https://github.com/guaishou74851/adcsr

(CVPR 2025) Adversarial Diffusion Compression for Real-World Image Super-Resolution [PyTorch]
https://github.com/guaishou74851/adcsr

adversarial-diffusion-compression adversarial-distillation computer-vision cvpr2025 deep-learning deep-neural-networks diffusion-models image-reconstruction image-restoration one-step-diffusion one-step-diffusion-model pruning python python3 pytorch super-resolution

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(CVPR 2025) Adversarial Diffusion Compression for Real-World Image Super-Resolution [PyTorch]

Host: GitHub
URL: https://github.com/guaishou74851/adcsr
Owner: Guaishou74851
License: apache-2.0
Created: 2025-03-06T06:49:39.000Z (9 months ago)
Default Branch: main
Last Pushed: 2025-04-05T03:32:15.000Z (8 months ago)
Last Synced: 2025-04-05T04:23:32.646Z (8 months ago)
Topics: adversarial-diffusion-compression, adversarial-distillation, computer-vision, cvpr2025, deep-learning, deep-neural-networks, diffusion-models, image-reconstruction, image-restoration, one-step-diffusion, one-step-diffusion-model, pruning, python, python3, pytorch, super-resolution
Language: Python
Homepage: https://arxiv.org/abs/2411.13383
Size: 36.2 MB
Stars: 49
Watchers: 2
Forks: 5
Open Issues: 3
Metadata Files:
- Readme: README.md
- License: LICENSE

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# (CVPR 2025) Adversarial Diffusion Compression for Real-World Image Super-Resolution [PyTorch]

[![icon](https://img.shields.io/badge/ArXiv-Paper-.svg)](https://arxiv.org/abs/2411.13383) [![Hugging Face](https://img.shields.io/badge/Code_&_Models-%F0%9F%A4%97%20Hugging%20Face-blue)](https://huggingface.co/Guaishou74851/AdcSR) ![visitors](https://visitor-badge.laobi.icu/badge?page_id=Guaishou74851.AdcSR)

[Bin Chen](https://scholar.google.com/citations?user=aZDNm98AAAAJ)^1,3,\*

| [Gehui Li](https://github.com/cvsym)^1,\*

| [Rongyuan Wu](https://scholar.google.com/citations?user=A-U8zE8AAAAJ)^2,3,\*

| [Xindong Zhang](https://scholar.google.com/citations?user=q76RnqIAAAAJ)³

| [Jie Chen](https://aimia-pku.github.io/)^1,†

| [Jian Zhang](https://jianzhang.tech/)^1,†

| [Lei Zhang](https://www4.comp.polyu.edu.hk/~cslzhang/)^2,3

¹ *School of Electronic and Computer Engineering, Peking University*

² *The Hong Kong Polytechnic University*, ³ *OPPO Research Institute*

^* Equal Contribution. ^† Corresponding Authors.

⭐ **If AdcSR is helpful to you, please star this repo. Thanks!** 🤗

## 📝 Overview

### Highlights

- **Adversarial Diffusion Compression (ADC).** We remove and prune redundant modules from the one-step diffusion network [OSEDiff](https://github.com/cswry/OSEDiff) and apply adversarial distillation to retain generative capabilities despite reduced capacity.

- **Real-Time [Stable Diffusion](https://huggingface.co/stabilityai/stable-diffusion-2-1)-Based Image Super-Resolution.** AdcSR super-resolves a 128×128 image to 512×512 **in just 0.03s 🚀** on an A100 GPU.

- **Competitive Visual Quality.** Despite **74% fewer parameters 📉** than [OSEDiff](https://github.com/cswry/OSEDiff), AdcSR achieves **competitive image quality** across multiple benchmarks.

### Framework

1. **Structural Compression**

   - **Removable modules** (VAE encoder, text prompt extractor, cross-attention, time embeddings) are eliminated.

   - **Prunable modules** (UNet, VAE decoder) are **channel-pruned** to optimize efficiency while preserving performance.



   



2. **Two-Stage Training**

   1. **Pretraining a Pruned VAE Decoder** to maintain its ability to decode latent representations.

   2. **Adversarial Distillation** to align compressed network features with the teacher model (e.g., [OSEDiff](https://github.com/cswry/OSEDiff)) and ground truth images.



