Creating AI Images: How Modern Image Generators Really Work
VeröffentlichtKategorie: Künstliche Intelligenz
Veröffentlicht am 16.05.2026
Creating AI Images: How Image Generators Work and Why Good Prompting Matters
AI images are no longer a toy. What looked like a technical experiment just a few years ago has now become a fixed part of many creative workflows. Images for social media, blog posts, presentations, mood boards, campaigns, product ideas or complete visual concepts can now be created within minutes.
At first, that sounds like magic. But it is not.
Behind AI image generators are complex models that analyze language, recognize visual relationships and create new images from that information. The quality does not only depend on the tool being used. It depends very strongly on how clearly the person describes what they actually want.
This is the key point: an AI image generator does not automatically replace design, photography, composition or visual thinking. It speeds up processes. It can make ideas visible. It can deliver variations. But it still needs direction, context and control.
Anyone who simply types “make me a beautiful image” usually gets exactly that: a generic image.
What is AI image generation?
AI image generation means that an artificial intelligence model creates an image from a text input. This text input is called a prompt.
A simple prompt
A cat on a sofa
The result will probably be correct, but it will feel rather generic.
A precise prompt
A black cat is lying on an old green velvet sofa in a dimly lit living room. Warm light falls through a window from the left onto its fur. The style feels like analog photography from the 1970s with subtle film grain and soft depth of field.
The result will be much more targeted, atmospheric and controlled.
The AI does not simply create any random image out of nothing. It processes the text, breaks it down into meaning and tries to calculate a visual representation from it. To do this, the models were previously trained on very large amounts of image-text relationships.
Through this training, the model learned which visual properties are associated with certain terms, styles, perspectives, materials or lighting moods.
How do modern AI image generators work?
In short: Many AI image models start with visual noise and gradually shape it into an image that matches the prompt.
Most well-known image generators are based on so-called diffusion models. These include Stable Diffusion, many SDXL models and Flux models.
Diffusion sounds complicated, but it can be explained quite simply.
The AI does not start with a finished image. It starts with noise — a kind of random pixel chaos. The model then removes this noise step by step and shapes it into an image that matches the prompt.
You can think of it like digital sculpting: in the beginning, there is only a rough block. With every step, more structure becomes visible. First rough shapes appear, then light, then edges, then details, then materials.
The prompt acts as a guardrail. It tells the model in which direction the image should develop.
What happens behind the scenes when using a prompt?
When you enter a prompt, the AI does not read the text like a human being. It converts it into mathematical information. This is done using a so-called text encoder.
The text encoder translates words, relationships and meanings into a form the image model can work with.
Example
A woman in a red dress at sunset by the sea
This becomes semantic information such as woman, red dress, sunset, sea, lighting mood, scene, color palette and possible composition.
The image model then uses this information to create a matching image from noise step by step.
At the end, the internally calculated result is converted into a visible image. In many models, this final step is handled by a so-called VAE, a Variational Autoencoder.
Put simply, this is the part that turns the internal image representation back into a normal image.
What do Steps, CFG and Sampler mean?
Anyone working locally with Stable Diffusion, SDXL or Flux will quickly come across technical terms such as Steps, CFG, Sampler or Scheduler. At first, these terms can seem intimidating, but they matter if you want more control over the result.
Steps
Steps describe the number of calculation steps used to build the image from noise.
More steps do not automatically mean a better image. At a certain point, the result barely improves, but the generation takes longer.
For classic diffusion models, values between 20 and 35 are often useful. Modern models can sometimes create good results with far fewer steps.
CFG
CFG stands for Classifier-Free Guidance. In simple terms, this value controls how strongly the model follows the prompt.
A low CFG value gives the AI more freedom. A very high CFG value pushes the model more strongly toward the prompt.
Excessive values can lead to harsh, unnatural or overcooked images.
Sampler
The sampler determines the mathematical method used to remove the noise.
Different samplers can create different visual results.
Some are faster, some create smoother transitions, while others may appear more detailed or more stable.
For beginners, a good prompt is more important than any technical fine-tuning. But if you work locally and want reproducible results, you should not ignore these settings completely.
Why do many AI images still look artificial?
Many AI images do not look artificial because the models are bad. They look artificial because the prompt is weak, unclear or overloaded.
Common mistakes
too many style terms
contradictory lighting instructions
unrealistic perspectives
exaggerated detail requirements
unclear subjects
missing composition
A classic example would be prompts like:
ultra realistic, masterpiece, 8k, cinematic, hyper detailed, unreal engine, octane render, volumetric lighting, award winning, perfect anatomy
At first glance, this looks professional. In practice, it is often just keyword spam.
