Apple ML Research: Accelerating Text-to-Video with Calibrated Sparse Attention
Apple ML Research · rss · 2026-07-21
Apple's Machine Learning Research team proposed a new method called Calibrated Sparse Attention to address the slow runtime of diffusion models in high-quality video generation.
Key Findings & Approach:
- The team identified that in large transformer-based video generation backbones, a significant fraction of token-to-token connections consistently yield negligible attention scores with repeating patterns. Skipping the computation of these trivial connections has little to no effect on the result.
- This observation holds true for connections among local token blocks as well.
- By calibrating and pruning these unnecessary attention connections, the method overcomes the spatiotemporal attention bottleneck, significantly accelerating video generation runtime without sacrificing quality.
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