LongT2IBench: A Benchmark for Evaluating Long Text-to-Image Generation with Graph-structured Annotations
1School of Artificial Intelligence, Xidian University
2State Key Laboratory of Electromechanical Integrated Manufacturing of High-Performance Electronic Equipments, Xidian University
*Corresponding author
News
-
[2025-12-21] The training code has been released.
-
[2025-12-09] The data and pre-trained models have been released.
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[2025-11-08] Our paper, "LongT2IBench: A Benchmark for Evaluating Long Text-to-Image Generation with Graph-structured Annotations", has been accepted for an oral presentation at AAAI 2026!
## Quick Start
This guide will help you get started with the LongT2IBench inference code.
### 1. Installation
First, clone the repository and install the required dependencies.
```bash
git clone https://github.com/yzc-ippl/LongT2IBench.git
cd LongT2IBench
pip install -r requirements.txt
```
### 2. Download Pre-trained Weights and Dataset
##### Prepare Pre-trained Weights
You can download the pre-trained model weights of [LongT2IExpert] from the following link: [**(Baidu Netdisk)**](https://pan.baidu.com/s/1Ltj77l31hyBkn6nLtYctnQ?pwd=i8ug)
Place the downloaded files in the `weights` directory.
- ``./weights/LongT2IBench-checkpoints``: The main model for generation and scoring.
Create the `weights` directory if it doesn't exist and place the files inside.
##### Prepare Datasets
You can download the dataset of [LongPrompt-3K] and [LongT2IBench-14K] from the following link: [**(Baidu Netdisk)**](https://pan.baidu.com/s/1M_tE9EfA2s0Vn7l9r0GebA?pwd=7b6d)
Place the downloaded files in the `data` directory.
Create the `data` directory if it doesn't exist and place the files inside.
```
LongT2IBench/
|-- weights/
| |-- LongT2IBench-checkpoints
| | |-- config.json
| | |-- ...
| |-- Qwen2.5-VL-7B-Instruct
| | |-- config.json
| | |-- ...
|-- data/
| |-- imgs
| |-- split
| | |-- train.json
| | |-- test.json
| | |-- val.json
|-- config.py
|-- dataset.py
|-- model.py
|-- requirements.txt
|-- README.md
|-- test_generation.py
|-- test_score.py
|-- train.py
```
### 3. Run Inference
The `LongT2IExpert` provides two main inference tasks: Long T2I Alignment Scoring and Long T2I Alignment Interpreting.
##### Long T2I Alignment Scoring
```
python test_score.py
```
##### Long T2I Alignment Interpreting
```
python test_generation.py
```
### 4. Run Training
You can run this code to train [LongT2IExpert] from start to finish.
Make sure the initially untrained weights are located at ``./weights/Qwen2.5-VL-7B-Instruct`` :
You can download the untrained weights from the following link [**(Baidu Netdisk)**](https://pan.baidu.com/s/17PcO4CvgB6FDHh6JBgM_Lg?pwd=3h8m)
```bash
python train.py
```
## Citation
If you find this work is useful, pleaes cite our paper!
```bibtex
@misc{yang2025longt2ibenchbenchmarkevaluatinglong,
title={LongT2IBench: A Benchmark for Evaluating Long Text-to-Image Generation with Graph-structured Annotations},
author={Zhichao Yang and Tianjiao Gu and Jianjie Wang and Feiyu Lin and Xiangfei Sheng and Pengfei Chen and Leida Li},
year={2025},
eprint={2512.09271},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2512.09271},
}
```
