iCATS: Fast Video Generation via Interaction-Aware Sparse Attention and Timestep-Adaptive Sparsity

arXiv:2610.11302 · cs.CV · Submitted 2026-10-08 · Read on arXiv

cs.CV

Submitted: 2026-10-08

Updated: 2026-10-08

Terminology

• Sparsity — 62× in this paper · explained in 4 episodes
• Sparse Attention — 58× in this paper · explained in 5 episodes
• Clustering — 56× in this paper · explained in 2 episodes
• Consistency — 14× in this paper · explained in 3 episodes
• Transformer — 12× in this paper · explained in 2 episodes
• Fidelity — 8× in this paper · explained in 2 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Accuracy — 4× in this paper · explained in 1 episode
• Diffusion Model — 3× in this paper · explained in 8 episodes
• Diffusion Models — 3× in this paper · explained in 6 episodes
• Generative Model — 3× in this paper · explained in 2 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• Hamming Distance — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Quantization — 2× in this paper · explained in 12 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Adaptivity — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Signal-to-noise ratio (SNR) — 1× in this paper · explained in 2 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Training-Free Framework — 1× in this paper · explained in 2 episodes
• Transferability — 1× in this paper · explained in 4 episodes

Sources

• Scalable Diffusion Models with Transformers
• Wan: Open and Advanced Large-Scale Video Generative Models
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer
• Seedance 1.0: Exploring the Boundaries of Video Generation Models
• RainFusion: Adaptive Video Generation Acceleration via Multi-Dimensional Visual Redundancy
• RainFusion2.0: Temporal-Spatial Awareness and Hardware-Efficient Block-wise Sparse Attention
• Sparse-vDiT: Unleashing the Power of Sparse Attention to Accelerate Video Diffusion Transformers
• Radial Attention: $O(n\log n)$ Sparse Attention with Energy Decay for Long Video Generation
• Rectified SpaAttn: Revisiting Attention Sparsity for Efficient Video Generation
• SLA2: Sparse-Linear Attention with Learnable Routing and QAT
• Flow Matching for Generative Modeling
• FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
• DSV: Exploiting Dynamic Sparsity to Accelerate Large-Scale Video DiT Training
• VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness

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