| | --- |
| | license: mit |
| | base_model: |
| | - CodeGoat24/UnifiedReward-2.0-qwen3vl-32b |
| | --- |
| | |
| | # Model Summary |
| | **UnifiedReward-Edit-qwen3vl-32b** is 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/`](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!! |
| |
|
| | 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](https://codegoat24.github.io) |
| |
|
| |
|
| | ## 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} |
| | } |
| | ``` |
