ChebBooster is a training‑free extrapolation framework that accelerates Diffusion Transformers (DiTs) by using Chebyshev polynomial theory. It employs a Barycentric formulation for numerically stable evaluation and separates the process into an offline weight precomputation phase and a lightweight online application stage. Experiments on DiT‑XL/2, PixArt‑Σ, and FLUX.1‑dev show consistent visual quality gains and up to 3.68× latency speedup and 5.12× FLOPs reduction compared to existing training‑free baselines.
By Chengjie Lu, Tianchi Deng, Zhengqi He, Chengwen Luo, Xueliang Li
arXiv:2602. 13357v3 Announce Type: replace-cross Abstract: Diffusion Transformers (DiTs) achieve state-of-the-art performance in high-fidelity image and video generation but suffer from expensive inference due to their iterative denoising structure.
By Dong Liu, Yanxuan Yu, Ben Lengerich, Ying Nian Wu
arXiv:2608. 07572v1 Announce Type: cross Abstract: Diffusion Transformers (DiTs) have demonstrated exceptional performance in high-fidelity image and video generation.
By Jinlong Yang, Jinke Wu, Lizilin, Yao Zhou
Diffusion models have achieved remarkable success in image and video generation, yet the high computational cost of iterative sampling remains a critical bottleneck for practical deployment. Feature c...
arXiv:2603.01623v2 Announce Type: replace
Abstract: Diffusion models have become the dominant tool for high-fidelity image and video generation, yet are critically bottlenecked by their inference spe...
By Jiaqi Han, Juntong Shi, Puheng Li, Haotian Ye, Qiushan Guo, Stefano Ermon
arXiv:2502. 10389v2 Announce Type: replace-cross Abstract: Diffusion models (DMs) have become the leading choice for generative tasks across diverse domains.
By Ziming Liu, Yifan Yang, Chengruidong Zhang, Yiqi Zhang, Lili Qiu, Yang You, Yuqing Yang
arXiv:2506. 13058v2 Announce Type: replace-cross Abstract: Diffusion probabilistic models (DPMs) have demonstrated remarkable success in visual generation.
By Hu Yu, Hao Luo, Xueyang Fu, Jie Huang, Fan Wang, Feng Zhao
ToDoPro introduces an early token‑reduction strategy for reference‑based image generation that uses a few denoising steps on 2× downsampled reference images to probe token importance with the existing Diffusion Transformer. Tokens are then hierarchically pruned: low‑importance tokens are discarded, mid‑importance tokens are replaced by their downsampled versions, and high‑importance tokens are retained. Experiments on three base models and two benchmarks show significant inference speedups, reduced peak GPU memory, and improved generation quality by filtering out noisy reference tokens.
By Caiyi Sun, Yujing Sun, Siu-Ming Yiu
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...
By Yuxiang Xiong, Ruiyan Wang, Wenqiang Wang, Teng Hu, Songhang Shen, Bohao Feng, Hongqian Deng, Ran Yi
The paper introduces DVG, a Dynamic Video Generation framework that allocates computation across time and space to accelerate diffusion-based video generation. By selecting content-aware acceleration strategies automatically, DVG achieves near-lossless speedups—up to 7× on HunyuanVideo models and 18× when combined with distillation—without manual tuning or retraining. The authors provide code on GitHub for reproducibility.
By Shikang Zheng, Jingkai Huang, Jiacheng Liu, Guantao Chen, Lixuan, Yuqi Lin, Peiliang Cai, Linfeng Zhang
arXiv:2606. 26778v1 Announce Type: cross Abstract: Diffusion Transformers (DiTs) have driven substantial progress in image and video generation but suffer from prohibitive computational costs.
By Xuyue Huang, Zhe Chen, Wang Shen, Xiao-Ping Zhang
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...
By Yasaman Haghighi, Alexandre Alahi