AI Image Generators Explained: How They Work and How to Choose One

Type a sentence, get a picture. AI image generators have turned that sentence into the most accessible creative tool ever built, and also one of the most misunderstood: they are neither search engines for pictures nor digital artists with intentions. They are statistical systems trained on enormous numbers of images and captions, and everything about using them well — choosing one, prompting it, trusting its output — follows from understanding that. This pillar guide explains how image generators work, walks through the main kinds you will meet, and gives you a grounded way to choose between them. Its five companion guides go deeper on each practical skill: creating your first images step by step, what free tiers actually include, why AI images look wrong, writing prompts that work, and how the same ideas extend to video.

How an image generator actually works

The cleanest public explanation of the core process comes from OpenAI’s page for its video model, which shares the approach: Sora “is a diffusion model, which generates a video by starting off with one that looks like static noise and gradually transforms it by removing the noise over many steps”. Image generators do the same for a single frame. During training, the model studied vast numbers of images alongside text captions, learning the statistical relationship between words and visual patterns: what “golden hour” does to light, what “watercolour” does to edges, what a retriever’s face is shaped like. At generation time it begins with random noise and repeatedly asks which small change would make the emerging picture match your description more closely, until a coherent image remains. Two consequences matter for every user. First, the output is genuinely new — synthesised from patterns, not copied from a stored picture — which is why the same prompt never returns the exact same image twice. Second, the model knows looks, not rules. It has no anatomy textbook, no dictionary and no physics engine; it has correlations. Hold those two facts together and every strength and failure in this guide becomes predictable.

The building blocks: prompts, styles and edits

Your control surface has three layers. The prompt is the first: the text you use to tell the tool what kind of image you want, in Midjourney’s definition from its official prompt guide, which adds that short and simple prompts typically generate its best images and that specific synonyms change results. Google DeepMind’s prompt guide for its image models decomposes a prompt into subject and style — who or what is pictured, in what medium and mood — and shows how the same scene can be reinterpreted across photographic, illustration and painting styles. The second layer is settings: aspect ratio, size and similar parameters; OpenAI’s help page notes ChatGPT can generate in any aspect ratio, chosen via a picker or written into the prompt. The third layer is editing: modern tools do not stop at generation. OpenAI describes asking ChatGPT to edit an existing image, add text or details, or make a background transparent, with details kept consistent between edits; Midjourney’s documentation likewise covers modifying creations, remixing prompts and using images themselves as prompts. Creation, in current tools, is a loop — describe, generate, judge, edit — and our step-by-step guide walks that loop for a first-timer.

The main generators, characterised by their own pages

ChatGPT Images (OpenAI) is the conversational route: image creation lives inside the chat product, is available on all tiers per OpenAI’s help centre, and emphasises editing, instruction-following — including adding text — and a saved library of everything you create. It is the natural starting point for anyone already chatting with AI. Adobe Firefly is the rights-first route: Adobe states its Firefly models are trained on licensed Adobe Stock and public-domain content and designed to be commercially safe, with a free plan that includes a set number of monthly credits for features such as Text to Image and Generative Fill, and deep integration with Adobe’s creative applications. Microsoft Designer / Image Creator is the free personal route: Microsoft’s FAQ says Designer is free to use with a Microsoft account, that a subscription may be required to create more frequently, and that it is licensed for personal, non-commercial use; its image generation is described as powered by DALL·E technology. Midjourney is the subscription-first, community-centred route whose own documentation walks users through choosing a paid plan, and whose prompt culture prizes short, specific phrasing and aesthetic exploration. Google’s Imagen line is the platform route: Imagen is Google DeepMind’s text-to-image family — described in its SynthID announcement as using input text to create photorealistic images — appearing across Google products and paired with the Veo video models. Rather than a league table, treat these as five philosophies: conversation, rights, free personal use, subscription craft, and platform integration. The free-tier detail behind the first three is unpacked in our free tiers guide.

