To quote Wikipedia (here): "Stable Diffusion is a deep learning, text-to-image model released in 2022. It is primarily used to generate detailed images conditioned on text descriptions, though it can also be applied to other tasks such as inpainting, outpainting, and generating image-to-image translations guided by a text prompt.". This of course sounds nice, but what makes it special and how can you use it?
When using AI models like Stable Diffusion, sometimes input images need to be of a specific size. In case of Stable Diffusion, multiples of 64 are required. Stable Diffusion (at least 1.5) works best with images of 512 pixels in width or height. If you for example take an image of 599 x 205 pixels and you resize it to 1496 x 512 (maintaining the aspect ratio), you end up with 1496, which is not a multiple of 64. In order to obtain a usable image, it needs to be padded to a size of 1536 x 512 to allow processing. In order to batch resize and pad images I created a Python script using OpenCV. Why create a script? Doing this for a large amount of images quickly becomes a chore and writing a script for this is fun. Why OpenCV? It is powerful, easy to use and popular.
