Papers › Constant Acceleration Flow

Constant Acceleration Flow

1 Nov 2024arXiv:2411.00322archive 2025-07-28

Dogyun Park, Sojin Lee, Sihyeon Kim, Taehoon Lee, Youngjoon Hong, Hyunwoo J. Kim

Rectified flow and reflow procedures have significantly advanced fast generation by progressively straightening ordinary differential equation (ODE) flows. They operate under the assumption that image and noise pairs, known as couplings, can be approximated by straight trajectories with constant velocity. However, we observe that modeling with constant velocity and using reflow procedures have limitations in accurately learning straight trajectories between pairs, resulting in suboptimal performance in few-step generation. To address these limitations, we introduce Constant Acceleration Flow (CAF), a novel framework based on a simple constant acceleration equation. CAF introduces acceleration as an additional learnable variable, allowing for more expressive and accurate estimation of the ODE flow. Moreover, we propose two techniques to further improve estimation accuracy: initial velocity conditioning for the acceleration model and a reflow process for the initial velocity. Our comprehensive studies on toy datasets, CIFAR-10, and ImageNet 64x64 demonstrate that CAF outperforms state-of-the-art baselines for one-step generation. We also show that CAF dramatically improves few-step coupling preservation and inversion over Rectified flow. Code is available at \href{https://github.com/mlvlab/CAF}{https://github.com/mlvlab/CAF}.

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caf_acc_defaults mlvlab/CAF/flow/script_util.py official repository ran · our draft was wrong fingerprinted no licence file found · pointer only · 0e98b4fde008d871 · report
caf_gan_defaults mlvlab/CAF/flow/script_util.py official repository ran · our draft was wrong fingerprinted no licence file found · pointer only · 535c58b2b7fb357a · report
caf_vel_defaults mlvlab/CAF/flow/script_util.py official repository ran · our draft was wrong fingerprinted no licence file found · pointer only · 20837e056bce0de2 · report

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Generation ImageNet 64x64 CAF FID 1.69 #16 of 65 Archive leaderboard report
Image Generation ImageNet 64x64 CAF Inception Score 62.03 #16 of 65 Archive leaderboard report
Image Generation ImageNet 64x64 CAF NFE 2 #16 of 65 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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