| <div align="center"> |
| <h2>GDPO-SR: Group Direct Preference Optimization for One-Step Generative Image Super-Resolution</h2> |
|
|
| [Qiaosi Yi](https://dblp.org/pid/249/8335.html)<sup>1,2</sup> |
| | [Shuai Li](https://scholar.google.com/citations?hl=zh-CN&user=Bd73ldQAAAAJ)<sup>1</sup> |
| | [Rongyuan Wu](https://scholar.google.com/citations?user=A-U8zE8AAAAJ&hl=zh-CN)<sup>1,2</sup> |
| | [Lingchen Sun](https://scholar.google.com/citations?hl=zh-CN&tzom=-480&user=ZCDjTn8AAAAJ)<sup>1,2</sup> |
| | [Zhengqiang zhang](https://scholar.google.com/citations?user=UX26wSMAAAAJ&hl=en)<sup>1,2</sup> |
| | [Lei Zhang](https://www4.comp.polyu.edu.hk/~cslzhang)<sup>1,2</sup> |
|
|
| <sup>1</sup>The Hong Kong Polytechnic University, <sup>2</sup>OPPO Research Institute |
| </div> |
|
|
| [](https://arxiv.org/pdf/2603.16769) [](https://huggingface.co/Joypop/GDPO/tree/main) |
|
|
|
|
| ## ⏰ Update |
| - **2026.3.19**: Paper is released on [ArXiv](https://arxiv.org/pdf/2603.16769). |
| - **2026.3.12**: The training code and testing code are released. |
| - **2026.3.10**: The repo is released. |
|
|
|
|
| ## ⚙ Dependencies and Installation |
| ```shell |
| ## git clone this repository |
| git clone https://github.com/Joyies/GDPO.git |
| cd GDPO |
| |
| |
| # create an environment |
| conda create -n GDPO python=3.10 |
| conda activate GDPO |
| pip install --upgrade pip |
| pip install -r requirements.txt |
| ``` |
|
|
| ## 🏂 Quick Inference |
|
|
| #### Step 1: Download the pretrained models |
| - Download the model weights [](https://huggingface.co/Joypop/GDPO/tree/main/ckp) and put the model weights in the `ckp/`: |
|
|
| #### Step 2: Prepare testing data and run testing command |
| You can modify input_path and output_path to run testing command. The input_path is the path of the test image and the output_path is the path where the output images are saved. |
| ``` |
| CUDA_VISIBLE_DEVICES=0, python GDPOSR/inferences/test.py \ |
| --input_path test_LR \ |
| --output_path experiment/GDPOSR \ |
| --pretrained_path ckp/GDPOSR \ |
| --pretrained_model_name_or_path stable-diffusion-2-1-base \ |
| --ram_ft_path ckp/DAPE.pth \ |
| --negprompt 'dotted, noise, blur, lowres, smooth' \ |
| --prompt 'clean, high-resolution, 8k' \ |
| --upscale 1 \ |
| --time_step=100 \ |
| --time_step_noise=250 |
| ``` |
| or |
| ``` |
| bash scripts/test/test.sh |
| ``` |
|
|
| ## 🚄 Training Phase |
|
|
| ### Step1: Prepare training data |
| Download the [OpenImage dataset](https://storage.googleapis.com/openimages/web/index.html) and [LSIDR dataset](https://github.com/ofsoundof/LSDIR). For each image in the LSDIR dataset, crop multiple 512×512 image patches using a sliding window with a stride of 64 pixels; |
|
|
|
|
| ### Step2: Train NAOSD. |
| ``` |
| bash scripts/train/train_NAOSD.sh |
| ``` |
| The hyperparameters in train_NAOSD.sh can be modified to suit different experimental settings. Besides, after training with NAOSD, you can use GDPOSR/mergelora.py to merge the LoRA into the UNet and VAE as base model for subsequent reinforcement learning training and inference. |
| |
| ### Step3: Train GDPO-SR |
| ``` |
| bash scripts/train/train_GDPOSR.sh |
| ``` |
| The hyperparameters in train_GDPOSR.sh can be modified to suit different experimental settings. Besides, after training with GDPO-SR, you can use GDPOSR/mergelora.py to merge the LoRA into the UNet for subsequent inference. |
| |
| ## 🔗 Citations |
| |
| ``` |
| @article{yi2026gdpo, |
| title={GDPO-SR: Group Direct Preference Optimization for One-Step Generative Image Super-Resolution}, |
| author={Yi, Qiaosi and Li, Shuai and Wu, Rongyuan and Sun, Lingchen and Zhang, Zhengqiang and Zhang, Lei}, |
| booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, |
| year={2026} |
| } |
| ``` |
| |
| ## ©️ License |
| This project is released under the [Apache 2.0 license](LICENSE). |
| |
| ## 📧 Contact |
| If you have any questions, please contact: qiaosiyijoyies@gmail.com |
| |
| |
