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

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 data gap for open text-to-image research. In this work, we present Fine-T2I, a large-scale, high-quality, and fully open dataset for open 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 models with carefully curated real images from professional photographers. Unlike previous datasets that simply present a set of samples, Fine-T2I is built with: 1) systematical dataset processing pipeline, 2) principled distribution design, 3) multi-granularity prompt design, 4) dynamic aspect-ratio & resolution strategy, and 5) advanced reasoning-VLM-as-a-judge protocol. Furthermore, 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 the open diffusion and autoregressive models we study, fine-tuning on Fine-T2I improves generation quality and instruction adherence, as validated by human evaluations and visual comparisons, with gains that depend on the base model's starting capability. Our analysis further shows that data aligned with a benchmark's distribution can inflate automatic scores without improving human-judged quality, underscoring the value of preference-oriented curation. We release Fine-T2I under an open license to help close the data gap in the open community.