zlab-princeton/i1-gptedit-tfrecord

i1: A Simple and Fully Open Recipe for Strong Text-to-Image Models<br> Boya Zeng, Tianze Luo, Shu Pu, Jucheng Shen, Taiming Lu, Gabriel Sarch, Zhuang Liu<br> Princeton University<br> [`arXiv`][`code`][`model`][`project page`]

<p align="center"> <img src="https://raw.githubusercontent.com/zlab-princeton/i1/main/docs/static/images/teaser.png" width="90%" alt="i1 teaser"> </p>

Overview

To prepare the dataset for training, we store the image-caption pairs as TFRecords.

This HuggingFace dataset contains the TFRecords corresponding to the `gptedit` dataset at 256×256 resolution.

It also serves as an example of what a dataset processed using our data processing pipeline looks like.

You can find processed TFRecords for other datasets and resolutions here.

Structure

dataset-train.tfrecord-*-of-00128 are TFRecord shards, where 128 is the total number of shards.<br> dataset_info.json and features.json are TFDS metadata files needed by tfds.builder_from_directory(...) to load and decode the dataset correctly.

Download

pip install -U "huggingface_hub"

hf download zlab-princeton/i1-gptedit-tfrecord \
    --repo-type dataset \
    --local-dir /path/to/save/dataset

Citation

If this dataset is useful for your research, please cite the following work:

@article{zeng2026i1,
  title={i1: A Simple and Fully Open Recipe for Strong Text-to-Image Models},
  author={Zeng, Boya and Luo, Tianze and Pu, Shu and Shen, Jucheng and Lu, Taiming and Sarch, Gabriel and Liu, Zhuang},
  journal={arXiv preprint arXiv:2606.11289},
  year={2026}
}