Diffusion360: Seamless 360 Degree Panoramic Image Generation based on
  Diffusion Models
Mengyang Feng Jinlin Liu Miaomiao Cui Xuansong Xie
This is a technical report on the 360-degree panoramic image generation task
based on diffusion models. Unlike ordinary 2D images, 360-degree panoramic
images capture the entire $360^\circ\times 180^\circ$ field of view. So the
rightmost and the leftmost sides of the 360 panoramic image should be
continued, which is the main challenge in this field. However, the current
diffusion pipeline is not appropriate for generating such a seamless 360-degree
panoramic image. To this end, we propose a circular blending strategy on both
the denoising and VAE decoding stages to maintain the geometry continuity.
Based on this, we present two models for \textbf{Text-to-360-panoramas} and
\textbf{Single-Image-to-360-panoramas} tasks. The code has been released as an
open-source project at
\href{https://github.com/ArcherFMY/SD-T2I-360PanoImage}{https://github.com/ArcherFMY/SD-T2I-360PanoImage}
and
\href{https://www.modelscope.cn/models/damo/cv_diffusion_text-to-360panorama-image_generation/summary}{ModelScope}