| # Qwen Image Layered - Docker Deployment for HF Inference Endpoints | |
| This directory contains a custom Docker-based deployment for `QwenImageLayeredPipeline`. | |
| ## Files | |
| - `Dockerfile`: Custom container with all bleeding-edge dependencies. | |
| - `app.py`: FastAPI server (HF-compatible API format). | |
| - `handler.py`: Model loading and inference logic. | |
| - `requirements.txt`: Python dependencies (all from git main). | |
| ## Deployment Steps | |
| ### 1. Build and Push Docker Image | |
| ```bash | |
| # Login to Docker Hub (or another registry) | |
| docker login | |
| # Build the image | |
| docker build -t yourusername/qwen-layered:latest . | |
| # Push to registry | |
| docker push yourusername/qwen-layered:latest | |
| ``` | |
| ### 2. Create HF Inference Endpoint | |
| 1. Go to [HF Inference Endpoints](https://ui.endpoints.huggingface.co/) | |
| 2. Click **New Endpoint** | |
| 3. Select **Custom Container** | |
| 4. Enter your Docker image URL: `docker.io/yourusername/qwen-layered:latest` | |
| 5. Select GPU (A10G or better, 24GB+ VRAM) | |
| 6. Deploy | |
| ### 3. Usage | |
| ```bash | |
| curl https://your-endpoint.endpoints.huggingface.cloud \ | |
| -X POST \ | |
| -d '{"inputs": {"prompt": "A cute cat"}}' \ | |
| -H "Authorization: Bearer hf_..." \ | |
| -H "Content-Type: application/json" | |
| ``` | |
| ## Local Testing | |
| ```bash | |
| docker build -t qwen-test . | |
| docker run --gpus all -p 8080:8080 qwen-test | |
| # Then: curl http://localhost:8080/health | |
| ``` | |