Update README.md with proper YAML metadata

README.md

@@ -1,10 +1,11 @@
 ---
-pipeline_tag: text-to-image
+language: en
+license: mit
 tags:
   - text-to-image
   - svg
   - vector-graphics
-license: mit
+pipeline_tag: text-to-image
 ---
 
 # DiffSketcher - Vector Graphics Generation
@@ -21,33 +22,50 @@ DiffSketcher is a state-of-the-art vector graphics generation model that creates
 import requests
 
 API_URL = "https://api-inference.huggingface.co/models/jree423/diffsketcher"
-headers = {"Authorization": "Bearer YOUR_API_TOKEN"}
+headers = {"Authorization": "Bearer YOUR_TOKEN"}
 
 def query(prompt):
     response = requests.post(API_URL, headers=headers, json={"inputs": prompt})
-    return response.content  # Returns the image directly
+    return response.content
 
 # Generate an image
 with open("output.png", "wb") as f:
     f.write(query("a beautiful mountain landscape"))
 ```
 
-## Examples
+You can also specify additional parameters:
 
-- "a beautiful mountain landscape"
-- "a red sports car"
-- "a portrait of a woman"
-- "a cat playing with a ball"
+```python
+response = requests.post(
+    API_URL,
+    headers=headers,
+    json={
+        "inputs": {
+            "text": "a beautiful mountain landscape",
+            "width": 512,
+            "height": 512,
+            "num_paths": 512,
+            "seed": 42
+        }
+    }
+)
+```
 
-## How It Works
+## Parameters
 
-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.
+- `text` (str): The text prompt to generate an image from.
+- `width` (int, optional): The width of the generated image. Default: 512.
+- `height` (int, optional): The height of the generated image. Default: 512.
+- `num_paths` (int, optional): The number of paths to use in the SVG. Default: 512.
+- `seed` (int, optional): The random seed to use for generation. Default: None (random).
 
-## Performance Considerations
+## Citation
 
-- The original implementation requires significant computational resources
-- Generation can take several minutes depending on the complexity
-- GPU acceleration is recommended for optimal performance
+```bibtex
+@inproceedings{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, Yong Jin},
+  booktitle={Advances in Neural Information Processing Systems},
+  year={2023}
+}
+```