Many AI-generated visuals share a similar style, making it hard for brands to differentiate. Experts say the key is not longer prompts, but a clear visual identity, reference images, and the right tools.
For publishers and content creators relying on generative image models, the challenge of visual sameness is becoming increasingly apparent. Many AI-generated images feature flawless lighting, polished composition, and a professional yet generic look. According to social and AI experts Lauren deVane and Michael Stelzner, this uniformity is less about the limitations of the image generators themselves and more about how users interact with them. When creators simply enter a description and accept the first result, they leave critical creative decisions to the model, often resulting in interchangeable visuals.
DeVane and Stelzner describe the process as a form of visual improvisation, emphasizing that there is no single button for originality. Even with standard AI workflows, users must actively correct and adjust outputs. Generative models, when given open-ended prompts, tend to default to patterns found in their training data. Research published in 2025 in “Scientific Reports” found that models like Stable Diffusion can produce stereotypical and repetitive depictions of people from certain backgrounds or professions, reinforcing the issue of visual homogenization.
Instead of relying on increasingly complex prompts, the experts recommend using concrete reference images to guide the model toward a specific visual outcome. For example, showing the AI a sample image that matches the desired aesthetic is often more effective than using vague terms like “modern” or “cinematic.” This approach also applies to people: providing an actual photo as a base can help achieve a more targeted result.
Using multiple image generators can further refine outcomes. Stelzner and deVane assign different tasks to different tools: Midjourney for stylized, cinematic images; Nano Banana for photorealistic visuals; and Ideogram for images where text is central. They advise against sticking to a single provider, instead recommending that creators select the best tool for each specific need.
Developing a distinctive visual language is crucial for brands and publishers. Stelzner suggests combining influences such as historical design styles, color palettes, moodboards, or brand elements. For instance, the term “Googie Architecture” is used to describe a Jetsons-inspired look, helping translate abstract ideas into reproducible prompts. However, research shows that prompt quality alone cannot fully solve the problem of sameness. Recent studies on iterative image-to-text-to-image processes reveal that AI systems often converge on a limited set of generic visual patterns, a phenomenon described as “visual elevator music.”
This means that brands and media organizations must continue to make deliberate choices about image concepts, style, and selection. While AI can speed up production, it does not automatically create a recognizable visual identity. As highlighted in a related discussion on how newsrooms communicate their impact to different audiences, the challenge of standing out and conveying unique value remains significant (see this analysis of newsroom impact communication).
Practical Steps for Unique AI Visuals
Stelzner and deVane offer a step-by-step guide for leveraging AI image generation effectively:
First, define specific use cases. AI-generated images are especially useful when many variations of a theme are needed. Companies can create a base prompt template and combine it with different product or brand references to produce seasonal campaigns or multiple ad creatives, all while maintaining a consistent visual language.
Second, clarify your visual identity before prompting. DeVane recommends analyzing personal preferences using reference images-identifying favored colors, perspectives, lighting, and compositions. A few well-chosen reference images can help the AI identify common design elements, which can then be used to build reusable prompts. For logos and brand colors, provide exact specifications, such as precise hex codes, to minimize interpretation by the AI.
Third, use deVane’s seven-dimension prompt model: medium, subject and action, environment, composition, lighting, aesthetic, and intended effect. Not every prompt needs all seven, but the goal is to leave as few key decisions as possible to the AI. Specifying actions, framing, lighting, and environment reduces the risk of generic results, with lighting and composition having the greatest impact on uniqueness.
Fourth, generate multiple variants and compare outputs from different models. Even a strong prompt may not yield the perfect image on the first try. Platforms like Magnific allow users to test prompts across several models simultaneously, increasing the chances of finding a suitable visual that can be further refined. This process is less about entering a prompt and receiving a finished image, and more about creative selection and iteration.
Expert Tips for Consistency and Precision
Stelzner and deVane also share several advanced tips: Use a developed prompt template as a visual foundation for entire campaigns, adding new products or motifs as references to maintain brand consistency. For brand colors, always specify exact hex codes rather than general color names to reduce ambiguity. When working with recurring characters, create a character contact sheet with front, side, and full-body views as a central reference for future scenes. Finally, avoid uploading too many references at once-choose only those that fit the specific task, as irrelevant images can confuse the model and reduce precision.
