Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment

Modern single-image super-resolution (SISR) models deliver photo-realistic results at the scale factors on which they are trained, but collapse when asked to magnify far beyond that regime. We address this scalability bottleneck with Chain-of-Zoom (CoZ), a model-agnostic framework that factorizes SISR into an autoregressive chain of intermediate scale-states with multi-scale-aware prompts. CoZ repeatedly re-uses a backbone SR model, decomposing the conditional probability into tractable sub-problems to achieve extreme resolutions without additional training. Because visual cues diminish at high magnifications, we augment each zoom step with multi-scale-aware text prompts generated by a vision-language model (VLM). The prompt extractor itself is fine-tuned using Generalized Reward Policy Optimization (GRPO) with a critic VLM, aligning text guidance towards human preference. Experiments show that a standard 4x diffusion SR model wrapped in CoZ attains beyond 256x enlargement with high perceptual quality and fidelity. Project Page:this https URL.

@article{kim2025_2505.18600,
  title={ Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment },
  author={ Bryan Sangwoo Kim and Jeongsol Kim and Jong Chul Ye },
  journal={arXiv preprint arXiv:2505.18600},
  year={ 2025 }
}