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

[2505.18600] Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2505.18600 (cs) [Submitted on 24 May 2025 ( v1 ), last revised 10 Apr 2026 (this version, v3)] Title: Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment Authors: Bryan Sangwoo Kim , Jeongsol Kim , Jong Chul Ye View a PDF of the paper titled Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment, by Bryan Sangwoo Kim and 2 other authors View PDF HTML (experimental) 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: this https URL . Comments: NeurIPS 2025 (Spotlight) Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2505.18600 [cs.CV] (or arXiv:2505.18600v3 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2505.18600 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jong Chul Ye [ view email ] [v1] Sat, 24 May 2025 08:50:08 UTC (6,599 KB) [v2] Tue, 27 May 2025 16:02:29 UTC (6,599 KB) [v3] Fri, 10 Apr 2026 11:08:57 UTC (6,602 KB) Full-text links: Access Paper: View a PDF of the paper titled Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment, by Bryan Sangwoo Kim and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2025-05 Change to browse by: cs cs.AI cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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