The correct option is More closely mirrors the brand signature style and design language even if overall variety may be reduced.

Prioritizing a smaller but curated set of proprietary images makes the model learn the label’s distinctive patterns, motifs, and color relationships more strongly. The training signal concentrates on consistent brand examples, so the generator better reproduces the brand’s aesthetic and design language. Because the data distribution is narrower than a large mixed corpus, the model tends to explore fewer directions and may trade off breadth of styles for fidelity to the brand identity.

Is cheaper to train yet may have difficulty learning the brand specific aesthetic is incorrect because emphasizing a curated in-house set actually improves learning of the brand look. While fewer images can reduce compute, the expected outcome described here conflicts with how representative proprietary data guides the model toward the desired aesthetic.

Generates a much broader spectrum of entirely novel styles that do not resemble the label identity is incorrect because that outcome aligns more with training on a huge heterogeneous public set. Prioritizing curated proprietary images narrows the style space toward the brand rather than expanding away from it.

Is far more likely to memorize and output near replicas of training images is incorrect because memorization risk depends on factors such as duplication, training regimen, and regularization. A curated set of around fifteen thousand images can still generalize well when trained properly, so near replicas are not the expected default outcome.

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