cjwbw / supir-v0q

Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild. This is the SUPIR-v0Q model and does NOT use LLaVA-13b.

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Input

Output

Examples

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Run time and cost

This model runs on Nvidia A40 (Large) GPU hardware. Predictions typically complete within 4 minutes. The predict time for this model varies significantly based on the inputs.

Readme

Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild

NOTE: This version uses the SUPIR-v0Q checkpoint and does not include the LLaVA-13b due to the memory constraint. Try https://replicate.com/cjwbw/supir-v0f for the SUPIR-v0F checkpoint or https://replicate.com/cjwbw/supir which is hosted on 80G A100 to include LLaVA-13b model.

  • SUPIR-v0Q: Default training settings with paper. High generalization and high image quality in most cases.
  • SUPIR-v0F: Training with light degradation settings. Stage1 encoder of SUPIR-v0F remains more details when facing light degradations.

BibTeX

@misc{yu2024scaling,
  title={Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild}, 
  author={Fanghua Yu and Jinjin Gu and Zheyuan Li and Jinfan Hu and Xiangtao Kong and Xintao Wang and Jingwen He and Yu Qiao and Chao Dong},
  year={2024},
  eprint={2401.13627},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}