Personalized text-to-image (T2I) synthesis based on diffusion models has attracted significant attention in recent research. However, existing methods primarily concentrate on customizing subjects or styles, neglecting the exploration of global geometry. In this study, we propose an approach that focuses on the customization of 360-degree panoramas, which inherently possess global geometric properties, using a T2I diffusion model. To achieve this, we curate a paired image-text dataset specifically designed for the task and subsequently employ it to fine-tune a pre-trained T2I diffusion model with LoRA. Nevertheless, the fine-tuned model alone does not ensure the continuity between the leftmost and rightmost sides of the synthesized images, a crucial characteristic of 360-degree panoramas. To address this issue, we propose a method called StitchDiffusion. Specifically, we perform pre-denoising operations twice at each time step of the denoising process on the stitch block consisting of the leftmost and rightmost image regions. Furthermore, a global cropping is adopted to synthesize seamless 360-degree panoramas. Experimental results demonstrate the effectiveness of our customized model combined with the proposed StitchDiffusion in generating high-quality 360-degree panoramic images. Moreover, our customized model exhibits exceptional generalization ability in producing scenes unseen in the fine-tuning dataset. Code is available at https://github.com/littlewhitesea/StitchDiffusion.

翻译：基于扩散模型的个性化文本到图像（T2I）合成技术在近期研究中备受关注。然而，现有方法主要聚焦于定制主体或风格，忽略了全局几何特性的探索。本研究提出一种方法，专注于利用T2I扩散模型定制本质上具有全局几何特性的360度全景图。为此，我们专门为该任务构建了成对的图像-文本数据集，并采用LoRA对预训练T2I扩散模型进行微调。但微调后的模型无法保证合成图像最左端与最右端之间的连续性——这是360度全景图的关键特性。针对该问题，我们提出StitchDiffusion方法：具体而言，在去噪过程的每个时间步上，对由最左端和最右端图像区域构成的拼接块执行两次预去噪操作；此外，采用全局裁剪策略合成无缝360度全景图。实验结果表明，所提出的定制模型结合StitchDiffusion能有效生成高质量360度全景图像。同时，定制模型在生成微调数据集中未出现的场景时展现出优异的泛化能力。代码开源地址：https://github.com/littlewhitesea/StitchDiffusion。