Answer in brief
An AI image generator is a text-to-image model: most start from random noise and clean it step by step, steered by your words. Choose one by what you can control (references, editing, fixed settings), how it handles text and size, and what its terms say about ownership and uploaded data. A controlled result comes from a written brief, references and one change per round.
How an AI image generator turns random noise into a picture
Most people who look for an ai image generator want a picture made from a sentence, without booking a photographer. Wikipedia describes the underlying tool, a text-to-image model, as a machine learning model that takes a prompt in natural language and produces an image matching that description. Most current ones are diffusion models. Wikipedia’s article on them explains the trick: the network is trained to reverse the process of adding noise to an image, so it can start from random noise and clean it up step by step.
The words steer that cleaning: the same article says these generators combine diffusion with text encoders and cross-attention modules. In plain terms, the model does not look a picture up in a catalogue; it develops a new one in the direction your text points. One request rarely gives the same picture twice, because each run starts from fresh noise. And the model knows nothing about your shop or your colours unless you show or describe them.
Inside one open model: Kandinsky 3 and its three parts
Open models publish their anatomy. Sber’s announcement on its company blog says Kandinsky 3.0 was developed by the Sber AI team. The model’s GitHub page calls it an open-source text-to-image diffusion model made of three parts: a Flan-UL2 text encoder with 8.6 billion parameters, a latent diffusion U-Net with 3 billion and a MoVQ encoder and decoder with 267 million. The same layout is a handy map for reading any other generator.
Each part answers a buyer’s question. The authors write that they improved text understanding and visual quality by enlarging the text encoder and the U-Net respectively, so the first reads your words and the second draws. Training data decides what a model draws with confidence: version 3.0 took in more data related to Russian culture so that it can generate pictures on those themes. The page also names the licence, Apache 2.0, the line to look for before any paid use.
What to compare between image generators before you pay
Start with control, because that separates a tool from a slot machine. Google’s guide to its Nano Banana image models says they can mix up to 14 reference images, edit a supplied picture by text and change its style or colour grading; it recommends a multi-turn conversation for iterating. OpenAI’s image generation guide offers edits, images built from references and masks for local changes. It is candid that the model may not follow the exact shape of a mask with complete precision.
Then check the output. Google lists built-in 1K, 2K and 4K generation; OpenAI accepts custom sizes with edges in multiples of 16 pixels, up to 3,840; Stability AI’s documentation gives 1.5 megapixels for its Stable Image Core service. OpenAI writes that its models can still struggle with precise text placement and clarity, while Google advises generating the text first and then the image that contains it. Google’s list of best-performing prompt languages includes Russian, Mexican Spanish and French.
Last, read the terms. The Gemini API Additional Terms say Google won’t claim ownership of generated content and may generate the same or similar content for others. On the unpaid tier, submitted content helps improve Google’s products, human reviewers may read it, and users are asked not to submit sensitive, confidential or personal information; the paid tier does not use prompts that way. Adobe states that Firefly models are trained on licensed content and public domain content whose copyright has expired.
A one-page brief written before the first image prompt
A controlled result starts before the tool is open. Write down where the image will live, its format, what it must show, what it must never resemble and three or four criteria a stranger could check. Google’s guide advises giving context and intent: asking for a logo for a high-end, minimalist skincare brand works better than asking for a logo. Add references, because one photo of the light you want says more than a paragraph. Then phrase constraints as what should be there, which Google calls a semantic negative prompt: an empty, deserted street instead of no cars.
Fix whatever the tool lets you fix. Stability AI’s documentation offers a separate negative prompt field and describes the seed as a value that guides the randomness of a generation; leave it out and a random one is used. The service returns the seed with each image, so a good result can be reproduced, and its aspect ratios run from 21:9 to 9:21. Written into the brief, these settings stop each round from changing three things at once.
One change per round: iterating on a generated image
The lottery feeling comes from changing everything every time. Google’s guide says not to expect a perfect image on the first try and suggests follow-ups that keep everything the same except, say, a character’s expression. For complex scenes it builds the request in steps: the background, then the object in front, then the detail on top. Make that a routine and change one variable per round: the light, then the framing, then the material.
Keep a log of the prompt, references, seed or conversation, model and date. Models change between versions, and an old result may not come back. Select against the brief rather than by first impression. Then finish the chosen picture by hand: crop, retouch, set the type in a layout program.
