🥇 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.
2025-03 | ✗ | 91.02 | 99.39 | 98.72 | 91.44 | 63.37 | 94.99 | 93.62 | 94.59 | 96.19 | 93.06 | 92.95 | 100.00 | 92.34 | 94.08 | 97.28 | 90.91 | 90.31 | 88.34 | 92.65 | 95.77 | 97.30 | 93.18 | 96.69 | 94.53 | 93.91 | 95.92 | 91.74 | 91.02 | 95.15 | 89.35 | 88.05 | 89.27 | 89.18 | 89.35 |