5 papers
Improved Mean Flows: On the Challenges of Fastforward Generative Models
Zhengyang Geng, Yiyang Lu, Zongze Wu +3
MeanFlow (MF) has recently been established as a framework for one-step generative modeling. However, its ``fastforward'' nature introduces key challenges in both the training obje…
Representation Fréchet Loss for Visual Generation
Jiawei Yang, Zhengyang Geng, Xuan Ju +2
We show that Fréchet Distance (FD), long considered impractical as a training objective, can in fact be effectively optimized in the representation space. Our idea is simple: deco…
Mean Flows for One-step Generative Modeling
Zhengyang Geng, Mingyang Deng, Xingjian Bai +2
We propose a principled and effective framework for one-step generative modeling. We introduce the notion of average velocity to characterize flow fields, in contrast to instantane…
One-Step Diffusion Distillation through Score Implicit Matching
Weijian Luo, Zemin Huang, Zhengyang Geng +2
Despite their strong performances on many generative tasks, diffusion models require a large number of sampling steps in order to generate realistic samples. This has motivated the…
Consistency Models Made Easy
Zhengyang Geng, Ashwini Pokle, William Luo +2
Consistency models (CMs) offer faster sampling than traditional diffusion models, but their training is resource-intensive. For example, as of 2024, training a state-of-the-art CM…