Update README with detailed Inference Endpoint setup instructions

README.md

@@ -92,11 +92,79 @@ Response format from the local server:
 
 ## Deployment on Hugging Face Inference Endpoints
 
-1. Push this repository to Hugging Face Hub or your Git repository
-2. Create a new Inference Endpoint on Hugging Face
-3. Select this repository as the source
-4. Configure compute resources (recommended: GPU with at least 16GB VRAM)
-5. Deploy the endpoint
+### Step 1: Push this repository to Hugging Face Hub
+
+1. Create a new repository on Hugging Face Hub:
+   ```bash
+   huggingface-cli repo create your-repo-name
+   ```
+
+2. Add the Hugging Face repository as a remote:
+   ```bash
+   git remote add huggingface https://huggingface.co/username/your-repo-name
+   ```
+
+3. Push your code to the Hugging Face repository:
+   ```bash
+   git push huggingface your-branch:main
+   ```
+
+### Step 2: Create an Inference Endpoint
+
+1. Go to your repository on Hugging Face Hub: https://huggingface.co/username/your-repo-name
+2. Click on "Deploy" in the top menu, then select "Inference Endpoints"
+3. Click "Create a new endpoint"
+4. Configure your endpoint with the following settings:
+   - Name: Give your endpoint a name
+   - Instance Type: Choose a GPU instance (recommended: at least 16GB VRAM for SDXL)
+   - Replicas: Start with 1 replica
+   - Autoscaling: Configure as needed
+5. Click "Create endpoint"
+
+The Hugging Face Inference Endpoints service will automatically detect and use your `EndpointHandler` class in the `handler.py` file.
+
+### Step 3: Test your Inference Endpoint
+
+Once deployed, you can test your endpoint using:
+
+```python
+import requests
+import json
+import base64
+from PIL import Image
+import io
+
+# Your Hugging Face API token and endpoint URL
+API_TOKEN = "your-hugging-face-api-token"
+API_URL = "https://api-inference.huggingface.co/models/username/your-repo-name"
+
+# Headers for the request
+headers = {
+    "Authorization": f"Bearer {API_TOKEN}",
+    "Content-Type": "application/json"
+}
+
+# Request payload
+payload = {
+    "inputs": "a beautiful landscape with mountains and a lake",
+    "parameters": {
+        "negative_prompt": "blurry, low quality",
+        "seed": 42,
+        "inference_steps": 30,
+        "guidance_scale": 7
+    }
+}
+
+# Send the request
+response = requests.post(API_URL, headers=headers, json=payload)
+result = response.json()
+
+# Convert the base64-encoded image to a PIL Image
+image_bytes = base64.b64decode(result[0]["generated_image"])
+image = Image.open(io.BytesIO(image_bytes))
+image.save("generated_image.jpg")
+print(f"Image saved with seed: {result[0]['seed']}")
+```
 
 ### Required Files