license: mit
task_categories:
- text-to-image
i1: A Simple and Fully Open Recipe for Strong Text-to-Image Models
Boya Zeng, Tianze Luo, Shu Pu, Jucheng Shen, Taiming Lu, Gabriel Sarch, Zhuang Liu
Princeton University
[arXiv][code][model][project page]
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 512×512 resolution. Concretely, we only retain raw images with a shorter edge of at least 512 pixels, center-crop them to squares, resize them to 512×512, and randomly subsample the resulting dataset to 1M images.
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.
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 i1-datasets/i1-gptedit-512-resolution-1m-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}
}