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且高质量内容的有效工具。然而，其在高分辨率图像生成（尤其是全景图像）中仍面临显著接缝与不连贯过渡等挑战。本文提出了一种优化框架TwinDiffusion，通过两项关键创新应对上述挑战：用于质量提升的裁剪融合技术，以及用于效率优化的交叉采样策略。我们引入了一种免训练优化阶段，用以增强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块。通过综合考虑连贯性、保真度、兼容性及效率等因素，将TwinDiffusion与现有方法进行全面评估。结果表明，我们的方法在生成无缝且连贯的全景图方面性能卓越，为全景图像生成的质量与效率树立了新标杆。