I tried to build a REACT STABLE DIFFUSION App in 15 minutes
TLDRIn this episode of Code That, the host attempts to build a React application that utilizes the Stable Diffusion API to generate images from text prompts in just 15 minutes. The tutorial covers setting up a FastAPI environment, importing necessary libraries, and creating an API endpoint. The host also demonstrates how to load the Stable Diffusion model, make generations based on prompts, and handle image encoding. After successfully building the API, the focus shifts to creating a React frontend using Chakra UI for a better-looking UI. The frontend includes an input for prompts and a button to trigger image generation. The process involves making API calls with Axios, managing state with React's useState, and displaying the generated images. The host also adds a loading indicator using Chakra UI's skeleton components to improve user experience. Despite the time constraint, the host provides a comprehensive guide on building a full-stack application for image generation using Stable Diffusion, with all the code available on GitHub.
Takeaways
- 🚀 The video demonstrates building a React application that integrates with a Stable Diffusion API for image generation.
- ⏱️ The challenge was to build the application within a 15-minute time limit, with penalties for using pre-existing code or going over time.
- 💻 Key technologies used include JavaScript, React, Fast API, and machine learning models for image generation.
- 🛠️ The presenter sets up a Python environment with necessary dependencies like Fast API, torch, and diffusers for the Stable Diffusion model.
- 🔍 An API is created with Fast API to handle requests and responses, including setting up middleware for CORS.
- 🖼️ The Stable Diffusion model is loaded using a pre-trained model ID and revision, allowing for GPU acceleration with reduced memory usage.
- 📈 The image generation process involves passing a prompt through the model with a guidance scale to generate images based on the input text.
- 🤖 The React application is built from scratch using Chakra UI for a better-looking interface and Axios for making API calls.
- 🔗 The app allows users to input prompts and generates images that are displayed in the browser, with a button to trigger the image generation.
- 🔄 A loading state is implemented to provide user feedback while the image is being generated, using Chakra UI's skeleton components.
- 📚 The code for the application and API is made available on GitHub for viewers to access and use.
- 🎁 The presenter offers an Amazon gift card as a reward for the first person who can successfully build the application within the time limit.
Q & A
What is the main focus of the video?
-The main focus of the video is to build a React application that interfaces with a Stable Diffusion API to generate images using machine learning models.
What are the two parts of the application being built?
-The two parts of the application being built are a full-blown Stable Diffusion API using Fast API and other libraries, and a full stack React application to render images from Stable Diffusion.
Why is there a need to build a custom API for Stable Diffusion?
-There is a need to build a custom API for Stable Diffusion because there isn't one available within Hugging Face that allows for the use of the Stable Diffusion model.
What are the rules set for the coding challenge?
-The rules include not using any pre-existing code outside of the React application shell, a time constraint of 15 minutes to complete the task with a 1-minute penalty for each pause, and a forfeit of a 50 Amazon gift card if the time limit is not met.
What is the penalty for not completing the task within the time limit?
-The penalty for not completing the task within the time limit is a forfeit of a 50 Amazon gift card to the viewers.
What is the technology stack used for building the API?
-The technology stack for building the API includes Python, Fast API, PyTorch, and the Diffusers library for the Stable Diffusion model.
How is the React application expected to communicate with the API?
-The React application is expected to communicate with the API using JavaScript's fetch API or a library like Axios to make HTTP requests.
What is the role of Chakra UI in the React application?
-Chakra UI is used in the React application to provide a set of customizable, accessible, and responsive components that make the UI look better.
How is the image generated by the API displayed in the React application?
-The image generated by the API is encoded in base64, sent back as a response, and then displayed in the React application using an `img` tag with the base64 string as the source.
What is the final step in the React application for image generation?
-The final step in the React application for image generation is to trigger an API call with the given prompt when the 'Generate' button is clicked, and then display the returned image.
What additional feature was added to improve the user experience?
-An additional feature added to improve the user experience is a loading skeleton screen, which indicates to the user that an image is being generated.
Outlines
🚀 Introduction to Building a Stable Diffusion API and React Application
The video begins with an introduction to advancements in AI image generation, particularly the stable diffusion model, and the lack of a user-friendly graphical user interface for such applications. The host proposes to build a stable diffusion API using Fast API and other libraries, as well as a full-stack React application to render images. The episode is structured as a challenge with time constraints and a penalty for not completing the task within the allotted time.
🛠️ Setting Up the API and Importing Dependencies
The host outlines the steps to create the API, including setting up a Python virtual environment, importing necessary dependencies like Fast API, torch, and diffusers, and detailing the process to enable CORS. The middleware setup is also discussed, allowing the API to receive requests from the JavaScript application. The focus then shifts to creating a route for generating images from prompts using the stable diffusion model.
🖼️ Generating and Returning Images with the API
The process of generating images using the stable diffusion model is explained. The host details the steps to load the model, make generations with a given prompt, and handle the device (CPU or GPU) for processing. The video demonstrates testing the API with sample prompts and discusses the need to encode the generated image as a base64 string to be sent back as a response.
🔄 Testing and Debugging the API
The host attempts to start the API server and encounters an error related to the command used. After correcting the command and restarting the server, the API is tested using Swagger UI with various prompts to generate images. The video shows the successful generation of images and the API's response, indicating that the setup is working correctly.
📱 Building the React Frontend for Image Generation
The focus shifts to building the React frontend application. The host uses Chakra UI for styling and sets up the basic structure of the app, including a heading, input field for prompts, and a button to trigger image generation. The application state is managed using React's useState hook, and the axios library is introduced for making API calls.
⚙️ Implementing API Calls and State Management in React
The video demonstrates how to make API calls from the React application using the axios library and handle the response to update the application's state with the generated image. The host also shows how to trigger the API call on button click and retrieve the prompt from the input field. The generated image is then displayed in the application.
🎉 Completing the Application and Adding Loading Indicators
The host wraps up the application by adding UI elements such as a link to the GitHub repo and a loading skeleton indicator using Chakra UI components. The loading state is managed to show a skeleton while the API call is in progress and the image is being generated. The video concludes with a demonstration of the completed application and a reminder that the code will be available on GitHub.
Mindmap
Keywords
💡React
💡Stable Diffusion
💡FastAPI
💡Hugging Face
💡API
💡Middleware
💡GPU
💡Base64 Encoding
💡Chakra UI
💡Axios
💡Swagger UI
Highlights
Building a React application to utilize the Stable Diffusion API for image generation.
Using FastAPI to create a custom API for the Stable Diffusion model.
Incorporating machine learning advancements in AI pad image generation.
Challenge of creating the application within a 15-minute time limit.
The use of an auth token for Hugging Face to access the Stable Diffusion model.
Leveraging FastAPI's middleware for cross-origin resource sharing (CORS).
Importing necessary libraries such as torch and diffusers for the API.
Setting up the API endpoint to generate images from prompts.
Loading the Stable Diffusion model with reduced memory usage for GPU compatibility.
Encoding images using base64 for efficient data transfer.
Building a full-stack application with React for the frontend.
Using Chakra UI for a better-looking user interface.
Implementing Axios for making API calls from the React frontend.
Managing application state with React's useState hook.
Creating a function to handle image generation on button click.
Adding a loading state to improve user experience during image generation.
Using ternary operators to conditionally render components based on state.
Styling the application with CSS for a polished look.
Integrating a GitHub link for users to access the project's repository.
Adding a skeleton loading indicator for better UI feedback during image generation.
The final application allows users to input prompts and generate images through the Stable Diffusion API.