Generative image models deliver appealing visuals quickly but tend towards the middle because they work on probabilities. Practitioners recommend an iterative process using reference images and several tools.
Generated images in social media content often share a recognisably uniform look. The cause lies in the method: an image model selects the most probable continuation and therefore drifts systematically towards the centre of its training distribution.
Social media practitioners Lauren deVane and Michael Stelzner therefore advise spending less time hunting for the one perfect prompt. More effective is an iterative process: work with reference images, use several tools, and define a house visual language against which results are judged.
For brands this is a cost question. Producing images without a defined visual language quickly yields a large volume of interchangeable material that weakens recognition. The effort shifts from production to selection.
What this means for decision-makers
- Define a written visual language covering colour, light and perspective rules before approving generated images.
- Work with reference images rather than ever longer text instructions.
- Budget time for selection and rework – generation is no longer the expensive step.
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