Where Compute Matters: Heterogeneous Attention for Efficient Video Diffusion

arXiv:2609.31050 · cs.CV · Submitted 2026-09-25 · Read on arXiv

cs.CV

Submitted: 2026-09-25

Updated: 2026-09-25

Terminology

• Transformer — 35× in this paper · explained in 2 episodes
• Distillation — 18× in this paper · explained in 2 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Consistency — 7× in this paper · explained in 3 episodes
• Diffusion Model — 7× in this paper · explained in 8 episodes
• Diffusion Models — 7× in this paper · explained in 6 episodes
• Sparse Attention — 6× in this paper · explained in 5 episodes
• Sparsity — 6× in this paper · explained in 4 episodes
• Distribution Matching — 5× in this paper · explained in 2 episodes
• Distribution Matching Distillation — 5× in this paper
• Attention Mechanism — 4× in this paper · explained in 3 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Redundancy — 4× in this paper · explained in 2 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Generative Model — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• International Conference on Learning Representations — 2× in this paper
• Temporal consistency — 2× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Fidelity — 1× in this paper · explained in 2 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Quantization — 1× in this paper · explained in 12 episodes

Sources

• Wan: Open and Advanced Large-Scale Video Generative Models
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• LTX-Video: Realtime Video Latent Diffusion
• Open-Sora Plan: Open-Source Large Video Generation Model
• Accelerating Diffusion Transformers with Token-wise Feature Caching
• Scaling Diffusion Transformers to 16 Billion Parameters
• VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness
• AMD-Hummingbird: Towards an Efficient Text-to-Video Model
• Taming Diffusion Transformer for Efficient Mobile Video Generation in Seconds
• MobileWan: Closing the Quality Gap for Mobile Video Diffusion
• HLA: Hadamard Linear Attention
• M4V: Multimodal Mamba for Efficient Text-to-Video Generation
• Qwen3 Technical Report
• Open-Sora: Democratizing Efficient Video Production for All

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models