# Less is Enough: Training-Free Video Diffusion Acceleration via Runtime-Adaptive Caching

arXiv: https://arxiv.org/abs/2507.02860 · HTML: https://mlsanity.com/paper/2507.02860/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- Placed in Video generation, one of 14 directions in Diffusion models.
- Builds on 40 of its 42 references that are in the map.
- First posted on arXiv 3 Jul 2025.

## Placed in

- Diffusion models / [Video generation](https://mlsanity.com/diffusion/video-generation/)

## Builds on (most cited first, 30 of 40)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://mlsanity.com/paper/2206.00927/) (2022)
- [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023)
- [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://mlsanity.com/paper/2412.03603/) (2024)
- [Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2304.08818/) (2023)
- [CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers](https://mlsanity.com/paper/2205.15868/) (2022)
- [WorldSimBench: Towards Video Generation Models as World Simulators](https://mlsanity.com/paper/2410.18072/) (2024)
- [On Distillation of Guided Diffusion Models](https://mlsanity.com/paper/2210.03142/) (2022)
- [Open-Sora: Democratizing Efficient Video Production for All](https://mlsanity.com/paper/2412.20404/) (2024)
- [Latte: Latent Diffusion Transformer for Video Generation](https://mlsanity.com/paper/2401.03048/) (2024)
- [Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation](https://mlsanity.com/paper/2501.12202/) (2025)
- [Reducing Activation Recomputation in Large Transformer Models](https://mlsanity.com/paper/2205.05198/) (2022)
- [Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model](https://mlsanity.com/paper/2411.19108/) (2024)
- [Token Merging for Fast Stable Diffusion](https://mlsanity.com/paper/2303.17604/) (2023)
- [Sparse VideoGen: Accelerating Video Diffusion Transformers with Spatial-Temporal Sparsity](https://mlsanity.com/paper/2502.01776/) (2025)
- [T2V-CompBench: A Comprehensive Benchmark for Compositional Text-to-video Generation](https://mlsanity.com/paper/2407.14505/) (2024)
- [Delta-DiT: A Training-Free Acceleration Method Tailored for Diffusion Transformers](https://mlsanity.com/paper/2406.01125/) (2024)

