Sparse Distillation Technology: How Fastwan AI Achieves Lightning Speed
Deep dive into the sparse distillation technique that enables Fastwan AI to generate videos 50x faster than traditional methods.
Key Innovation
Sparse distillation combines video sparse attention (VSA) with distribution matching distillation (DMD) to achieve unprecedented speed improvements while maintaining quality.
The Problem with Traditional Video Generation
Traditional video diffusion models face two major bottlenecks: the need for 50+ denoising steps and quadratic attention costs when processing long sequences. For a 5-second 720P video, models must handle over 80,000 tokens.
Video Sparse Attention (VSA)
VSA dynamically identifies important tokens during training, making it fully compatible with distillation techniques. Unlike previous sparse attention methods that rely on multi-step redundancy, VSA learns data-dependent sparsity patterns.
Performance Improvements
How Sparse Distillation Works
The technique uses three components: a sparse student network, a real score network, and a fake score network. The student uses VSA for efficiency while leveraging full-attention supervision during training.
Training Efficiency
Training FastWan2.1-1.3B requires only 768 GPU hours on H200s, costing approximately $2,603 with cloud pricing. This makes advanced video generation accessible to research institutions and companies.