arXiv AI
EpaCache: Error-Propagation-Aware Caching for Accelerating Diffusion-Based Visual Generation
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Computer Vision
Oct 8
MORCA: Offline-to-Online Reinforcement Learning for Adaptive Cache Reuse in Video Diffusion Acceleration
arXiv:2610.10457v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) achieve remarkable performance in video synthesis, but their iterative denoising process suffers from high inference late...
arXiv Machine Learning
Sep 22
SenCache: Accelerating Diffusion Model Inference via Sensitivity-Aware Caching
arXiv:2602.24208v2 Announce Type: replace-cross Abstract: Diffusion models achieve state-of-the-art video generation quality, but their inference remains expensive due to the large number of sequenti...
arXiv AI
Aug 14
From Local Mismatch to Global Impact: Optimizing Cache Reuse Policy for Efficient Diffusion
arXiv:2608. 13043v1 Announce Type: new Abstract: Diffusion models have achieved dominant performance in visual generation but suffer from substantial inference overhead.
arXiv Machine Learning
Aug 3
OnlineCache: Learning Dynamic Caching Policies with Error Correction for Efficient Diffusion Inference
arXiv:2607. 29398v1 Announce Type: new Abstract: Diffusion models have revolutionized generative tasks but incur high latency due to iterative denoising.
Hugging Face Trending Papers
Jul 26
OmniCache: Multidimensional Hierarchical Feature Caching For Diffusion Models
High-resolution image and video diffusion models, including SD3, FLUX, and recent video diffusion transformers, have substantially improved generative quality but remain expensive at inference time because they repeatedly evaluate attention-heavy denoisers over many sampling steps. We address this inefficiency by exploiting redundancy in intermediate diffusion features rather than changing model weights or retraining.
arXiv Computer Vision
4d ago
SoftCache: Adaptive Step Size Caching for Diffusion Transformers
SoftCache introduces an adaptive step size caching mechanism for diffusion transformers, replacing hard binary caching with a soft approach that uses a headroom signal to determine how far cached steps advance. By allowing steps to jump beyond scheduled nodes and shrink as a refresh nears, SoftCache achieves significant speedups—between 3.5× and 6.9×—across image and video models such as FLUX, HunyuanVideo, and Wan2.1, outperforming all baselines on four fidelity metrics.