Shift-and-Sum Quantization for Visual Autoregressive Models
Researchers have developed a new post-training quantization (PTQ) framework specifically designed for visual autoregressive models (VAR). This framework addresses two main challenges: high reconstruction errors in attention-value products and a mismatch between calibration data sampling frequencies and predicted probabilities. The proposed solution includes a shift-and-sum quantization method and a resampling strategy for calibration data, which collectively improve performance in various image generation tasks. AI
IMPACT This new quantization technique could lead to more efficient deployment of visual autoregressive models for various image generation tasks.