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arXiv · 2605.23518

VINS-120K: Ultra High-Resolution Image Editing with A Large-Scale Dataset

Zhizhou Chen·Shanyan Guan·Zhanxin Gao·En Ci·Yanhao Ge·Wei Li·Zhenyu Zhang·Jian Yang·Ying Tai
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Abstract

Directly editing ultra-high-resolution (UHR) images is valuable but underexplored, primarily due to the lack of high-quality data and the challenge in modeling high-frequency texture details. We introduce VINS-120K, the first large-scale dataset for instruction-based UHR image editing, comprising 120K carefully curated triplets of instruction, input image, and edited image. Each image exceeds 4K resolution ($\geq$4096 $\times$ 4096) and is filtered through a rigorous multi-stage pipeline to ensure visual quality, instruction alignment, and aesthetic fidelity. Built on VINS-120K, we further develop a high-frequency-aware post-adaptation strategy to extend pretrained non-high-resolution models to the UHR regime. We also present VINS-4KEval, a benchmark covering diverse editing types, to facilitate consistent evaluation in UHR settings. Experiments confirm that our work improves fine-grained detail synthesis and texture realism in UHR image editing.

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Zhizhou Chen, Shanyan Guan, Zhanxin Gao, En Ci, Yanhao Ge, Wei Li, Zhenyu Zhang, Jian Yang, Ying Tai. 2026-05-22. VINS-120K: Ultra High-Resolution Image Editing with A Large-Scale Dataset. https://arxiv.org/abs/2605.23518

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