title: Constant Acceleration Flow
summary: Rectified flow and reflow procedures have significantly advanced fast
  generation by progressively straightening ordinary differential equation (ODE)
  flows. They operate under the assumption that image and noise pairs, known as
  couplings, can be approximated by straight trajectories with constant velocity.
  However, we observe that modeling with constant velocity and using reflow
  procedures have limitations in accurately learning straight trajectories
  between pairs, resulting in suboptimal performance in few-step generation. To
  address these limitations, we introduce Constant Acceleration Flow (CAF), a
  novel framework based on a simple constant acceleration equation. CAF
  introduces acceleration as an additional learnable variable, allowing for more
  expressive and accurate estimation of the ODE flow. Moreover, we propose two
  techniques to further improve estimation accuracy: initial velocity
  conditioning for the acceleration model and a reflow process for the initial
  velocity. Our comprehensive studies on toy datasets, CIFAR-10, and ImageNet
  64x64 demonstrate that CAF outperforms state-of-the-art baselines for one-step
  generation. We also show that CAF dramatically improves few-step coupling
  preservation and inversion over Rectified flow. Code is available at
  https://github.com/mlvlab/CAF{https://github.com/mlvlab/CAF}.
paper_authors._id: 67289d08a53d5d0e84be8c8b
paper_authors.name: Dogyun Park
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-04T14:19:44.694Z
paper_authors._id: 64e6d90c4a408888f9dcc95e
paper_authors.avatarUrl: /avatars/829394448bbe53bf133d6315e51a90b7.svg
paper_authors.fullname: Dogyun Park
paper_authors.type: user
paper_authors.user: DogyunPark
paper_authors._id: 67289d08a53d5d0e84be8c8c
paper_authors.name: Sojin Lee
paper_authors._id: 67289d08a53d5d0e84be8c8d
paper_authors.name: Sihyeon Kim
paper_authors._id: 67289d08a53d5d0e84be8c8e
paper_authors.name: Taehoon Lee
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-05T07:58:40.319Z
paper_authors._id: 637db20b64686bd9011f51ba
paper_authors.avatarUrl: /avatars/3b57ccd35d856958a84db6f0dd4e405e.svg
paper_authors.fullname: Taehoon Lee
paper_authors.type: user
paper_authors.user: dlxogns183
paper_authors._id: 67289d08a53d5d0e84be8c8f
paper_authors.name: Youngjoon Hong
paper_authors._id: 67289d08a53d5d0e84be8c90
paper_authors.name: Hyunwoo J. Kim
paper_title: Constant Acceleration Flow
paper_summary: Rectified flow and reflow procedures have significantly advanced fast
  generation by progressively straightening ordinary differential equation (ODE)
  flows. They operate under the assumption that image and noise pairs, known as
  couplings, can be approximated by straight trajectories with constant velocity.
  However, we observe that modeling with constant velocity and using reflow
  procedures have limitations in accurately learning straight trajectories
  between pairs, resulting in suboptimal performance in few-step generation. To
  address these limitations, we introduce Constant Acceleration Flow (CAF), a
  novel framework based on a simple constant acceleration equation. CAF
  introduces acceleration as an additional learnable variable, allowing for more
  expressive and accurate estimation of the ODE flow. Moreover, we propose two
  techniques to further improve estimation accuracy: initial velocity
  conditioning for the acceleration model and a reflow process for the initial
  velocity. Our comprehensive studies on toy datasets, CIFAR-10, and ImageNet
  64x64 demonstrate that CAF outperforms state-of-the-art baselines for one-step
  generation. We also show that CAF dramatically improves few-step coupling
  preservation and inversion over Rectified flow. Code is available at
  https://github.com/mlvlab/CAF{https://github.com/mlvlab/CAF}.
paper_ai_summary: A novel framework called Constant Acceleration Flow (CAF) improves few-step image generation by incorporating acceleration into the ODE flow model, demonstrating superior performance compared to existing methods.
paper_ai_keywords: Rectified flow reflow procedures ordinary differential equation (ODE) couplings constant velocity Constant Acceleration Flow (CAF) acceleration model coupling preservation inversion