Constant Acceleration Flow
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}.
67289d08a53d5d0e84be8c8b
Dogyun Park
admin_assigned
2024-11-04T14:19:44.694Z
64e6d90c4a408888f9dcc95e
/avatars/829394448bbe53bf133d6315e51a90b7.svg
Dogyun Park
user
DogyunPark
67289d08a53d5d0e84be8c8c
Sojin Lee
67289d08a53d5d0e84be8c8d
Sihyeon Kim
67289d08a53d5d0e84be8c8e
Taehoon Lee
claimed_verified
2024-11-05T07:58:40.319Z
637db20b64686bd9011f51ba
/avatars/3b57ccd35d856958a84db6f0dd4e405e.svg
Taehoon Lee
user
dlxogns183
67289d08a53d5d0e84be8c8f
Youngjoon Hong
67289d08a53d5d0e84be8c90
Hyunwoo J. Kim
claimed_verified
2025-06-16T15:51:34.522Z
63183a30a760ff8cff318be5
/avatars/1b314afd8d3319423915ffc2ae56d567.svg
Hyunwoo Kim
user
hkimcvml
Constant Acceleration Flow
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}.
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.
Rectified flow reflow procedures ordinary differential equation (ODE) couplings constant velocity Constant Acceleration Flow (CAF) acceleration model coupling preservation inversion