  Deep learning-based image stitching pipelines are typically divided into
three cascading stages: registration, fusion, and rectangling. Each stage
requires its own network training and is tightly coupled to the others, leading
to error propagation and posing significant challenges to parameter tuning and
system stability. This paper proposes the Simple and Robust Stitcher
(SRStitcher), which revolutionizes the image stitching pipeline by simplifying
the fusion and rectangling stages into a unified inpainting model, requiring no
model training or fine-tuning. We reformulate the problem definitions of the
fusion and rectangling stages and demonstrate that they can be effectively
integrated into an inpainting task. Furthermore, we design the weighted masks
to guide the reverse process in a pre-trained largescale diffusion model,
implementing this integrated inpainting task in a single inference. Through
extensive experimentation, we verify the interpretability and generalization
capabilities of this unified model, demonstrating that SRStitcher outperforms
state-of-the-art methods in both performance and stability. Code:
https://github.com/yayoyo66/SRStitcher
