Facial parts swapping aims to selectively transfer interest regions from the source image onto the target image while maintaining the rest of the target image unchanged.Most studies on face swapping designed specifically for full-face swapping, are either unable or significantly limited when it comes to swapping individual facial parts, which affects fine-grained and customized character designs.However, designing such an approach specifically for face parts swapping is challenged by a rational multiple reference feature fusion, which needs to be both efficient and effective.To overcome this challenge, FuseAnyPart is proposed to facilitate the seamless ``fuse-any-part" customization of the face.In FuseAnyPart, organ parts from different people are assembled into a complete face in the latent space within the Mask-based Fusion Module.Subsequently, the consolidated feature is dispatched to the Addition-based Injection Module forfusion within the UNet of the diffusion model to create novel characters.Extensive experiments qualitatively and quantitatively validate the superiority and robustness of FuseAnyPart.