CAR-Flow: Condition-Aware Reparameterization Aligns Source and Target for Better Flow Matching

• URL: http://arxiv.org/abs/2509.19300v2
• Date: Thu, 23 Oct 2025 20:16:25 GMT
• Title: CAR-Flow: Condition-Aware Reparameterization Aligns Source and Target for Better Flow Matching
• Authors: Chen Chen, Pengsheng Guo, Liangchen Song, Jiasen Lu, Rui Qian, Xinze Wang, Tsu-Jui Fu, Wei Liu, Yinfei Yang, Alex Schwing,
• Abstract summary: Conditional generative modeling aims to learn a conditional data distribution from samples containing data-condition pairs.<n>We propose Condition-Aware Re parameterization for Flow Matching (CAR-Flow) to ease the demand on the model.<n>CAR-Flow shortens the probability path the model must learn, leading to faster training in practice.
• Score: 32.53595655791657
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 and conditional injection. To ease the demand on the model, we propose Condition-Aware Reparameterization for Flow Matching (CAR-Flow) -- a lightweight, learned shift that conditions the source, the target, or both distributions. By relocating these distributions, CAR-Flow 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-Flow. On higher-dimensional natural image data (ImageNet-256), equipping SiT-XL/2 with CAR-Flow reduces FID from 2.07 to 1.68, while introducing less than 0.6% additional parameters.

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