Frequency-Step Cache: Separating Synchronization and Repair for Efficient Autoregressive Video Diffusion
On the map: Diffusion models → Video generation
Abstract (arXiv)
Autoregressive (AR) video generation enables long-horizon synthesis but requires repeated forward passes through large diffusion transformers. Training-free caching reduces this cost by reusing intermediate computation, yet existing controllers often couple two decisions: when to synchronize the cache exactly and how much computation to repair between synchronizations. We introduce Frequency-Step Cache (FSC), which separates these roles. FSC schedules exact full-computation anchors on a dense-to-sparse trajectory, while a lightweight latent-space risk based on frequency-resolved drift and token age controls inter-anchor repair through whole-step replay, selective token recomputation, or full repair. On SkyReels-V2, whole-step reuse provides most of the acceleration, while selective token computation is most effective as fidelity repair. A controlled ablation shows that using one reactive signal for both synchronization and repair reduces fidelity at matched latency, with the same separation advantage observed on FramePack and Matrix-Game. Across multiple autoregressive video backbones, FSC improves speed-quality trade-offs. On SkyReels-V2, FSC-Speed reaches 2.36x speedup versus MotionCache's 2.275x while improving SSIM by 0.019 and PSNR by 0.5 dB. At MotionCache-matched speed, FSC-Quality improves SSIM by 0.031 and PSNR by 1.0 dB and lowers LPIPS by 0.024. Without retuning, FSC-Quality transfers to SkyReels-V2 14B at 2.12x speedup versus 1.79x for MotionCache.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Video generation, one of 14 directions in Diffusion models. Builds on 24 of its 28 references that are in the map. First posted on arXiv 11 Oct 2026.
Builds on
24 of 28 references.
Closest ancestors · 4
Same direction, newest first
- 2026Not All Frames Deserve Full Computation: Accelerating Autoregressive Video Generation via Selective Computation and Predictive Extrapolation3 Apr
- 2025Frame Context Packing and Drift Prevention in Next-Frame-Prediction Video Diffusion Models17 Apr
- 2024From Slow Bidirectional to Fast Autoregressive Video Diffusion Models10 Dec
- 2024Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model28 Nov
Other references · 20
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
- 2022Scalable Diffusion Models with Transformerscited by 6,054
- 2023VBench: Comprehensive Benchmark Suite for Video Generative Modelscited by 1,822
- 2024Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusioncited by 681
- 2023DeepCache: Accelerating Diffusion Models for Freecited by 359
- 2024Delta-DiT: A Training-Free Acceleration Method Tailored for Diffusion Transformerscited by 175
- 2024Accelerating Diffusion Transformers with Token-wise Feature Cachingcited by 156
- 2024FORA: Fast-Forward Caching in Diffusion Transformer Accelerationcited by 137
- 2025From Reusing to Forecasting: Accelerating Diffusion Models with TaylorSeerscited by 136
- 2025DiCache: Let Diffusion Model Determine Its Own Cachecited by 42
- 2024Rethinking Token-wise Feature Caching: Accelerating Diffusion Transformers with Dual Feature Cachingcited by 28
- 2026Flow caching for autoregressive video generationcited by 25
8 more
- 2025FreqCa: Accelerating Diffusion Models via Frequency-Aware Cachingcited by 20
- 2025ERTACache: Error Rectification and Timesteps Adjustment for Efficient Diffusioncited by 12
- 2026SeaCache: Spectral-Evolution-Aware Cache for Accelerating Diffusion Modelscited by 11
- 2026Denoising as Path Planning: Training-Free Acceleration of Diffusion Models with DPCachecited by 8
- 2026PreciseCache: Precise Feature Caching for Efficient and High-fidelity Video Generationcited by 8
- 2026Motion-Aware Caching for Efficient Autoregressive Video Generationcited by 6
- 2026Frequency-Aware Error-Bounded Caching for Accelerating Diffusion Transformerscited by 4
- 2025ProCache: Constraint-Aware Feature Caching with Selective Computation for Diffusion Transformer Accelerationcited by 2
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- 2026DSA: Dynamic Step Allocation for Fast Autoregressive Video Generation
- 2026Heuristics are Not Enough: Calibrated and Corrected Caching for Video Diffusion Acceleration
- 2026MORCA: Offline-to-Online Reinforcement Learning for Adaptive Cache Reuse in Video Diffusion Acceleration
- 2025MagCache: Fast Video Generation with Magnitude-Aware Cache
- 2026Future Forcing: Future-aware Training-free KV Cache Policy for Autoregressive Video Generation
- 2025Playing with Transformer at 30+ FPS via Next-Frame Diffusion
- 2025VideoSSM: Autoregressive Long Video Generation with Hybrid State-Space Memory
- 2026SAGA: Stable Acceleration Guidance for Autoregressive Video Generation