CodeGoat24/FLUX.2-klein-base-9B-UnifiedReward-Flex-lora

Model Summary

This model is GRPO trained using UnifiedReward-Flex as reward on the training dataset of UniGenBench.

🚀 The inference code is available at Github.

For further details, please refer to the following resources:

• 📰 Paper: https://arxiv.org/abs/2602.02380
• 🪐 Project Page: https://codegoat24.github.io/UnifiedReward/flex
• 🤗 Model Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-flex
• 🤗 Dataset: https://huggingface.co/datasets/CodeGoat24/UnifiedReward-Flex-SFT-90K
• 👋 Point of Contact: Yibin Wang

Qualitative Results

Quantitative Results

UniGenBench

Model	Overall	Style	World Knowledge	Attribute	Action	Relationship	Compound	Grammar	Logical Reasoning	Entity Layout	Text Generation
FLUX2.Klein-base-9B	78.93%	97.50%	91.61%	83.65%	77.00%	86.42%	78.61%	76.87%	53.41%	88.43%	55.75%
Ours	81.54%	97.60%	91.93%	85.47%	78.42%	86.42%	81.96%	76.97%	58.64%	88.43%	69.54%

T2I-CompBench

Model	Overall	Color	Shape	Texture	2D-Spatial	3D-Spatial	Numeracy	Non-Spatial	Complex
FLUX2.Klein-base-9B	53.72%	85.90%	60.81%	72.24%	41.46%	36.87%	64.36%	31.11%	37.04%
Ours	58.75%	85.93%	63.36%	74.69%	46.77%	43.18%	70.60%	30.73%	54.73%

GenEval

Model	Overall	Single Object	Two Object	Counting	Colors	Position	Color Attr
FLUX2.Klein-base-9B	78.99%	99.69%	92.93%	77.50%	92.55%	66.75%	44.50%
Ours	81.55%	99.69%	93.94%	84.69%	93.22%	70.75%	47.00%

Citation

@article{unifiedreward-flex,
  title={Unified Personalized Reward Model for Vision Generation},
  author={Wang, Yibin and Zang, Yuhang and Han, Feng and Bu, Jiazi and Zhou, Yujie and Jin, Cheng and Wang, Jiaqi},
  journal={arXiv preprint arXiv:2602.02380},
  year={2026}
}