Photo retouching models are trained on pairs of original and retouched photographs, usually edited by experts. Expert edits are costly, so the two main public sets, FiveK and PPR10K, hold only 16,161 originals. Good photographs on their own are far easier to find: commercial stock collections and free ones such as Pixabay and Unsplash hold millions. We learn retouching from them alone: we damage each photograph the way a camera or a careless edit would, which gives a worse and a better version of the same photograph. From these pairs, our model, ATune, learns how 13 measured attributes, such as brightness, contrast and sharpness, should change to raise a photograph's quality. The edit is declarative: ATune sets target values for the attributes, and a simple search adjusts the sliders of an out-of-the-box photo editor until the photograph reaches them. Every edit is a list of slider values a person can read and adjust. The same model can drive any editor that makes those changes; we test RawTherapee and darktable. We train on 10M photographs from a commercial stock collection and test on FiveK, PPR10K and SPAQ. Nine quality judges score every edit, and in a user study 157 human raters made 9,253 choices between our edit and another method's edit of the same photograph. When Claude Opus 5 and GPT-6 Astra edit the same photographs in the same editor, raters prefer our edits to GPT-6 Astra's (54.3%) and rate them even with Claude's (50.5%). We also compare with supervised retouchers on their home ground: NeurOP on FiveK and AdaInt on PPR10K, each trained on that set's own expert edits. Raters chose our edits in 46.3% of those comparisons. ATune, a 54M-parameter model trained without a single expert edit, now matches frontier models at setting an editor's sliders.

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