Build a free Stable Diffusion app with a GPU backend

In this tutorial, we created a simple web app for the text-to-image model Stable Diffusion. We used Google Colab as a cloud-based platform for running Python code. We started by installing necessary dependencies such as Flask and diffusers library using pip install command in a new Python 3 notebook. Next, we created a Jinja2 HTML template with an embedded image tag that will be populated at runtime with the base64 encoded string of our generated image. We then implemented the Flask application which listens for incoming HTTP requests on two different endpoints: / and /submit-caption. The / endpoint returns the initial web page that is shown when the app is accessed, while the /submit-caption endpoint handles every time a caption is submitted. For this, we used torch to load the pretrained Stable Diffusion model (which has been trained on large amounts of image and text data) into GPU memory so as to enable efficient processing for incoming requests. Finally, we ran the Flask application and obtained both a localhost URL at which it can be locally accessed (on the server) and an ngrok URL at which the app can be publicly accessed. By going to use the web app section of this article.