InstaRevive: One-Step Image Enhancement via Dynamic Score Matching (ICLR 2025)

Yixuan Zhu* $\dagger$, Haolin Wang* , Ao Li, Wenliang Zhao*, Yansong Tang, Jingxuan Niu, Lei Chen $\ddagger$, Jie Zhou, Jiwen Lu

* Equal contribution   $\dagger$ Project leader   $\ddagger$ Corresponding author

[Paper]

The repository contains the official implementation for the paper "InstaRevive: One-Step Image Enhancement via Dynamic Score Matching" (ICLR 2025).

We propose InstaRevive, a straightforward yet powerful image enhancement framework that employs score-based diffusion distillation to harness potent generative capability and minimize the sampling steps.

📋 To-Do List

• Release model and inference code.
• Release code for training dataloader.

💡 Pipeline

😀Quick Start

⚙️ 1. Installation

We recommend you to use an Anaconda virtual environment. If you have installed Anaconda, run the following commands to create and activate a virtual environment.

conda create -n instarevive python==3.9.0
conda activate instarevive
conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.7 -c pytorch -c nvidia

git clone https://github.com/EternalEvan/InstaRevive.git

cd InstaRevive
pip install -r requirements.txt

🗂️ 2. Download Checkpoints

Please download our pretrained checkpoints from this link and put them under ./weights. The file directory should be:

|-- checkpoints
|--|-- InstaRevive_v1.ckpt
...

📊 4. Test & Evaluation

You can test InstaRevive with following commands:

python inference.py --ckpt ./weights/InstaRevive_v1.ckpt  --input /data/testdata/  --output ./outputs/bsr_exp --sr_scale 4

• Quick Test

For a quick test, we collect some test samples in ./assets. You can run the demo for real-world ISR:

python inference.py --ckpt ./weights/InstaRevive_v1.pth  --input ./assets/inputs/  --output ./outputs/bsr_exp --tiled --sr_scale 1

You can use --tiled for patch-based inference and use --sr_scale tp set the super-resolution scale, like 2 or 4. You can set CUDA_VISIBLE_DEVICES=1 to choose the devices.

The evaluation process can be done with one Nvidia GeForce RTX 3090 GPU (24GB VRAM). You can use more GPUs by specifying the GPU ids.

🫰 Acknowledgments

We would like to express our sincere thanks to the author of DiffBIR for the clear code base and quick response to our issues.

We also thank PixArt, Real-ESRGAN and LoRA, for our code is partially borrowing from them.

🔖 Citation

Please cite us if our work is useful for your research.

@misc{zhu2025instarevive,
      title={InstaRevive: One-Step Image Enhancement via Dynamic Score Matching}, 
      author={Yixuan Zhu, Haolin Wang, Ao Li, Wenliang Zhao, Yansong Tang, Jingxuan Niu, Lei Chen, Jie Zhou, Jiwen Lu},
      year={2025},
      eprint={2504.00508},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

🔑 License

This code is distributed under an MIT LICENSE.