ProReflow：基于速度分解的渐进式重排

ProReflow: Progressive Reflow with Decomposed Velocity

March 5, 2025

作者: Lei Ke, Haohang Xu, Xuefei Ning, Yu Li, Jiajun Li, Haoling Li, Yuxuan Lin, Dongsheng Jiang, Yujiu Yang, Linfeng Zhang

cs.AI

摘要

扩散模型在图像和视频生成领域取得了显著进展，但仍面临巨大的计算成本问题。作为一种有效解决方案，流匹配旨在将扩散模型的扩散过程重新调整为直线路径，以实现少步甚至一步生成。然而，本文指出，流匹配的原始训练流程并非最优，并引入了两种技术加以改进。首先，我们提出了渐进式回流，通过在局部时间步长上逐步回流扩散模型，直至整个扩散过程完成，从而降低了流匹配的难度。其次，我们引入了对齐的v预测，强调了流匹配中方向匹配相较于幅度匹配的重要性。在SDv1.5和SDXL上的实验结果表明了我们方法的有效性，例如，在SDv1.5上仅用4个采样步骤就在MSCOCO2014验证集上达到了10.70的FID，接近我们的教师模型（32步DDIM，FID = 10.05）。

English

Diffusion models have achieved significant progress in both image and video generation while still suffering from huge computation costs. As an effective solution, flow matching aims to reflow the diffusion process of diffusion models into a straight line for a few-step and even one-step generation. However, in this paper, we suggest that the original training pipeline of flow matching is not optimal and introduce two techniques to improve it. Firstly, we introduce progressive reflow, which progressively reflows the diffusion models in local timesteps until the whole diffusion progresses, reducing the difficulty of flow matching. Second, we introduce aligned v-prediction, which highlights the importance of direction matching in flow matching over magnitude matching. Experimental results on SDv1.5 and SDXL demonstrate the effectiveness of our method, for example, conducting on SDv1.5 achieves an FID of 10.70 on MSCOCO2014 validation set with only 4 sampling steps, close to our teacher model (32 DDIM steps, FID = 10.05).