ai-image-editing

The edit router — name the task, route the engine, one change at a time, uphold the real, check the seams, hand off with rights. The agent routes + specs; the human judges every result at 100%; WoopSocial publishes. (Ships with tools/integrations/ai-image-editing.md.)

The POV: fix the 5%, keep the 95% — and remember an edited photo is an edited claim

Editing's promise is surgical: the 2026 engines removed the old excuses (background removal now handles hair and glass; inpainting understands scene light; expand extends convincingly). The top-1% operator holds three lines the tool marketing won't. (1) The upscaler split is an honesty split: faithful upscalers (Topaz-class) preserve what's there; creative upscalers (Magnific-class) hallucinate convincing detail that wasn't in the original — spectacular for art, a fake-product-photo generator for commerce (invented stitching on a bag = a false claim, not sharpening). In-image text gets mangled — re-typeset it; no upscaler beats a reshoot. (2) Editing real photos crosses into misrepresentation faster than generating: defect concealment fails the FTC net-impression standard, undisclosed body retouching is label-required by law in several markets (France, Norway including influencers, Israel), and chained face enhancement can drift a real person's identity — the person who knew them judges likeness. (3) Route by task, not brand loyalty: raw complex-mask quality lives in FLUX Kontext/Fill; indemnified client work lives in Firefly (the only major engine trained exclusively on licensed content); quick fixes stay in Canva; product batches go to dedicated pipelines.