The automatic generation of implants holds significant importance in cranial repair surgery. Recent studies have attempted to use diffusion models to complete defective skull point clouds and obtain implants through a voxelization network. However, its multi-step sampling in point cloud diffusion is slow ( \(\sim 1,000\) s), often requiring tens of inference steps to get satisfactory results. In this paper, we explore a recent method called Rectified Flow, which straightens the trajectories of probability flows, and enables one-step generation while maintaining high quality. Moreover, we finetune the voxelization network to enhance its adaptability to the output of the point cloud completion network, thereby reducing the iteration of training required. Leveraging our new pipeline, each implant requires \(\sim 56\) s to generate, whereas point cloud completion alone consumes just \(\sim 0.75\) s. To evaluate the effectiveness of our method, we conducted experiments on SkullBreak and SkullFix datasets. The results, measured using the Dice score (DSC), the 10mm boundary DSC (bDSC), and 95 percentile Hausdorff Distance (HD95) metrics showed a clear performance advantage compared to other proposed methods.