🥇 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.
2025-12 | ✗ | 95.62 | 99.49 | 99.68 | 90.60 | 93.60 | 96.55 | 92.14 | 94.23 | 98.08 | 99.31 | 95.62 | 100.00 | 95.52 | 96.15 | 98.91 | 96.32 | 93.81 | 92.86 | 95.28 | 97.83 | 97.97 | 97.22 | 100.00 | 95.31 | 97.13 | 99.23 | 94.95 | 91.98 | 95.90 | 92.13 | 87.70 | 93.80 | 93.28 | 94.32 |