36 lines
1.8 KiB
Markdown
36 lines
1.8 KiB
Markdown
---
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license: mit
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base_model:
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- CodeGoat24/UnifiedReward-2.0-qwen-3b
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---
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# UnifiedReward-Edit-qwen-7B
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[2025/10/23] 🔥🔥🔥 We release **UnifiedReward-Edit**-3b, a unified reward model for **both Text-to-Image and Image-to-Image generation**!!
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For image editing reward task, our models support:
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>1. Pairwise Rank — directly judge which of two edited images is better.
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>
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>2. Pairwise Score — assign a separate score to each image in a pair.
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>
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>3. Pointwise Score — rate a single image on two axes: instruction-following and overall image quality.
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🚀 The image editing reward inference code is available at [`UnifiedReward-Edit/`](https://github.com/CodeGoat24/UnifiedReward/tree/main/UnifiedReward-Edit) directory, while T2I inference code is unchanged from previous models. The editing training data is preprocessed from [EditScore](https://huggingface.co/datasets/EditScore/EditScore-Reward-Data) and [EditReward](https://huggingface.co/datasets/TIGER-Lab/EditReward-Data) and will be released soon. We sincerely appreciate all contributors!!
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For further details, please refer to the following resources:
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- 📰 Paper: https://arxiv.org/pdf/2503.05236
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- 🪐 Project Page: https://codegoat24.github.io/UnifiedReward/
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- 🤗 Model Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-models-67c3008148c3a380d15ac63a
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- 🤗 Dataset Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-training-data-67c300d4fd5eff00fa7f1ede
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- 👋 Point of Contact: [Yibin Wang](https://codegoat24.github.io)
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## Citation
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```
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@article{unifiedreward,
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title={Unified reward model for multimodal understanding and generation},
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author={Wang, Yibin and Zang, Yuhang and Li, Hao and Jin, Cheng and Wang, Jiaqi},
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journal={arXiv preprint arXiv:2503.05236},
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year={2025}
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}
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``` |