Hi Beautiful People. Today I'm going to talk about a technique called Stable Diffusion, which is a technique that seems to occur time and time again in the field of Generative AI. Firstly Generative AI stands for Generative Artificial Intelligence and is a collection of AI techniques that allow you produce new things. This could be works of art, real-life pictures or the creation of new types of music. In particularly the latest tool that is doing the rounds is OpenAI's DALL-E, which uses stable diffusion and ChatGPT, which uses a transformer technique.. If you haven't heard of either of those, you must of been living under a rock, so feel free to learn more by experimentation in my previous blog post:
What is Stable Diffiusion?
Stable diffusion is a mathematical process that describes how a something spreads out over time in a stable manner. It is a type of diffusion process in which the distribution of the quantity remains unchanged over time. In other words, the spread of the quantity is consistent and does not vary significantly over time.
Stable diffusion processes are often used to model physical phenomena, such as the movement of particles in a fluid or the spread of a chemical through a substance. They can also be used to model the spread of information or ideas within a population.
In Generative AI, stable diffusion is a deep learning, text-to-image model. Deep Learning is a technique that helps computers learn in a similar manner to the brain. It does this using a neural network and learns over time, very similar to how a human learns by experimentation over time. Text-to-image models are used in AI to help generate new images from a simple sentence, just like DALL-E above.
Here is a great video from OpenAI, that explains very simply how DALL-E works:
How is it used in Generative AI?
Stable diffusion processes can be used in generative AI in a variety of ways. One example is the use of a generative model that is trained to generate new, synthetic data that is similar to a training dataset. Generative models can be used to synthesize a wide variety of data, including images, text, and audio.
One way that stable diffusion is used in a generative model is as a way to model the distribution of data in the training dataset. For example, if the training dataset consists of images of faces, a stable diffusion process could be used to model the distribution of features in the images, such as the shapes of the faces and the colors of the eyes. This could help the generative model to generate new images of faces that are realistic and consistent with the training data.
Another way that stable diffusion can be used in generative AI is as a way to explore the space of possible data points that could be generated by a model. For example, a stable diffusion process could be used to generate a sequence of data points that are drawn from the distribution modeled by a generative model. This could be used to explore the range of outputs that the model is capable of producing, or to identify areas of the data space that are poorly represented in the training data. This could help generate music that is not random and is ordered according to a harmonic major or minor system. This would produce music that is pleasing to the ear and could be used in film, tv or pop music.
Here is another great video that talks about Stable Diffusion. It starts off simple but then gets deeper and deeper. Simply click the stop button when you've had enough:
I hope you enjoyed this post. Stay safe, and I'll catch you all soon.
Paul Colmer is an AWS Senior Technical Trainer. Paul has an infectious passion for inspring others to learn and to applying disruptive thinking in an engaging and positive way.