1. Tutorial Introduction

Chain-of-Zoom is a Chained Zoom (COZ) framework released by the KAIST AI research team on May 26, 2025. The framework solves the problem that modern single image super-resolution (SISR) models fail when required to zoom far beyond that range. The standard 4x diffusion SR model encapsulated in the COZ framework can achieve more than 256x zoom while maintaining high perceptual quality and fidelity. The related paper results are "Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment".

The computing resources used in this tutorial are dual-card RTX 4090.

2. Effect display

3. Operation steps

1. Start the container

2. Usage steps

If "Bad Gateway" is displayed, it means the model is initializing. Since the model is large, please wait about 2-3 minutes and refresh the page.

Specific parameters:

• Input Image: Input image.
• Color Alignment Method:
  • wavelet: No color correction is performed.
  • adain: Color correction with adaptive instance normalization.
  • nofix: Use wavelet transform for finer color correction.

result 

4. Discussion

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Citation Information

Thanks to Github user SuperYang  Deployment of this tutorial. The reference information of this project is as follows:

@article{kim2025chain,
  title={Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment},
  author={Kim, Bryan Sangwoo and Kim, Jeongsol and Ye, Jong Chul},
  journal={arXiv preprint arXiv:2505.18600},
  year={2025}
}