Image stitching is a crucial prerequisite step for unmanned aerial vehicle （UAV） remote sensing applications， while the stitched images using most of the current image stitching methods often suffer from large irregular boundaries and multiple stitching seams， which can seriously affect subsequent analysis and applications. Existing improved methods typically cannot simultaneously address these two issues， and integrating the two types of methods in sequence is a straightforward way to solve the two problems， while this often can not obtain satisfactory performance because of the inevitable error propagation problem. This paper proposes an inpainting method for the UAV image stitching task based on the denoising diffusion probability model （DDPM）. The method uniformly designs masks for irregular boundaries and stitching seams， and a diffusion model is then utilized with manifold gradient prior constraints to complete the masked regions. By doing so， both irregular boundaries and stitching seams are simultaneously eliminated， thereby improving the quality of the stitching results. Comparative experiments are conducted using four datasets established for different scenarios. The experimental results demonstrated the efficacy of the proposed method in effectively eliminating irregular boundaries and seams in the image stitching. Moreover， from patches to pictures quality （PaQ-2-PiQ） and multi-scale image quality （MUSIQ） scores increased by 4.36% and 15.37%， respectively. Furthermore， at the locations of irregular boundaries， the structural similarity （SSIM） and peak signal-to-noise ratio （PSNR） values improved by 20.22% and 33.69%， respectively. Compared with state-of-the-art methods and other conventional image stitching algorithms， the proposed method performs better in both subjective and objective quality metric scores， has good robustness and generalization， and can be widely applied to UAV image stitching scenarios.