I tried to build a ML Text to Image App with Stable Diffusion in 15 Minutes

Nicholas Renotte
20 Sept 202218:43

TLDRIn this episode of 'Code That', the host attempts to build a text-to-image generation app using Stable Diffusion and Python's Tkinter library within a 15-minute timeframe. The app allows users to input a prompt and generates an image through machine learning. The host outlines the rules, including a time limit and a penalty for looking at pre-existing code. They proceed to create the app's interface, including a prompt entry field, an image placeholder, and a 'Generate' button. The process involves importing necessary libraries, setting up the Stable Diffusion pipeline with a model ID from Hugging Face, and configuring the app to run on a GPU. Despite encountering memory issues, they successfully generate images from text prompts, showcasing the capabilities of Stable Diffusion as a free alternative to other models. The host also mentions the possibility of saving the generated images for further use and provides resources for finding more prompts to test the app's capabilities.


  • ๐Ÿš€ The video demonstrates building a text-to-image generation app using Stable Diffusion in a short time frame.
  • โฐ The challenge is to build the app within a 15-minute time limit, with penalties for looking at pre-existing code or exceeding time.
  • ๐Ÿ“ The app uses a text prompt to generate images through machine learning, specifically the Stable Diffusion model.
  • ๐Ÿ’ป The development environment includes Python with libraries such as Tkinter, Torch, and the Diffusers library for Stable Diffusion.
  • ๐Ÿ”‘ An authentication token from Hugging Face is required to access the Stable Diffusion model.
  • ๐Ÿ–ผ๏ธ The app creates a user interface with an entry field for prompts, a button to trigger image generation, and a frame to display the generated image.
  • ๐Ÿ› ๏ธ The Stable Diffusion model is loaded into the GPU for efficient processing, with considerations for memory and data types.
  • ๐Ÿ” The 'guidance scale' parameter influences how closely the generated image adheres to the input prompt.
  • ๐Ÿ“‰ The video shows troubleshooting memory issues and ensuring the correct data types are used for the model's inputs.
  • ๐ŸŽจ The generated images can be saved and used elsewhere, showcasing the capabilities of the Stable Diffusion model.
  • ๐ŸŒ The video encourages viewers to experiment with the model themselves and provides resources like 'prompt hero' for additional inspiration.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is building a text-to-image generation app using Stable Diffusion and the Python library, Pkinter, within a 15-minute time frame.

  • What is the Stable Diffusion model mentioned in the video?

    -Stable Diffusion is a deep learning model used for text-to-image generation, which is one of the most expensive and interesting models of its time.

  • What is the programming challenge presented in the video?

    -The challenge is to create a text-to-image app without looking at any pre-existing code or documentation. If the presenter fails to complete the task within 15 minutes, there is a one-minute time penalty.

  • What is the penalty for failing to meet the time limit?

    -If the presenter fails to build the app within the 15-minute time limit, there will be a 50 Amazon gift card given away to the viewers.

  • What is the purpose of the entry field in the app?

    -The entry field allows users to type in a prompt, which the app will use to generate an image through machine learning or AI.

  • What is the role of the 'generate' button in the app?

    -The 'generate' button is used to trigger the image generation process using the input prompt from the user.

  • What is the significance of the 'guidance scale' in Stable Diffusion?

    -The guidance scale determines how closely the Stable Diffusion model follows the user's input prompt when generating the image. A higher value makes the model adhere more strictly to the prompt, while a lower value allows for more flexibility.

  • What is the model ID used for in the video?

    -The model ID is used to specify the pre-trained Stable Diffusion model that the app will use for generating images.

  • How does the presenter handle the GPU memory issue?

    -The presenter attempts to resolve the GPU memory issue by revising the code to use torch.float16 instead of torch.float32, which is a lower precision but requires less memory.

  • What is the final outcome of the video?

    -The presenter successfully builds the text-to-image app within the time limit and demonstrates its functionality by generating images based on various prompts.

  • How can viewers get their hands on the code used in the video?

    -The presenter will provide a link to all the code in the comments section below the video.

  • What is the presenter's final thought on Stable Diffusion?

    -The presenter considers Stable Diffusion to be an amazing and powerful tool, offering state-of-the-art deep learning capabilities and being a free alternative to other models like Dali 2.



๐Ÿš€ Introduction to Text-to-Image Generation with Stable Diffusion

The video begins with an introduction to a text-to-image generation app using the stable diffusion model. The host outlines the challenge of building the app within a 15-minute time limit, with a penalty of a 50 Amazon gift card if the time limit is exceeded. The process starts with setting up the app environment by importing necessary libraries and modules, such as tkinter for GUI, PIL for image rendering, and the stable diffusion pipeline from the 'diffusers' package. The host also emphasizes the need to use an auth token from Hugging Face for accessing the model.


๐Ÿ› ๏ธ Building the Application Interface

The host proceeds to build the user interface for the application using tkinter. A text entry field is created for the user to input a prompt, which will be used to generate an image. The entry field is styled with a height of 40, a width of 512, and a specific font and color scheme. A placeholder frame is also set up for the generated image, which is intended to be 512x512 pixels, matching the output size of the stable diffusion model. Additionally, a 'Generate' button is created to trigger the image generation process, and its position is calculated to center it within the application window.


