| title | Perceptual Flow Matching for Few-Step Generative Modeling |
|---|---|
| authors | Chuyang Zhao, Yifei Song, Hongfa Wang, Jianlong Yuan, Yuan Zhang, Siming Fu, Zhineng Chen, Huilin Deng, Haoyang Huang, Nan Duan |
| date | 2026-07-03 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.03524 |
| affiliation | jingdong |
| upvotes | 16 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | We propose Perceptual Flow Matching (PFM), a simple yet effective framework for few-step generation in flow-matching models. Rather than performing velocity regression in the conventional VAE latent space, PFM supervises flow matching in a perceptual feature space using pretrained perceptual models. This simple change substantially improves the few-step generation capability of flow-matching models, reducing the number of sampling steps from 35-50 to 4-8 while preserving generation quality. Unlike existing acceleration and distillation approaches, PFM requires neither teacher models nor auxiliary score networks and can be integrated into standard flow-matching training pipelines with minimal modifications. Extensive experiments on image generation, video generation, and image editing tasks demonstrate that PFM consistently produces high-quality results while producing fewer artifacts than existing distillation-based methods. We further show that perceptual supervision shifts the regression minimizer from mean-seeking to mode-seeking, biasing predictions toward on-manifold modes that remain accurate under coarse few-step integration. Our results reveal that standard flow-matching training can naturally yield high-quality few-step generators when supervised in an appropriate representation space. We hope this insight inspires future research into representation-aware objectives for efficient generative modeling. |
cite
@misc{zhao2026perceptual,
title={Perceptual Flow Matching for Few-Step Generative Modeling},
author={Chuyang Zhao and Yifei Song and Hongfa Wang and Jianlong Yuan and Yuan Zhang and Siming Fu and Zhineng Chen and Huilin Deng and Haoyang Huang and Nan Duan},
year={2026},
eprint={2607.03524},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.03524}
}