An unsupervised diffeomorphic registration network is proposed for fast bin-to-bin 3D non-rigid respiratory motion estimation, which enables efficient motion-compensated coronary MR image reconstruction in less than 20s. It achieves similar motion-corrected reconstruction quality to a conventional state-of-the-art registration method. The proposed approach holds great potential for clinical application due to the fast computation speed and no need of ground truth for training.