Diffusion models have emerged as effective tools for generating diverse and high-quality content. However, their capability in high-resolution image generation, particularly for panoramic images, still faces challenges such as visible seams and incoherent transitions. In this paper, we propose TwinDiffusion, an optimized framework designed to address these challenges through two key innovations: Crop Fusion for quality enhancement and Cross Sampling for efficiency optimization. We introduce a training-free optimizing stage to refine the similarity of the adjacent image areas, as well as an interleaving sampling strategy to yield dynamic patches during the cropping process. A comprehensive evaluation is conducted to compare TwinDiffusion with the existing methods, considering factors including coherence, fidelity, compatibility, and efficiency. The results demonstrate the superior performance of our approach in generating seamless and coherent panoramas, setting a new standard in quality and efficiency for panoramic image generation.

翻译：扩散模型已成为生成多样化和高质量内容的有效工具。然而，它们在高分辨率图像生成（尤其是全景图像）方面仍面临可见接缝和不连贯过渡等挑战。本文提出TwinDiffusion，一种通过两项关键创新应对这些挑战的优化框架：用于质量增强的Crop Fusion和用于效率优化的Cross Sampling。我们引入了一种免训练的优化阶段，以提升相邻图像区域的相似性，同时采用交错采样策略在裁剪过程中生成动态区块。通过考虑一致性、保真度、兼容性和效率等因素，对TwinDiffusion与现有方法进行了全面评估。结果表明，我们的方法在生成无缝且连贯的全景图方面具有卓越性能，为全景图像生成的质量和效率树立了新标准。