README.md

---
pipeline_tag: other
library_name: diffusers
---

# SounDiT: Geo-Contextual Soundscape-to-Landscape Generation

SounDiT is a diffusion transformer (DiT)-based model designed for the **Geo-contextual Soundscape-to-Landscape (GeoS2L)** generation task. It synthesizes geographically realistic landscape images from environmental soundscapes by incorporating geo-contextual scene conditioning.

- **Paper:** [SounDiT: Geo-Contextual Soundscape-to-Landscape Generation](https://huggingface.co/papers/2505.12734)
- **Project Page:** [https://gisense.github.io/SounDiT-Page/](https://gisense.github.io/SounDiT-Page/)
- **Repository:** [https://github.com/GISense/SounDiT](https://github.com/GISense/SounDiT)

## Overview
Recent audio-to-image models often struggle to reconstruct real-world landscapes from environmental soundscapes. SounDiT addresses this gap using a DiT architecture that leverages diverse environmental soundscapes and scene conditioning to ensure geographical coherence. To evaluate this task, the authors introduced the Place Similarity Score (PSS) framework, which captures multi-level generation consistency across element, scene, and human perception.

## Code Usage

### Environment Setup

```bash
conda env create -f environment.yml
conda activate SounDiT
```

### Inference

```bash
bash ./scripts/inference.sh
```

## Citation

If you use SounDiT in your research, please cite the following paper:

```bibtex
@misc{wang2025sounditgeocontextualsoundscapetolandscapegeneration,
  title={SounDiT: Geo-Contextual Soundscape-to-Landscape Generation},
  author={Junbo Wang and Haofeng Tan and Bowen Liao and Albert Jiang and Teng Fei and Qixing Huang and Zhengzhong Tu and Shan Ye and Yuhao Kang},
  year={2025},
  eprint={2505.12734},
  archivePrefix={arXiv},
  primaryClass={cs.SD},
  url={https://arxiv.org/abs/2505.12734}
}
```