TraFlow: Trajectory Distillation on Pre-Trained Rectified Flow

• URL: http://arxiv.org/abs/2502.16972v1
• Date: Mon, 24 Feb 2025 08:57:19 GMT
• Title: TraFlow: Trajectory Distillation on Pre-Trained Rectified Flow
• Authors: Zhangkai Wu, Xuhui Fan, Hongyu Wu, Longbing Cao,
• Abstract summary: We propose a trajectory distillation method, modelname, that enjoys the benefits of both and enables few-step generations.<n>TraFlow adopts the settings of consistency trajectory models, and further enforces the properties of self-consistency and straightness throughout the entire trajectory.
• Score: 31.56008127287467
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Majorities of distillation methods on pre-trained diffusion models or on pre-trained rectified flow, focus on either the distillation outputs or the trajectories between random noises and clean images to speed up sample generations from pre-trained models. In those trajectory-based distillation methods, consistency distillation requires the self-consistent trajectory projection to regulate the trajectory, which might avoid the common ODE approximation error {while still be concerning about sampling efficiencies}. At the same time, rectified flow distillations enforce straight trajectory for fast sampling, although an ODE solver is still required. In this work, we propose a trajectory distillation method, \modelname, that enjoys the benefits of both and enables few-step generations. TraFlow adopts the settings of consistency trajectory models, and further enforces the properties of self-consistency and straightness throughout the entire trajectory. These two properties are pursued by reaching a balance with following three targets: (1) reconstruct the output from pre-trained models; (2) learn the amount of changes by pre-trained models; (3) satisfy the self-consistency over its trajectory. Extensive experimental results have shown the effectiveness of our proposed method.

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