title: ProReflow: Progressive Reflow with Decomposed Velocity
summary: 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).
paper_authors._id: 67cec02929ee2ca420c0c395
paper_authors.name: Lei Ke
paper_authors._id: 67cec02929ee2ca420c0c396
paper_authors.name: Haohang Xu
paper_authors._id: 67cec02929ee2ca420c0c397
paper_authors.name: Xuefei Ning
paper_authors._id: 67cec02929ee2ca420c0c398
paper_authors.name: Yu Li
paper_authors._id: 67cec02929ee2ca420c0c399
paper_authors.name: Jiajun Li
paper_authors._id: 67cec02929ee2ca420c0c39a
paper_authors.name: Haoling Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-10T11:08:46.234Z
paper_authors._id: 650be23ec4e52db6a4db63ef
paper_authors.avatarUrl: /avatars/03af548029b38bee49ec295fefe74f9a.svg
paper_authors.fullname: Haoling Li
paper_authors.type: user
paper_authors.user: Ringo1110
paper_authors._id: 67cec02929ee2ca420c0c39b
paper_authors.name: Yuxuan Lin
paper_authors._id: 67cec02929ee2ca420c0c39c
paper_authors.name: Dongsheng Jiang
paper_authors._id: 67cec02929ee2ca420c0c39d
paper_authors.name: Yujiu Yang
paper_authors._id: 67cec02929ee2ca420c0c39e
paper_authors.name: Linfeng Zhang
paper_title: ProReflow: Progressive Reflow with Decomposed Velocity
paper_summary: 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).
paper_ai_keywords: diffusion models image generation video generation computation costs flow matching straight line progressive reflow local timesteps aligned v-prediction direction matching magnitude matching FID MSCOCO2014 validation set sampling steps DDIM steps teacher model