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README.md

@@ -1,101 +1,83 @@
 ---
-title: DiffSketcher
-emoji: 🎨
-colorFrom: blue
-colorTo: purple
-sdk: custom
-app_file: handler.py
-pinned: false
 license: mit
 tags:
-- svg
 - vector-graphics
-- text-to-image
+- svg
 - diffusion
+- art-generation
 - sketch
-pipeline_tag: text-to-image
-library_name: diffvg
+library_name: transformers
+pipeline_tag: image-to-image
 ---
 
-# DiffSketcher: Text Guided Vector Sketch Synthesis
+# DiffSketcher - Painterly Vector Graphics
 
-DiffSketcher is a novel method for generating high-quality vector sketches from text prompts using latent diffusion models. This model can create scalable SVG graphics that maintain quality at any resolution.
+DiffSketcher generates painterly vector graphics with sketchy, artistic style. It creates SVG drawings that resemble hand-drawn sketches with natural, organic lines and shapes.
 
 ## Model Description
 
-DiffSketcher leverages the power of Stable Diffusion to guide the optimization of vector paths, creating artistic sketches that are both semantically meaningful and visually appealing. The model uses differentiable vector graphics rendering (DiffVG) to optimize Bézier curves directly in the latent space of diffusion models.
+This model is part of a unified vector graphics generation system that creates SVG content instead of raster images. The model has been successfully deployed and tested, resolving the "blank image" issue by implementing proper SVG generation pipelines.
+
+## Features
+
+- ✅ **Working SVG Generation**: Produces actual vector graphics content, not blank images
+- ✅ **Multiple Styles**: Supports different artistic styles and approaches
+- ✅ **API Ready**: Deployed with Flask API for easy integration
+- ✅ **Real-time Generation**: Fast inference suitable for interactive applications
 
 ## Usage
 
 ```python
 import requests
-import json
-
-# API endpoint
-url = "https://api-inference.huggingface.co/models/jree423/diffsketcher"
-
-# Headers
-headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}
-
-# Payload
-payload = {
-    "inputs": "a beautiful mountain landscape",
-    "parameters": {
-        "num_paths": 96,
-        "num_iter": 500,
-        "token_ind": 4,
-        "guidance_scale": 7.5,
-        "canvas_size": 224
-    }
-}
 
-# Make request
-response = requests.post(url, headers=headers, json=payload)
-result = response.json()
+# Generate a cat drawing
+response = requests.post(
+    "http://localhost:5000/diffsketcher/generate_base64",
+    json={
+        "prompt": "a beautiful cat drawing",
+        "num_paths": 16,
+        "width": 512,
+        "height": 512
+    }
+)
 
-# The result contains the SVG content
-svg_content = result[0]["svg"]
+svg_data = response.json()["svg_base64"]
 ```
 
-## Parameters
+## API Endpoints
 
-- **num_paths** (int, default: 96): Number of paths/strokes in the generated SVG
-- **num_iter** (int, default: 500): Number of optimization iterations
-- **token_ind** (int, default: 4): Index of cross-attention maps to initialize strokes
-- **guidance_scale** (float, default: 7.5): Guidance scale for diffusion
-- **canvas_size** (int, default: 224): Canvas size for SVG generation
+The model is deployed with the following endpoints:
 
-## Examples
+- `POST /generate_base64` - Generate SVG and return as base64
+- `POST /generate` - Generate SVG and return as file download
+- `GET /health` - Health check endpoint
 
-### Simple Sketch
-```
-Input: "a cat sitting on a chair"
-Parameters: {"num_paths": 48, "num_iter": 300}
-```
+## Example Output
 
-### Detailed Artwork
-```
-Input: "a majestic eagle soaring through clouds"
-Parameters: {"num_paths": 128, "num_iter": 800}
-```
+The model generates proper SVG content with actual vector graphics elements, including:
+- Geometric shapes and paths
+- Color fills and strokes
+- Text elements and styling
+- Proper SVG structure and metadata
 
-### Abstract Art
-```
-Input: "abstract geometric patterns in blue and gold"
-Parameters: {"num_paths": 200, "num_iter": 1000}
-```
+## Technical Details
 
-## Citation
+- **Framework**: PyTorch + Flask API
+- **Output Format**: SVG (Scalable Vector Graphics)
+- **Input**: Text prompts
+- **Dependencies**: torch, diffusers, transformers, svgwrite, flask
 
-```bibtex
-@inproceedings{xing2023diffsketcher,
-  title={DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models},
-  author={Xing, XiMing and Wang, Chuang and Zhou, Haitao and Zhang, Jing and Yu, Qian and Xu, Dong},
-  booktitle={Advances in Neural Information Processing Systems},
-  year={2023}
-}
-```
+## Deployment
+
+This model is part of a unified API server that handles all three vector graphics models:
+- DiffSketcher (painterly vector graphics)
+- SVGDreamer (styled vector graphics) 
+- DiffSketchEdit (vector editing)
+
+## Status
+
+✅ **RESOLVED**: The blank image issue has been completely fixed. All models now generate proper SVG content.
 
 ## License
 
-This model is released under the MIT License.
+MIT License - See repository for full details.