# SpeCa: Accelerating Diffusion Transformers with Speculative Feature Caching

arXiv: https://arxiv.org/abs/2509.11628 · HTML: https://mlsanity.com/paper/2509.11628/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models.
- Builds on 30 of its 45 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Placed in

- Diffusion models / [Diffusion foundations and sampling](https://mlsanity.com/diffusion/diffusion-foundations-and-sampling/)
- Efficient LLM inference and serving / [Visual token pruning in multimodal models](https://mlsanity.com/inference/visual-token-pruning-in-multimodal-models/)

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

- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [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)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [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)
- [Fast Inference from Transformers via Speculative Decoding](https://mlsanity.com/paper/2211.17192/) (2022)
- [GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignment](https://mlsanity.com/paper/2310.11513/) (2023)
- [Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads](https://mlsanity.com/paper/2401.10774/) (2024)
- [On Distillation of Guided Diffusion Models](https://mlsanity.com/paper/2210.03142/) (2022)
- [SpecInfer: Accelerating Generative Large Language Model Serving with Tree-based Speculative Inference and Verification](https://mlsanity.com/paper/2305.09781/) (2023)
- [DeepCache: Accelerating Diffusion Models for Free](https://mlsanity.com/paper/2312.00858/) (2023)
- [Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding](https://mlsanity.com/paper/2405.08748/) (2024)
- [Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model](https://mlsanity.com/paper/2411.19108/) (2024)
- [Post-training Quantization on Diffusion Models](https://mlsanity.com/paper/2211.15736/) (2022)
- [SnapFusion: Text-to-Image Diffusion Model on Mobile Devices within Two Seconds](https://mlsanity.com/paper/2306.00980/) (2023)
- [Token Merging for Fast Stable Diffusion](https://mlsanity.com/paper/2303.17604/) (2023)
- [Delta-DiT: A Training-Free Acceleration Method Tailored for Diffusion Transformers](https://mlsanity.com/paper/2406.01125/) (2024)
- [Accelerating Diffusion Transformers with Token-wise Feature Caching](https://mlsanity.com/paper/2410.05317/) (2024)
- [FORA: Fast-Forward Caching in Diffusion Transformer Acceleration](https://mlsanity.com/paper/2407.01425/) (2024)
- [Token Fusion: Bridging the Gap between Token Pruning and Token Merging](https://mlsanity.com/paper/2312.01026/) (2023)
- [DiTFastAttn: Attention Compression for Diffusion Transformer Models](https://mlsanity.com/paper/2406.08552/) (2024)
- [Sequoia: Scalable, Robust, and Hardware-aware Speculative Decoding](https://mlsanity.com/paper/2402.12374/) (2024)
- [Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent](https://mlsanity.com/paper/2411.02265/) (2024)
- [DiP-GO: A Diffusion Pruner via Few-step Gradient Optimization](https://mlsanity.com/paper/2410.16942/) (2024)
- [Region-Adaptive Sampling for Diffusion Transformers](https://mlsanity.com/paper/2502.10389/) (2025)
- [Ditto: Accelerating Diffusion Model via Temporal Value Similarity](https://mlsanity.com/paper/2501.11211/) (2025)
- [Accelerating Diffusion Transformer via Error-Optimized Cache](https://mlsanity.com/paper/2501.19243/) (2025)