Choosing one: a decision framework that survives next month’s launches

Models leapfrog each other constantly, so choose by durable criteria rather than this week’s benchmark. First, rights: what may you do with the output? Firefly’s pages foreground commercial safety; Microsoft’s foreground a personal-use licence; if the image will earn money or represent a business, the licence line outranks every aesthetic preference. Second, workflow: do you want to converse and edit (ChatGPT), design around images (Designer, Firefly), explore aesthetics in a community (Midjourney), or build into a platform (Google)? The best tool is the one whose loop matches how you already work. Third, your real bottleneck: if it is prompting skill, every tool will disappoint equally until your descriptions improve — spend a week with our prompt checklist before spending money. If it is volume or resolution, tiers and credits become the deciding numbers, read on the makers’ own current pages rather than in reviews. Fourth, failure tolerance: photorealistic people, in-image text and exact product representation are the hardest tasks for every current model; if your use case lives there, budget for editing and selection time, or keep a camera in the loop. Finally, start free, because the free routes above are genuinely capable, and let a specific, repeated limit — not curiosity — tell you when a subscription is earned.

Limits, watermarks and honest use

Every generator shares the structural limits that follow from diffusion: plausible looks over true rules. Hands gain or lose fingers, lettering dissolves into texture, counts drift, reflections misbehave — the full catalogue, with workarounds, is in why AI images look wrong. The makers increasingly engineer for accountability alongside quality. Google DeepMind’s SynthID embeds a digital watermark directly into the pixels of images created by Imagen — imperceptible to the eye, detectable by scanning — so synthetic images can be identified as synthetic. OpenAI described red-teaming and provenance metadata plans for its video work in similar terms. The user-side responsibilities are the mirror image: do not present generated images as photographs of real events, respect each tool’s licence, label AI imagery where a viewer could be misled, and remember that “the AI made it” transfers no responsibility away from the person who publishes it. Used with that honesty, these tools are what their best demos suggest: a way for anyone who can describe a picture to begin making one — including the growing number who then animate those pictures with the text-to-video tools covered in the final guide of this series.

What this means

The deepest change here is not technical but distributive. Image-making used to be gated by years of hand skill, then by expensive software and stock libraries; a text prompt has collapsed that gate to the cost of a clear description. That collapse does not abolish expertise — it relocates it, from the hand to the eye and the sentence. The users who thrive with these systems are developing three literacies at once: verbal precision, because prompts reward exact words; visual judgement, because generation is cheap and selection is the real work; and provenance awareness, because synthetic images now circulate in the same feeds as photographs and carry watermarks, licences and responsibilities with them. For the tools themselves, expect the competition described in this guide to keep rotating — today’s differentiator is text rendering or editing consistency, tomorrow’s will be something else — while the durable skills stay constant. Learn the diffusion idea well enough to predict failures, learn to describe subject, style, light and framing, learn to read a licence before a feature list, and you are equipped for whichever generator leads next year. That is the real promise of the technology: not that anyone can make a picture, which is now trivially true, but that anyone willing to learn how pictures work can make good ones.

One more thing this changes, and it is easy to miss: the skill moves upstream. When generating an image costs seconds, the valuable work is no longer producing a picture — it is judging one. Is this the right image for the story? Do the details survive a second look — hands, text, reflections, the things generators still fumble? Does it show something real, or something that merely looks real? Readers can feel that difference even when they cannot name it, and trust is built or lost exactly there. The generators will keep getting faster and cheaper. The eye that decides what is good enough to publish stays stubbornly, reassuringly human.

Related Guides

• How to Create AI Images Step by Step (Even If You Cannot Draw)
• Free AI Image Generators: What the Free Tiers Actually Let You Do
• Why AI Images Look Wrong: Hands, Text and Other Common Problems Explained
• How to Write AI Image Prompts That Actually Work: A Checklist
• AI Video Generators Explained: How Text-to-Video AI Works

Bottom line

These systems turn clear description into pictures and collapse the old gate of hand skill. What decides quality now is verbal precision, visual judgement and knowing where an image came from — skills anyone can build.

Read Next

• How to Create AI Images Step by Step (Even If You Cannot Draw)
• AI Video Generators Explained: How Text-to-Video AI Works

Sources

• OpenAI, “Sora: Creating video from text” — openai.com (accessed 6 Nov 2026)
• OpenAI Help Center, “Images in ChatGPT” — help.openai.com (accessed 6 Nov 2026)
• Google DeepMind, “Identifying AI-generated images with SynthID” — deepmind.google (accessed 6 Nov 2026)
• Adobe, “AI Drawing Generator” (Firefly) — adobe.com (accessed 6 Nov 2026)