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--- |
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license: mit |
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tags: |
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- code |
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--- |
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<!-- |
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* @Author: Yaozzz666 |
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* @Date: 2025-03-21 13:49:25 |
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* @LastEditors: Yaozzz666 |
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* @LastEditTime: 2025-03-22 11:11:04 |
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* |
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* Copyright (c) 2025 by ${Yaozzz666}, All Rights Reserved. |
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--> |
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<h1 align="center"> |
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<a href="https://arxiv.org/abs/2504.11271"> |
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Distillation-Supervised Convolutional Low-Rank Adaptation for Efficient Image Super-Resolution |
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</a> |
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</h1> |
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<div align="center"> |
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<a href="https://arxiv.org/abs/2504.11271" style="text-decoration:none;"> |
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<img src="https://img.shields.io/badge/cs.CV-2504.11271-%23B22222" alt="cs.CV"> |
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</a> |
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</div> |
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## 📖 The Architecture of DSCLoRA Model |
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<div align=center> |
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<img src="https://huggingface.co/sssefe/DSCLoRA/resolve/main/figs/DSCF_arch.png" width="1500px"/> |
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</div> |
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We replace the SPAB module with the proposed SConvLB module and incorporate |
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ConvLoRA layers into both the pixel shuffle block and its preceding convolutional layer. Spatial Affinity Distillation Loss is calculated |
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between each feature map. |
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## 🚀 Updates |
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* [2025.04.21] ✅ Upload our model here. |
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* [2025.04.15] 🎉 Our [paper](https://arxiv.org/abs/2504.11271) is accepted to CVPR 2025 Workshop! |
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* [2025.03.26] 🏆 Our team won **1st** place in the [NTIRE 2025 Efficient SR Challenge](https://cvlai.net/ntire/2025/). Challenge report is [here](https://arxiv.org/abs/2504.10686). |
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* [2025.03.21] ✅ Release our code on [github](https://github.com/Yaozzz666/DSCF-SR). |
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## 🔧 The Environments |
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The evaluation environments adopted by us is recorded in the `requirements.txt`. After you built your own basic Python (Python = 3.9 in our setting) setup via either *virtual environment* or *anaconda*, please try to keep similar to it via: |
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- Step1: install Pytorch first: |
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`pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117` |
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- Step2: install other libs via: |
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```pip install -r requirements.txt``` |
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or take it as a reference based on your original environments. |
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## ⚡ How to test the model? |
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1. Run the [`run.sh`](./run.sh) |
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```bash |
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CUDA_VISIBLE_DEVICES=0 python test_demo.py --data_dir [path to your data dir] --save_dir [path to your save dir] --model_id 23 |
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``` |
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- Be sure the change the directories `--data_dir` and `--save_dir`. |
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## 🥰 Citation |
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If our work is useful to you, please use the following BibTeX for citation. |
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``` |
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@inproceedings{Chai2025DistillationSupervisedCL, |
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title={Distillation-Supervised Convolutional Low-Rank Adaptation for Efficient Image Super-Resolution}, |
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author={Xinning Chai and Yao Zhang and Yuxuan Zhang and Zhengxue Cheng and Yingsheng Qin and Yucai Yang and Li Song}, |
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year={2025}, |
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url={https://api.semanticscholar.org/CorpusID:277787382} |
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} |
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``` |
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## 📜 License and Acknowledgement |
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This code repository is release under [MIT License](LICENSE). |
