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--- |
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license: mit |
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base_model: |
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- CodeGoat24/UnifiedReward-2.0-qwen3vl-32b |
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--- |
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# Model Summary |
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**UnifiedReward-Edit-qwen3vl-32b** is 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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>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). 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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``` |
