Intro to Stable Diffusion
Stable diffusion is a tool used in artificial intelligence to generate images.
It’s an open-source tool that can create images that don’t exist in the real world, based on prompts or instructions given to it.
For example, we could give it a prompt to create an image of an apple, and it would generate an image of an apple that doesn’t exist in the real world.
In this course, we’ll learn how to use stable diffusion to generate images.
We’ll start by learning the basics of Stable Diffusion, and then we’ll learn how to put it into practice. This means we’ll learn how to use it to make our own images.
It’s a step-by-step process, but don’t worry, we’ll go through it together. By the end of the course, you’ll be able to create unique images using Stable Diffusion.
What is Stable Diffusion?
Stable diffusion is a deep learning model used for image generation.
It’s a type of diffusion model, which are generative models designed to create new data similar to the data they were trained on. In this case, stable diffusion creates images.
The model works by reducing random Gaussian noise, meaning it takes random numbers as input and generates images based on these values.
One of the key features of stable diffusion is its ability to generate images from keyword correlations.
The images it creates do not exist in the real world and are entirely generated by the algorithm.
For example, if you input the text “a photograph of an astronaut riding a horse,” the model will generate an image that fits this description.
Key Features
Image to Image
Another key feature from Stable Diffusion is text-guided image-to-image generation. This allows you to use an initial image as input to guide the generation of a new image. For example, you could input a child’s drawing and the model would generate a realistic image based on it.
Inpainting
The model also has an inpainting feature, which allows you to select a specific part of an image to change or remove. For example, you could select a dog in an image and replace it with a cat.
Fina tuning
Fine tuning is another feature of stable diffusion. This is a customized training used to insert a new concept, like a new person or object, into the image.
Upscaling
Finally, the model also has a super resolution feature for upscaling images and making them sharper, and an outpainting or image extension feature, which allows you to extend image regions that have been cropped or do not exist.
Outpainting
Outpainting is a feature that allows you to extend image regions
Types of Image Generation
There are 3 types of image generation in stable diffusion:
- Unconditional image generation
- The generation of images conditioned by text
- The generation of images based on other image
Unconditional image generation
In unconditional image generation, the model generates images without any specific guidance or conditions. It simply creates images that are similar to those it was trained on. For example, if it was trained on images of cats, it will generate new images of cats.
The generation of images conditioned by text
In conditional image generation, the model generates images based on certain conditions or inputs. One of these methods is text-to-image generation. In this case, you provide a text prompt, and the model generates an image that corresponds to the description in the text.
For example, if you provide the text prompt “a red apple on a tree”, the model will generate an image of a red apple on a tree. This is done by converting the text into numerical representations, or embeddings, which the model can understand and use to generate the image.
This method is particularly useful in situations where you want to generate specific images based on certain descriptions or concepts. It’s also a fascinating demonstration of how advanced these models have become, as they can understand and visually interpret textual descriptions.
The generation of images based on other image
image-to-image generation is another form of conditional image generation. In this case, instead of a text prompt, an initial image is provided to the model. The model then generates a new image that is in some way related to or based on the initial image.
For example, you could provide an image of a day scene and instruct the model to generate a similar scene but at night. Or you could provide a sketch or outline of an image, and the model could generate a full-color, detailed version of that sketch.
This method is particularly useful in tasks such as style transfer (where the style of one image is applied to another), colorization of black and white images, turning sketches into realistic images, and many more. It provides a lot of control over the final output and can lead to some very creative results.
How Diffusion Models Works
