MixFlow: Mixed Source Distributions Improve Rectified Flows

TL;DR

MixFlow introduces a novel training strategy that combines mixed source distributions to reduce path curvature in rectified flows, leading to faster sampling and improved image generation quality.

Contribution

The paper proposes MixFlow, a new method that trains on mixtures of distributions to better align source and data, enhancing efficiency and quality in rectified flow models.

Findings

Improves generation quality by 12% in FID over standard rectified flow.

Reduces required sampling steps for high-quality generation.

Accelerates training convergence significantly.

Abstract

Diffusion models and their variations, such as rectified flows, generate diverse and high-quality images, but they are still hindered by slow iterative sampling caused by the highly curved generative paths they learn. An important cause of high curvature, as shown by previous work, is independence between the source distribution (standard Gaussian) and the data distribution. In this work, we tackle this limitation by two complementary contributions. First, we attempt to break away from the standard Gaussian assumption by introducing $\kappa\texttt{-FC}$, a general formulation that conditions the source distribution on an arbitrary signal $\kappa$ that aligns it better with the data distribution. Then, we present MixFlow, a simple but effective training strategy that reduces the generative path curvatures and considerably improves sampling efficiency. MixFlow trains a flow model on linear mixtures of a fixed unconditional distribution and a $\kappa\texttt{-FC}$-based distribution. This simple mixture improves the alignment between the source and data, provides better generation quality with less required sampling steps, and accelerates the training convergence considerably. On average, our training procedure improves the generation quality by 12\% in FID compared to standard rectified flow and 7\% compared to previous baselines under a fixed sampling budget. Code available at: $\href{https://github.com/NazirNayal8/MixFlow}{https://github.com/NazirNayal8/MixFlow}$