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高质量内容的有效工具，然而其在生成高分辨率图像（尤其是全景图像）时仍面临可见接缝与不连贯过渡等挑战。本文提出TwinDiffusion优化框架，通过两大创新应对上述挑战：用于质量增强的裁剪融合技术与用于效率优化的交叉采样策略。我们引入无需训练的优化阶段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图像块。通过综合评估对比TwinDiffusion与现有方法在连贯性、保真度、兼容性及效率方面的表现，实验结果表明本方法在生成无缝连贯全景图方面具有卓越性能，为全景图像生成的质量与效率树立了新标杆。