## Built on by (most cited first, 30 of 45)

- [DiCache: Let Diffusion Model Determine Its Own Cache](https://mlsanity.com/paper/2508.17356/) (2025)
- [StreamDiffusionV2: A Streaming System for Dynamic and Interactive Video Generation](https://mlsanity.com/paper/2511.07399/) (2025)
- [Out of Sight but Not Out of Mind: Hybrid Memory for Dynamic Video World Models](https://mlsanity.com/paper/2603.25716/) (2026)
- [A Survey on Cache Methods in Diffusion Models: Toward Efficient Multi-Modal Generation](https://mlsanity.com/paper/2510.19755/) (2025)
- [Sol-Attn: Accelerating Video Generation Inference via On-the-Fly Attention Sparsification](https://mlsanity.com/paper/2607.24027/) (2026)
- [PreciseCache: Precise Feature Caching for Efficient and High-fidelity Video Generation](https://mlsanity.com/paper/2603.00976/) (2026)
- [Sol Video Inference Engine: Agent-Native Full-Stack Acceleration Framework for Efficient Video Generation](https://mlsanity.com/paper/2606.23743/) (2026)
- [Efficient Video Diffusion Models: Advancements and Challenges](https://mlsanity.com/paper/2604.15911/) (2026)
- [SuperGen: An Efficient Ultra-high-resolution Video Generation System with Sketching and Tiling](https://mlsanity.com/paper/2508.17756/) (2025)
- [RegionE: Adaptive Region-Aware Generation for Efficient Image Editing](https://mlsanity.com/paper/2510.25590/) (2025)
- [WorldCache: Accelerating World Models for Free via Heterogeneous Token Caching](https://mlsanity.com/paper/2603.06331/) (2026)
- [1.x-Distill: Breaking the Diversity, Quality, and Efficiency Barrier in Distribution Matching Distillation](https://mlsanity.com/paper/2604.04018/) (2026)
- [Fast3Dcache: Training-free 3D Geometry Synthesis Acceleration](https://mlsanity.com/paper/2511.22533/) (2025)
- [Fast-SAM3D: 3Dfy Anything in Images but Faster](https://mlsanity.com/paper/2602.05293/) (2026)
- [WorldCache: Content-Aware Caching for Accelerated Video World Models](https://mlsanity.com/paper/2603.22286/) (2026)
- [ReCache: Learning Budget-Aware Caching Schedules for Diffusion Models via REINFORCE](https://mlsanity.com/paper/2606.06060/) (2026)
- [CachedSearch: Training-Free Cached Exploration for Test-Time Search in Video Diffusion](https://mlsanity.com/paper/2607.23159/) (2026)
- [LightCache: Memory-Efficient, Training-Free Acceleration for Video Generation](https://mlsanity.com/paper/2510.05367/) (2025)
- [No Cache Left Idle: Accelerating diffusion model via Extreme-slimming Caching](https://mlsanity.com/paper/2512.12604/) (2025)
- [EditYourself: Audio-Driven Generation and Manipulation of Talking Head Videos with Diffusion Transformers](https://mlsanity.com/paper/2601.22127/) (2026)
- [FastSTAR: Spatiotemporal Token Pruning for Efficient Autoregressive Video Synthesis](https://mlsanity.com/paper/2603.07192/) (2026)
- [Seeing Realism from Simulation: Efficient Video Transfer for Vision-Language-Action Data Augmentation](https://mlsanity.com/paper/2605.02757/) (2026)
- [Accelerating Rectified Flow Models via Trajectory-Aware Caching](https://mlsanity.com/paper/2605.16789/) (2026)
- [PixelWizard: Towards Efficient High-Fidelity Video Generation at Ultra-Large Spatial Resolution](https://mlsanity.com/paper/2605.25801/) (2026)
- [NaviCache: Test-Time Self-Calibration Caching for Video Generation](https://mlsanity.com/paper/2606.26795/) (2026)
- [WorldDynCache: Risk-Controlled Latent Dynamics Approximation for Diffusion World Model](https://mlsanity.com/paper/2608.01845/) (2026)
- [Not All Tokens Need 40 Steps: Heterogeneous Step Allocation in Diffusion Transformers for Efficient Video Generation](https://mlsanity.com/paper/2605.06892/) (2026)
- [VDE: Training-Free Accelerating Rectified Flow Model via Velocity Decomposition and Estimation](https://mlsanity.com/paper/2605.23381/) (2026)
- [LearniBridge: Learnable Calibration of Feature Caching for Diffusion Models Acceleration](https://mlsanity.com/paper/2606.26778/) (2026)
- [ACID: Adaptive Caching for vIDeo generation](https://mlsanity.com/paper/2607.12358/) (2026)

## Abstract (arXiv)

Video generation models have demonstrated remarkable performance, yet their broader adoption remains constrained by slow inference speeds and substantial computational costs, primarily due to the iterative nature of the denoising process. Addressing this bottleneck is essential for democratizing advanced video synthesis technologies and enabling their integration into real-world applications. This work proposes EasyCache, a training-free acceleration framework for video diffusion models. EasyCache introduces a lightweight, runtime-adaptive caching mechanism that dynamically reuses previously computed transformation vectors, avoiding redundant computations during inference. Unlike prior approaches, EasyCache requires no offline profiling, pre-computation, or extensive parameter tuning. We conduct comprehensive studies on various large-scale video generation models, including OpenSora, Wan2.1, and HunyuanVideo. Our method achieves leading acceleration performance, reducing inference time by up to 2.1-3.3× compared to the original baselines while maintaining high visual fidelity with a significant up to 36% PSNR improvement compared to the previous SOTA method. This improvement makes our EasyCache a efficient and highly accessible solution for high-quality video generation in both research and practical applications. The code is available at https://github.com/H-EmbodVis/EasyCache.