title: Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and
    Preference Alignment
summary: 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/ .
paper_authors._id: 6837fe7664391bba7e477747
paper_authors.name: Bryan Sangwoo Kim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-29T20:19:04.953Z
paper_authors._id: 6628efe14e1fa854f48d3a28
paper_authors.avatarUrl: /avatars/aa5421149a07a82b5c2a25978f9b6926.svg
paper_authors.fullname: Sangwoo Kim
paper_authors.type: user
paper_authors.user: bryanswkim
paper_authors._id: 6837fe7664391bba7e477748
paper_authors.name: Jeongsol Kim
paper_authors._id: 6837fe7664391bba7e477749
paper_authors.name: Jong Chul Ye
paper_title: Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and
    Preference Alignment
paper_summary: 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/ .
paper_ai_summary: Chain-of-Zoom (CoZ) enhances single-image super-resolution models by using an autoregressive chain of intermediate scale-states and multi-scale-aware prompts to achieve extreme magnifications with high quality.
paper_ai_keywords: single-image super-resolution Chain-of-Zoom autoregressive chain multi-scale-aware prompts backbone SR model diffusion SR model prompt extractor Generalized Reward Policy Optimization critic VLM