Why AI Image Batches Lose Visual Consistency and How to Fix It

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When generating product images in bulk using AI tools, visual drift — inconsistent colours, margins, and subject sizes — tends to emerge around the 80th image in a batch. Three root causes drive this: prompt ambiguity, random model seeds, and misaligned reference inputs, two of which cannot be solved by rewriting prompts alone. A structured workflow involving three anchor reference images, numerical constraints, and a brand kit configuration can significantly reduce drift before scaling up. Running a pilot batch of ten images side by side — rather than paging through them individually — is recommended to catch inconsistencies early and cheaply. Scaling in smaller concurrent batches, rather than one large queue, also limits costly rework when something goes wrong.

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