通过混合分布提升生成路径平滑度，显著加快扩散模型采样速度
MixFlow: Mixed Source Distributions Improve Rectified Flows
- 引入κ-FC让源分布适配数据分布，减少生成路径弯曲
- 混合训练策略使采样步数更少，FID降低12%（相比标准修正流）
- 适合关注采样效率与生成质量平衡的研究者
扩散模型及其变体如修正流虽能生成高质量图像，但受限于高度弯曲的生成路径导致采样速度慢。此前研究指出，源分布（标准高斯）与数据分布独立是造成高曲率的重要原因。本文提出两项互补贡献：首先引入κ-FC，以任意信号κ条件化源分布，使其更贴近数据分布；其次提出MixFlow训练策略，通过固定无条件分布与κ-FC分布的线性混合，有效降低生成路径曲率，大幅提升采样效率。该方法显著改善生成质量，在固定采样预算下，相比标准修正流提升12% FID，较先前基线提升7%。代码已公开。
原文摘要 · Abstract (English)
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 $κ\texttt{-FC}$, a general formulation that conditions the source distribution on an arbitrary signal $κ$ 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 $κ\texttt{-FC}$-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: $\href{https://github.com/NazirNayal8/MixFlow}{https://github.com/NazirNayal8/MixFlow}$
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