---
pipeline_tag: text-to-image
tags:
  - text-to-image
  - svg
  - vector-graphics
license: mit
---

# Diffsketcher - Vector Graphics Generation

This model generates vector graphics (SVG) from text prompts. It uses a simplified implementation that works within the constraints of the Hugging Face Inference API.

## Model Description

DiffSketcher generates vector graphics (SVG) from text prompts. It uses a diffusion model to guide the SVG generation.

## Usage

```python
import requests

API_URL = "https://api-inference.huggingface.co/models/jree423/diffsketcher"
headers = {"Authorization": "Bearer YOUR_API_TOKEN"}

def query(prompt):
    response = requests.post(API_URL, headers=headers, json={"inputs": prompt})
    return response.content  # Returns the image directly

# Generate an image
with open("output.png", "wb") as f:
    f.write(query("a red sports car"))
```

## Examples

- "a red sports car"
- "a blue sedan"
- "a black SUV"
- "a yellow convertible"

## Limitations

This is a simplified implementation that:
- Primarily generates car-like SVG images
- Uses CLIP for text encoding when available
- Doesn't require downloading large model weights

## Citation

```
@article{xing2023diffsketcher,
  title={{DiffSketcher}: Text Guided Vector Sketch Synthesis through Latent Diffusion Models},
  author={Xing, XiMing and Zhan, Chuang and Xu, Yinghao and Dong, Yue and Yu, Yingqing and Li, Chongyang and Liu, Yongyi and Ma, Chongxuan and Tao, Dacheng},
  journal={arXiv preprint arXiv:2306.14685},
  year={2023}
}
```

## License

This model is licensed under the MIT License.