Stable Diffusion is a powerful text-to-image model that uses deep learning to generate high-quality images based on textual prompts. It belongs to a class of models called latent diffusion models, which work by compressing images into a lower-dimensional latent space and learning how to denoise this space to create realistic outputs.

To generate images using Stable Diffusion, you typically need a prompt (e.g., “a futuristic cityscape at sunset”) and access to the model. This can be done locally with a GPU or through online platforms like Hugging Face, Replicate, or DreamStudio. After entering your prompt, the model processes it using a neural network trained on billions of images and generates an image that visually matches the text.

The process involves tokenizing the input text, mapping it into a latent space, applying the diffusion process, and decoding the result into an image. Users can adjust parameters such as guidance scale, steps, or seed for more control.

Stable Diffusion is widely used in art, game design, and rapid prototyping. If you want to understand how this model works in depth and learn how to build or fine-tune such models, consider taking a Generative AI online course.