Depth Adaptive Efficient Visual Autoregressive Modeling
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910145865318400 |
|---|---|
| author | Li, Chunliang Cao, Tianze Zhao, Sanyuan |
| author_facet | Li, Chunliang Cao, Tianze Zhao, Sanyuan |
| contents | Visual Autoregressive (VAR) modeling inefficiently applies a fixed computational depth to each position when generating high-resolution images. While existing methods accelerate inference by pruning tokens using frequency maps, their binary hard-pruning approach is fundamentally limited and fails to improve quality even with better frequency estimation. Observing that VAR models possess significant depth redundancy, we propose a paradigm shift from pruning entire tokens to adaptively allocating per-token computational depth. To this end, we introduce DepthVAR, a training-free framework that dynamically allocates computation. It integrates an adaptive depth scheduler, which assigns computational depth via a cyclic rotated schedule for balanced, non-static refinement, with a dynamic inference process that translates these depths into layer-major masks, selectively applies transformer blocks, and blends the resulting codes to ensure each token's influence is proportional to its processing depth. Extensive experiments show that DepthVAR achieves 2.3$\times$-3.1$\times$ acceleration with minimal quality loss, offering a competitive compute-performance trade-off compared to existing hard-pruning approaches. Code is available at https://github.com/STOVAGtz/DepthVAR |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_17286 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Depth Adaptive Efficient Visual Autoregressive Modeling Li, Chunliang Cao, Tianze Zhao, Sanyuan Computer Vision and Pattern Recognition Visual Autoregressive (VAR) modeling inefficiently applies a fixed computational depth to each position when generating high-resolution images. While existing methods accelerate inference by pruning tokens using frequency maps, their binary hard-pruning approach is fundamentally limited and fails to improve quality even with better frequency estimation. Observing that VAR models possess significant depth redundancy, we propose a paradigm shift from pruning entire tokens to adaptively allocating per-token computational depth. To this end, we introduce DepthVAR, a training-free framework that dynamically allocates computation. It integrates an adaptive depth scheduler, which assigns computational depth via a cyclic rotated schedule for balanced, non-static refinement, with a dynamic inference process that translates these depths into layer-major masks, selectively applies transformer blocks, and blends the resulting codes to ensure each token's influence is proportional to its processing depth. Extensive experiments show that DepthVAR achieves 2.3$\times$-3.1$\times$ acceleration with minimal quality loss, offering a competitive compute-performance trade-off compared to existing hard-pruning approaches. Code is available at https://github.com/STOVAGtz/DepthVAR |
| title | Depth Adaptive Efficient Visual Autoregressive Modeling |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.17286 |