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

• URL: http://arxiv.org/abs/2410.18830v2
• Date: Sun, 06 Apr 2025 16:44:55 GMT
• Title: Multi-Scale Diffusion: Enhancing Spatial Layout in High-Resolution Panoramic Image Generation
• Authors: Xiaoyu Zhang, Teng Zhou, Xinlong Zhang, Jia Wei, Yongchuan Tang,
• Abstract summary: This paper introduces the Multi-Scale Diffusion (MSD), an optimized framework that extends the panoramic image generation framework to multiple resolution levels.<n>Our method leverages gradient descent techniques to incorporate structural information from low-resolution images into high-resolution outputs.
• Score: 12.588962705218103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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