---
license: mit
task_categories:
- text-to-image
---

**i1: A Simple and Fully Open Recipe for Strong Text-to-Image Models**<br>
[Boya Zeng](https://boyazeng.github.io), [Tianze Luo](https://luotianze666.github.io), [Shu Pu](https://urrealhero.github.io/MyPersonalWeb/), [Jucheng Shen](https://juchengshen.github.io), [Taiming Lu](https://taiminglu.com), [Gabriel Sarch](http://gabesarch.me), [Zhuang Liu](https://www.cs.princeton.edu/~zhuangl)<br>
Princeton University<br>
[[`arXiv`](https://arxiv.org/abs/2606.11289)][[`code`](https://github.com/zlab-princeton/i1)][[`model`](https://huggingface.co/zlab-princeton/i1-3B)][[`project page`](https://zlab-princeton.github.io/i1/)]


<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 `rendered_text` 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](https://github.com/zlab-princeton/i1/tree/main/data_processing) looks like.

You can find processed TFRecords for other datasets and resolutions [here](https://huggingface.co/i1-datasets).

## 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

```bash
pip install -U "huggingface_hub"

hf download i1-datasets/i1-rendered_text-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:

```bibtex
@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}
}
```