Automatic Curation

Using quality signals, behavioral patterns, and visual analysis to surface the highest-value assets from a large generative library without manual rating. Signals include technical quality scores, selection frequency, within-session diversity, and aesthetic assessments from embedding-based classifiers.

The volume of AI-generated content makes manual curation impractical at scale. A productive creator might generate 500+ images per day, making it impossible to individually review and rate every output. Automatic curation provides a first pass by applying multiple quality signals.

Technical quality scoring detects artifacts, blank outputs, and corrupted generations. Behavioral signals — which images the creator downloads, shares, opens repeatedly, or uses as img2img inputs — indicate implicit quality judgments. Visual diversity scoring identifies the most distinctive outputs within a session. These signals combine to create a quality ranking that surfaces the most promising assets for human review, reducing the decision space from hundreds to dozens.