HACK (Head-Aware KV Cache Compression) introduces a novel approach to reduce memory usage in Visual Autoregressive Models while maintaining generation quality.

• Identifies two distinct types of attention heads: Structural and Content-Enriching
• Achieves 2-3x memory reduction with minimal quality loss
• Enables processing of longer visual sequences with existing hardware
• Demonstrates compatibility across multiple visual generation models

This engineering breakthrough addresses a critical limitation in visual AI systems, allowing more efficient deployment of visual generation capabilities in memory-constrained environments like mobile devices and edge computing.

Head-Aware KV Cache Compression for Efficient Visual Autoregressive Modeling