CAR-Flow: Condition-Aware Reparameterization Aligns Source and Target
  for Better Flow Matching
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. 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.
68d34ffd0e215259d193b1ab
Chen Chen
admin_assigned
2025-09-24T15:34:36.964Z
65409e53d07194a81ecdd427
/avatars/73a88ffe7f78f05b6fc8eb57e9f1e587.svg
Chen Chen
user
ultra7chen
68d34ffd0e215259d193b1ac
Pengsheng Guo
68d34ffd0e215259d193b1ad
Liangchen Song
admin_assigned
2025-09-24T15:34:53.552Z
63d9ea2aaefaa7352587151c
/avatars/2f02ee1b2a0018c74ef1d47883782bd5.svg
Liangchen Song
user
lsongx
68d34ffd0e215259d193b1ae
Jiasen Lu
admin_assigned
2025-09-24T15:35:00.187Z
62b6b0397523238923221df9
/avatars/77068771dd51df7519516cd502a88789.svg
Jiasenlu
user
Jiasenlu
68d34ffd0e215259d193b1af
Rui Qian
68d34ffd0e215259d193b1b0
Xinze Wang
admin_assigned
2025-09-24T15:35:12.106Z
64980a5be35ff69cfd9df735
/avatars/ec0c84611cc600e2f81c528d633090bc.svg
wangxinze
user
wangxinze
68d34ffd0e215259d193b1b1
Tsu-Jui Fu
admin_assigned
2025-09-24T15:35:18.721Z
638cb2efc8912be69c149a46
https://cdn-avatars.huggingface.co/v1/production/uploads/638cb2efc8912be69c149a46/qxMNOWoSUG525DgNPpHcK.jpeg
Tsu-Jui Fu
user
tsujuifu
68d34ffd0e215259d193b1b2
Wei Liu
68d34ffd0e215259d193b1b3
Yinfei Yang
68d34ffd0e215259d193b1b4
Alex Schwing
CAR-Flow: Condition-Aware Reparameterization Aligns Source and Target
  for Better Flow Matching
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. 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.
Condition-Aware Reparameterization for Flow Matching (CAR-Flow) enhances conditional generative modeling by repositioning distributions, leading to faster training and improved performance on image data.
conditional generative modeling diffusion flow-based methods learned shift source distribution target distribution probability path SiT-XL/2 FID