Fine-T2I: An Open, Large-Scale, and Diverse Dataset for High-Quality T2I Fine-Tuning

• URL: http://arxiv.org/abs/2602.09439v1
• Date: Tue, 10 Feb 2026 06:06:54 GMT
• Title: Fine-T2I: An Open, Large-Scale, and Diverse Dataset for High-Quality T2I Fine-Tuning
• Authors: Xu Ma, Yitian Zhang, Qihua Dong, Yun Fu,
• Abstract summary: Fine-T2I is a large-scale, high-quality, and fully open dataset for text-to-image fine-tuning.<n>All samples are rigorously filtered for text-image alignment, visual fidelity, and prompt quality.<n>The final dataset contains over 6 million text-image pairs, around 2 TB on disk.
• Score: 39.35923155873977
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High-quality and open datasets remain a major bottleneck for text-to-image (T2I) fine-tuning. Despite rapid progress in model architectures and training pipelines, most publicly available fine-tuning datasets suffer from low resolution, poor text-image alignment, or limited diversity, resulting in a clear performance gap between open research models and enterprise-grade models. In this work, we present Fine-T2I, a large-scale, high-quality, and fully open dataset for T2I fine-tuning. Fine-T2I spans 10 task combinations, 32 prompt categories, 11 visual styles, and 5 prompt templates, and combines synthetic images generated by strong modern models with carefully curated real images from professional photographers. All samples are rigorously filtered for text-image alignment, visual fidelity, and prompt quality, with over 95% of initial candidates removed. The final dataset contains over 6 million text-image pairs, around 2 TB on disk, approaching the scale of pretraining datasets while maintaining fine-tuning-level quality. Across a diverse set of pretrained diffusion and autoregressive models, fine-tuning on Fine-T2I consistently improves both generation quality and instruction adherence, as validated by human evaluation, visual comparison, and automatic metrics. We release Fine-T2I under an open license to help close the data gap in T2I fine-tuning in the open community.

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