Memory-Efficient Visual Autoregressive Modeling with Scale-Aware KV
  Cache Compression
Authors: name: Kunjun Li, name: Zigeng Chen, name: Cheng-Yen Yang, name: Jenq-Neng Hwang

Visual Autoregressive (VAR) modeling has garnered significant attention for
its innovative next-scale prediction approach, which yields substantial
improvements in efficiency, scalability, and zero-shot generalization.
Nevertheless, the coarse-to-fine methodology inherent in VAR results in
exponential growth of the KV cache during inference, causing considerable
memory consumption and computational redundancy. To address these bottlenecks,
we introduce ScaleKV, a novel KV cache compression framework tailored for VAR
architectures. ScaleKV leverages two critical observations: varying cache
demands across transformer layers and distinct attention patterns at different
scales. Based on these insights, ScaleKV categorizes transformer layers into
two functional groups: drafters and refiners. Drafters exhibit dispersed
attention across multiple scales, thereby requiring greater cache capacity.
Conversely, refiners focus attention on the current token map to process local
details, consequently necessitating substantially reduced cache capacity.
ScaleKV optimizes the multi-scale inference pipeline by identifying
scale-specific drafters and refiners, facilitating differentiated cache
management tailored to each scale. Evaluation on the state-of-the-art
text-to-image VAR model family, Infinity, demonstrates that our approach
effectively reduces the required KV cache memory to 10% while preserving
pixel-level fidelity.