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

by Xu Ma, Yitian Zhang, Qihua Dong, Yun Fu

Northeastern Univeristy

Please see our [Dataset Explore] to view detailed samples (loading is slow, be patient).

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🆕 What's New

• [2026.02.20]: Fine-T2I reaches the #1 spot among Hugging Face Datasets Trending list ⭐️⭐️⭐️

• [2026.02.16]: Fine-T2I tops the Hugging Face Datasets Trending list, reaching the #2 spot and #1 spot for image datasets! ⭐️⭐️⭐️

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Fine-T2I is a large-scale, high-quality, and fully open dataset designed to advance SOTA text-to-image (T2I) fine-tuning. Comprising over 6 million text–image pairs (approximately 2 TB), Fine-T2I was constructed to bridge the performance gap between open community models and enterprise-grade models. The dataset distinguishes itself through a rigorous construction pipeline that combines high-fidelity synthetic data with professional real-world photography, ensuring exceptional visual quality and precise instruction adherence.

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Key Features

• Massive Scale & Quality: Contains ~6.15M synthetic samples generated by SOTA diffusion models (Z-Image, FLUX2) and ~168k curated real images from professional sources (Pexels, Pixabay, Unsplash).
• Diverse Distributions: Synthetic samples span 10 task combinations, 32 prompt categories, and 11 distinct visual styles.
• Dual Prompt Annotations: Each image is paired with two types of prompts (original and enhanced) to support diverse user behaviors (The prompt for generating synthetic image is saved in txt).
• High Resolution: Fine-T2I supports randomized aspect ratios and high resolutions (mostly >1K).
• Rigorous Filtering: The data underwent a strict cleaning pipeline involving prompt semantic deduplication, safety checks, aesthetic filter, a VLM-thinking-based "Strict Visual Quality Auditor" and other filtering steps, filtered out total >95% of candidates to ensure flawless text–image alignment and zero artifacts.

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Statistical Analysis

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Useage

Fine-T2I is organized in the WebDataset format, each sample consists of three corresponding files: jpg, txt, and json.

You can easily load the dataset in streaming mode (without downloading the entire 2TB) using the Hugging Face datasets library.

from datasets import load_dataset
import json
# we use synthetic_enhanced_prompt_random_resolution as an example
url_pattern = "https://huggingface.co/datasets/ma-xu/fine-t2i/resolve/main/synthetic_enhanced_prompt_random_resolution/train-*.tar"
dataset = load_dataset(
    "webdataset", 
    data_files={"train": url_pattern}, 
    split="train", 
    streaming=True  # please do streaming, or you will download the whole dataset ~2TB
)
for i, sample in enumerate(dataset):
    if i >= 10:
        break
    print(f"\n\nImage info: {sample['jpg'].size}\n") 
    print(f"Text: {sample['txt']}\n")
    print(f"Meta json data: {json.dumps(sample['json'], indent=4, ensure_ascii=False)}\n")
    print(f"=========="*5)

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Cite

If you find the data useful, please cite:

@misc{ma2026finet2i,
      title={Fine-T2I: An Open, Large-Scale, and Diverse Dataset for High-Quality T2I Fine-Tuning}, 
      author={Xu Ma and Yitian Zhang and Qihua Dong and Yun Fu},
      year={2026},
      eprint={2602.09439},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2602.09439}, 
}

Contact

For any issues or questions, please feel free to contact Xu Ma: ma.xu1@northeastern.edu

Thanks

We are very grateful to the other datasets（like LAION, BLIP3o, JourneyDB, etc） that laid the foundation for text-to-image generation, and we also appreciate the templates provided by BLIP3o. Thanks.