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

TL;DR

This paper presents Multi-Scale Diffusion, a novel framework that improves the spatial layout and coherence of high-resolution panoramic images generated by diffusion models, by integrating multi-resolution structural guidance.

Contribution

The paper introduces Multi-Scale Diffusion, a new method that extends panoramic image generation to multiple resolutions, enhancing spatial consistency in high-resolution outputs.

Findings

Significant improvement in spatial layout coherence.

Enhanced quality of high-resolution panoramic images.

Outperforms previous methods in qualitative and quantitative evaluations.

Abstract

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.