# A Survey on Cache Methods in Diffusion Models: Toward Efficient Multi-Modal Generation

arXiv: https://arxiv.org/abs/2510.19755 · HTML: https://mlsanity.com/paper/2510.19755/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models.
- Builds on 118 of its 124 references that are in the map.
- First posted on arXiv 22 Oct 2025.

## Placed in

- Diffusion models / [Diffusion foundations and sampling](https://mlsanity.com/diffusion/diffusion-foundations-and-sampling/)

## Builds on (most cited first, 30 of 118)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [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)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https://mlsanity.com/paper/2311.12022/) (2023)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://mlsanity.com/paper/2307.08691/) (2023)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://mlsanity.com/paper/2206.00927/) (2022)
- [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023)
- [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://mlsanity.com/paper/2412.03603/) (2024)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Qwen-Image Technical Report](https://mlsanity.com/paper/2508.02324/) (2025)
- [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://mlsanity.com/paper/2211.01095/) (2022)
- [Large Language Diffusion Models](https://mlsanity.com/paper/2502.09992/) (2025)
- [WorldSimBench: Towards Video Generation Models as World Simulators](https://mlsanity.com/paper/2410.18072/) (2024)

## Built on by (most cited first, 16 of 16)

- [HY-World 2.0: A Multi-Modal World Model for Reconstructing, Generating, and Simulating 3D Worlds](https://mlsanity.com/paper/2604.14268/) (2026)
- [LiveEdit: Towards Real-Time Diffusion-Based Streaming Video Editing](https://mlsanity.com/paper/2606.26740/) (2026)
- [TC-Pad'e: Trajectory-Consistent Pad'e Approximation for Diffusion Acceleration](https://mlsanity.com/paper/2603.02943/) (2026)
- [LESA: Learnable Stage-Aware Predictors for Diffusion Model Acceleration](https://mlsanity.com/paper/2602.20497/) (2026)
- [Predict to Skip: Linear Multistep Feature Forecasting for Efficient Diffusion Transformers](https://mlsanity.com/paper/2602.18093/) (2026)
- [Beyond Fixed Formulas: Data-Driven Linear Predictor for Efficient Diffusion Models](https://mlsanity.com/paper/2604.26365/) (2026)
- [MSEditor: Toward Consistent Multi-Shot Video Editing](https://mlsanity.com/paper/2608.17559/) (2026)
- [LinCa: Accelerating Diffusion Models via Learnable Decomposed Feature Caching](https://mlsanity.com/paper/2608.17973/) (2026)
- [On Approaches to Building Surrogate ODE Models for Diffusion Bridges](https://mlsanity.com/paper/2512.12671/) (2025)
- [Accelerating Masked Image Generation by Learning Controlled Latent Dynamics](https://mlsanity.com/paper/2602.23996/) (2026)
- [Whispers in the Noise: Surrogate-Guided Concept Awakening via a Multi-Agent Framework](https://mlsanity.com/paper/2605.18150/) (2026)
- [Token-Operations-Oriented Inference Optimization Techniques for Large Models](https://mlsanity.com/paper/2606.20295/) (2026)
- [MagicPrompt: Ultra-Lightweight Prompt Tuning for Video Generation](https://mlsanity.com/paper/2607.14595/) (2026)
- [GeoCache: Training-Free Acceleration of Multi-View Texture Diffusion via Geometric Delta Transport](https://mlsanity.com/paper/2608.13255/) (2026)
- [AViTS: Adaptive Spatiotemporal Token Selection for Efficient Dynamic-Resolution Generation](https://mlsanity.com/paper/2608.17995/) (2026)
- [Two Halves are More than One: Phase-wise Velocity Distillation for Fast and High-Quality Image Generation](https://mlsanity.com/paper/2610.08070/) (2026)

## Abstract (arXiv)

Diffusion Models have become a cornerstone of modern generative AI for their exceptional generation quality and controllability. However, their inherent textitmulti-step iterations and textitcomplex backbone networks lead to prohibitive computational overhead and generation latency, forming a major bottleneck for real-time applications. Although existing acceleration techniques have made progress, they still face challenges such as limited applicability, high training costs, or quality degradation. Against this backdrop, Diffusion Caching offers a promising training-free, architecture-agnostic, and efficient inference paradigm. Its core mechanism identifies and reuses intrinsic computational redundancies in the diffusion process. By enabling feature-level cross-step reuse and inter-layer scheduling, it reduces computation without modifying model parameters. This paper systematically reviews the theoretical foundations and evolution of Diffusion Caching and proposes a unified framework for its classification and analysis. Through comparative analysis of representative methods, we show that Diffusion Caching evolves from textitstatic reuse to textitdynamic prediction. This trend enhances caching flexibility across diverse tasks and enables integration with other acceleration techniques such as sampling optimization and model distillation, paving the way for a unified, efficient inference framework for future multimodal and interactive applications. We argue that this paradigm will become a key enabler of real-time and efficient generative AI, injecting new vitality into both theory and practice of textitEfficient Generative Intelligence.