How does AI image generation work
AI image generation relies on models that have been trained to recognise visual patterns across huge sets of real photographs. Instead of copying those images, the model absorbs the structure, textures and general logic of how things usually look, and later applies this knowledge when it creates an image from a text prompt. In our case, everything runs on Stable Diffusion XL, a latent diffusion model built to handle detailed, high-resolution output.
The generation process doesn’t start with shapes or colours. It begins with noise — a completely random field of pixels — and the model gradually reshapes it into something meaningful. SDXL reads the prompt, breaks it down into objects, style cues and relationships, and then refines the frame step by step. At each stage, more noise is removed and more structure appears. Because the model has learned from a broad range of imagery, it can recreate lighting, depth and texture in a way that feels consistent with real photographs or chosen artistic styles.
