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FasterCache: Training-Free Video Diffusion Model Acceleration with High Quality

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arxiv 2410.19355 v2 pith:HR34R4SA submitted 2024-10-25 cs.CV

Zhengyao Lv , Chenyang Si , Junhao Song , Zhenyu Yang , Yu Qiao , Ziwei Liu , Kwan-Yee K. Wong This is my paper · ORCID
classification cs.CV
keywords videofastercachequalityacceleratediffusiongenerationinferenceacceleration
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abstract

In this paper, we present \textbf{\textit{FasterCache}}, a novel training-free strategy designed to accelerate the inference of video diffusion models with high-quality generation. By analyzing existing cache-based methods, we observe that \textit{directly reusing adjacent-step features degrades video quality due to the loss of subtle variations}. We further perform a pioneering investigation of the acceleration potential of classifier-free guidance (CFG) and reveal significant redundancy between conditional and unconditional features within the same timestep. Capitalizing on these observations, we introduce FasterCache to substantially accelerate diffusion-based video generation. Our key contributions include a dynamic feature reuse strategy that preserves both feature distinction and temporal continuity, and CFG-Cache which optimizes the reuse of conditional and unconditional outputs to further enhance inference speed without compromising video quality. We empirically evaluate FasterCache on recent video diffusion models. Experimental results show that FasterCache can significantly accelerate video generation (\eg 1.67$\times$ speedup on Vchitect-2.0) while keeping video quality comparable to the baseline, and consistently outperform existing methods in both inference speed and video quality.

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Cited by 18 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. EchoCache: Energy-Guided Cross-Modal Caching for Efficient Audio-Driven Video Generation

    cs.CV 2026-08 conditional novelty 6.0 of 10

    Audio time-frequency energy guides which video latents get recomputed during diffusion denoising, yielding up to 2.46x faster audio-driven video generation with competitive quality.

  2. CachedSearch: Training-Free Cached Exploration for Test-Time Search in Video Diffusion

    cs.AI 2026-07 conditional novelty 6.0 of 10

    Using cached drafts to select the winner and regenerating only that winner captures 94.7% of best-of-8 search gain at 63% of the cost.

  3. Importance-Aware OBS Pruning for Diffusion Models

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Injecting spatial importance maps (e.g., CFG delta) into the OBS Hessian improves subject preservation in pruned diffusion models at high sparsity, but gains over the baseline are small and without error bars.

  4. SVG-EAR: Parameter-Free Linear Compensation for Sparse Video Generation via Error-aware Routing

    cs.CV 2026-03 conditional novelty 6.0 of 10

    Parameter-free centroid compensation plus error-aware block routing yields a better quality–density Pareto frontier for sparse attention in video DiTs than score-based sparsification.

  5. Phase-Aligned RoPE for Mixed-Resolution Diffusion Transformer

    cs.CV 2025-11 conditional novelty 6.0 of 10

    Expressing all RoPE positions on the query's grid ('one attention, one scale') plus a small boundary content-exchange step restores mixed-resolution diffusion generation that naive position interpolation destroys.

  6. VMoBA: Mixture-of-Block Attention for Video Diffusion Models

    cs.CV 2025-06 conditional novelty 6.0 of 10

    VMoBA is a sparse attention mechanism for video diffusion models that combines cyclic 1D-2D-3D block partitioning with global and threshold-based block selection to reduce training FLOPs while keeping generation quality.

  7. Dual-Expert Consistency Model for Efficient and High-Quality Video Generation

    cs.CV 2025-06 conditional novelty 6.0 of 10

    By training a semantic expert and a LoRA-based detail expert, DCM reaches nearly teacher-level VBench scores with 4-step video sampling on HunyuanVideo and CogVideoX.

  8. SRDiffusion: Accelerate Video Diffusion Inference via Sketching-Rendering Cooperation

    cs.GR 2025-05 conditional novelty 6.0 of 10

    SRDiffusion accelerates video diffusion by switching from a large model to a smaller sibling model after early high-noise steps, using an adaptive threshold for the switch.

  9. dKV-Cache: The Cache for Diffusion Language Models

    cs.CL 2025-05 conditional novelty 6.0 of 10

    dKV-Cache reuses cached key and value states of decoded tokens during diffusion LM denoising, delivering 2-10x faster inference with near-lossless quality on several benchmarks.

  10. SPADE: An Input-Adaptive Sparse Attention Engine for Fast Video Diffusion Models Inference

    cs.CV 2026-08 conditional novelty 5.0 of 10

    SPADE combines static, semi-static, and dynamic block-sparse attention with a cheap SICS-based blocking heuristic to speed up video diffusion inference by up to 1.80x end-to-end.

  11. OmniCache: Multidimensional Hierarchical Feature Caching For Diffusion Models

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Training-free hierarchical feature caching across token, frame, block, and layer axes cuts diffusion inference latency up to 35% while preserving quality better than averaging-based token merging.

  12. ACID: Adaptive Caching for vIDeo generation

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Adaptive threshold switching on the drift signal's rate of change expands the quality-vs-speed Pareto frontier of TeaCache, EasyCache, and DiCache across HunyuanVideo, Wan 2.1, and CogVideoX.

  13. EC-Diff: Fast and High-Quality Edge-Cloud Collaborative Inference for Diffusion Models

    cs.CV 2025-07 reject novelty 5.0 of 10

    EC-Diff accelerates edge-cloud diffusion inference with a k-step noise approximation strategy and a two-stage greedy search for the cloud-edge handoff point, claiming about 2x speedup with preserved quality.

  14. PromptTea: Let Prompts Tell TeaCache the Optimal Threshold

    cs.CV 2025-07 conditional novelty 5.0 of 10

    PromptTea tunes diffusion-model cache reuse thresholds using prompt-derived complexity, achieving up to 2.79x speedup on Wan2.1 with PSNR 23.0 dB.

  15. Sparse-vDiT: Unleashing the Power of Sparse Attention to Accelerate Video Diffusion Transformers

    cs.CV 2025-06 conditional novelty 5.0 of 10

    Sparse-vDiT replaces dense attention with fixed per-head sparse patterns chosen offline, achieving 1.58-1.85x end-to-end speedups on CogVideoX1.5, HunyuanVideo, and Wan2.1 with minimal quality loss.

  16. Weather-Magician: Reconstruction and Rendering Framework for 4D Weather Synthesis In Real Time

    cs.CV 2025-05 conditional novelty 5.0 of 10

    A real-time framework that reconstructs clear scenes with 3D Gaussian Splatting and renders them under controllable fog, rain, snow, and snow-cover effects.

  17. Predictive Feature Caching for Training-free Acceleration of Molecular Geometry Generation

    cs.LG 2025-10 conditional novelty 4.0 of 10

    Predictive feature caching, borrowed from image diffusion, speeds up molecular flow-matching generation by 2-3x at near-matched quality by forecasting hidden features instead of recomputing them.

  18. UniCP: A Unified Caching and Pruning Framework for Efficient Video Generation

    cs.CV 2025-02 reject novelty 4.0 of 10

    UniCP combines error-aware caching and PCA-based pruning to speed up diffusion-transformer video generation by about 1.6x.

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