🥇 UniGenBench Leaderboard (English)
📚 UniGenBench is a unified benchmark for T2I generation that integrates diverse prompt themes with a comprehensive suite of fine-grained evaluation criteria.
🔧 You can use the official GitHub repo to evaluate your model on UniGenBench.
😊 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.
📝 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.
2025-12 | ✗ | 95.77 | 99.19 | 99.20 | 88.76 | 97.39 | 96.33 | 90.71 | 92.31 | 99.03 | 97.92 | 97.50 | 100.00 | 94.84 | 95.51 | 95.63 | 95.59 | 91.15 | 97.02 | 94.81 | 96.94 | 96.96 | 96.67 | 99.44 | 93.75 | 98.17 | 98.45 | 97.87 | 92.27 | 97.77 | 90.28 | 88.10 | 94.56 | 93.31 | 95.83 |