UnifiedReward-Edit-qwen-7B

[2025/10/23] 🔥🔥🔥 We release UnifiedReward-Edit-7b, a unified reward model for both Text-to-Image and Image-to-Image generation!! For image editing reward task, our models support:

1. Pairwise Rank — directly judge which of two edited images is better.

2. Pairwise Score — assign a separate score to each image in a pair.

3. Pointwise Score — rate a single image on two axes: instruction-following and overall image quality.

🚀 The image editing reward inference code is available at UnifiedReward-Edit/ directory, while T2I inference code is unchanged from previous models. The editing training data is preprocessed from EditScore and EditReward and will be released soon. We sincerely appreciate all contributors!!

For further details, please refer to the following resources:

• 📰 Paper: https://arxiv.org/pdf/2503.05236
• 🪐 Project Page: https://codegoat24.github.io/UnifiedReward/
• 🤗 Model Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-models-67c3008148c3a380d15ac63a
• 🤗 Dataset Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-training-data-67c300d4fd5eff00fa7f1ede
• 👋 Point of Contact: Yibin Wang

Citation

@article{unifiedreward,
  title={Unified reward model for multimodal understanding and generation},
  author={Wang, Yibin and Zang, Yuhang and Li, Hao and Jin, Cheng and Wang, Jiaqi},
  journal={arXiv preprint arXiv:2503.05236},
  year={2025}
}