ChiaroRetouch: Recoverable Color Actions for Stagewise Local Photo Retouching

Reasoning photo retouching translates abstract user intent into precise tone and color adjustments. Generative editors regenerate pixels and reach impressive quality at the cost of slow inference and capped output resolution, whereas driving a differentiable executor with compact editing parameters is efficient and resolution-independent but has largely been limited to global operations. Extending such efficient models to local retouching requires explicitly modeling *where* to adjust and *what* action to apply. From a per-pixel perspective, we factorize region control along chromatic, luminance, and semantic dimensions, and build a six-stage process over Hue, three luminance bands (Shadows, Midtones, and Highlights), and Subject supports, together with a Global stage. Learning such structured local execution, however, requires stagewise trajectories with spatial supervision, which are difficult to collect at scale. We therefore treat recoverability as a design constraint and propose ChiaroRetouch, whose fixed-basis Gaussian executor stays nonlinear in color yet linear in its executable action coefficients, so every masked state transition determines its action by a deterministic support-weighted solve. This property lets MaskGrade synthesize and numerically verify local stagewise trajectories at scale via controlled degradation. Experiments show that ChiaroRetouch achieves a favorable trade-off between reference fidelity and instruction adherence across multiple expert retouching benchmarks; its closed-form recovery is over 94x faster than iterative fitting, and it processes a 1K image in 0.35 s while supporting local retouching at original resoluti