Text-to-image AI: Creating images from descriptions
Text-to-image AI is developing rapidly and is increasingly integrated into everyday workflows. At the same time, pressure from regulators and platforms to create transparency in AI-generated content is growing. This is leading to a new normal where images, while quickly created by text command, will also need to be recognizable as synthetic in the future. Those who today work with 'Create an image of...' will, in 2026, no longer be faced with just a creative question, but also a responsibility question.
Source: Own Representation
Fundamentals of Text-to-Image AI
A text-to-image model converts a natural language description into a corresponding image. This enables the creation of product shots, infographics, or photo scenes without a traditional camera or illustration. The barrier to entry has lowered as these systems no longer appear as specialized software but are integrated into familiar interfaces like chat applications, as OpenAI in den ChatGPT Release Notes describes.
The core remains unchanged: language controls composition, style, details, and often even text elements in the image. For example, if a team quickly needs a banner motif for a landing page, variants can be generated and iteratively refined in minutes today, instead of starting a complex photo or design process. The OpenAI-Plattformdokumentation explains image generation from text prompts.
OpenAI has introduced image generation in ChatGPT as "4o image generation" and emphasizes that the system follows more precise instructions and can use chat context. This is more than just convenience: those who clarify requirements in the same dialogue ('clean, medical, no logos, neutral colors') and then generate images directly, reduce misunderstandings that previously arose between briefing and implementation. DALL·E 3 is an example of this integration.
For developers, image generation has become a feature that can be integrated into products like shipping or payment services. The official announcement of OpenAI nennt gpt-image-1 als Modell, to integrate image generation into their own tools.
Large generators like DALL·E, Midjourney, Stable Diffusion, and Firefly dominate the market. DALL·E 3 allows ChatGPT to generate more detailed prompts and adjust images with a few words. Midjourney explains that image generation begins with a "prompt", a text that tells the system what image to create. Parameters allow for control of aspect ratios and other properties, as described in the Midjourney-Dokumentation described.
Stable Diffusion is presented by Stability AI as a "text-to-image model" that generates images from text. SDXL 1.0 is a further development of these models. Adobe positions Firefly as a text-to-image tool that converts text prompts into images, which is also documented in the Support-Seiten documented.
Across all systems, it is evident that the "tool" is rarely just a model but a workflow of prompt, variations, upscaling, editing, and repeating.
Source: user-added
The fusion of artificial intelligence and human creativity enables the generation of unique visual content from text descriptions.
Prompting and Image Development
Midjourney describes a prompt as a "text or phrase" that can range from a single word to a complete phrase. The quality, however, often depends on specific details: perspective, lighting, material, context, image type (photo, illustration, diagram), and clear exclusions. A prompt like "studio photo, neutral lighting, no brand logos, wooden surface, slight depth of field, 4:5" usually leads to usable results faster than "burger on a plate," as the OpenAI-Dokumentation for image generation clarifies.
The second lever is iteration. DALL·E 3 is explicitly described by OpenAI as a system where images can be "tweaked" with a few words. This is the difference between "generating an image" and "developing an image idea": one approaches a motif as in editorial work, with clear criteria and repeated corrections.
Regulation and Labeling
The EU Commission is working on a „Code of Practice“ on labeling and marking of AI-generated content to support transparency obligations. This refers to obligations under Artikel 50 des AI Acts, addressing transparency for AI-generated or manipulated content. Synthetic content such as deepfakes must be marked as artificially generated.
In parallel, platforms are implementing their own rules. YouTube requires creators to disclose "meaningfully altered or synthetically generated content that seems realistic." TikTok can automatically label content with "AI-generated" and considers "Content Credentials" from C2PA. Meta hat angekündigt, labeling AI-generated images on Facebook, Instagram, and Threads to enable users to recognize photorealistic AI content.
The key point is provenance: it's not just about whether AI is involved, but also where the content comes from and whether it has been altered. The C2PA describes itself as an open standard for making the origin and changes of digital content traceable. OpenAI erklärt, that C2PA can embed metadata for verifying origin and relevant information in media. OpenAI has begun C2PA-Metadaten zu Bildern hinzuzufügen, that are generated or edited with DALL·E 3 in ChatGPT and via the OpenAI API.
For editorial teams, government agencies, and companies, this is an important difference from visible watermarks: metadata can be machine-checked without visually "branding" the image, as long as it is not removed. Verification tools like the Verify-Seite der Content Authenticity Initiative can read Content Credentials. Nevertheless, the reality remains that metadata only helps if platforms adopt them, users don't remove them, and standards are broadly implemented, as the C2PA Explainer darlegt.
Source: user-added
The fusion of human imagination and artificial intelligence enables the creation of new images from text descriptions.
Challenges and Risks
The risks are not abstract but manifest in real-world cases, such as the Guardian Anfang Januar 2026 berichtete: AI-generated videos of Spain's Princess Leonor were used for fraud. Such examples illustrate why labeling became politically enforceable: not because every AI image is dangerous, but because individual realistic-looking fakes can cause significant damage. States are also following suit: Reuters berichtete 2025 on a Spanish draft law with high penalties for not labeling AI-generated content, in the context of EU regulations.
While transparency about outputs is growing, transparency about training data remains a point of contention. AP News beschreibt, that Getty dropped certain copyright claims in a UK case against Stability AI. The dispute revolved, among other things, around the accusation that images were used for training without permission. The Guardian stellte diesen Fall as a signal of how complex it becomes to legally separate training, storage, and output.
For users, this means: "I am allowed to use the image" and "the model was trained cleanly" are two different questions that may need to be checked separately in some environments.
Practical Application for Businesses
In many teams, image AI is already used for quick drafts – for social posts, mockups, header images, or visuals in presentations. The break occurs when these drafts are published externally. Then platform disclosure rules, as demanded by YouTube für realistisch wirkende synthetische Inhalte apply. At the same time, technical signals like Content Credentials are emerging, which TikTok ausdrücklich erwähnt.
A typical scenario in marketing: a product photo is missing, the shoot is next week, but the shop needs a visual for A/B testing today. Text-to-image helps here, as long as it remains clear internally that it is a synthetic image – and it is clearly labeled externally if it looks realistic and the platform requires it.
A second scenario from training and knowledge transfer: teams generate diagrams, flowcharts, or simple "how-to" images because 4o Image Generation explicitly targets precise instruction following and rendering of text. This saves time but can create new verification obligations: anyone publishing a diagram that looks like an official medical illustration should take origin signals and internal approvals as seriously as with traditional design, as the EU Code of Practice nahelegt.
By 2026, text-to-image will no longer be a "toy" but a production tool, as providers have anchored image generation from text into chat interfaces and APIs. At the same time, the EU and platforms are pushing transparency forward – through labeling requirements and standards like C2PA, which are intended to make provenance machine-readable. Those who generate images via prompts today gain speed, but lose the excuse that provenance can no longer be traced: rules, labels, and metadata are here and will become part of the workflow.