Taming Stable Diffusion for Text to 360° Panorama Image Generation
Cheng Zhang Qianyi Wu Camilo Cruz Gambardella Xiaoshui Huang Dinh Phung Wanli Ouyang Jianfei Cai
Generative models, e.g., Stable Diffusion, have enabled the creation of
photorealistic images from text prompts. Yet, the generation of 360-degree
panorama images from text remains a challenge, particularly due to the dearth
of paired text-panorama data and the domain gap between panorama and
perspective images. In this paper, we introduce a novel dual-branch diffusion
model named PanFusion to generate a 360-degree image from a text prompt. We
leverage the stable diffusion model as one branch to provide prior knowledge in
natural image generation and register it to another panorama branch for
holistic image generation. We propose a unique cross-attention mechanism with
projection awareness to minimize distortion during the collaborative denoising
process. Our experiments validate that PanFusion surpasses existing methods
and, thanks to its dual-branch structure, can integrate additional constraints
like room layout for customized panorama outputs. Code is available at
https://chengzhag.github.io/publication/panfusion.