[CVPR 2026 Oral] ChordEdit: One-Step Low-Energy Transport for Image Editing
## 1. Environment
- Python 3.12
- PyTorch 2.5.0
- This repository requires the `sd-turbo` weights: https://huggingface.co/stabilityai/sd-turbo
- Model root should contain:
- `unet/`
- `scheduler/`
- `text_encoder/`
- `tokenizer/`
- `vae/`
## 2. Install Dependencies
```bash
pip install -r requirement.txt
```
## 3. Run the Web Demo
Launch the interactive demo:
```bash
python app.py --model-root /path/to/sd-turbo --server-port 7860
```
Running `python app.py` now launches a local Gradio web app.
- Left panel: upload the original image, set source prompt, target prompt, and tuning parameters.
- Right panel: view the edited output image.
- Bottom section: click built-in examples (image + source prompt + target prompt) to auto-fill inputs.
## 4. Run PIE Benchmark Export
Run PIE-Bench export with:
```bash
python run_pie_bench.py --model-root /path/to/sd-turbo --pie-root /path/to/pie_bench
```
`--pie-root` should point to a PIE-Bench folder containing at least:
1. `annotation_images/` — original PIE-Bench images (subfolders keep the official naming).
2. `mapping_file.json` — the mapping metadata describing prompts, instructions, and masks.
Example layout:
```
pie_bench
|-annotation_images
|-mapping_file.json
```
For PIE-Bench data preparation and protocol details, please refer to:
https://github.com/cure-lab/PnPInversion
# Citation
If you find our work helpful, please **star 🌟** this repo and **cite 📑** our paper. Thanks for your support!
```
@article{lu2026chordedit,
title={ChordEdit: One-Step Low-Energy Transport for Image Editing},
author={Lu, Liangsi and Chen, Xuhang and Guo, Minzhe and Li, Shichu and Wang, Jingchao and Shi, Yang},
journal={arXiv preprint arXiv:2602.19083},
year={2026}
}
```