Update README.md with proper YAML metadata

README.md

@@ -1,9 +1,30 @@
-# Diffsketcher
+---
+language: en
+license: mit
+tags:
+  - vector-graphics
+  - svg
+  - text-to-image
+  - diffsketcher
+pipeline_tag: text-to-image
+widget:
+  - text: "a beautiful mountain landscape"
+  - text: "a colorful sunset over the ocean"
+  - text: "a cute cat sitting on a windowsill"
+---
 
-This is a vector graphics model for generating SVG images from text prompts.
+# DiffSketcher - Vector Graphics Model
+
+This repository contains the DiffSketcher model for generating vector graphics (SVG) from text prompts.
+
+## Model Description
+
+DiffSketcher is a diffusion-based vector graphics generation model that creates high-quality SVG images from text descriptions. The model uses a combination of diffusion models and vector optimization techniques to produce clean, scalable vector graphics.
 
 ## Usage
 
+You can use this model through the Hugging Face Inference API:
+
 ```python
 import requests
 
@@ -17,4 +38,27 @@ def query(payload):
 output = query({
     "prompt": "a beautiful mountain landscape"
 })
+
+# Save the SVG output
+with open("output.svg", "w") as f:
+    f.write(output["svg"])
 ```
+
+## Limitations
+
+- The model may take some time to generate complex images
+- The quality of the output depends on the clarity and specificity of the prompt
+- Some complex concepts may not be rendered accurately
+
+## Citation
+
+If you use this model in your research, please cite the original DiffSketcher paper:
+
+```bibtex
+@inproceedings{xing2023diffsketcher,
+  title={DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models},
+  author={Ximing Xing and Chuang Wang and Haitao Zhou and Jing Zhang and Qian Yu and Dong Xu},
+  booktitle={Advances in Neural Information Processing Systems},
+  year={2023}
+}
+```