---
language:
  - en
license: mit
library_name: diffvg
tags:
  - vector-graphics
  - svg
  - text-to-image
  - diffusion
  - stable-diffusion
pipeline_tag: text-to-image
inference: true
---

# DiffSketcher

**Text-guided vector graphics synthesis**

## Model Description

DiffSketcher is a vector graphics model that converts text descriptions into scalable vector graphics (SVG). It was developed based on the research from the [original repository](https://github.com/ximinng/DiffSketcher) and adapted for the Hugging Face ecosystem.

## How to Use

You can use this model through the Hugging Face Inference API:

```python
import requests
import base64
from PIL import Image
import io

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

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

# Example
payload = {"prompt": "a house with a chimney"}
output = query(payload)

# Save SVG
with open("output.svg", "w") as f:
    f.write(output["svg"])

# Save image
image_data = base64.b64decode(output["image"])
image = Image.open(io.BytesIO(image_data))
image.save("output.png")
```

## Model Parameters

* `prompt` (string, required): Text description of the desired output
* `negative_prompt` (string, optional): Text to avoid in the generation
* `num_paths` (integer, optional): Number of paths in the SVG
* `guidance_scale` (float, optional): Guidance scale for the diffusion model
* `seed` (integer, optional): Random seed for reproducibility

## Limitations

* The model works best with descriptive, clear prompts
* Complex scenes may not be rendered with perfect accuracy
* Generation time can vary based on the complexity of the prompt