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[CVPR 2025] PyTorch implementation of Diff-II

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# Inversion Circle Interpolation: Diffusion-based Image Augmentation for Data-scarce Classification (CVPR 2025)

**Diff-II is a novel and effective generative data augmentation method.**

![](https://raw.githubusercontent.com/HKUST-LongGroup/Diff-II/main/teaser.jpg)

> [**Inversion Circle Interpolation: Diffusion-based Image Augmentation for Data-scarce Classification**](https://arxiv.org/pdf/2408.16266)
> 
> Yanghao Wang, Long Chen

# Updates
- (02/2025) Diff-II was accepted by CVPR 2025.
- (12/2024) Code is released.

# Get Start
## Environment
Create a conda environment and activate it with the following command:
```bash
conda env create -f environment.yaml
conda activate diff-ii
```
If this takes too long, `conda config --set solver libmamba` sets conda to use the `libmamba` solver and could speed up installation.

## Download datasets, learned concepts, and suffixes
This may take some time.
```bash
bash scripts/download.sh
```

## Run code
We implemented it on 8 x 3090Ti GPUs. Thus, you can adjust the number of GPUs by modifying the code's hyperparameters.
Depending on your needs, modify the scripts/run.sh file and run.
```bash
bash scripts/run.sh
```

# Citing Diff-II

If you use Diff-II in your research or wish to refer to the baseline results published here, please use the following BibTeX entry.

```BibTeX
@article{wang2024improving,
  title={Improving diffusion-based data augmentation with inversion spherical interpolation},
  author={Wang, Yanghao and Chen, Long},
  journal={arXiv preprint arXiv:2408.16266},
  year={2024}
}
```

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