Adam/final touches

playbooks/supplemental/open-webui-chat/README.md

@@ -757,6 +757,7 @@ This demonstrates that Open WebUI can send multimodal requests (text + image) th
 
 ---
 
+<!-- @os:windows -->
 ### Activity 3: Generate an Image from a Text Prompt (Stable Diffusion)
 
 Stable Diffusion models don't support text generation, they only generate images through the Images API. 
@@ -771,7 +772,60 @@ Stable Diffusion models don't support text generation, they only generate images
    - **OpenAI API Base URL:** `http://localhost:13305/api/v1`
    - **OpenAI API Key:** `-`
    - **Model:** `SDXL-Turbo` or `SDXL-Base-1.0`
-4. If you want to add more parameters, add them to the text field as JSON. For example: `{ "steps": 4, "cfg_scale": 1 }`. See available parameters at [Image Generation (Stable Diffusion CPP)](https://lemonade-server.ai/models.html).
+4. If you want to add more parameters, add them to the text field as JSON. For example: `{ “steps”: 4, “cfg_scale”: 1 }`. See available parameters at [Image Generation (Stable Diffusion CPP)](https://lemonade-server.ai/models.html).
+
+   <p align=”center”>
+     <img src=”assets/images_settings.png” alt=”Open WebUI Image Generation settings” width=”600”/>
+   </p>
+
+5. Save
+
+
+#### Step 2: Allow Image Generation for the model
+This step ensures that you enable Image Generation as a capability for your model.
+1. Go to **Admin Settings → Models** (http://localhost:8080/admin/settings/models) and choose your model
+2. Turn on `Image Generation`
+
+   <p align="center">
+     <img src="assets/model_settings.png" alt="Model Settings" width="45%"/>
+     <img src="assets/edit_model.png" alt="Edit Model" width="50%"/>
+   </p>
+
+#### Step 3: Generate an image from the chat screen
+
+1. Go back to chat at `http://localhost:8080`.
+2. Select a **Text Generation LLM** in the model dropdown (example: Qwen, Llama). **Do not select a Stable Diffusion model** as this is a chat model selector.
+3. In the message area, click on **Integrations**, and toggle **Image** ON.
+4. Use a prompt like: `A cinematic photo of heavy traffic at sunset, ultra detailed`.
+5. An image is generated and appears in the chat.
+
+   <p align="center">
+     <img src="assets/image_gen_prompt.png" alt="Image Generation" width="49%"/>
+     <img src="assets/image_gen_response.png" alt="Generated image response" width="32.5%"/>
+   </p>
+
+This establishes that Open WebUI can coordinate a “two-part” workflow:
+  - The LLM helps refine the prompt
+  - The image is generated via Lemonade’s Images endpoint using Stable Diffusion
+<!-- @os:end -->
+
+<!-- @os:linux -->
+<!-- @device:halo,stx,krk,rx7900xt,rx9070xt -->
+### Activity 3: Generate an Image from a Text Prompt (Stable Diffusion)
+
+Stable Diffusion models don’t support text generation, they only generate images through the Images API. 
+
+#### Step 1: Configure Image Generation in Open WebUI
+
+1. In the Lemonade GUI (`http://localhost:13305`), search for `SDXL-Turbo` (fast) or `SDXL-Base-1.0` (higher quality) and download it.
+2. Go to **Admin Settings → Images** (http://localhost:8080/admin/settings/images)
+3. Set:
+   - **Image Generation:** ON
+   - **Image Generation Engine:** Default (OpenAI)
+   - **OpenAI API Base URL:** `http://localhost:13305/api/v1`
+   - **OpenAI API Key:** `-`
+   - **Model:** `SDXL-Turbo` or `SDXL-Base-1.0`
+4. If you want to add more parameters, add them to the text field as JSON. For example: `{ “steps”: 4, “cfg_scale”: 1 }`. See available parameters at [Image Generation (Stable Diffusion CPP)](https://lemonade-server.ai/models.html).
 
