Authors Oscar Davis†**, Anastasiia Filippova, Victor Turrisi, Amitis Shidani, Pierre Ablin, Marco Cuturi, Louis Béthune
View publication：https://arxiv.org/abs/2605.07820
Score Distillation of Flow Matching Models
December 16, 2025 research area Computer Vision：/research/?domain=Computer%20Vision, research area Methods and Algorithms：/research/?domain=Methods%20and%20Algorithms
Diffusion models achieve high-quality image generation but are limited by slow iterative sampling. Distillation methods alleviate this by enabling one- or few-step generation. Flow matching, originally introduced as a distinct framework, has since been shown to be theoretically equivalent to diffusion under Gaussian assumptions, raising the question of whether distillation techniques such as score distillation transfer directly. We provide a…
CAR-Flow: Condition-Aware Reparameterization Aligns Source and Target for Better Flow Matching
November 12, 2025 research area Computer Vision：/research/?domain=Computer%20Vision conference NeurIPS：/research/?event=NeurIPS
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…
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