In this paper, we propose an automated Euler's time-step adjustment scheme for diffeomorphic image registration using stationary velocity fields (SVFs). The proposed variational problem aims at bounding the inverse consistency error by adaptively adjusting the number of Euler's step required to realize the time integration. This particular formulation allows us to gain computationally since only relevant number of time steps are taken. We parameterize the SVFs using multi-scale Wendland kernels through the kernel bundle framework. In terms of performance, the proposed scheme reaches the same accuracy as a fixed time-step scheme however at a much less computational cost.