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

• URL: http://arxiv.org/abs/2505.18600v2
• Date: Tue, 27 May 2025 16:02:29 GMT
• Title: Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment
• Authors: Bryan Sangwoo Kim, Jeongsol Kim, Jong Chul Ye,
• Abstract summary: Chain-of-zoom (CoZ) is a framework that factorizes SISR into a chain of intermediate scale-states with multi-scale-aware prompts.<n>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)<n>Experiments show that a standard 4x diffusion SR model wrapped in CoZ attains beyond 256x enlargement with high perceptual quality and fidelity.
• Score: 51.99765487172328
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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/ .

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