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cs.CV2026

HACK++: Towards More Effective Head-Aware Key-Value Compression for Efficient Visual Autoregressive Modeling

Ziran Qin, Yuchen Jiang, Mingbao Lin +4

Visual Autoregressive (VAR) models adopt a next-scale prediction paradigm, offering high-quality generation with substantially fewer decoding steps. However, existing VAR models su…

cs.CV2026

PreciseCache: Precise Feature Caching for Efficient and High-fidelity Video Generation

Jiangshan Wang, Kang Zhao, Jiayi Guo +5

High computational costs and slow inference hinder the practical application of video generation models. While prior works accelerate the generation process through feature caching…

cs.CV2026

Elastic Diffusion Transformer

Jiangshan Wang, Zeqiang Lai, Jiarui Chen +5

Diffusion Transformers (DiT) have demonstrated remarkable generative capabilities but remain highly computationally expensive. Previous acceleration methods, such as pruning and di…

cs.CV2026

Efficient Autoregressive Video Diffusion with Dummy Head

Hang Guo, Zhaoyang Jia, Jiahao Li +5

The autoregressive video diffusion model has recently gained considerable research interest due to its causal modeling and iterative denoising. In this work, we identify that the m…

cs.CV2025

Head-Aware KV Cache Compression for Efficient Visual Autoregressive Modeling

Ziran Qin, Youru Lv, Mingbao Lin +4

Visual Autoregressive (VAR) models adopt a next-scale prediction paradigm, offering high-quality content generation with substantially fewer decoding steps. However, existing VAR m…