arXiv Machine Learning
Oct 9

Few-Step Generation via Data-Space Iteration

The paper introduces data‑space iteration, a few‑step generation framework that eliminates the need for a fixed probability‑flow discretization. By refining predictions directly in data space with a shared generator, each iteration is trained to produce the best possible sample within its capacity. Experiments on class‑conditional ImageNet 256×256 show that data‑space iteration outperforms standard discretization baselines and matches or improves upon schedule‑searched variants without schedule‑specific training.

By Shanchuan Lin, Yansong Peng, Fu-Yun Wang, Haoqi Fan
arXiv Machine Learning
Oct 8

Consistent Distribution Matching for Data-Free Diffusion Distillation

Consistent Distribution Matching is a data‑free distillation technique that accelerates diffusion and flow models by unifying sample generation and score estimation in a single student network. The method requires only a frozen teacher and a trainable student, optimizing a single objective that provably leads to Wasserstein convergence of the student’s flow‑map to the teacher’s marginals. On ImageNet 256×256, it achieves an FID of 2.04 with one function evaluation and 1.37 with four evaluations after just 40 training epochs, outperforming existing data‑free baselines.

By Yuxiang Fu, Qi Yan, Zike Wu, Yongxing Zhang, Purang Abolmaesumi, Lele Wang, Renjie Liao
arXiv Machine Learning
Jun 9

Optimizing Few-Step Generation with Adaptive Matching Distillation

arXiv:2602. 07345v2 Announce Type: replace-cross Abstract: Distribution Matching Distillation (DMD) is a powerful acceleration paradigm, yet its stability is often compromised in Forbidden Zone, regions where the real teacher provides unreliable guidance while the fake teacher exerts insufficient repulsive force.

By Lichen Bai, Zikai Zhou, Shitong Shao, Wenliang Zhong, Shuo Yang, Shuo Chen, Bojun Chen, Zeke Xie
arXiv Machine Learning
Sep 24

WTF?! Simulation-Free Reinforcement Learning with Wasserstein-Tilted Flow Maps

The paper introduces Wasserstein‑Tilted Flow Maps (WTF), a simulation‑free reinforcement learning method that fine‑tunes pre‑trained flow‑based generative models by adding an optimal transport regularizer derived from the model’s drift. Unlike traditional KL‑reward tilting, WTF transports individual samples toward higher reward, framing the problem as a deterministic optimal control task on the flow map. Experiments on ImageNet‑256 and text‑to‑image demonstrate that WTF achieves higher reward and comparable or better diversity while reducing training compute by up to 280×.

By Abbas Mammadov, Jerry Y. Huang, Justin Lin, Partha Kaushik, Sheel Shah, Kartik Nair, Yee Whye Teh, Nicholas M. Boffi