We propose FocusX, the first mobile-deployable system achieving artifact-free all-in-focus synthesis in dynamic scenes. Our approach introduces three key innovations: 1) For focal stack acquisition, our depth prior-based dynamic focusing method that adaptively selects focus distances using real-time scene depth distribution analysis and depth-of-field constrained spatial clustering, reducing redundant captures while ensuring full depth coverage; 2) To reduce pixel misalignment caused by lens breathing, we adopt a one-time offline calibration to map the relationship between field-of-view and focus distance, aligning the images by cropping accordingly; 3) We design the Diff-MotionAIFNet, a conditional diffusion-based model that decouples moving-static components for artifact-free AIF reconstruction in dynamic scene while preserving scene fidelity. We further contribute DynaAIFSet, containing 5,500 dynamic scenes (120K images) for training and evaluation. Experiments show FocusX achieves state-of-the-art performance, outperforming baselines up by 59.6% in SSIM and 49.1% in PSNR, respectively. The deployment latency of FocusX is 4.8s on Honor Magic7 Pro. This work bridges computational photography theory with mobile implementation constraints, delivering practical AIF enhancement for user-generated content.