通过强制边界条件提升流模型生成质量
Improving Rectified Flow with Boundary Conditions
- 在神经网络中引入边界约束，修正速度场估计偏差
- ImageNet上使用ODE采样时FID降低8.01%，SDE采样时降低8.98%
- 适用于对生成质量要求高的图像生成任务
Rectified Flow通过学习速度场实现高质量生成建模，但直接用无约束神经网络建模速度场存在缺陷：学习到的速度场难以满足特定边界条件，导致与期望的常微分方程（ODE）偏离。这一问题在推理阶段的随机采样中尤为严重，因得分函数误差在边界附近被放大。为此，我们提出边界强化的矩形流模型（Boundary RF Model），仅需最小代码修改即可强制施加边界条件。该模型在ImageNet上使用ODE采样时，FID得分提升8.01%；使用SDE采样时，提升达8.98%。
原文摘要 · Abstract (English)
Rectified Flow offers a simple and effective approach to high-quality generative modeling by learning a velocity field. However, we identify a limitation in directly modeling the velocity with an unconstrained neural network: the learned velocity often fails to satisfy certain boundary conditions, leading to inaccurate velocity field estimations that deviate from the desired ODE. This issue is particularly critical during stochastic sampling at inference, as the score function's errors are amplified near the boundary. To mitigate this, we propose a Boundary-enforced Rectified Flow Model (Boundary RF Model), in which we enforce boundary conditions with a minimal code modification. Boundary RF Model improves performance over vanilla RF model, demonstrating 8.01% improvement in FID score on ImageNet using ODE sampling and 8.98% improvement using SDE sampling.
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