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arXiv · 2410.18830

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

Xiaoyu Zhang·Teng Zhou·Xinlong Zhang·Jia Wei·Yongchuan Tang
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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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Xiaoyu Zhang, Teng Zhou, Xinlong Zhang, Jia Wei, Yongchuan Tang. 2024-10-24. Multi-Scale Diffusion: Enhancing Spatial Layout in High-Resolution Panoramic Image Generation. https://arxiv.org/abs/2410.18830

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