Empowering Local Image Generation: Harnessing Stable Diffusion for Machine Learning and AI

Abstract

This paper examines the ability to use Stable Diffusion's diffusion models to get state-of-the-art synthesis results on image data and other types of data. Also, a guiding interface can be used to control the process of making images by converting text to images and image to image. But because these models usually work directly in pixel space, optimizing strong DMs often needs more GPU VRAM to run. Using Stable Diffusion and diffusion models on local hardware like this lets more information and depth be added while generating images, which greatly improves the quality detail of the image. By combining diffusion models to model architecture, I have made diffusion models into powerful and flexible producers for general conditioning inputs, such as when using XL-XDXL 1.0 and LoRA models. Overall, the paper highlights how a normal person can run their own Midjourney like AI image generation with the help of machine learning and generative AI.

Keywords

• stable diffusion
• machine learning
• image generation
• generative ai
• vram
• gpu
• diffusion models
• prompt