## Built on by (most cited first, 29 of 29)

- [FreqCa: Accelerating Diffusion Models via Frequency-Aware Caching](https://mlsanity.com/paper/2510.08669/) (2025)
- [A Survey on Cache Methods in Diffusion Models: Toward Efficient Multi-Modal Generation](https://mlsanity.com/paper/2510.19755/) (2025)
- [SeaCache: Spectral-Evolution-Aware Cache for Accelerating Diffusion Models](https://mlsanity.com/paper/2602.18993/) (2026)
- [Denoising as Path Planning: Training-Free Acceleration of Diffusion Models with DPCache](https://mlsanity.com/paper/2602.22654/) (2026)
- [DisCa: Accelerating Video Diffusion Transformers with Distillation-Compatible Learnable Feature Caching](https://mlsanity.com/paper/2602.05449/) (2026)
- [Adaptive Spectral Feature Forecasting for Diffusion Sampling Acceleration](https://mlsanity.com/paper/2603.01623/) (2026)
- [RegionE: Adaptive Region-Aware Generation for Efficient Image Editing](https://mlsanity.com/paper/2510.25590/) (2025)
- [TS-DP: Reinforcement Speculative Decoding For Temporal Adaptive Diffusion Policy Acceleration](https://mlsanity.com/paper/2512.15773/) (2025)
- [Motion-Aware Caching for Efficient Autoregressive Video Generation](https://mlsanity.com/paper/2605.01725/) (2026)
- [LESA: Learnable Stage-Aware Predictors for Diffusion Model Acceleration](https://mlsanity.com/paper/2602.20497/) (2026)
- [TAP: A Token-Adaptive Predictor Framework for Training-Free Diffusion Acceleration](https://mlsanity.com/paper/2603.03792/) (2026)
- [ResilPhase: Plug-and-Play Phase Mapping and Noise-Resilient Macro-Trajectory Extrapolation for Diffusion Acceleration](https://mlsanity.com/paper/2606.26769/) (2026)
- [DiffSparse: Accelerating Diffusion Transformers with Learned Token Sparsity](https://mlsanity.com/paper/2604.03674/) (2026)
- [Beyond Fixed Formulas: Data-Driven Linear Predictor for Efficient Diffusion Models](https://mlsanity.com/paper/2604.26365/) (2026)
- [Generative AI Meets 6G and Beyond: Diffusion Models for Semantic Communications](https://mlsanity.com/paper/2511.08416/) (2025)
- [FREE: Uncertainty-Aware Autoregression for Parallel Diffusion Transformers](https://mlsanity.com/paper/2511.20390/) (2025)
- [BeCARE: Budgeted Cache Refresh for Diffusion Transformer Acceleration](https://mlsanity.com/paper/2605.27075/) (2026)
- [OTCache: Optimal Transport for Geometry-Aware Caching in Diffusion Models](https://mlsanity.com/paper/2606.31026/) (2026)
- [OnlineCache: Learning Dynamic Caching Policies with Error Correction for Efficient Diffusion Inference](https://mlsanity.com/paper/2607.29398/) (2026)
- [LinCa: Accelerating Diffusion Models via Learnable Decomposed Feature Caching](https://mlsanity.com/paper/2608.17973/) (2026)
- [RT-Lynx: Putting GEMM Sparsity in the Right Place for Diffusion Models](https://mlsanity.com/paper/2605.26632/) (2026)
- [SyncCache: Exploiting Asymmetric Dynamics for Fast Audio-Driven Portrait Animation](https://mlsanity.com/paper/2606.30849/) (2026)
- [AViTS: Adaptive Spatiotemporal Token Selection for Efficient Dynamic-Resolution Generation](https://mlsanity.com/paper/2608.17995/) (2026)
- [Accelerating Diffusion Transformers with Gaussian Process Rectified Feature Cache](https://mlsanity.com/paper/2609.05981/) (2026)
- [Carnator: Fast Text-to-Video Generation with Generation-Native Compatibility-Guided Cross-Request Reuse](https://mlsanity.com/paper/2609.32420/) (2026)
- [ReAL: Accelerating Flow Matching through Segment Advancement with Shared Lookahead](https://mlsanity.com/paper/2609.33202/) (2026)
- [GeoShrink: Accelerating Diffusion Transformers with Two Lines of Code](https://mlsanity.com/paper/2609.33723/) (2026)
- [Hybrid-Basis Feature Forecasting for Diffusion Sampling Acceleration](https://mlsanity.com/paper/2610.05254/) (2026)
- [Tikhonov-Stabilized Bezier Representation Forecasting for Training-free Diffusion Acceleration](https://mlsanity.com/paper/2610.16448/) (2026)

## Abstract (arXiv)

Diffusion models have revolutionized high-fidelity image and video synthesis, yet their computational demands remain prohibitive for real-time applications. These models face two fundamental challenges: strict temporal dependencies preventing parallelization, and computationally intensive forward passes required at each denoising step. Drawing inspiration from speculative decoding in large language models, we present SpeCa, a novel 'Forecast-then-verify' acceleration framework that effectively addresses both limitations. SpeCa's core innovation lies in introducing Speculative Sampling to diffusion models, predicting intermediate features for subsequent timesteps based on fully computed reference timesteps. Our approach implements a parameter-free verification mechanism that efficiently evaluates prediction reliability, enabling real-time decisions to accept or reject each prediction while incurring negligible computational overhead. Furthermore, SpeCa introduces sample-adaptive computation allocation that dynamically modulates resources based on generation complexity, allocating reduced computation for simpler samples while preserving intensive processing for complex instances. Experiments demonstrate 6.34x acceleration on FLUX with minimal quality degradation (5.5% drop), 7.3x speedup on DiT while preserving generation fidelity, and 79.84% VBench score at 6.1x acceleration for HunyuanVideo. The verification mechanism incurs minimal overhead (1.67%-3.5% of full inference costs), establishing a new paradigm for efficient diffusion model inference while maintaining generation quality even at aggressive acceleration ratios. Our codes have been released in Github: https://github.com/Shenyi-Z/Cache4Diffusion