Abstract
Flow-based super-resolution (SR) models have demonstrated astonishing capabilities in generating high-quality images. However, these methods encounter several challenges during image generation, such as grid artifacts, exploding inverses, and suboptimal results due to a fixed sampling temperature. To overcome these issues, this work introduces a conditional learned prior to the inference phase of a flow-based SR model. This prior is a latent code predicted by our proposed latent module conditioned on the low-resolution image, which is then transformed by the flow model into an SR image. Our framework is designed to seamlessly integrate with any contemporary flow-based SR model without modifying its architecture or pre-trained weights. We evaluate the effectiveness of our proposed framework through extensive experiments and ablation analyses. The proposed framework successfully addresses all the inherent issues in flow-based SR models and enhances their performance in various SR scenarios. Our code is available at: this https URL
Abstract (translated)
基于流的超分辨率（SR）模型已经在生成高质量图像方面表现出惊人的能力。然而，在图像生成过程中，这些方法遇到了多个挑战，例如网格伪影、爆炸逆和由于固定采样温度而导致的最优结果。为了克服这些问题，本文在推理阶段引入了一个有条件的先验。这个先验是我们基于低分辨率图像提出的latent模块，然后通过流模型转换为SR图像。我们的框架旨在与任何当代流式SR模型无缝集成，而不会修改其架构或预训练权重。通过广泛的实验和消融分析来评估我们提出的框架的有效性。我们成功解决了基于流SR模型的所有固有问题，并在各种SR场景中提高了其性能。我们的代码可在此处下载：https://this URL。
URL
https://arxiv.org/abs/2403.10988