Modern models are getting better at understanding natural language. A clearly written sentence is often stronger than a long list of effect terms.
The key difference: old prompt logic vs. new prompt logic
In the past, many image models worked particularly well with keyword lists. This was especially common with Stable Diffusion 1.5 and many models trained on top of it.
Old prompt logic
portrait, woman, dramatic lighting, 85mm lens, shallow depth of field, cinematic, ultra detailed, studio photography
This still works in some cases, especially with local models, anime models or specialized checkpoints.
New prompt logic
Create a calm portrait of a woman sitting by the window in a small café. It is raining outside. The light is soft and comes from the left. The mood should feel thoughtful, warm and slightly melancholic.
For Flux, GPT Image or Gemini, natural language is often easier to understand.
Newer models handle natural language much better. It is easier for people to write and, in many cases, even better for modern models.
Which AI image generators matter right now?
There are now many AI image generators. Not every tool is useful for every purpose. What matters is what you actually want to achieve.
Midjourney
Midjourney is strong when it comes to aesthetic, atmospheric and visually impressive images. It often delivers very strong results for mood images, concept art, fashion, surreal scenes, social media visuals and artistic looks.
The advantage: the images often look good straight away.
The downside: precise control is not always easy. For layouts, fixed text, corporate design requirements or exact corrections, Midjourney is not always the best choice.
Flux
Flux comes from Black Forest Labs and has quickly established itself as a strong model for high-quality image generation.
What makes Flux especially interesting is how well it understands prompts written in natural language.
That is a clear advantage for realistic, complex or more narrative image ideas. Flux is also relevant for local workflows.
GPT Image
OpenAI’s GPT Image models are especially interesting when image generation is combined with language, editing and layout understanding.
Their strengths include image editing, consistent subjects, multi-step instructions, text inside images, infographics, layouts and working with existing images.
When an image should not only look good but also serve a specific purpose, these models can be very useful.
Gemini / Nano Banana
Google’s image models around Gemini and Nano Banana are especially interesting for explanatory images, infographics, image editing and visuals with stronger contextual meaning.
Their advantage is that they do not only recognize image patterns, but can also interpret context more effectively through language and world knowledge.
That makes them useful for blog graphics, explanatory visuals and visual concepts.
Stable Diffusion and SDXL
Stable Diffusion and SDXL remain important, especially for local workflows. If you want maximum control, these systems are hard to ignore.
The big advantage is the open ecosystem: there are countless models, LoRAs, ControlNet extensions, workflows, interfaces and community resources.
The downside: getting started is more technical. For regular beginners, ChatGPT, Gemini, Midjourney or Canva are often easier. But for advanced users and creative control, local models remain extremely interesting.
Why text in AI images has long been a problem
Text has long been one of the biggest weaknesses of AI image generators.
The reason is simple: an image model does not automatically understand letters like a word processor does. It has learned that certain shapes appear on posters, signs or packaging, but in earlier models these shapes were often just visual patterns.
That is why AI-generated images often contained misspelled words, distorted logos or fantasy lettering.
Practical note: For professional use, this still applies: AI can provide a strong starting point, but final typography should always be checked and often set manually.
Newer models have become much better at this because they connect language and image more closely. Even so, text inside images remains a demanding task.
Why hands and anatomy are difficult
Hands used to be the classic giveaway for AI-generated images.
Too many fingers, fused fingers, incorrect joints or strange gripping poses were common.
The reason is that hands are extremely complex. They consist of many small elements, constantly change shape and often interact with objects.
A face follows more stable patterns. A hand, on the other hand, can grip, point, hold, fold, cover or twist.
Modern models have become much better at this. Still, hands, teeth, jewelry, tools, guitars, bicycles or complex interactions remain good tests for the quality of an image generator.
How do you write a good prompt?
A good prompt does not just describe a subject. It describes an image idea.
Just a subject
A man is standing on a street.
An image idea
An older man is standing alone at night on a rain-soaked street in a big city. The light from a red neon sign reflects on the asphalt. The camera is close at eye level, the background is blurred, and the mood feels lonely and cinematic.
The most important building blocks of a good prompt
The clearer these points are, the better the model can work.
Prompting is image direction
Many people think prompting is just about text. In reality, it is much closer to directing an image.
