Update: Add model card for original implementation

README.md

@@ -9,11 +9,11 @@ license: mit
 
 # Diffsketcher - Vector Graphics Generation
 
-This model generates vector graphics (SVG) from text prompts. It uses a versatile implementation that analyzes the prompt to determine what type of object to generate.
+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 analyzes the prompt to determine what type of object to generate and creates appropriate SVG images.
+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.
 
 ## Usage
 
@@ -29,48 +29,29 @@ def query(prompt):
 
 # Generate an image
 with open("output.png", "wb") as f:
-    f.write(query("a red sports car"))
+    f.write(query("a beautiful mountain landscape"))
 ```
 
 ## Examples
 
-### Cars
+- "a beautiful mountain landscape"
 - "a red sports car"
-- "a blue sedan"
-- "a black SUV"
-
-### Landscapes
-- "a mountain landscape with a lake"
-- "a forest with a river"
-- "a beach at sunset"
-
-### Animals
-- "a brown dog"
-- "a black cat"
-- "a colorful bird"
-
-### Buildings
-- "a small house with a garden"
-- "a tall skyscraper"
-- "a medieval castle"
-
-### Faces
-- "a smiling woman"
-- "a man with a beard"
-- "a girl with long hair"
-
-### Abstract
-- "colorful abstract art"
-- "geometric shapes"
-- "vibrant colors and patterns"
+- "a portrait of a woman"
+- "a cat playing with a ball"
 
 ## How It Works
 
-1. **Prompt Analysis**: The model analyzes the prompt to determine what type of object to generate.
-2. **CLIP Integration**: The model uses CLIP to encode the prompt when available.
-3. **SVG Generation**: Based on the detected object type, the model creates an appropriate SVG.
+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
 
 ```