Hugging Face Trending Papers June 25, 2026

ResilPhase: Plug-and-Play Phase Mapping and Noise-Resilient Macro-Trajectory Extrapolation for Diffusion Acceleration

Read the original on Hugging Face Trending Papers →

The adoption of powerful diffusion models is hindered by their significant inference latency. Recent ``cache-then-forecast'' schemes alleviate this issue by accelerating DiTs using derivative-based polynomials, but they suffer from severe quality degradation at high acceleration ratios.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

arXiv AI

Jun 26

ResilPhase: Plug-and-Play Phase Mapping and Noise-Resilient Macro-Trajectory Extrapolation for Diffusion Acceleration

arXiv:2606. 26769v1 Announce Type: new Abstract: The adoption of powerful diffusion models is hindered by their significant inference latency.

By Qicheng Zhao, Yu Li, Qi Sun, Zheyu Yan

diffusion efficiency benchmarks

arXiv AI

Aug 11

BRACE: Taming Sharp Irregularities via Barycentric Rational Forecasting for Fast Diffusion Transformers Inference

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

llms diffusion benchmarks

arXiv AI

Jul 28

Differencing the Diffusion Trajectory toward Uncertain Components for Time Series Forecasting

arXiv:2607. 22599v1 Announce Type: new Abstract: Diffusion models have become a widely used framework for probabilistic time series forecasting, modeling the distribution of future values given an observed history.

By Chen Su, Yuanhe Tian, Yan Song

diffusion benchmarks

arXiv AI

Aug 12

SynBoost: A Synergistic Framework for Fast Sampling of Diffusion Models

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

diffusion

arXiv Machine Learning

Jun 16

ANCHOR: Error-Controlled Adaptive Numerical Correction for Neural Operator Time Marching

arXiv:2512. 19643v2 Announce Type: replace Abstract: Numerical simulation of time-dependent partial differential equations (PDEs) is central to scientific and engineering applications, but high-fidelity solvers are often prohibitively expensive for long-horizon or time-critical settings.

By Rajyasri Roy, Dibyajyoti Nayak, Somdatta Goswami

arXiv Machine Learning

Aug 7

Kastor: An efficient fine-tuning strategy for generative emulation of PDE simulations

arXiv:2608. 06107v1 Announce Type: new Abstract: Machine learning offers a promising avenue to accelerate physical simulations by replacing computationally expensive traditional Partial Differential Equation (PDE) solvers with fast, differentiable surrogate models.

By Guillaume Couairon, Alexis Jacq, Yu-Han Wu, Renu Singh, Yana Hasson, Quentin Berthet, Romuald Elie

diffusion fine-tuning benchmarks

Related stories

ResilPhase: Plug-and-Play Phase Mapping and Noise-Resilient Macro-Trajectory Extrapolation for Diffusion Acceleration

BRACE: Taming Sharp Irregularities via Barycentric Rational Forecasting for Fast Diffusion Transformers Inference

Differencing the Diffusion Trajectory toward Uncertain Components for Time Series Forecasting

SynBoost: A Synergistic Framework for Fast Sampling of Diffusion Models

ANCHOR: Error-Controlled Adaptive Numerical Correction for Neural Operator Time Marching

Kastor: An efficient fine-tuning strategy for generative emulation of PDE simulations