Papers › Boosting Flow-based Generative Super-Resolution Models via Learned Prior
Boosting Flow-based Generative Super-Resolution Models via Learned Prior
Li-Yuan Tsao, Yi-Chen Lo, Chia-Che Chang, Hao-Wei Chen, Roy Tseng, Chien Feng, Chun-Yi Lee
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/BFSR
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Super-Resolution | DIV2K val - 4x upscaling | LINF-LP | LPIPS | 0.105 | #5 of 21 | Archive leaderboard | report |
| Image Super-Resolution | DIV2K val - 4x upscaling | LINF-LP | LRPSNR | 47.3 | #5 of 21 | Archive leaderboard | report |
| Image Super-Resolution | DIV2K val - 4x upscaling | LINF-LP | PSNR | 28.00 | #5 of 21 | Archive leaderboard | report |
| Image Super-Resolution | DIV2K val - 4x upscaling | LINF-LP | SSIM | 0.78 | #5 of 21 | Archive leaderboard | report |
| Image Super-Resolution | DIV2K val - 4x upscaling | SRFlow-LP | LPIPS | 0.109 | #6 of 21 | Archive leaderboard | report |
| Image Super-Resolution | DIV2K val - 4x upscaling | SRFlow-LP | LRPSNR | 51.51 | #6 of 21 | Archive leaderboard | report |
| Image Super-Resolution | DIV2K val - 4x upscaling | SRFlow-LP | PSNR | 27.51 | #6 of 21 | Archive leaderboard | report |
| Image Super-Resolution | DIV2K val - 4x upscaling | SRFlow-LP | SSIM | 0.78 | #6 of 21 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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