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--- |
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title: FaceForge |
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emoji: 🧑🎨 |
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colorFrom: indigo |
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colorTo: pink |
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sdk: gradio |
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sdk_version: "4.44.1" |
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app_file: app.py |
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pinned: false |
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--- |
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# faceforge |
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Interactive latent space editor for face generation using pretrained GANs and diffusion models. |
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## 🚀 Deploy on Hugging Face Spaces (Recommended) |
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FaceForge is ready to run as a Gradio app on [Hugging Face Spaces](https://huggingface.co/spaces): |
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1. **Push your code to a public GitHub repository.** |
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2. **Create a new Space** at https://huggingface.co/spaces (choose the Gradio SDK or Docker SDK). |
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3. **Add your `requirements.txt` and the provided `Dockerfile` to your repo.** |
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4. **Set the entrypoint to `app.py`** (which integrates both the API and UI components). |
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5. **Deploy!** Your app will be live at `https://<your-username>.hf.space`. |
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### Example Dockerfile (already included): |
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```Dockerfile |
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FROM huggingface/transformers:latest |
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WORKDIR /app |
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COPY requirements.txt ./ |
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RUN pip install --no-cache-dir -r requirements.txt |
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RUN pip install --no-cache-dir transformers |
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COPY . . |
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EXPOSE 7860 |
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ENV PYTHONPATH="/app" |
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ENV PYTHONUNBUFFERED=1 |
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ENV API_URL="/api" |
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ENV MOCK_API="true" |
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ENV BASE_URL="" |
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CMD ["python", "app.py"] |
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``` |
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## Local Development (Optional) |
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You can still run FaceForge locally: |
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```bash |
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pip install -r requirements.txt |
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python main.py |
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``` |
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This will start the integrated application with both the API and UI components available: |
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- UI accessible at http://localhost:7860/ |
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- API accessible at http://localhost:7860/api/ |
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## Architecture |
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FaceForge uses a modular architecture: |
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1. **Core Components** (`faceforge_core/`): Core algorithms and utilities |
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2. **API Layer** (`faceforge_api/`): FastAPI endpoints for model interaction |
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3. **UI Layer** (`faceforge_ui/`): Gradio interface for user interaction |
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The main application integrates these components into a single FastAPI application where: |
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- The API is mounted at `/api/` |
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- The Gradio UI is mounted at the root path `/` |
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## Features |
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- Latent space exploration and manipulation |
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- Attribute direction discovery (PCA/classifier) |
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- Custom attribute-preserving loss |
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- Modular, testable core |
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- Gradio UI for interactive exploration |
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## Controls (Gradio UI) |
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- Enter prompts (comma-separated) |
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- Choose sampling mode (distance/circle) |
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- Adjust player position sliders |
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- Click "Generate" to see results |
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## Testing |
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Run all tests with: |
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```bash |
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pytest tests/ |
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``` |
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## Debugging |
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If you encounter Gradio schema-related errors like: |
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``` |
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TypeError: argument of type 'bool' is not iterable |
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``` |
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The application includes a patch that should fix the issue automatically. This patch addresses a known issue with schema processing in older Gradio versions. |
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### Common Issues: |
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#### API Connection Errors |
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If you see errors like: |
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``` |
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Invalid URL '/api/generate': No scheme supplied. Perhaps you meant https:///api/generate? |
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``` |
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This indicates an issue with URL formatting. The application should handle this automatically with the following settings: |
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1. For the integrated app, set `BASE_URL=""` in the environment |
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2. For separate UI/API components, set `BASE_URL="http://localhost:7860"` (or your server URL) |
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3. Using relative URLs within the same server requires the correct base URL configuration |
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The updated app uses proper URL formatting that works in both integrated and separated modes. |
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#### Missing Dependencies |
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If you see errors about missing Python packages like `transformers`, you have options: |
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1. Install the missing package: `pip install transformers` |
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2. Use mock mode: Set `MOCK_API="true"` in environment variables |
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3. Use the Docker image which includes all dependencies: `docker build -t faceforge .` |
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#### Environment Variables |
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- `MOCK_API`: Set to "true" to use mock API responses (for testing without ML dependencies) |
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- `API_URL`: Override the API endpoint URL |
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- `BASE_URL`: Base URL for relative API paths (leave empty for integrated deployment) |
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- `PORT`: Set the port for the server (default: 7860) |
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## Notes |
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- The backend and frontend are fully integrated for Spaces deployment. |
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- The application will use the actual ML framework when dependencies are available, and fall back to mock implementations when they're missing. |
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- For custom model integration, edit the core and backend modules as needed. |
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