Update: Improve model card

README.md

@@ -7,13 +7,9 @@ tags:
 license: mit
 ---
 
-# Diffsketcher - Vector Graphics Generation
+# Vector Graphics Generation
 
-This model generates vector graphics (SVG) from text prompts. It uses the original implementation from the official repository.
-
-## Model Description
-
-DiffSketcher generates vector graphics (SVG) from text prompts. It uses a diffusion model to guide the SVG generation and creates sketches with a specified number of paths.
+This model generates vector graphics (SVG) from text prompts.
 
 ## Usage
 
@@ -38,31 +34,3 @@ with open("output.png", "wb") as f:
 - "a red sports car"
 - "a portrait of a woman"
 - "a cat playing with a ball"
-
-## How It Works
-
-1. **Text Encoding**: The text prompt is encoded using CLIP.
-2. **Diffusion Process**: A diffusion model generates a latent representation.
-3. **SVG Generation**: The latent representation is used to generate an SVG.
-4. **PNG Conversion**: The SVG is converted to PNG for display.
-
-## Performance Considerations
-
-- The original implementation requires significant computational resources
-- Generation can take several minutes depending on the complexity
-- GPU acceleration is recommended for optimal performance
-
-## 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.