🥇 UniGenBench Leaderboard (Chinese)
📚 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.
2025-11 | ✗ | 93.82 | 99.50 | 97.47 | 82.34 | 95.69 | 94.55 | 90.97 | 96.15 | 95.75 | 95.14 | 91.25 | 100.00 | 94.96 | 94.23 | 94.57 | 97.06 | 92.35 | 95.24 | 96.70 | 96.07 | 96.96 | 91.67 | 97.83 | 97.66 | 94.20 | 96.68 | 91.67 | 89.04 | 94.49 | 90.74 | 81.92 | 94.40 | 96.32 | 92.42 |