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Optimizing the Post-Processing Workflow: Why I use Imagenomic Portraiture.
For centuries, Western portraiture operated under what John Berger called the “likeness contract”: the subject sat, the artist observed, and the resulting image stood as a trace of a unique individual. Photography intensified this contract, promising chemical indexicality. Yet today, a smartphone portrait is not a single exposure but a computational composite—stitched from multiple frames, enhanced by neural networks, and filtered through real-time facial landmark detection. When this image is then fed into a generative model (e.g., Stable Diffusion, Midjourney) to produce “variations,” we witness a fundamental rupture: the portrait no longer refers backward to a sitting body but forward to a family of possible images.
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We term this new regime —from imago (likeness, image) and genome (the complete set of genetic instructions). Just as a genome encodes an organism’s potential variations, the imagenomic portrait encodes a subject’s visual potentials, ready to be expressed (rendered), replicated (shared), and mutated (edited or synthesized).
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The imagenomic portrait challenges foundational concepts:
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