---
title: Stable Diffusion Playground
description: Launch an interactive web app for Stable Diffusion
icon: "circle-2"
version: EN
---

This example deploys a simple web app for Stable Diffusion. You will learn how you can set up an interactive workload for inference -- mounting models from Hugging Face and opening up a port for user inputs. For a more in-depth guide, refer to our [blog post](https://blog.vessl.ai/thin-plate-spline-motion-model-for-image-animation). 

<CardGroup cols={2}>
  <Card icon="sparkles" title="Try it on VESSL Hub"  href="https://vessl.ai/hub/ssd-1b-inference">
    Try out the Quickstart example with a single click on VESSL Hub.
  </Card>

  <Card icon="github" title="See the final code" href="https://github.com/vessl-ai/hub-model/tree/main/SSD-1B">
    See the completed YAML file and final code for this example. 
  </Card>
</CardGroup>

## What you will do

<img
  style={{ borderRadius: '0.5rem' }}
  src="/images/get-started/ssd-title.png"
/>

- Host a GPU-accelerated web app built with [Streamlit](https://streamlit.io/) 
- Mount model checkpoints from [Hugging Face](https://huggingface.co/)
- Open up a port to an interactive workload for inference

## Writing the YAML 

Let's fill in the `stable-diffusion.yml` file.

<Steps titleSize="h3">
  <Step title="Spin up an interactive workload">
    We already learned how you can launch an interactive workload in our [previous](/get-started/gpu-notebook) guide. Let's copy & paste the YAML we wrote for `notebook.yml`.

    ```yaml
    name: Stable Diffusion Playground
    description: An interactive web app for Stable Diffusion
    resources:
      cluster: vessl-gcp-oregon
      preset: gpu-l4-small
    image: quay.io/vessl-ai/torch:2.1.0-cuda12.2-r3
    interactive:
      jupyter:
        idle_timeout: 120m
      max_runtime: 24h
    ```
  </Step>

  <Step title="Configure an interactive run">
    Let's mount a [GitHub repo](https://github.com/vessl-ai/hub-model/tree/main/SSD-1B) and import a model checkpoint from Hugging Face. We already learned how you can mount a codebase from our [Quickstart](/get-started/quickstart) guide.  
    
    VESSL AI comes with a native integration with Hugging Face so you can import models and datasets simply by referencing the link to the Hugging Face repository. Under `import`, let's create a working directory `/model/` and import the [model](https://huggingface.co/VESSL/SSD-1B/tree/main). 

    ```yaml
    name: Stable Diffusion Playground
    description: An interactive web app for Stable Diffusion
    resources:
      cluster: vessl-gcp-oregon
      preset: gpu-l4-small
    image: quay.io/vessl-ai/torch:2.1.0-cuda12.2-r3
		import:
			/code/:
				git:
					url: https://github.com/vessl-ai/hub-model
					ref: main
			/model/: hf://huggingface.co/VESSL/SSD-1B
    interactive:
      jupyter:
        idle_timeout: 120m
      max_runtime: 24h
    ```
  </Step> 

  <Step title="Open up a port for inference">

    The `ports` key expose the workload ports where VESSL AI listens for HTTP requests. This means you will be able to interact with the remote workload -- sending input query and receiving an generated image through port `80` in this case. 

    ```yaml
    name: Stable Diffusion Playground
    description: An interactive web app for Stable Diffusion
    resources:
      cluster: vessl-gcp-oregon
      preset: gpu-l4-small
    image: quay.io/vessl-ai/torch:2.1.0-cuda12.2-r3
		import:
			/code/:
				git:
					url: https://github.com/vessl-ai/hub-model
					ref: main
			/model/: hf://huggingface.co/VESSL/SSD-1B
    interactive:
      jupyter:
        idle_timeout: 120m
      max_runtime: 24h
    ports:
      - name: streamlit
        type: http
        port: 80
    ```
  </Step>

    <Step title="Write the run commands">

    Let's install additional Python dependencies with [`requirements.txt`](https://github.com/vessl-ai/hub-model/blob/main/SSD-1B/requirements.txt) and finally run our app [`ssd_1b_streamlit.py`](https://github.com/vessl-ai/hub-model/blob/main/SSD-1B/ssd_1b_streamlit.py). 
    
    Here, we see how our Streamlit app is using the port we created previously with the `--server.port=80` flag. Through the port, the app receives a user input and generates an image with the Hugging Face model we mounted on `/model/`. 

    ```yaml
    name: Stable Diffusion Playground
    description: An interactive web app for Stable Diffusion
    resources:
      cluster: vessl-gcp-oregon
      preset: gpu-l4-small
    image: quay.io/vessl-ai/torch:2.1.0-cuda12.2-r3
    import:
      /code/:
        git:
          url: https://github.com/vessl-ai/hub-model
          ref: main
      /model/: hf://huggingface.co/VESSL/SSD-1B
    run:
      - command: |-
        pip install -r requirements.txt
        streamlit run ssd_1b_streamlit.py --server.port=80
        workdir: /code/SSD-1B
    interactive:
      max_runtime: 24h
      jupyter:
        idle_timeout: 120m
    ports:
      - name: streamlit
        type: http
        port: 80
    ```
  </Step>
</Steps>

## Running the app

Once again, running the workload will guide you to the workload Summary page. 

```
vessl run create -f stable-diffusion.yml
```

Under ENDPOINTS, click the `streamlit` link to launch the app. 

<img
  style={{ borderRadius: '0.5rem' }}
  src="/images/get-started/ssd-summary.jpeg"
/>

<img
  style={{ borderRadius: '0.5rem' }}
  src="/images/get-started/ssd-streamlit.jpeg"
/>

## Using our web interface

You can repeat the same process on the web. Head over to your [Organization](https://vessl.ai), select a project, and create a New run. 

<iframe 
  src="https://scribehow.com/embed/Stable_Diffusion_Playground__D9ujQM9ZQtGz_Aj9oiyXSg?skipIntro=true&removeLogo=true"
  width="100%" height="640" allowfullscreen frameborder="0"
  style={{ borderRadius: '0.5rem' }} >
</iframe>

## What's next?

See how VESSL AI takes care of the infrastructural challenges of fine-tuning a large language model with a custom dataset. 

<CardGroup cols={2}>
  <Card title="Llama 2 Fine-tuing" href="get-started/llama2">
    Launch an interactive web application for Stable Diffusion
  </Card>
</CardGroup>