ProReflow: Progressive Reflow with Decomposed Velocity
Authors: name: Lei Ke, name: Haohang Xu, name: Xuefei Ning, name: Yu Li, name: Jiajun Li, name: Haoling Li, name: Yuxuan Lin, name: Dongsheng Jiang, name: Yujiu Yang, name: Linfeng Zhang

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).