恆定加速流

Constant Acceleration Flow

November 1, 2024

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

cs.AI

摘要

糾正流程和重新流程程序已顯著推進快速生成，逐漸使普通微分方程（ODE）流變得更加直線。它們的運作基於一個假設，即圖像和噪聲對，稱為耦合，可以通過具有恆定速度的直線軌跡來近似。然而，我們觀察到，使用恆定速度建模和重新流程程序在準確學習對之間的直線軌跡方面存在局限性，導致在少步生成中表現不佳。為了解決這些限制，我們引入了常加速流（CAF），這是一個基於簡單的恆定加速方程的新框架。CAF引入了加速度作為一個額外可學習的變量，可以更具表現力和準確地估計ODE流。此外，我們提出了兩種進一步提高估計準確性的技術：加速度模型的初始速度條件和用於初始速度的重新流程。我們對玩具數據集、CIFAR-10和ImageNet 64x64進行了全面研究，結果顯示CAF在一步生成方面優於最先進的基準。我們還展示了CAF在幾步耦合保留和反演方面明顯優於糾正流。代碼可在https://github.com/mlvlab/CAF{https://github.com/mlvlab/CAF}找到。

English

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 https://github.com/mlvlab/CAF{https://github.com/mlvlab/CAF}.