Deep Learning Application with Stable Diffusion

Overview

• Creating a deep learning application to generate images from text.
• Using Stable Diffusion for image generation and super sampling for enlargement.
• Application will run inside a Docker container, utilizing the GPU for high performance.

Setting Up Docker

1. Installing Docker Desktop

   • Go to the Docker website and download Docker Desktop.
   • Set up the engine and enable WSL if using a WSL terminal.

2. Cloning Starter Files

   • Use Git to clone starter files from GitHub:

     git clone <repository-url>  
     

   • Navigate to the new directory:

     cd stable_diffusion_gooey_app/starter_files  
     

3. Initializing Docker

   • Use docker init to automate setup.
   • Specify Python version and port settings (port 8000).

Creating the Application

1. Running the Docker Container

   • Start the container with:

     docker-compose up --build  
     

   • Access the application at localhost:8000.

2. Image Generation Logic

   • Enter text prompt (e.g., "Canadian bear eating fish in the river").
   • Initially, application only collects input and prints it.

3. Integrating Stable Diffusion

   • Download Stable Diffusion model (requires 40GB of space) using Git LFS.
   • Add necessary libraries to requirements file:
     • diffusers
     • torch (PyTorch)
     • accelerate

Coding the Image Generation

1. Importing and Setting up Pipeline

   • Import Stable Diffusion pipeline from diffusers.
   • Set up the pipeline and customize quality parameters.

2. Generating and Saving Images

   • Pass user input to generate images and save them in the static folder.
   • To allow local file changes without rebuilding, set the debug mode and configure volumes in docker-compose.yml.

Using GPU for Efficient Processing

1. Enabling GPU Support

   • Ensure compatible Nvidia GPU and install necessary drivers/toolkit.
   • Update the Docker compose file to include GPU settings.

2. Testing GPU Availability

   • Check if CUDA is available using PyTorch.
   • Update stable diffusion pipeline to delegate tasks to the GPU.

Enabling Super Sampling

1. Loading the EDSR Model

   • Use EDSR (Enhanced Deep Super Resolution) model for image upscaling.
   • Download model and install OpenCV dependencies.

2. Processing Output Images

   • Load demo images for super sampling and convert them from NPI array to image format.
   • Save enlarged images with unique names using date and time as identifiers.

Licensing and Sharing the Application

1. Handles Licenses for Models Used

   • Obtain licenses for EDSR, Stable Diffusion, and any other models used.

2. Pushing to Docker Hub

   • Create a new repository on Docker Hub and push the updated application image.
   • Clean up unnecessary files and perform a final build with Docker.

3. Testing Deployment

   • Test the app in a new directory, generating high-definition images successfully.

Conclusion

• Full cycle of creating a deep learning application from installation to deployment, with considerations for performance and licensing.
• Encouragement to provide feedback and suggestions for future content.