LightDial:Progression-Conditioned Attention for Continuous Portrait Relighting

Portrait relighting plays an important role in portrait enhancement and creative editing by reshaping how illumination defines facial structure, subject appearance, and visual atmosphere. Despite recent advances in instruction-based image editing, current models still struggle to produce consistently plausible and visually coherent portrait lighting across diverse effects, while offering limited control over how strongly each effect is expressed. A key challenge is that increasing lighting strength is not a uniform change in edit magnitude: different effects evolve through distinct, spatially structured changes in illumination. In this work, we present LightDial, which formulates lighting strength through a continuous progression variable. This progression-based formulation turns strength control from magnitude scaling into learning structured changes along an ordered lighting progression. By modulating the interactions among text, reference-image, and noisy-output representations according to the requested progression state, and by explicitly constraining response changes between neighboring states during training, LightDial enables smooth and structured evolution of lighting effects while preserving identity and scene content. To provide the effect diversity and ordered progression supervision required by this formulation, we construct PortraitLight-50K, a large-scale portrait relighting dataset covering structurally diverse lighting effects with multi-level progression sequences. Extensive experiments demonstrate high-quality portrait relighting and strong identity preservation, together with smoother and more consistent progression responses than existing continuous-control approaches.