Just a few years ago, creating a realistic image required the skills of an artist, photographer, or designer and hours of work. Today, it’s enough to describe the desired picture with text – and in a few seconds, an AI neural network will produce a result that is sometimes indistinguishable from a professional illustrator’s work or a photograph.

This technology has transformed marketing, design, illustration, and even sparked serious discussions about copyright and the future of creative professions. Let’s break down how it works and how to use this tool effectively.

How an AI Neural Network Turns Text into an Image

The technology underlying most modern image generators is called a diffusion model. Its working principle can be explained through the reverse process.

Imagine a photograph that is gradually “noised” – random interference is added until only chaotic noise remains, similar to static on an old TV. The neural network is trained on a vast number of such pairs: an original image and its noised version at different stages.

After training, the model learns to do the opposite: take pure noise and gradually “denoise” it, reconstructing a meaningful image. The text description (prompt) serves as a guide – it directs the noise restoration process so that the final image matches the description.

Technically, this happens through a connection with a language model that converts the text prompt into a mathematical representation (vector) understandable by the image generator. This is why the prompt’s phrasing so strongly influences the result – the model literally uses the text as a navigation map for the generation process.

Key Image Generators: What’s the Difference?

Midjourney

Considered a leader in artistic expressiveness and aesthetics. Midjourney images often look like professional illustrations or concept art – the model is specifically tuned to produce visually appealing results, sometimes even at the expense of strictly following the prompt.

It operates via the Discord messenger or a web interface. The subscription is paid, with virtually no free tier.

Best for: artistic illustrations, concept art, creative and atmospheric images.

DALL-E (by OpenAI)

Integrated into ChatGPT, making it convenient for users already working with this assistant. DALL-E’s specialty is a better understanding of complex, compound queries and more precise adherence to prompt details, including text within the image (though not perfect).

Best for: illustrations with specific details, images with text, quick generation without special skills.

Stable Diffusion

An open-source model that can be run locally on your own computer without a subscription and without sending data to external servers. It requires a graphics card with sufficient memory but offers full control over the process and deep customization possibilities through additional models and extensions.

Best for: users with technical skills, those who value privacy, and those who want precise control through additional tools.

Adobe Firefly

Integrated into Adobe products (Photoshop, Illustrator) and trained exclusively on licensed materials, which resolves many legal questions about copyright for commercial use.

Best for: designers already working with Adobe products, commercial projects with high legal compliance requirements.

How to Write an Effective Prompt for Image Generation

Structure of a Good Prompt

An effective prompt usually includes several layers of information:

Subject – what or who is depicted (“red fox,” “futuristic city,” “portrait of an elderly woman”)

Style – artistic direction (“watercolor style,” “photorealism,” “like video game concept art,” “minimalist style”)

Composition and Angle – (“close-up,” “overhead view,” “panoramic shot”)

Lighting and Atmosphere – (“soft sunset light,” “dramatic shadows,” “foggy morning”)

Technical Details – (“shot with a wide-angle lens,” “high detail,” “4K”)

Example of a complete prompt: “A red fox sits on a snow-covered stump in a winter forest, soft morning light filtering through the trees, photorealism, shallow depth of field, calm atmosphere”

Negative Prompts

Many generators allow you to specify what should not be in the image – this is called a negative prompt. It’s useful for excluding unwanted artifacts: extra limbs, distorted faces, watermarks, text, blurriness.

Iterative Approach

The first result is rarely perfect. Professionals usually generate several variations, select the most successful one, then refine the prompt or use functions to edit specific parts of the image (inpainting).

Ethical and Legal Questions

The topic of generative images is inextricably linked to complex copyright issues. Models were trained on vast datasets of images, many of which are copyrighted, without the explicit consent of the creators – this has led to lawsuits by artists against development companies in several countries.

Using generated images for commercial purposes has legal nuances that vary depending on the country and the specific service. Before using them in a commercial project, it’s advisable to check the terms of use of the particular generator and current legislation.

It’s also important to avoid creating images of real people without their consent, especially in a compromising or misleading context – this can violate personal rights laws and lead to serious consequences.

Where It’s Already Applied

AI image generation is already actively used in marketing for creating advertising materials, in game development for rapid concept art creation, in publishing for illustrations, in architecture and interior design for visualizing ideas in early stages, and in personal creativity – from podcast covers to gift cards.

Conclusion

AI image generation is one of the most vivid and impressive demonstrations of modern AI’s capabilities. The tool is accessible to everyone, requires no artistic skills for basic use, but demands an understanding of prompt composition principles to achieve quality results.

Like any powerful technology, it raises new questions – about authorship, originality, and the future of creative professions. These questions do not yet have definitive answers, but understanding the technology’s principles helps to use it consciously and effectively.