Boosting Flow-based Generative Super-Resolution Models via Learned Prior
CVPR 2024(2024)
摘要
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:
https://github.com/liyuantsao/FlowSR-LP
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