  Recently, Google proposes DDVM which for the first time demonstrates that a
general diffusion model for image-to-image translation task works impressively
well on optical flow estimation task without any specific designs like RAFT.
However, DDVM is still a closed-source model with the expensive and private
Palette-style pretraining. In this technical report, we present the first
open-source DDVM by reproducing it. We study several design choices and find
those important ones. By training on 40k public data with 4 GPUs, our
reproduction achieves comparable performance to the closed-source DDVM. The
code and model have been released in
https://github.com/DQiaole/FlowDiffusion_pytorch.
