Multi-Scale Diffusion: Enhancing Spatial Layout in High-Resolution Panoramic Image Generation

Diffusion models have recently gained recognition for generating diverse and high-quality content, especially in image synthesis. These models excel not only in creating fixed-size images but also in producing panoramic images. However, existing methods often struggle with spatial layout consistency when producing high-resolution panoramas due to the lack of guidance on the global image layout. This paper introduces the Multi-Scale Diffusion (MSD), an optimized framework that extends the panoramic image generation framework to multiple resolution levels. Our method leverages gradient descent techniques to incorporate structural information from low-resolution images into high-resolution outputs. Through comprehensive qualitative and quantitative evaluations against prior work, we demonstrate that our approach significantly improves the coherence of high-resolution panorama generation.

@article{zhang2025_2410.18830,
  title={ Multi-Scale Diffusion: Enhancing Spatial Layout in High-Resolution Panoramic Image Generation },
  author={ Xiaoyu Zhang and Teng Zhou and Xinlong Zhang and Jia Wei and Yongchuan Tang },
  journal={arXiv preprint arXiv:2410.18830},
  year={ 2025 }
}