Face swapping, or deepfake generation, remains a challenging task that requires balancing identity preservation, attribute consistency, and photorealistic realism. We propose a novel training-free, three-stage face swapping framework that improves realism by explicitly aligning illumination and skin appearance prior to diffusion-based synthesis. Our approach refines photometric consistency and skin tone while preserving facial structure and integrates seamlessly with an off-the-shelf diffusion face swapping model. Experiments on the CelebAMask-HQ dataset demonstrate significant improvements in both visual realism and attribute preservation, achieving an FID score of 7.16 compared to the baseline. The proposed method provides an efficient and robust solution for realistic face swapping under varying illumination and appearance conditions without additional model training.