ACID: Adaptive Caching for Faster Video Generation Without Quality Loss

Jul 15, 2026

Researchers introduce ACID, a training-free wrapper that adaptively adjusts caching thresholds during video diffusion model inference. By dynamically switching between low and high thresholds based on the rate of change in the drift signal, ACID achieves up to 2.16x speedup over no-caching baselines and up to 38% additional speedup over conservative fixed-threshold caching, with negligible quality degradation (<0.3 dB PSNR). ACID is compatible with existing caching methods such as TeaCache, EasyCache, and DiCache, and works across multiple video diffusion models.

Why it matters: This approach advances the speed-quality tradeoff in video diffusion models, making them more practical for real-time applications without retraining.

Full story at: arXiv Computer Vision ↗