---
library_name: diffusers
license: mit
pipeline_tag: text-to-image
base_model:
- black-forest-labs/FLUX.2-klein-base-9B
---

# Model Summary
This model is GRPO trained using [UnifiedReward-Flex](https://huggingface.co/collections/CodeGoat24/unifiedreward-flex) as reward on the training dataset of [UniGenBench](https://github.com/CodeGoat24/UniGenBench).

🚀 The inference code is available at [Github](https://github.com/CodeGoat24/Pref-GRPO/blob/main/inference/flux2_klein_dist_infer.sh).


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](https://codegoat24.github.io)

# Qualitative Results
![image](https://cdn-uploads.huggingface.co/production/uploads/654c6845bac6e6e49895a5b5/zAWUuvKDgG43GaeyMFk7-.png)



# 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

```bibtex
@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}
}
```