A seed is essentially a starting point for the random number generator (RNG) that drives how an AI model creates or builds an image.
Without a seed, every generation is random, even with the same prompt. By using a seed you can anchor the randomness, so the model follows a similar creative path each time, even when the prompt changes.
A seed doesn’t lock everything in place but it creates some form of repeatability within the randomness.
Using seeds for consistency
Reproducing a ‘look’: If you generate an image that has a ‘look’ that you really like, then save the seed value and the prompt and use them later to regenerate similar images.
Iterative tweaks: If you like the core structure and layout of an image, use the seed value and make iterative tweaks to your prompt (e.g. adjusting the lighting in the prompt)
Creating cohesive sets: You can use seeds to maintain a core focal point (e.g. product or location) and adjust your prompt to change other elements within the image.
Testing changes: Use seeds to control randomness while experimenting with different prompts. This approach could also be used within creative ideation.
Best practices:
For an image you like, always save the seed and the full prompt
Ensure you are using the same AI model when re-using a seed
Be appreciative of the fact that seeds guide strongly, but some randomness might still appear
Different models behave differently when it comes to seeds - don’t expect to see consistent results working with seeds across different models. Even small model updates can slightly alter how seeds behave.
Seeds give you more control when you want it, but still leave room for creativity. Experimenting with seeds by fixing the seed but adjusting the prompt is a great way to get a feel for how they work.
By learning to use seeds, you can get the best of both worlds: creative variety when you want it, and reliable consistency when you need it.