๐Ÿง™โ€โ™‚๏ธ Implementing the Stable Diffusion Model

The video continues with the implementation of the stable diffusion model. The host specifies a model ID for the stable diffusion model and creates a pipeline to load the model. The model is then sent to the GPU for processing. The host outlines the steps to generate an image using the model, which includes setting up autocast for the device, obtaining the user's prompt, and specifying a guidance scale to determine how closely the generated image should follow the prompt. The generated image is then converted to a format suitable for display in the application.


๐ŸŽจ Testing the Application and Generating Images

The host tests the application by running it and attempting to generate an image using a sample prompt. Initially, there are some technical difficulties with memory and data type issues, but these are resolved by correcting the data type to 'torch.float16'. Once the application is running smoothly, the host demonstrates the ability to generate images with various prompts, such as 'space trip landing on Mars' and 'Rick and Morty planning a space heist'. The host also mentions the ability to save the generated images for further use. The video concludes with a reminder that the stable diffusion model is open-source and encourages viewers to experiment with it. The host provides a link to the code in the video description and thanks the viewers for their support.



๐Ÿ’กStable Diffusion

Stable Diffusion is a deep learning model that is used for text-to-image generation. It is one of the most advanced models in the field of AI, capable of creating images from textual descriptions. In the video, the host uses Stable Diffusion to generate images based on prompts entered by the user, showcasing the model's ability to interpret and visualize text.

๐Ÿ’กText-to-Image Generation

Text-to-image generation is a process where a machine learning model converts textual descriptions into visual images. It is a form of AI that requires understanding and creativity. In the context of the video, the host is building an app that uses Stable Diffusion to perform text-to-image generation, allowing users to input prompts and receive generated images.

๐Ÿ’กMachine Learning

Machine learning is a subset of artificial intelligence that involves the use of algorithms to parse data, learn from that data, and make informed decisions based on what they've learned. In the video, machine learning is central to the functionality of the Stable Diffusion model, which learns from vast amounts of data to generate images from text.


AI, or artificial intelligence, refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the video, AI is used to describe the broader field of which machine learning and the Stable Diffusion model are a part, emphasizing the intelligent behavior of the model in generating images.


Tkinter is a Python library used for creating graphical user interfaces (GUIs). In the video, the host uses Tkinter to build the user interface for the text-to-image app, allowing users to input prompts and interact with the generated images.

๐Ÿ’กAuth Token

An auth token, or authentication token, is a unique combination of letters, numbers, and sometimes symbols that is used to identify and authenticate users in a system. In the context of the video, the host imports an auth token from Hugging Face to access the Stable Diffusion model's API.

๐Ÿ’กHugging Face

Hugging Face is a company that provides tools and libraries for natural language processing (NLP) and machine learning. In the video, the host obtains an auth token from Hugging Face to use the Stable Diffusion model, indicating that the model is hosted or provided through their platform.

๐Ÿ’กImage Rendering

Image rendering refers to the process of generating an image from a data source, which in this case is a textual prompt. The video demonstrates image rendering through the Stable Diffusion model, where the textual input from the user is converted into a visual image.


In the context of the video, a prompt is a textual description or a phrase that the user inputs into the app to guide the Stable Diffusion model in generating an image. The host discusses how the user can type in a prompt to create a corresponding image.

๐Ÿ’กGuidance Scale

The guidance scale is a parameter in the Stable Diffusion model that determines how closely the generated image should adhere to the textual prompt provided by the user. A higher guidance scale means the model will follow the prompt more strictly, while a lower scale allows for more creative freedom in the image generation.

๐Ÿ’กDeep Learning Model

A deep learning model is a type of artificial intelligence algorithm inspired by the structure and function of the brain in animals. It is designed to learn complex patterns from large amounts of data. In the video, the Stable Diffusion model is an example of a deep learning model used for generating images from text.


Building a text-to-image generation app using Stable Diffusion and Python's tkinter in just 15 minutes.

Importing necessary dependencies like tkinter, torch, and diffusers.

Setting up the app geometry and appearance mode for a better user interface.

Creating an entry field for users to input their text prompt.

Designing a button to trigger the image generation process.

Configuring the Stable Diffusion model with a specific model ID and using an auth token from Hugging Face.

Loading the model into GPU memory for efficient processing.

Writing a function to handle the image generation using the Stable Diffusion pipeline.

Specifying the guidance scale to control how closely the generated image follows the input prompt.

Generating the image using the input prompt and displaying it within the app.

Saving the generated image as a PNG file for further use.

Successfully generating an image of a spaceship landing on Mars using the app.

Demonstrating the generation of various other images like Rick and Morty planning a space heist.

Mentioning the use of the open-source Stable Diffusion model as an alternative to DALL-E 2.

Discussing the ability to find and use prompts from websites like Prompt Hero.

Sharing the final working app and providing a link to the code in the comments.

Encouraging viewers to try out the app and explore the capabilities of Stable Diffusion.

Highlighting the importance of community support and thanking viewers for their engagement.