   



## 😍 Visual Results

[](https://imgsli.com/MzU2MjU1) [](https://imgsli.com/MzU2MjU2) [](https://imgsli.com/MzU2MjU3)

[](https://imgsli.com/MzU2NTg4) [](https://imgsli.com/MzU2NTkw) [](https://imgsli.com/MzU2NTk1)

[](https://imgsli.com/MzU2OTE0) [](https://imgsli.com/MzU2OTE1)

https://github.com/user-attachments/assets/1211cefa-8704-47f5-82cd-ec4ef084b9ec



## ⚙ Installation

```shell

git clone https://github.com/Guaishou74851/AdcSR.git

cd AdcSR

conda create -n AdcSR python=3.10

conda activate AdcSR

pip install --upgrade pip

pip install -r requirements.txt

chmod +x train.sh train_debug.sh test_debug.sh evaluate_debug.sh

```

## ⚡ Test

1. **Download test datasets** (`DIV2K-Val.zip`, `DRealSR.zip`, `RealSR.zip`) from [Hugging Face](https://huggingface.co/Guaishou74851/AdcSR) or [PKU Disk](https://disk.pku.edu.cn/link/AAD499197CBF054392BC4061F904CC4026).

2. **Unzip** them into `./testset/`, ensuring the structure:

   ```

   ./testset/DIV2K-Val/LR/xxx.png

   ./testset/DIV2K-Val/HR/xxx.png

   ./testset/DRealSR/LR/xxx.png

   ./testset/DRealSR/HR/xxx.png

   ./testset/RealSR/LR/xxx.png

   ./testset/RealSR/HR/xxx.png

   ```

3. **Download model weights** (`net_params_200.pkl`) from the same link and place it in `./weight/`.  

4. **Run the test script** (or modify and execute `./test_debug.sh` for convenience):  

   ```bash

   python test.py --LR_dir=path_to_LR_images --SR_dir=path_to_SR_images

   ```

   The results will be saved in `path_to_SR_images`.

5. **Test Your Own Images**:

   - Place your **low-resolution (LR)** images into `./testset/xxx/`.

   - Run the command with `--LR_dir=./testset/xxx/ --SR_dir=./yyy/`, and the model will perform **x4 super-resolution**.

## 🍭 Evaluation

Run the evaluation script (or modify and execute `./evaluate_debug.sh` for convenience):

```bash

python evaluate.py --HR_dir=path_to_HR_images --SR_dir=path_to_SR_images

```

## 🔥 Train

This repo provides code for **Stage 2** training (**adversarial distillation**). For **Stage 1** (pretraining the channel-pruned VAE decoder), refer to our paper and use the code of [Latent Diffusion Models](https://github.com/CompVis/latent-diffusion) repo.

1. **Download pretrained model weights** (`DAPE.pth`, `halfDecoder.ckpt`, `osediff.pkl`, `ram_swin_large_14m.pth`) from [Hugging Face](https://huggingface.co/Guaishou74851/AdcSR) or [PKU Disk](https://disk.pku.edu.cn/link/AAD499197CBF054392BC4061F904CC4026), and place them in `./weight/pretrained/`.

2. **Download the [LSDIR](https://huggingface.co/ofsoundof/LSDIR) dataset** and store it in your preferred location.

3. **Modify the dataset path** in `config.yml`:

   ```yaml

   dataroot_gt: path_to_HR_images_of_LSDIR

   ```

4. **Run the training script** (or modify and execute `./train.sh` or `./train_debug.sh`):

   ```bash

   CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.run --nproc_per_node=8 --master_port=23333 train.py

   ```

   The trained model will be saved in `./weight/`.

   

## 🥰 Acknowledgement

This project is built upon the codes of [Latent Diffusion Models](https://github.com/CompVis/latent-diffusion), [Diffusers](https://github.com/huggingface/diffusers), [BasicSR](https://github.com/XPixelGroup/BasicSR), and [OSEDiff](https://github.com/cswry/OSEDiff). We sincerely thank the authors of these repos for their significant contributions.

## 🎓 Citation

If you find our work helpful, please consider citing:

```latex

@inproceedings{chen2025adversarial,

  title={Adversarial Diffusion Compression for Real-World Image Super-Resolution},

  author={Chen, Bin and Li, Gehui and Wu, Rongyuan and Zhang, Xindong and Chen, Jie and Zhang, Jian and Zhang, Lei},

  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},

  year={2025}

}

```

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