{"total":19,"rows":[{"id":"2505.13389","title":"VSA: Faster Video Diffusion with Trainable Sparse Attention","created":"2025-05-19","cited_by_count":104,"area":"diffusion"},{"id":"2502.21079","title":"Training-free and Adaptive Sparse Attention for Efficient Long Video Generation","created":"2025-02-28","cited_by_count":69,"area":"diffusion"},{"id":"2505.16864","title":"Training-Free Efficient Video Generation via Dynamic Token Carving","created":"2025-05-22","cited_by_count":34,"area":"diffusion"},{"id":"2506.03065","title":"Sparse-vDiT: Unleashing the Power of Sparse Attention to Accelerate Video Diffusion Transformers","created":"2025-06-03","cited_by_count":34,"area":"diffusion"},{"id":"2509.01085","title":"Bidirectional Sparse Attention for Faster Video Diffusion Training","created":"2025-09-01","cited_by_count":19,"area":"diffusion"},{"id":"2504.10317","title":"Analysis of Attention in Video Diffusion Transformers","created":"2025-04-14","cited_by_count":15,"area":"diffusion"},{"id":"2509.24899","title":"Attention Surgery: An Efficient Recipe to Linearize Your Video Diffusion Transformer","created":"2025-09-29","cited_by_count":15,"area":"diffusion"},{"id":"2508.04016","title":"S$^2$Q-VDiT: Accurate Quantized Video Diffusion Transformer with Salient Data and Sparse Token Distillation","created":"2025-08-06","cited_by_count":13,"area":"diffusion"},{"id":"2510.08318","title":"LinVideo: A Post-Training Framework towards O(n) Attention in Efficient Video Generation","created":"2025-10-09","cited_by_count":11,"area":"diffusion"},{"id":"2602.18993","title":"SeaCache: Spectral-Evolution-Aware Cache for Accelerating Diffusion Models","created":"2026-02-22","cited_by_count":11,"area":"diffusion"},{"id":"2601.04342","title":"ReHyAt: Recurrent Hybrid Attention for Video Diffusion Transformers","created":"2026-01-07","cited_by_count":7,"area":"diffusion"},{"id":"2604.15911","title":"Efficient Video Diffusion Models: Advancements and Challenges","created":"2026-04-17","cited_by_count":7,"area":"diffusion"},{"id":"2607.28611","title":"Chimera: Designing and Chinchilla-Scaling Hybrid Visual Diffusion Transformers","created":"2026-07-30","cited_by_count":3,"area":"diffusion"},{"id":"2603.09721","title":"FrameDiT: Diffusion Transformer with Matrix Attention for Efficient Video Generation","created":"2026-03-10","cited_by_count":1,"area":"diffusion"},{"id":"2604.20470","title":"DynamicRad: Content-Adaptive Sparse Attention for Long Video Diffusion","created":"2026-04-22","cited_by_count":1,"area":"diffusion"},{"id":"2609.06712","title":"RoLA: Rotary-Positioned Low-Rank Linear Attention for Efficient Diffusion Transformers","created":"2026-09-06","cited_by_count":1,"area":"diffusion"},{"id":"2603.07057","title":"SODA: Sensitivity-Oriented Dynamic Acceleration for Diffusion Transformer","created":"2026-03-07","cited_by_count":0,"area":"diffusion"},{"id":"2606.16317","title":"Training-free sparse attention based on cumulative energy filtering","created":"2026-06-15","cited_by_count":0,"area":"diffusion"},{"id":"2608.23696","title":"Renormalization Group Flow Matching for Scalable Local Generative Modeling","created":"2026-08-24","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}