VAR architectures, establishing a new state of the art in PTQ for VAR.

Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

arXiv:2606.16131 [cs.CV]

(or arXiv:2606.16131v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2606.16131 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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