ProReflow: Progressive Reflow with Decomposed Velocity
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).
67cec02929ee2ca420c0c395
Lei Ke
67cec02929ee2ca420c0c396
Haohang Xu
67cec02929ee2ca420c0c397
Xuefei Ning
67cec02929ee2ca420c0c398
Yu Li
67cec02929ee2ca420c0c399
Jiajun Li
67cec02929ee2ca420c0c39a
Haoling Li
claimed_verified
2025-03-10T11:08:46.234Z
650be23ec4e52db6a4db63ef
/avatars/03af548029b38bee49ec295fefe74f9a.svg
Haoling Li
user
Ringo1110
67cec02929ee2ca420c0c39b
Yuxuan Lin
67cec02929ee2ca420c0c39c
Dongsheng Jiang
67cec02929ee2ca420c0c39d
Yujiu Yang
67cec02929ee2ca420c0c39e
Linfeng Zhang
claimed_verified
2025-03-25T08:21:37.134Z
642ec9831d1737803dc1c30a
/avatars/c9ded838bad09004c15a27200e66a108.svg
linfeng zhang
user
linfengZ
ProReflow: Progressive Reflow with Decomposed Velocity
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).
Flow matching is improved with progressive reflow and aligned v-prediction, reducing computational costs while maintaining high image generation quality.
diffusion models flow matching progressive reflow aligned v-prediction FID MSCOCO2014 sampling steps DDIM steps