> ## Documentation Index
> Fetch the complete documentation index at: https://docs.aion.xyz/llms.txt
> Use this file to discover all available pages before exploring further.

# Serve Stable Diffusion using Ray

Deploy a GPU-backed Kubernetes cluster on AION, install NVIDIA GPU Operator + KubeRay Operator + Ray Cluster from Marketplace, then apply the RayService Stable Diffusion example and validate it by port-forwarding and sending a request.

## Step 1: Create a Kubernetes cluster with GPUs

1. In the left navigation, click **Kubernetes**.
2. Click **Create K8s Cluster**.
3. **Select a region** with GPU availability that fits your needs.
4. **Create or select a project**.
5. **Configure the cluster** (cluster name, nodes, image, SSH key, version, etc.).
6. Click **Launch** and wait until the cluster status is ready/active.
7. Copy **KubeConfig** from the cluster card/details and setup the kubeconfig file.

> Recommended: Have at least one GPU-capable node available for Stable Diffusion serving.

## Step 2: Install required Marketplace apps

Go to **Kubernetes → Marketplace** and install, in this order:

### 2.1 Install NVIDIA GPU Operator

1. Open **NVIDIA GPU Operator** → **View Details**
2. Choose **Existing Cluster** = your cluster
3. Click **Deploy Application**

<Frame>
  <img src="https://mintcdn.com/aion-7ea50436/xARrCFFNr_4Yiona/images/cookbooks/nvidia-gpu-operator.png?fit=max&auto=format&n=xARrCFFNr_4Yiona&q=85&s=84ba37ab8d154e6d671af32184a52ec8" width="2856" height="1598" data-path="images/cookbooks/nvidia-gpu-operator.png" />
</Frame>

Verify:

```bash theme={null}
kubectl get pods -n gpu-operator
```

### 2.2 Install KubeRay Operator

1. Open **KubeRay Operator** → **View Details**
2. Choose **Existing Cluster**
3. Click **Deploy Application**

<Frame>
  <img src="https://mintcdn.com/aion-7ea50436/xARrCFFNr_4Yiona/images/cookbooks/kuberay-operator.png?fit=max&auto=format&n=xARrCFFNr_4Yiona&q=85&s=c9b9ab833f44f1bafed1001cfad3b28e" width="2642" height="1460" data-path="images/cookbooks/kuberay-operator.png" />
</Frame>

Verify CRDs:

```bash theme={null}
kubectl get crd | grep ray
```

### 2.3 Install Ray Cluster

1. Open **Ray Cluster** → **View Details**
2. Choose **Existing Cluster**
3. Click **Deploy Application**

<Frame>
  <img src="https://mintcdn.com/aion-7ea50436/xARrCFFNr_4Yiona/images/cookbooks/ray-cluster.png?fit=max&auto=format&n=xARrCFFNr_4Yiona&q=85&s=c631068a29353028d493e117042b62d8" width="2622" height="1508" data-path="images/cookbooks/ray-cluster.png" />
</Frame>

Verify Ray is present:

```bash theme={null}
kubectl get ray-cluster -A
kubectl get pods -A | grep -i ray
```

## Step 3: Install the Stable Diffusion RayService

Apply the example RayService manifest:

```bash theme={null}
kubectl apply -f https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray-service.stable-diffusion.yaml
```

Check the RayService:

```bash theme={null}
kubectl get rayservices -A
kubectl describe rayservices.ray.io stable-diffusion
```

Wait until the RayService **Ready** condition becomes `True`.

## Step 3: Forward the Serve port

First, find the service:

```bash theme={null}
kubectl get services
```

Then port-forward the serve service:

```bash theme={null}
kubectl port-forward svc/stable-diffusion-serve-svc 8000
```

Keep this terminal running.

## Step 4: Run the Gradio client

Download the request script

```bash theme={null}
curl -L -o diffusion-run.py "https://gist.githubusercontent.com/rb-aion/f01e3abecaae3346fd3ef94122ea0caf/raw/5f46b75360c262963a535f6fbd954a56f2055592/diffusion-run.py"
```

Install dependencies

```
python -m pip install --upgrade pip
python -m pip install gradio requests
```

Start the app

```
python diffusion-run.py
```

Gradio will print a local URL (typically [http://127.0.0.1:7860](http://127.0.0.1:7860)). Open it in your browser to generate images.