Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and
  Preference Alignment
Authors: name: Bryan Sangwoo Kim, name: Jeongsol Kim, name: Jong Chul Ye

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:
https://bryanswkim.github.io/chain-of-zoom/ .