Perceptual Flow Matching for Few-Step Generative Modeling
For generative modeling practitioners, PFM offers a simple, training-integrated method to accelerate flow-matching models without complex distillation, achieving competitive few-step generation.
Perceptual Flow Matching (PFM) reduces sampling steps in flow-matching models from 35-50 to 4-8 by supervising velocity regression in a perceptual feature space instead of VAE latent space, preserving generation quality without needing teacher models or auxiliary networks. Experiments on image/video generation and editing show fewer artifacts than distillation methods.
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.