Multi-Scale Diffusion: Enhancing Spatial Layout in High-Resolution Panoramic Image Generation
This work addresses layout consistency issues in panoramic image generation for applications like virtual reality and photography, representing an incremental improvement over existing diffusion-based methods.
The paper tackles the problem of spatial layout inconsistency in high-resolution panoramic image generation by introducing Multi-Scale Diffusion (MSD), which uses gradient descent to integrate structural information from low-resolution images, resulting in significantly improved coherence in outputs.
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.