# Accelerating Diffusion Transformer via Error-Optimized Cache

arXiv: https://arxiv.org/abs/2501.19243 · HTML: https://mlsanity.com/paper/2501.19243/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models.
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 31 Jan 2025.

## Placed in

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

## Builds on (most cited first, 7 of 7)

- [Locating and Editing Factual Associations in GPT](https://mlsanity.com/paper/2202.05262/) (2022)
- [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://mlsanity.com/paper/2206.00927/) (2022)
- [Enhancing Zero-Shot Vision Models by Label-Free Prompt Distribution Learning and Bias Correcting](https://mlsanity.com/paper/2410.19294/) (2024)
- [Relational Diffusion Distillation for Efficient Image Generation](https://mlsanity.com/paper/2410.07679/) (2024)
- [MPQ-DM: Mixed Precision Quantization for Extremely Low Bit Diffusion Models](https://mlsanity.com/paper/2412.11549/) (2024)
- [Selective Vision-Language Subspace Projection for Few-shot CLIP](https://mlsanity.com/paper/2407.16977/) (2024)
- [Boosting Few-Shot Learning via Attentive Feature Regularization](https://mlsanity.com/paper/2403.17025/) (2024)

## Built on by (most cited first, 14 of 14)

- [From Reusing to Forecasting: Accelerating Diffusion Models with TaylorSeers](https://mlsanity.com/paper/2503.06923/) (2025)
- [SpeCa: Accelerating Diffusion Transformers with Speculative Feature Caching](https://mlsanity.com/paper/2509.11628/) (2025)
- [FastCache: Fast Caching for Diffusion Transformer Through Learnable Linear Approximation](https://mlsanity.com/paper/2505.20353/) (2025)
- [FreqCa: Accelerating Diffusion Models via Frequency-Aware Caching](https://mlsanity.com/paper/2510.08669/) (2025)
- [Accelerating Diffusion Transformer via Gradient-Optimized Cache](https://mlsanity.com/paper/2503.05156/) (2025)
- [Enhancing CLIP Robustness via Cross-Modality Alignment](https://mlsanity.com/paper/2510.24038/) (2025)
- [DisCa: Accelerating Video Diffusion Transformers with Distillation-Compatible Learnable Feature Caching](https://mlsanity.com/paper/2602.05449/) (2026)
- [Evolutionary Caching to Accelerate Your Off-the-Shelf Diffusion Model](https://mlsanity.com/paper/2506.15682/) (2025)
- [Hierarchical Semantic Alignment for Image Clustering](https://mlsanity.com/paper/2512.00904/) (2025)
- [Thinking with Frames: Generative Video Distortion Evaluation via Frame Reward Model](https://mlsanity.com/paper/2601.04033/) (2026)
- [DiffSparse: Accelerating Diffusion Transformers with Learned Token Sparsity](https://mlsanity.com/paper/2604.03674/) (2026)
- [Accelerating Frequency Domain Diffusion Models with Error-Feedback Event-Driven Caching](https://mlsanity.com/paper/2604.22901/) (2026)
- [BeCARE: Budgeted Cache Refresh for Diffusion Transformer Acceleration](https://mlsanity.com/paper/2605.27075/) (2026)
- [Accelerating Masked Image Generation by Learning Controlled Latent Dynamics](https://mlsanity.com/paper/2602.23996/) (2026)

## Abstract (arXiv)

Diffusion Transformer (DiT) is a crucial method for content generation. However, it needs a lot of time to sample. Many studies have attempted to use caching to reduce the time consumption of sampling. Existing caching methods accelerate generation by reusing DiT features from the previous time step and skipping calculations in the next, but they tend to locate and cache low-error modules without focusing on reducing caching-induced errors, resulting in a sharp decline in generated content quality when increasing caching intensity. To solve this problem, we propose the Error-Optimized Cache (EOC). This method introduces three key improvements: (1) Prior knowledge extraction: Extract and process the caching differences; (2) A judgment method for cache optimization: Determine whether certain caching steps need to be optimized; (3) Cache optimization: reduce caching errors. Experiments show that this algorithm significantly reduces the error accumulation caused by caching, especially excessive caching. On the ImageNet dataset, without substantially increasing the computational load, this method improves the FID of the generated images when the rule-based model FORA has a caching level of 75%, 50%, and 25%, and the training-based model Learning-to-cache has a caching level of 22%. Specifically, the FID values change from 30.454 to 21.690 (28.8%), from 6.857 to 5.821 (15.1%), from 3.870 to 3.692 (4.6%), and from 3.539 to 3.451 (2.5%) respectively. Code is available at https://github.com/qiujx0520/EOCMM2025.git.