You decide: What is in the foreground? What is in the background? Where does the light come from? Should the image feel calm or dynamic? Is the camera close or far away? Should it look documentary, commercial, cinematic or illustrative?
These are classic visual design decisions.
That is why people with experience in design, photography, film, illustration or advertising have a clear advantage when creating AI images.
They know what information an image needs in order to work.
Why photography terms work so well
Many AI image generators respond very well to terms from photography and film. That is because such terms are often linked to specific visual effects in training data.
creates a close framing.
creates a wide scene.
often creates a classic portrait look.
creates a blurred background and a sharper subject.
creates warm light shortly after sunrise or shortly before sunset.
creates light from behind the subject.
creates an analog grain texture.
These kinds of instructions are often more effective than vague terms like “beautiful”, “professional” or “high-quality”.
Why “beautiful” is not a good prompt
“Beautiful” is subjective. An AI can only work with that to a limited extent.
It is better to describe what beautiful should mean in this context: soft, bright, minimalist, warm, reduced, luxurious, documentary, natural, high-contrast, elegant, raw, technical, playful or dark.
What is a negative prompt?
A negative prompt describes what should not appear in the image.
This can be helpful if a model repeatedly generates unwanted elements.
Example
You prompt a tennis ball on a meadow, but the model keeps adding a tennis court in the background. In that case, you can exclude “tennis court” in the negative prompt.
Negative prompts should be used sparingly.
Long lists such as “bad quality, bad anatomy, extra fingers, ugly, blurry, distorted” do not always help. With some models they can be useful, with others they may even make the result worse.
A strong positive prompt is usually more important than an overloaded negative prompt.
What is img2img?
img2img means Image-to-Image. Instead of starting with an empty prompt, you start with an existing image.
This can be a sketch, a photo, a screenshot, a mood board or another AI-generated image.
The AI uses this image as a starting point and changes it according to your instructions.
An important value here is the strength of the change. This is often called the denoise value.
Low denoise value
The result stays close to the original.
High denoise value
The AI gets much more creative freedom.
img2img is especially useful when you want to keep a basic composition but change style, light, material or atmosphere.
What is inpainting?
Inpainting is one of the most important functions in AI image editing.
You select a specific area inside the image and only that area gets recalculated.
This is extremely useful because AI-generated images are rarely perfect on the first try.
Maybe the face looks good, but the hand does not. Or the subject works, but an object in the background is distracting. Or you want to change the clothing.
Practical advantage: With inpainting, you do not have to regenerate the entire image. You only correct the problematic area.
For professional workflows, this makes far more sense than endlessly generating completely new images.
What are LoRAs?
LoRAs are small add-on models that extend an existing image model.
They can introduce specific styles, characters, objects, brand aesthetics or visual concepts.
A base model knows a lot of general information. A LoRA specializes it for something specific.
This could be a certain illustration style, a product category, a character, a visual language or a recurring aesthetic.
Important: LoRAs must fit the base model. Many LoRAs also require a specific trigger word inside the prompt.
What is ControlNet?
ControlNet is a tool for much more precise image control.
While a regular prompt only describes what should be created, ControlNet can take over certain structures from a reference image.
For example: edges, poses, depth information, perspective or composition.
A typical example is OpenPose. It allows you to transfer a body pose from one image while generating a completely new character, outfit, style or environment.
ControlNet is especially powerful when the goal is not just beautiful images, but intentional composition.
That matters in professional design. Complex image structures are often difficult to control accurately with text prompts alone.
What is an IP-Adapter?
An IP-Adapter uses reference images to transfer style, composition or visual characteristics into a new generation.
It is less rigid than ControlNet, but extremely useful when you want to keep a look, color palette, person or character more consistent across multiple images.
Consistency is one of the biggest challenges in AI-generated imagery, especially for campaigns, character concepts or image series.
IP-Adapters and similar reference-image techniques help reduce that inconsistency.
Why aspect ratios matter
The aspect ratio completely changes the visual impact of an image.
1:1
Works well for many social media posts.
9:16
Ideal for Stories, Reels and smartphone formats.
16:9
Great for headers, presentations and YouTube content.
21:9
Creates a very wide cinematic look.
If you crop the image afterwards, you often lose important visual elements.
It is usually better to think about the desired format from the start and define it directly inside the prompt or tool settings.
Which styles work especially well?
AI image generators can imitate or combine almost any visual style. Still, some styles are especially common and effective in real-world use.
Photorealism
Photorealistic prompts work best when they are written like real photographic instructions.
Instead of simply writing “realistic photo”, it is better to describe camera, lighting, perspective and mood.
