Introduction to Stable Diffusion

Stability AI, a startup with operations in London and California, has made waves by introducing Stable Diffusion, a cutting-edge AI model designed to generate images that mimic human artistry. This model is distributed under the Creative ML OpenRAIL-M license, allowing for both personal and commercial use.

The Basics of Stable Diffusion

This text-to-image latent diffusion model has been developed by a team of researchers and engineers. You can explore more about it on the official Hugging Face platform.

In this guide, we will demonstrate how to run this groundbreaking technology on Google Colaboratory directly from your PC. As it operates in the cloud, it is compatible with any operating system, including Windows, Mac, and Linux.

Getting Started with Google Colab

To begin, you need to set up a project in Google Colab. You can either run the program directly from the original Colab page or copy it to your Google Drive for personal use.

Creating a Hugging Face Account

Next, it's essential to create a Hugging Face account to obtain an access token. Sign up or log in, navigate to “Settings”, then “Access Token” to generate your token. It's advisable to keep this token handy for future use.

Accessing the Model Card

To proceed, access the model card, verify your license, and then select “Access Repository.” You will need to log in to your Hugging Face account or create one if you don't have it yet.

Run the Program

After successfully logging in, you will be prompted to enter the token you generated earlier. Once you input the token, click Login, and if everything goes smoothly, the model will start generating images, such as astronauts riding horses.

Generating Custom Images

To create images of your choice, you can modify the prompt in the code. For example, you can change “a photograph of an astronaut riding a horse” to any concept you wish to visualize.

num_images = 3

prompt = ["a photograph of an astronaut riding a horse"] * num_images

with autocast("cuda"):

images = pipe(prompt)["sample"]

grid = image_grid(images, rows=1, cols=3)

grid

Here are some of the results I achieved with different prompts:

“a photograph of a woman with a glass of wine”

“Cars on the highway”

Important Note on Content Restrictions

While experimenting with prompts, be mindful that using inappropriate language may lead to restrictions. For instance, attempts to generate explicit content will result in a black image, indicating that potential NSFW content was detected.

The Future of AI in Art

This revolutionary technology poses significant implications for the art world and data collection, extending beyond just entertainment. It's crucial to harness this innovation responsibly for the benefit of society.

Chapter 2: Video Resources for Further Learning

To enhance your understanding of Stable Diffusion and its implementation, here are some valuable video resources:

The first video, titled "2024! Stable Diffusion in Colab Notebook for FREE with no disconnects. FREE Midjourney alternative," provides a comprehensive overview of how to use Stable Diffusion effectively in a Colab environment.

The second video, titled "Running Stable Diffusion on Google Colab," delves into the practical steps of setting up and running Stable Diffusion, making it a must-watch for anyone interested in this technology.