Deep Dive: How It Works
The Attribution Database
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All training data is vectorized and stored in a dedicated attribution database.
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Each image is decomposed into concept-level representations across multiple dimensions.
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Sophisticated deduplication mechanisms identify and handle similar or duplicate content, preventing any single image from being over-counted in attribution calculations.
Real-Time Analysis
When a visual is generated or modified, the attribution engine activates automatically. The system searches for similarity patterns between the output and the training set across multiple dimensions:
Composition — Spatial arrangement, framing, visual hierarchy
Style — Artistic treatment, rendering approach, aesthetic qualities Objects — Subject matter, elements, recognizable items
Objects — Subject matter, elements, recognizable items
Texture — Surface qualities, material representation, detail patterns
Background — Environmental context, scene setting, depth
Foreground — Primary subjects, focal elements, layering
This multidimensional analysis captures how different training images influence different aspects of the generation—one image might contribute primarily to composition, another to color palette, a third to object rendering.
The Attribution Vector
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For every generation, the system creates an irreversible attribution vector. This vector is a one-way mathematical transformation that:
Maps which training content influenced the output
Calculates the relative weight of each contribution
Captures concept-level influence, not pixel-level copying
Cannot be reversed to reconstruct source images