UniGenBench: Unified Text-to-Image Generation Benchmark

Hunyuan, Tencent & UnifiedReward Team

🔥 News

😊 We are actively gathering feedback from the community to improve our benchmark. We welcome your input and encourage you to stay updated through our repository!!

📝 To add your own model to the leaderboard, please send an Email to Yibin Wang, then we will help with the evaluation and updating the leaderboard.

Please leave us a star ⭐ if you find our benchmark helpful.

• [2025/9] 🔥🔥 We release UniGenBench Leaderboard (Chinese) and we will continue to update it regularly.
• [2025/9] 🔥🔥 We release all generated images from the T2I models evaluated in our UniGenBench on UniGenBench-Eval-Images. Feel free to use any evaluation model that is convenient and suitable for you to assess and compare the performance of your models.
• [2025/9] 🔥🔥 GPT-4o, Imagen-4-Ultra, Nano Banana, Seedream-3.0, Qwen-Image, FLUX-Kontext-[Max/Pro] are added to the 🏅Leaderboard.
• [2025/8] 🔥🔥 We release paper, project page, and UniGenBench Leaderboard (English).

Introduction

We propose UniGenBench, a unified and versatile benchmark for image generation that integrates diverse prompt themes with a comprehensive suite of fine-grained evaluation criteria.

✨ Highlights:

• Comprehensive and fine-grained evaluation: covering 10 primary dimensions and 27 sub-dimensions, enabling systematic and fine-grained assessment of diverse model capabilities.

• Rich prompt theme coverage: organized into 5 primary themes and 20 sub-themes, comprehensively spanning both realistic and imaginative generation scenarios.

• Efficient yet comprehensive: unlike other benchmarks, UniGenBench requires only 600 prompts, with each prompt targeting 1–5 specific testpoint, ensuring both coverage and efficiency.

• Reliable MLLM Evaluation: Each testpoint of the prompt is accompanied by a detailed description, explaining how the testpoint is reflected in the prompt, assisting MLLM in conducting precise evaluations.

• Bilingual support: providing test prompts in both English and Chinese, together with evaluation pipelines for both languages, thus enabling fair and broad cross-lingual benchmarking.

📑 Prompt Introduction

Each prompt in our benchmark is recorded as a row in a .csv file, combining with structured annotations for evaluation.

• index

• prompt: The full English prompt to be tested

• sub_dims: A JSON-encoded field that organizes rich metadata, including:

  • Primary / Secondary Categories – prompt theme (e.g., Creative Divergence → Imaginative Thinking)
  • Subjects – the main entities involved in the prompt (e.g., Animal)
  • Sentence Structure – the linguistic form of the prompt (e.g., Descriptive)
  • Testpoints – key aspects to evaluate (e.g., Style, World Knowledge, Attribute - Quantity)
  • Testpoint Description – evaluation cues extracted from the prompt (e.g., classical ink painting, Egyptian pyramids, two pandas)

• English Test set: data/test_prompts_en.csv

• Chinese Test set: data/test_prompts_zh.csv

• Training set: train_prompt.txt

🚀 Inference

We provide reference code for multi-node inference based on FLUX.1-dev.

# English Prompt
bash inference/flux_en_dist_infer.sh

# Chinese Prompt
bash inference/flux_zh_dist_infer.sh

For each test prompt, 4 images are generated and stored in the following folder structure:

output_directory/
  ├── 0_0.png
  ├── 0_1.png
  ├── 0_2.png
  ├── 0_3.png
  ├── 1_0.png
  ├── 1_1.png
  ...

The file naming follows the pattern promptID_imageID.png

✨ Evaluation with Gemini2.5-pro

We are using the API version:

gemini-2.5-pro:

Release stage: General Availability (GA)

Release date: June 17, 2025

1. Evaluation

#!/bin/bash

# API
API_KEY="sk-xxxxxxx"
BASE_URL=""

DATA_PATH="flux_output"  # Directory of generated images
CSV_FILE="data/test_prompts_en.csv" # English test prompt file

# English Evaluation
python eval/gemini_en_eval.py \
  --data_path "$DATA_PATH" \
  --api_key "$API_KEY" \
  --base_url "$BASE_URL" \
  --csv_file "$CSV_FILE"

# Chinese Evaluation
CSV_FILE="data/test_prompts_zh.csv" # Chinese test prompt file

python eval/gemini_zh_eval.py \
  --data_path "$DATA_PATH" \
  --api_key "$API_KEY" \
  --base_url "$BASE_URL" \
  --csv_file "$CSV_FILE"

• After evaluation, scores across all dimensions will be printed to the console.
• A detailed .csv results file will also be saved in the ./results directory.

2. Calculate Score

You can also load the results file to re-print or further analyze the scores.

python eval/calculate_score.py

✨ Evaluation with Qwen2.5-VL-72b

1. Deploy vLLM server

1. Install vLLM

pip install vllm==0.9.0.1 transformers==4.52.4

2. Start server

echo ${LOCAL_IP}

CUDA_VISIBLE_DEVICES=0,1,2,3 vllm serve Qwen/Qwen2.5-VL-72B-Instruct \
    --host ${LOCAL_IP} \
    --trust-remote-code \
    --served-model-name QwenVL \
    --gpu-memory-utilization 0.9 \
    --tensor-parallel-size 4 \
    --pipeline-parallel-size 1 \
    --limit-mm-per-prompt image=2 \
    --port 8080 

2. Evaluation

#!/bin/bash

# vLLM request url
API_URL=http://${LOCAL_IP}:8080

DATA_PATH="flux_output"  # Directory of generated images
CSV_FILE="data/test_prompts_en.csv" # English test prompt file

# English Evaluation
python eval/qwenvl_72b_en_eval.py \
  --data_path "$DATA_PATH" \
  --api_url "$API_URL" \
  --csv_file "$CSV_FILE"

# Chinese Evaluation
CSV_FILE="data/test_prompts_zh.csv" # Chinese test prompt file

python eval/qwenvl_72b_zh_eval.py \
  --data_path "$DATA_PATH" \
  --api_url "$API_URL" \
  --csv_file "$CSV_FILE"

• After evaluation, scores across all dimensions will be printed to the console.
• A detailed .csv results file will also be saved in the ./results directory.

3. Calculate Score

You can also load the results file to re-print or further analyze the scores.

python eval/calculate_score.py

📧 Contact

If you have any comments or questions, please open a new issue or feel free to contact Yibin Wang.

⭐ Citation

@article{Pref-GRPO&UniGenBench,
  title={Pref-GRPO: Pairwise Preference Reward-based GRPO for Stable Text-to-Image Reinforcement Learning},
  author={Wang, Yibin and Li, Zhimin and Zang, Yuhang and Zhou, Yujie and Bu, Jiazi and Wang, Chunyu and Lu, Qinglin, and Jin, Cheng and Wang, Jiaqi},
  journal={arXiv preprint arXiv:2508.20751},
  year={2025}
}