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arXiv · 2411.17178

LiteVAR: Compressing Visual Autoregressive Modelling with Efficient Attention and Quantization

Rui Xie·Tianchen Zhao·Zhihang Yuan·Rui Wan·Wenxi Gao·Zhenhua Zhu·Xuefei Ning·Yu Wang
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Abstract

Visual Autoregressive (VAR) has emerged as a promising approach in image generation, offering competitive potential and performance comparable to diffusion-based models. However, current AR-based visual generation models require substantial computational resources, limiting their applicability on resource-constrained devices. To address this issue, we conducted analysis and identified significant redundancy in three dimensions of the VAR model: (1) the attention map, (2) the attention outputs when using classifier free guidance, and (3) the data precision. Correspondingly, we proposed efficient attention mechanism and low-bit quantization method to enhance the efficiency of VAR models while maintaining performance. With negligible performance lost (less than 0.056 FID increase), we could achieve 85.2% reduction in attention computation, 50% reduction in overall memory and 1.5x latency reduction. To ensure deployment feasibility, we developed efficient training-free compression techniques and analyze the deployment feasibility and efficiency gain of each technique.

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Rui Xie, Tianchen Zhao, Zhihang Yuan, Rui Wan, Wenxi Gao, Zhenhua Zhu, Xuefei Ning, Yu Wang. 2024-11-26. LiteVAR: Compressing Visual Autoregressive Modelling with Efficient Attention and Quantization. https://arxiv.org/abs/2411.17178

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