Perceptual Flow Matching for Few-Step Generative Modeling

2026-07-03

AI Summary

This work addresses the limitations of existing few-step generation methods, which often rely on teacher models or auxiliary networks, thereby compromising efficiency and generalization. The authors propose Perceptual Flow Matching (PFM), a novel framework that, for the first time, transfers flow matching supervision from the conventional VAE latent space to a pretrained perceptual feature space, replacing standard velocity regression. PFM requires neither additional networks nor knowledge distillation; instead, it achieves highly efficient few-step synthesis solely through a shift in representation space. The approach reveals that perceptual supervision effectively guides optimization toward authentic data manifold modes. Evaluated across image and video generation as well as image editing tasks, PFM reduces sampling steps from 35–50 to just 4–8 while maintaining high visual quality and significantly suppressing artifacts, outperforming current distillation-based methods.

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.

Problem

• few-step generation
• flow matching
• perceptual supervision
• generative modeling
• sampling efficiency

Innovation

• Perceptual Flow Matching
• few-step generation
• flow matching
• perceptual feature space
• mode-seeking regression