AI image generation is the use of a trained model to create or edit pictures from a text description, a reference image or both. This guide explains how the models work, compares the main tools by what they are good at, gives a prompt formula with real examples and covers ownership, copyright and commercial use.
- Pick the tool by job: chat-based tools for quick edits and text in images, Midjourney for stylized art, Firefly when commercial safety matters, open models for control and privacy.
- A strong image prompt names the subject, setting, composition, lighting, style and format, in that order; exact text goes in quotes.
- Edit instead of regenerating: change one thing per turn and keep what already works.
- In the US, images created purely from prompts are not protected by copyright; human selection, arrangement or modification can be.
- Read each tool's terms before commercial use, and avoid real people, trademarks and living artists' names in prompts.
What is AI image generation?
AI image generation is creating or editing images with a model trained on large collections of pictures and their descriptions. You describe what you want in words, optionally add reference images, and the model produces new pixels that match the description.
People use it for a wide range of everyday work:
- Marketing and social: ad variations, blog headers, social posts, thumbnails.
- Product and ecommerce: lifestyle scenes around a product photo, background swaps, seasonal variants.
- Design and concept work: mood boards, early concepts, storyboards, UI mockups.
- Personal projects: illustrations, cards, game assets, restoring or editing photos.
The tools fall into four groups: image features inside chat assistants (ChatGPT, Gemini), dedicated art tools (Midjourney, Ideogram), design suites with built-in generation (Adobe Firefly, Canva) and open models you can run yourself (FLUX, Stable Diffusion). Which group fits depends more on the job than on which model tops this month's comparison.
How do AI image generators work?
Most image generators learn the relationship between text and images from millions of captioned examples, then create a new image by gradually turning random noise into a picture that matches your prompt. That process is called diffusion.
A simplified version of the pipeline:
- Your prompt becomes numbers. A text encoder converts the description into a vector that captures its meaning.
- The model starts from noise. It begins with a field of random pixels (or a compressed version of them).
- It removes noise in steps. At each step, the model predicts what a slightly cleaner image matching your prompt would look like, and moves toward it.
- The result is decoded into a full-resolution image.
Some newer generators are built into large multimodal models that handle text and images together. In practice, this family tends to follow long, detailed instructions well and is good at conversational editing ("now make the jacket red"), which is why image features inside chat assistants improved so quickly.
Two consequences matter for everyday use:
- Models know patterns, not facts. They have no reliable idea what your company's logo or your living room looks like unless you show them.
- Every generation is a sample. The same prompt gives different results each time. Generate several and pick, rather than expecting one perfect image.
Which AI image generator should you use?
Choose by what you need to make. The table below compares the main options by strength rather than by ranking, because each one leads in a different area and model versions change every few months.
| Tool | Best for | Watch out for |
|---|---|---|
| ChatGPT (OpenAI's GPT Image models) | Conversational edits, following detailed instructions, text in images | Generation can be slow on complex prompts; usage limits by plan |
| Gemini (Google's Nano Banana models) | Editing photos, combining several images, consistent characters | Adds visible and invisible watermarks; best model needs a paid plan |
| Midjourney | Stylized, cinematic and artistic images | Less literal prompt following; check plan terms for commercial and private use |
| Adobe Firefly | Commercial work inside Photoshop, Illustrator and Express | Partner models in the app do not carry the same assurances as Firefly's own |
| Ideogram | Posters, logos and layouts with lots of typography | Narrower style range than art-focused tools |
| FLUX and Stable Diffusion (open models) | Privacy, fine control, training on your own style or product | Setup, GPU cost and license terms vary by model |
A few facts behind the table, from the vendors' own pages:
- OpenAI's image generation docs list current GPT Image models with editing, masks for inpainting and transparent backgrounds, and note that complex prompts can take up to two minutes. The same page lists known limits: text placement, consistency of recurring characters and precise layout can still be imperfect.
- Google's Gemini image page lists a free Nano Banana model and a newer one for paid Google AI plans, and says Gemini adds both an invisible SynthID watermark and a visible one.
- Adobe says Firefly's own models are trained on licensed content such as Adobe Stock and public domain content, and that it has never trained Firefly on user content. Its generative AI approach page also says that for partner models in Adobe apps, it is the creator's responsibility to judge whether they suit a project.
- Black Forest Labs lists an open-weights member of its FLUX family alongside API-only models; check the license before commercial use.
If you use AI images mostly for campaigns, our guide to AI for marketing covers where generated visuals fit in a content workflow and where stock or photography still wins.
How do you write a good image prompt?
A good image prompt describes the picture the way a photographer or art director would brief it: subject, setting, composition, lighting, style and format. Specific, concrete words beat a list of adjectives like "beautiful, stunning, 8k".
Use this order as a template:
[Subject and action], [setting and time], [composition and camera angle],[lighting and color], [style or medium], [format and aspect ratio].Text in the image: "[exact words]"Compare a weak prompt with a strong one:
A cool picture of a coffee shop.A barista pouring latte art at a wooden counter in a small neighborhood coffeeshop, early morning, shot from a low angle across the counter with shallow depthof field, warm window light from the left and soft steam visible, documentaryphotography style, natural colors, horizontal 16:9 format. No people's faces infocus, no logos.Prompting habits that consistently help:
- Lead with the subject. Models weight the start of the prompt heavily.
- Name the camera or medium. "35mm photo", "flat vector illustration", "watercolor on rough paper" and "isometric 3D render" each change the result more than "high quality".
