Paper#image generation, autoregressive models, speculative decoding🔬 ResearchAnalyzed: Jan 3, 2026 23:58
Accelerating Visual Autoregressive Models with Adaptive Draft Trees
Published:Dec 26, 2025 04:45
•1 min read
•ArXiv
Analysis
This paper addresses the slow inference speed of autoregressive (AR) image models, which is a significant bottleneck. It proposes a novel method, Adjacency-Adaptive Dynamical Draft Trees (ADT-Tree), to accelerate inference by dynamically adjusting the draft tree structure based on the complexity of different image regions. This is a crucial improvement over existing speculative decoding methods that struggle with the spatially varying prediction difficulty in visual AR models. The results show significant speedups on benchmark datasets.
Key Takeaways
- •Addresses the slow inference problem of autoregressive image models.
- •Proposes Adjacency-Adaptive Dynamical Draft Trees (ADT-Tree) for faster inference.
- •ADT-Tree dynamically adjusts draft tree structure based on image region complexity.
- •Achieves significant speedups on benchmark datasets.
- •Integrates with relaxed sampling methods for further acceleration.
Reference
“ADT-Tree achieves speedups of 3.13x and 3.05x, respectively, on MS-COCO 2017 and PartiPrompts.”