FaceKeeper: Multi-Expert LoRA for High-Fidelity Portrait Retouching

Portrait retouching must balance blemish removal, texture preservation, and identity consistency. Existing learning or diffusion methods often struggle to balance these three objectives: high-frequency details are lost, skin is overly smoothed, or multiple conflicting objectives are compressed into a single LoRA, leading to trade-offs and degradation. This paper proposes FaceKeeper, a multi-expert LoRA framework that internalizes these objectives into a unified training process and gradually resolves conflicts across three dimensions: In terms of supervision, we propose a semantically guided spatial-high-frequency coupled detail oversampling strategy that focuses on salient local regions; at the parameter level, we adopt a three-stage adaptation process:freezing, dense training, and sparse refinement,where the shared projection A is frozen, and the intent-specific factor B is merged into a lightweight adapter via mask-aware integration after dense training and sparse refinement; at the semantic level, we bridge the gap between pixel restoration and texture perception through multi-expert reward pre-alignment. Based on FLUX.2-klein-base-9B, our method outperforms dedicated retouching models and general-purpose editors on the self-built FaceKeeper-Bench and CelebA-HQ benchmarks for reference-free metrics, while remaining competitive for reference-based metrics. User studies further validate its excellent balance between blemish removal and the preservation of texture and identity.