DFU: scale-robust diffusion model for zero-shot super-resolution image generation

• URL: http://arxiv.org/abs/2401.06144v2
• Date: Mon, 22 Jan 2024 17:11:57 GMT
• Title: DFU: scale-robust diffusion model for zero-shot super-resolution image generation
• Authors: Alex Havrilla, Kevin Rojas, Wenjing Liao, Molei Tao
• Abstract summary: We present a novel deep-learning architecture, Dual-FNO UNet (DFU), which approximates the score operator by combining both spatial and spectral information at multiple resolutions. We propose a fine-tuning strategy to further enhance the zero-shot super-resolution image-generation capability of our model, leading to a FID of 11.3 at 1.66 times the maximum training resolution on FFHQ.
• Score: 15.689418447376587
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion generative models have achieved remarkable success in generating images with a fixed resolution. However, existing models have limited ability to generalize to different resolutions when training data at those resolutions are not available. Leveraging techniques from operator learning, we present a novel deep-learning architecture, Dual-FNO UNet (DFU), which approximates the score operator by combining both spatial and spectral information at multiple resolutions. Comparisons of DFU to baselines demonstrate its scalability: 1) simultaneously training on multiple resolutions improves FID over training at any single fixed resolution; 2) DFU generalizes beyond its training resolutions, allowing for coherent, high-fidelity generation at higher-resolutions with the same model, i.e. zero-shot super-resolution image-generation; 3) we propose a fine-tuning strategy to further enhance the zero-shot super-resolution image-generation capability of our model, leading to a FID of 11.3 at 1.66 times the maximum training resolution on FFHQ, which no other method can come close to achieving.

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