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

• URL: http://arxiv.org/abs/2410.18830v1
• Date: Thu, 24 Oct 2024 15:18:51 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: We introduce the Multi-Scale Diffusion (MSD) framework, a plug-and-play module that extends the existing panoramic image generation framework to multiple resolution levels. By utilizing gradient descent techniques, our method effectively incorporates structural information from low-resolution images into high-resolution outputs.
• Score: 12.588962705218103
• License:
• Abstract: Diffusion models have recently gained recognition for generating diverse and high-quality content, especially in the domain of 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 of the global image layout. In this paper, we introduce the Multi-Scale Diffusion (MSD) framework, a plug-and-play module that extends the existing panoramic image generation framework to multiple resolution levels. By utilizing gradient descent techniques, our method effectively incorporates structural information from low-resolution images into high-resolution outputs. A comprehensive evaluation of the proposed method was conducted, comparing it with the prior works in qualitative and quantitative dimensions. The evaluation results demonstrate that our method significantly outperforms others in generating coherent high-resolution panoramas.

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