chore: import upstream snapshot with attribution

doc/source/cluster/kubernetes/examples/stable-diffusion-rayservice.md

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+(kuberay-stable-diffusion-rayservice-example)=
+
+# Serve a StableDiffusion text-to-image model on Kubernetes
+
+> **Note:** The Python files for the Ray Serve application and its client are in the [ray-project/serve_config_examples](https://github.com/ray-project/serve_config_examples) repository
+and [the Ray documentation](https://docs.ray.io/en/latest/serve/tutorials/stable-diffusion.html).
+
+## Step 1: Create a Kubernetes cluster with GPUs
+
+See [aws-eks-gpu-cluster.md](kuberay-eks-gpu-cluster-setup) or [gcp-gke-gpu-cluster.md](kuberay-gke-gpu-cluster-setup) or [ack-gpu-cluster.md](kuberay-ack-gpu-cluster-setup) to create a Kubernetes cluster with 1 CPU node and 1 GPU node.
+
+## Step 2: Install KubeRay operator
+
+Follow [this document](kuberay-operator-deploy) to install the latest stable KubeRay operator using the Helm repository. Note that the YAML file in this example uses `serveConfigV2`. This feature requires KubeRay v0.6.0 or later.
+
+## Step 3: Install a RayService
+
+```sh
+kubectl apply -f https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray-service.stable-diffusion.yaml
+```
+
+This RayService configuration contains some important settings:
+
+* In the RayService, the head Pod doesn't have any `tolerations`. Meanwhile, the worker Pods use the following `tolerations` so the scheduler won't assign the head Pod to the GPU node.
+    ```yaml
+    # Please add the following taints to the GPU node.
+    tolerations:
+        - key: "ray.io/node-type"
+        operator: "Equal"
+        value: "worker"
+        effect: "NoSchedule"
+    ```
+* It includes `diffusers` in `runtime_env` since this package isn't included by default in the `ray-ml` image.
+
+## Step 4: Forward the port of Serve
+
+First get the service name from this command.
+
+```sh
+kubectl get services
+```
+
+Then, port forward to the serve.
+
+```sh
+# Wait until the RayService `Ready` condition is `True`. This means the RayService is ready to serve.
+kubectl describe rayservices.ray.io stable-diffusion
+
+# [Example output]
+#   Conditions:
+#     Last Transition Time:  2025-02-13T07:10:34Z
+#     Message:               Number of serve endpoints is greater than 0
+#     Observed Generation:   1
+#     Reason:                NonZeroServeEndpoints
+#     Status:                True
+#     Type:                  Ready
+
+# Forward the port of Serve
+kubectl port-forward svc/stable-diffusion-serve-svc 8000
+```
+
+## Step 5: Send a request to the text-to-image model
+
+```sh
+# Step 5.1: Download `stable_diffusion_req.py`
+curl -LO https://raw.githubusercontent.com/ray-project/serve_config_examples/master/stable_diffusion/stable_diffusion_req.py
+
+# Step 5.2: Set your `prompt` in `stable_diffusion_req.py`.
+
+# Step 5.3: Send a request to the Stable Diffusion model.
+python stable_diffusion_req.py
+# Check output.png
+```
+
+* You can refer to the document ["Serving a Stable Diffusion Model"](https://docs.ray.io/en/latest/serve/tutorials/stable-diffusion.html) for an example output image.