README.md

---
license: cc
datasets:
- QWW/Syncd_filtered
base_model:
- black-forest-labs/FLUX.1-Kontext-dev
pipeline_tag: image-text-to-image
---
# Model Card for Model ID

This is the pre-trained model weight for paper **DivRL: Disentangled Self-Similarity Rewards for Diverse Subject-Driven Generation**.

- **Finetuned from model:** [FLUX.1-Kontext-dev](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev)
- **Repository:** [https://github.com/QianWangX/DivRL](https://github.com/QianWangX/DivRL)
- **Paper:** [https://arxiv.org/abs/2606.23950](https://arxiv.org/abs/2606.23950)
- **Demo:** [https://qianwangx.github.io/DivRL/](https://qianwangx.github.io/DivRL/)

## Uses

Stage-1 weight is trained with nSSM as reward model only. Built on top of Stage-1 weight, Stage-2 weight is further trained on nSSM + VSM collaboratively to obtain the final results shown in the paper.

You can refer to the Stage-1 weight for generation with high diversity but low consistency, and the Stage-2 weight for generation with both high diversity and high consistency.

## How to Get Started with the Model

Please refer to [https://github.com/QianWangX/DivRL](https://github.com/QianWangX/DivRL).

## Training Details

### Training Data

We provide the training data at [QWW/Syncd_filtered](https://huggingface.co/datasets/QWW/Syncd_filtered).


<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```
@misc{wang2026divrl,
      title={DivRL: Disentangled Self-Similarity Rewards for Diverse Subject-Driven Generation}, 
      author={Qian Wang and Zhenyu Li and Abdelrahman Eldesokey and Peter Wonka},
      year={2026},
      eprint={2606.23950},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2606.23950}, 
}
```