AI image generation — explained

A lightweight fine-tuning method that teaches a base image model a new face, style or object using a tiny add-on file.

A slider that controls how literally the model follows your prompt versus how much creative freedom it takes.

The number that determines a model's random starting noise — same seed and same prompt always gives the same image.

The algorithm that turns random noise into a finished image step by step.

How many de-noising iterations the model runs — more steps = more detail but slower and more expensive.

A model that constrains image generation to match a reference's pose, depth, edges or layout.

Generate a new image guided by a reference image instead of starting from pure noise.

Replace or refine only the masked region of an image while keeping the rest untouched.

A saved snapshot of a fully-trained image model — the file that defines the base aesthetic.

An older full fine-tuning method that bakes a new subject directly into a checkpoint.

Words and concepts you want the model to actively avoid in the output.

Syntax that boosts or reduces the importance of specific words in your prompt.

Enlarging an image to higher resolution while restoring or inventing detail.

The component that converts between pixels and the latent space the diffusion model actually works in.

The class of generative AI that creates images by repeatedly de-noising random static.

A fully synthetic social-media persona whose photos and videos are created entirely by generative AI rather than a real human.

AI image generation configured to produce adult or explicit content that mainstream platforms restrict by default.

Running a diffusion model without the content-classification layer that would otherwise block or blur certain outputs.

The emerging market where creators use AI-generated content — instead of (or alongside) filmed footage — to produce sponsored posts, ads, and brand deals.

A head-to-head comparison of the two most important open-weight image generation architectures.

The step-by-step process of teaching a diffusion model a new face, object, or style using a small set of training images.

A complete guide to writing negative prompts that eliminate artefacts, bad anatomy, and unwanted styles from your AI images.

How to choose the right guidance scale value for any model to get sharp, properly-prompted images without burning the output.

Why seeds matter for reproducibility, creative iteration, and sharing exact images with other users.

A step-by-step guide to using image-to-image generation to restyle, refine, or repurpose existing photos and renders.

How to use AI inpainting to fix, replace, or add elements within a specific region of an existing image.

How ControlNet works and which control types — pose, depth, canny, normal map — to use for different creative goals.

Techniques for generating the same person's face reliably across different prompts, poses, and scenes in AI image generation.

Who owns AI-generated images, what licences govern the models that create them, and what you can legally do with the output.

The automated systems that classify, filter, and block AI-generated images based on safety, legality, and platform policy.