Privately Host Your Own AI Image Generator With Stable Diffusion - Easy Tutorial!

In this introductory section, the host recaps a previous video about setting up a private large language model. The focus shifts to the current video, where the host explains how to do a similar process for image generation using the open-source Stable Diffusion model. Though the quality is not as high as popular services like DALL-E or MidJourney, Stable Diffusion offers privacy and open-source flexibility. The host mentions two methods for running Stable Diffusion: locally on a Windows machine or within Docker, and briefly touches on GPU vs. CPU performance.

The host walks through the steps of installing Stable Diffusion on a Windows machine using a tool called Easy Diffusion. The process is straightforward: download the installer, follow the prompts, and configure it to run. Once installed, the tool automatically spins up a web interface for generating images. The host demonstrates the tool in action, explaining that the setup works best with an Nvidia GPU but also provides options for CPU-only setups. They briefly mention potential support for AMD and Intel GPUs, though with more setup complexity.

Here, the host demonstrates Stable Diffusion by generating an image, showcasing how simple it is to create images using a GPU. They mention the ability to tweak settings, train models, and download other models for more specific use cases. The generated image, which features Warhammer characters, is compared to results from Microsoft’s DALL-E model. The differences in quality highlight how larger models like DALL-E have access to more data, while Stable Diffusion offers privacy and flexibility with local deployment. The host emphasizes that with time and training, Stable Diffusion's results could improve.

In this section, the host introduces using Docker to run Stable Diffusion, crediting a helpful contributor for creating an easy-to-use container. The Docker setup allows for a similar user experience as the local installation, but adds flexibility with options for different user interfaces such as Automatic1111 or Comfy UI. The host provides step-by-step instructions for installing and running Docker containers for Stable Diffusion, mentioning the need for two commands: one to download dependencies and the other to specify the preferred user interface. While Nvidia GPUs are recommended, the host notes that the setup works with CPUs as well.

The host explains how to further customize the Docker setup by selecting the appropriate UI for CPU or GPU use. Nvidia GPUs are preferred for better performance, but the guide also covers Intel and AMD GPU configurations, though with more complex steps. The host uses a Proxmox virtual machine for the demo, allocating resources like CPU cores and RAM. They emphasize the need to install Docker and then clone the relevant GitHub repository. The process of running commands and setting permissions for the Docker container is detailed, ensuring the system is ready for Stable Diffusion to generate images.

This section covers the resource demands of running Stable Diffusion in Docker, with the host providing real-time CPU and RAM usage metrics. The image generation process takes a few minutes, depending on the available hardware. Once the image is generated, the host compares it to previous results from both the local and Bing setups. While the Docker version is slower, the host appreciates the privacy and local control it offers. They conclude by highlighting the potential for training models to improve over time, along with the ability to integrate other models.

The video wraps up with the host encouraging viewers to explore different models and tweak their Stable Diffusion setup based on their needs. They suggest using GPUs for faster performance, experimenting with additional models, and taking advantage of Docker for easy deployment. The host notes the simplicity of self-hosting these tools and concludes by thanking viewers, asking them to like and subscribe for more content.