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README.md

@@ -9,6 +9,9 @@ tags:
   - diffusion
 pipeline_tag: text-to-image
 inference: true
+inference_providers:
+  - cpu
+  - gpu
 model-index:
   - name: DiffSketcher
     results:
@@ -26,28 +29,17 @@ model-index:
 
 # DiffSketcher
 
-**Text-guided vector sketch synthesis**
+**Text-guided vector graphics synthesis**
 
 </div>
 
-## [S1] DiffSketcher is a text to vector graphics model.
-## [S2] You provide a text prompt and get SVG output.
-## [S1] Amazing! Let me try it.
-
-<div align="center">
-<img src="https://huggingface.co/jree423/diffsketcher/resolve/main/model_preview.svg" alt="DiffSketcher Preview" width="600"/>
-</div>
+## 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 and adapted for the Hugging Face ecosystem.
 
-## Features
+## How to Use
 
-- Generate vector graphics from text descriptions
-- Output both SVG and PNG formats
-- Scalable and editable results
-- Controllable generation parameters
-
-## Usage with Inference API
+You can use this model through the Hugging Face Inference API:
 
 ```python
 import requests
@@ -89,16 +81,3 @@ image.save("output.png")
 - 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
-
-## Citation
-
-If you use this model in your research, please cite the original work:
-
-```
-@inproceedings{ximing2023vectorgraphics,
-  title="Vector Graphics Synthesis",
-  author="Author, A. and Author, B.",
-  booktitle="Conference",
-  year="2023"
-}
-```