most citedAB-Cache: Training-Free Acceleration of Diffusion Models via Adams-Bashforth Cached Feature Reuse

1 citations · 2 across the 8 of their papers we have counts for

collaborators

9 papers

cs.CV2026

MAR-GRPO: Stabilized GRPO for AR-diffusion Hybrid Image Generation

Xiaoxiao Ma, Jiachen Lei, Tianfei Ren +6

Reinforcement learning (RL) has been successfully applied to autoregressive (AR) and diffusion models. However, extending RL to hybrid AR-diffusion frameworks remains challenging d…

cs.CV2025

VideoScaffold: Elastic-Scale Visual Hierarchies for Streaming Video Understanding in MLLMs

Naishan Zheng, Jie Huang, Qingpei Guo +1

Understanding long videos with multimodal large language models (MLLMs) remains challenging due to the heavy redundancy across frames and the need for temporally coherent represent…

cs.CV2025

Fast-ARDiff: An Entropy-informed Acceleration Framework for Continuous Space Autoregressive Generation

Zhen Zou, Xiaoxiao Ma, Jie Huang +2

Autoregressive(AR)-diffusion hybrid paradigms combine AR's structured modeling with diffusion's photorealistic synthesis, yet suffer from high latency due to sequential AR generati…

cs.CV2025

Towards Better & Faster Autoregressive Image Generation: From the Perspective of Entropy

Xiaoxiao Ma, Feng Zhao, Pengyang Ling +6

In this work, we first revisit the sampling issues in current autoregressive (AR) image generation models and identify that image tokens, unlike text tokens, exhibit lower informat…

cs.CV2025

Unleashing the Potential of the Semantic Latent Space in Diffusion Models for Image Dehazing

Zizheng Yang, Hu Yu, Bing Li +3

Diffusion models have recently been investigated as powerful generative solvers for image dehazing, owing to their remarkable capability to model the data distribution. However, th…

cs.CV2025

InfoScale: Unleashing Training-free Variable-scaled Image Generation via Effective Utilization of Information

Guohui Zhang, Jiangtong Tan, Linjiang Huang +4

Diffusion models (DMs) have become dominant in visual generation but suffer performance drop when tested on resolutions that differ from the training scale, whether lower or higher…