CAR: Condition-Aware Reparameterization Aligns Source and Target for Better Flow Matching
Chen Chen, Pengsheng Guo, Liangchen Song, Jiasen Lu, Rui Qian, Tsu-Jui Fu, Xinze Wang, Wei Liu, Yinfei Yang, Alex Schwing
Conditional generative modeling aims to learn a conditional data distribution from samples containing data-condition pairs. For this, diffusion and flow-based methods have attained compelling results. These methods use a learned (flow) model to transport an initial standard Gaussian noise that ignores the condition to the conditional data distribution. The model is hence required to learn both mass transport \emph{and} conditional injection. To ease the demand on the model, we propose \emph{Condition-Aware Reparameterization} (CAR)--a lightweight, learned \emph{shift} that conditions the source, the target, or both distributions. By relocating these distributions, CAR shortens the probability path the model must learn, leading to faster training in practice. On low-dimensional synthetic data, we visualize and quantify the effects of CAR. On higher-dimensional natural image data (ImageNet-256), we show that adding CAR to SiT-XL/2 reduces FID from 2.07 to 1.68, while introducing less than \(0.6\%\) additional parameters.