一致性流匹配：定義具有速度的直流 一致性

Consistency Flow Matching: Defining Straight Flows with Velocity Consistency

July 2, 2024

作者: Ling Yang, Zixiang Zhang, Zhilong Zhang, Xingchao Liu, Minkai Xu, Wentao Zhang, Chenlin Meng, Stefano Ermon, Bin Cui

cs.AI

摘要

流匹配（Flow matching，FM）是一個通用框架，透過常微分方程（ODEs）來定義機率路徑，以在噪音和數據樣本之間進行轉換。最近的方法試圖將這些流軌跡拉直，以通過迭代校正方法或最優運輸解決方案生成質量更高的樣本，通常需要較少的函數評估。在本文中，我們介紹了一種新的流匹配方法，即一致性流匹配（Consistency Flow Matching，Consistency-FM），它明確地強制在速度場中實現自一致性。Consistency-FM直接定義了從不同時間開始到同一終點的直線流，對其速度值施加約束。此外，我們提出了一種多段訓練方法，用於Consistency-FM以增強表達能力，實現更好的折衷方案，即在採樣質量和速度之間取得更好的平衡。初步實驗表明，我們的Consistency-FM通過比一致性模型快4.4倍和校正流模型快1.7倍的收斂速度顯著提高了訓練效率，同時實現了更好的生成質量。我們的代碼可在以下鏈接找到：https://github.com/YangLing0818/consistency_flow_matching

English

Flow matching (FM) is a general framework for defining probability paths via Ordinary Differential Equations (ODEs) to transform between noise and data samples. Recent approaches attempt to straighten these flow trajectories to generate high-quality samples with fewer function evaluations, typically through iterative rectification methods or optimal transport solutions. In this paper, we introduce Consistency Flow Matching (Consistency-FM), a novel FM method that explicitly enforces self-consistency in the velocity field. Consistency-FM directly defines straight flows starting from different times to the same endpoint, imposing constraints on their velocity values. Additionally, we propose a multi-segment training approach for Consistency-FM to enhance expressiveness, achieving a better trade-off between sampling quality and speed. Preliminary experiments demonstrate that our Consistency-FM significantly improves training efficiency by converging 4.4x faster than consistency models and 1.7x faster than rectified flow models while achieving better generation quality. Our code is available at: https://github.com/YangLing0818/consistency_flow_matching