    <p align="center">
      <img src="assets/images_settings.png" alt="Open WebUI Image Generation settings" width="600"/>
@@ -806,6 +860,8 @@ This step ensures that you enable Image Generation as a capability for your mode
 This establishes that Open WebUI can coordinate a “two-part” workflow:
   - The LLM helps refine the prompt
   - The image is generated via Lemonade’s Images endpoint using Stable Diffusion
+<!-- @device:end -->
+<!-- @os:end -->
 
 ---
 

playbooks/supplemental/vllm-inference/README.md

@@ -44,13 +44,13 @@ There is no host-side vLLM installation step. Start vLLM with:
 vllm-launch
 ```
 
-The launcher starts the container, targets the integrated GPU, and exposes a local OpenAI-compatible vLLM server.
+The launcher starts the container, targets the integrated GPU, and exposes a local OpenAI-compatible vLLM server. Alternatively, click the vLLM icon in the taskbar.
 
 ## Quick Start
 
 ### 1. Confirm the vLLM Server Is Running
 
-After `vllm-launch` starts, the server is available at `http://localhost:8001`. Keep the launch terminal open because the server runs in the foreground, then open a separate terminal for the remaining steps. The examples below use `Qwen/Qwen3-1.7B`; if your launcher is configured for a different model, substitute that model ID in the requests.
+The `vllm-launch` may take a couple minutes to initialize everything. Once it starts, the server is available at `http://localhost:8001`. Keep the launch terminal open because the server runs in the foreground, then open a separate terminal for the remaining steps. The examples below use `Qwen/Qwen3-1.7B`; if your launcher is configured for a different model, substitute that model ID in the requests.
 
 ### 2. Send a Prompt
 
@@ -113,14 +113,6 @@ Make sure the server is running:
 curl http://localhost:8001/health
 ```
 
-## Requirements
-
-### For vLLM Server
-- Linux
-- `vllm-launch` container launcher
-- AMD system with a supported integrated GPU
-- Sufficient memory for the selected model
-
 ## Summary
 
 In this playbook, you learned how to:
@@ -133,7 +125,13 @@ In this playbook, you learned how to:
 
 You now have a containerized vLLM deployment for serving large language models with optimized performance on the integrated GPU.
 
+## Next Steps
+
+- **Try different models** — Swap the model in the `vllm-launch` configuration to experiment with different LLMs and compare performance.
+- **Build an application** — Use the OpenAI-compatible API to integrate vLLM into a Python app, chatbot, or automation workflow.
+- **Fine-tune and serve** — Fine-tune a model using LoRA or QLoRA, then deploy it with vLLM for optimized inference.
+
 ## Additional Resources
 
-- **[vLLM Official Documentation](https://docs.vllm.ai/)** - Comprehensive guides and API references
-- **[vLLM GitHub Repository](https://github.com/vllm-project/vllm)** - Source code, issues, and community discussions
+- **[vLLM Official Documentation](https://docs.vllm.ai/)** — Comprehensive guides and API references
+- **[vLLM GitHub Repository](https://github.com/vllm-project/vllm)** — Source code, issues, and community discussions

playbooks/supplemental/vllm-inference/playbook.json

@@ -26,7 +26,7 @@
   },
   "difficulty": "beginner",
   "isNew": false,
-  "isFeatured": true,
+  "isFeatured": false,
   "developed": true,
   "published": true,
   "tags": [

website/src/components/PlaybooksSection.tsx

@@ -258,7 +258,7 @@ export default function PlaybooksSection({ activeDevice, selectedDevice, onSelec
                             New
                           </span>
                         )}
-                        {isHaloSelected && featuredPlaybook.category === "core" && featuredPlaybook.id !== "lmstudio-rocm-llms" && (
+                        {isHaloSelected && featuredPlaybook.category === "core" && (
                           <PreinstalledBadge />
                         )}
                         <DifficultyBadge difficulty={featuredPlaybook.difficulty} />
@@ -322,7 +322,7 @@ export default function PlaybooksSection({ activeDevice, selectedDevice, onSelec
                         </svg>
                       </div>
                       <div className="flex items-center gap-1.5">
-                        {isHaloSelected && playbook.category === "core" && playbook.id !== "lmstudio-rocm-llms" && (
+                        {isHaloSelected && playbook.category === "core" && !(platformFilter === "linux" && (playbook.id === "lmstudio-rocm-llms" || playbook.id === "vscode-qwen3-coder")) && (
                           <PreinstalledBadge />
                         )}
                         {playbook.isNew && (