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20242026
most citedSparse VideoGen: Accelerating Video Diffusion Transformers with Spatial-Temporal Sparsity

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cs.CV2026

TurboT2VA: Fast Large-Scale Text-to-Video-Audio Generation via Score-Regularized Consistency Distillation

Xiaoda Yang, Yuxiang Liu, Kaiwen Zheng +12

Joint text-to-video-audio generation produces synchronized visual and acoustic content, but the long sampling trajectories and heterogeneous multimodal computation of large models…

cs.CV2026

Vidu S1: A Real-Time Interactive Video Generation Model

Jintao Zhang, Kai Jiang, Jintao Chen +24

We introduce Vidu S1, a real-time interactive video generation model supporting voice control of digital characters. Users can control video generation content at any moment throug…

cs.CV2026

Causal-rCM: A Unified Teacher-Forcing and Self-Forcing Open Recipe for Autoregressive Diffusion Distillation in Streaming Video Generation and Interactive World Models

Kaiwen Zheng, Guande He, Min Zhao +7

Autoregressive video diffusion with causal diffusion transformers has emerged as a major paradigm for real-time streaming video generation and action-conditioned interactive world…

cs.DC2026

TurboServe: Serving Streaming Video Generation Efficiently and Economically

Youhe Jiang, Haoxu Wang, Haotong Bao +5

Streaming video generation is emerging as a new serving workload in which users interact with long-lived sessions that generate video progressively, chunk by chunk. Unlike offline…

cs.CV2026

6Bit-Diffusion: Inference-Time Mixed-Precision Quantization for Video Diffusion Models

Rundong Su, Jintao Zhang, Zhihang Yuan +3

Diffusion transformers have demonstrated remarkable capabilities in generating videos. However, their practical deployment is severely constrained by high memory usage and computat…

cs.CV2026

SpargeAttention2: Trainable Sparse Attention via Hybrid Top-k+Top-p Masking and Distillation Fine-Tuning

Jintao Zhang, Kai Jiang, Chendong Xiang +5

Many training-free sparse attention methods are effective for accelerating diffusion models. Recently, several works suggest that making sparse attention trainable can further incr…

cs.LG2026

SLA2: Sparse-Linear Attention with Learnable Routing and Quantization-aware Training

Jintao Zhang, Haoxu Wang, Kai Jiang +6

Sparse-Linear Attention (SLA) combines sparse and linear attention to accelerate diffusion models and has shown strong performance in video generation. However, (i) SLA relies on a…