Natural portrait of a woman near a window, shot with an 85mm lens, soft daylight from the left, shallow depth of field, subtle skin texture, documentary photography style.
Cinematic look
The cinematic look is popular, but often overdone.
What matters are clear cinematic decisions: light source, color palette, focal length, contrast, framing and atmosphere.
Digital illustration
Digital illustration works especially well for fantasy, science fiction, editorial visuals, campaign ideas and creative concepts.
Style direction, level of detail, color palette and lighting are especially important here.
Flat design and infographics
Flat design is not always easy for AI because many models tend to add unnecessary details.
If you want reduced graphics, you need to clearly describe simplicity: few colors, clean shapes, no unnecessary details, lots of white space, clean lines and simple icons.
Watercolor
Watercolor styles rely on simplicity, soft transitions, paper texture and imperfect edges. Too much detail often ruins the effect.
Oil painting
Oil painting prompts benefit from canvas texture, visible brush strokes, impasto, baroque lighting or impressionistic color work.
Vintage and retro
Retro images become much stronger when you mention a specific decade or photographic technique such as 35mm, Kodachrome or film grain.
Why AI images do not replace real design
From a professional perspective, this is probably the most important point.
An AI-generated image is not automatically a finished design.
Good design requires typography, grids, spacing, hierarchy, brand understanding, audience awareness, readability, recognition and technical production quality.
AI can generate image material. It can visualize ideas. It can create variations. But it does not automatically replace design.
Especially for logos, branding, advertisements, websites, exhibition graphics or print production, professional expertise still matters.
The legal and ethical side
AI-generated imagery is not just a technical topic. It is also a legal and ethical one.
Responsibility increases especially with photorealistic images. If an AI-generated image looks like a real photo, people may also perceive it as one.
That may not matter much for harmless mood imagery. But it becomes much more critical in news, politics, health, public events or socially sensitive topics.
AI-generated images should therefore be used consciously and transparently.
Where AI-generated images are actually useful
AI image generators are especially powerful during early creative phases.
Instead of spending hours searching through stock photo libraries, you can directly generate the scene you actually need.
That saves time and opens up new creative possibilities.
But the closer the image gets to final professional communication, the more important post-processing becomes.
The real skill: visual thinking
The tools are becoming easier and easier to use. That is exactly why the difference between good and bad results will not disappear.
It is simply shifting.
In the past, the technical side was the biggest barrier. Today almost anyone can generate images.
But not everyone can judge whether an image actually works.
The real skill lies in:
composition
visual taste
image structure
understanding light
understanding audiences
understanding branding
technical control
critical selection
AI creates quantity. Humans still need to recognize quality.
Practical tips for better AI images
1. Do not start with effects
Before writing a prompt, be clear about what the image is supposed to achieve: explain something, sell something, create emotion, document a situation, create tension or attract attention.
2. Define the main subject clearly
The AI needs to understand what the image is actually about. Too many equally important elements often lead to chaotic results.
3. Describe lighting intentionally
Soft daylight, hard studio light, backlighting, neon light, candlelight or an overcast sky all create completely different moods.
4. Define the perspective
Close-ups, wide shots, bird’s-eye views, low angles or centered perspectives completely change how an image feels.
5. Do not overload the style
One style is often enough. If you ask for photorealistic watercolor 3D cinematic vintage flat design all at once, the result usually falls apart.
6. Work iteratively
Generate a base idea, review the result, improve it step by step, fix distracting areas and refine it afterwards.
7. Use inpainting instead of restarting
If 80 percent of the image already works, there is no reason to regenerate everything from scratch. Targeted corrections are much more efficient.
8. Use reference images
Reference images help the AI understand direction, style, pose and composition more clearly.
9. Always check text manually
Even though modern models are getting better at rendering text, you should never trust it blindly. Professional typography should usually still be done manually.
10. Be selective
Not every visually impressive image is actually a good image. What matters is not only aesthetics, but also function.
My conclusion
AI image generators are powerful tools. But they are not a guarantee for good design.
They make creative processes faster, broader and more experimental. They help visualize ideas, test variations and generate image material that used to require far more effort.
But they do not automatically replace concepts, visual language, design understanding or professional control.
The difference is no longer who can use AI. Many people can do that now.
The real difference lies in who can recognize strong images, guide them intentionally and use them meaningfully.
That is exactly why AI will not be the end of design. It will change design. And the people who truly understand how these tools work will always get better results than those who simply copy prompts.