Where image generators stop: layouts, recurring characters, brand rules
Vendors document their weak spots. OpenAI lists four for its image models: complex prompts may take up to two minutes, precise text remains hard, recurring characters or brand elements may drift across generations, and exact placement in layout-sensitive compositions is difficult. Google sets per-model limits on how many characters keep their resemblance in one workflow. Together these lists show what a generator does not replace: a menu or a marketplace card is a layout first, and a brand is a set of rules a model has never seen unless you supply them.
Some limits are not technical. OpenAI writes that all prompts and generated images are filtered under its content policy. Stability AI returns a blurred image with a special finish reason when an output violated its moderation policy. A likeness of a real person, another company’s logo or a famous character is a question for a lawyer before it is a question for a prompt.
Who owns a generated picture and how to disclose it honestly
Terms of service and copyright law answer different questions. In its January 2025 report, the U.S. Copyright Office concluded that copyright does not extend to purely AI-generated material and that prompts alone do not give enough control to make a user the author; selecting an output is not authorship either. Perceptible human work, creative changes and a creative selection and arrangement can be protected. In one case the report cites, the selection and arrangement of generated images with human-written text in a comic book were found protectable as a compilation.
Article 1228 of the Civil Code of the Russian Federation says the author of a result of intellectual activity is the citizen whose creative labour created it. Neither text settles a particular project, and a contract or a trade mark needs a specialist. Labelling is simpler. Google says all images from its models include a SynthID watermark, which Google DeepMind describes as invisible and designed to withstand cropping, filters and lossy compression, and Adobe says Firefly outputs include Content Credentials indicating AI use. Telling the client which parts were generated stays the author’s job.
A week of practice with an AI image generator, then the course
Much of this can be practised alone in a week on a real task: three newsletter covers, an illustration for a café menu, a banner for a seasonal sale. Write the brief on one page and choose one ai image generator. Read its pages on limits and terms, then run three rounds with one change each, logging every attempt. Finish one picture by hand and judge it against the criteria written at the start; the free prompt worksheet in the VITON13 SCHOOL library gives the brief a ready structure.
A week alone does not give a method that holds across tasks, or feedback on it. The AI for designers course at VITON13 SCHOOL has eight modules: visual concept, art direction, image generation with control of frame, style and detail, systemic prompting with templates and variables, brand systems, interfaces, selection and finishing, and rights and ethics. A learner leaves with a moodboard and a written concept, a library of visual prompts, a short brand-system guide and interface screens, built on a project adapted to their own task. The course page carries the current terms.
Practical checklist
- Write a one-page brief: where the image lives, its format, what it must show and the criteria you will judge it by.
- Collect two or three reference images and describe constraints as what should be in the frame, not only what should not.
- Read the limits and the terms page of the generator you picked, including what happens to the files you upload.
- Fix the seed or keep one conversation, and change a single variable per round while logging prompt, model and date.
- Set all lettering by hand in a layout program and tell the client which parts of the work were generated.
Questions and answers
Why does the same image prompt give a different picture every time?
A diffusion model starts each generation from random noise and cleans it towards your text, so a new start gives a new picture. Tools that expose a seed, such as Stability AI’s service, let you repeat the starting point and vary one setting at a time.
Can an AI image generator write readable text on a poster?
Sometimes, but not reliably. OpenAI says its image models can still struggle with precise text placement and clarity, and Google suggests creating the text first. For menus, prices and claims, set the type yourself in a layout program on top of the generated picture.
Is it safe to upload client photos to a free image generator?
Check the terms first. Google’s Gemini API terms say that on unpaid services submitted content helps improve its products and may be read by human reviewers, and they ask users not to submit sensitive, confidential or personal information. Paid tiers are handled differently.
Do I own the copyright in a picture made from my prompt?
The U.S. Copyright Office says prompts alone do not make you the author; your own perceptible work, creative edits and arrangement can be protected. Russian law ties authorship to a citizen’s creative labour. For a contract or a trade mark, ask a lawyer about your case.
How do I get a consistent series instead of random image results?
Write a brief with criteria, use the same references, fix the seed or stay in one conversation, and change one variable per round. Vendors admit that recurring characters and brand elements can drift, so check each image of the series against your written rules.