少步生成建模的感知流匹配

摘要

我们提出了感知流匹配（Perceptual Flow Matching，PFM），一种简单而有效的框架，用于流匹配模型中的少步生成。与在传统VAE潜空间中进行速度回归不同，PFM利用预训练的感知模型在感知特征空间中对流匹配进行监督。这一简单变化显著提升了流匹配模型的少步生成能力，将采样步数从35-50步减少至4-8步，同时保持生成质量。与现有的加速和蒸馏方法不同，PFM既不需要教师模型，也不需要辅助评分网络，并且只需最小程度的修改即可集成到标准流匹配训练流程中。在图像生成、视频生成和图像编辑任务上的大量实验表明，PFM始终能产生高质量结果，同时比现有基于蒸馏的方法产生更少的伪影。我们进一步证明，感知监督将回归最小化器从均值寻求转变为模式寻求，使预测偏向于流形上的模式，这些模式在粗糙的少步积分下仍保持准确。我们的结果揭示，当在合适的表示空间中进行监督时，标准流匹配训练自然能产生高质量的少步生成器。我们希望这一见解能启发未来针对高效生成建模的表示感知目标函数的研究。

English

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.