- Describe light. "Overcast daylight", "golden hour backlight" and "studio softbox from above" set the mood.
- Put exact text in quotes and keep it short. A headline of two to five words renders far more reliably than a paragraph.
- Say what to leave out in plain words. "No text, no logos, no people" works in chat-based tools; some tools also offer a separate negative prompt field.
- Set the format. Square for feeds, vertical 9:16 for stories and reels, horizontal 16:9 for headers and slides.
The general principles of prompt engineering apply here too: give context, show examples (reference images are the visual version of examples) and change one thing at a time.
How do you edit AI images instead of starting over?
Edit the image you almost like instead of regenerating from scratch. Current tools can change one region, swap a background or adjust a detail while keeping the rest, which is faster and more predictable than rolling the dice again.
A reliable editing workflow:
- Generate four to eight options from your prompt and pick the closest.
- Fix the big things first: composition, subject, background.
- Change one thing per request: "Make the mug dark green. Keep everything else the same."
- Use inpainting for small areas: select or mask the region (a hand, a sign, a stray object) and describe only what should be there.
- Upscale and do final touches in your usual editor: crop, color correction, real typography for anything important.
For brand work, add your logo and final copy in a design tool after generation. AI-rendered logos are often subtly wrong, and real vector text stays sharp at any size.
For consistency across a series (the same character, product or style), give the model a reference image each time and describe what must stay fixed. Chat tools that keep the conversation context, and Gemini's support for combining several images, make this much easier than it was with one-shot generators.
Who owns AI-generated images, and can you use them commercially?
Two separate questions decide this: what the tool's terms allow, and whether copyright law protects the image at all. Tool terms usually let you use outputs, including commercially on paid plans, but in the US an image produced purely from a prompt is not protected by copyright.
The U.S. Copyright Office's January 2025 report on copyrightability concluded that:
- Outputs are protected only where a human author has determined sufficient expressive elements.
- The mere provision of prompts is not enough.
- Protection can apply where a human-authored work is perceptible in the output, or where a person makes creative arrangements or modifications of it.
- Using AI to assist in creating a work does not bar copyright.
In practice, this means a competitor could reuse a raw AI image of yours without infringing your copyright, while a composition you substantially edited, combined with your own photography or arranged into a larger design may be protected. Laws differ by country, so get local advice for anything high stakes.
Separate from copyright, these create real risk regardless of the tool:
- Real people. Generating recognizable people, especially in endorsements or compromising scenes, can violate publicity rights and platform rules.
- Trademarks and characters. Brand logos, mascots and famous characters belong to someone else.
- Living artists' styles by name. Prompting "in the style of [living artist]" is legally unsettled and often unwelcome. Describe the visual qualities instead.
Check each tool's current terms before commercial use. Some services link commercial rights, private generation or higher limits to specific paid plans, and enterprise plans from some vendors add IP indemnification.
How do you use AI images responsibly?
Use AI images in ways that do not mislead your audience: disclose generation where people would care, keep provenance metadata intact and never present a generated image as a real event or a real customer.
Practical rules many teams adopt:
- Label AI images in news, documentary and testimonial contexts, and follow platform rules on synthetic media.
- Keep Content Credentials. Many tools attach C2PA metadata and Google adds SynthID; avoid workflows that strip them on purpose.
- Do not fake proof. No AI "photos" of product results, events or people who did not exist.
- Review for bias. Models reproduce stereotypes from their training data. Check who appears in generated "doctor", "CEO" or "family" images and correct the prompt when results are one-sided.
Our guide to responsible AI use includes a short policy template you can adapt for image generation.
How to start with AI image generation today
You can produce useful images this afternoon with a tool you already have:
- Pick one real task: a blog header, a product scene or a social post.
- Use the chat assistant you already pay for, or a free tier, before buying a dedicated tool.
- Write the prompt with the formula above, including format and what to leave out.
- Generate several options, pick one and refine it with one-change edits.
- Add final text and logos in a design tool, and save the version history.
- Read the tool's terms once, so you know what you can do commercially.
When you are ready for motion, our guide to AI video generation covers generative video tools and code-based video. If you end up scripting image or video production with a coding agent, Orkio runs that agent on an always-on cloud box with a ready Video (Remotion) kit.
Frequently asked questions
What is the best free AI image generator?
As of October 2026, Google's Gemini app offers a free Nano Banana image model, and several other assistants include limited image generation on their free plans. Free tiers change often and usually limit volume and resolution, so check the current plan page before relying on one for regular work.
Can I use AI-generated images commercially?
Usually yes, but it depends on the tool's terms and on what is in the image. Many paid tools allow commercial use of outputs, while some tie commercial rights or private generation to specific plans. Separately, an image that shows a real person, a trademark or a copyrighted character can create legal problems no matter which tool made it.
Why does AI get hands and text wrong?
Image models learn statistical patterns of pixels, not the structure of a hand or the spelling of a word. Newer models handle both much better than earlier ones, but small text, long phrases and complex hand poses still fail sometimes. Keep text short, put it in quotes, and fix leftovers with an inpainting edit.
Are AI images watermarked?
Many are. Google adds an invisible SynthID watermark to images made in Gemini, and several vendors attach C2PA Content Credentials metadata that records how an image was made. Metadata can be stripped when a file is re-saved, so treat it as a signal rather than proof.
Is Stable Diffusion still worth using?
Open models such as Stable Diffusion and the open-weight FLUX models are worth it when you need privacy, fine control or custom training on your own style or product. They take more setup and a capable GPU, so for occasional use a hosted tool is faster.
