Differentially private (DP) image synthesis enables the generation of realistic images while bounding privacy leakage, facilitating secure data sharing across organizations. However, the Gaussian noise injected during DP training, such as via DP-SGD, often severely degrades synthesis quality by disrupting model convergence. To address this, we introduce RPGen, a novel framework that enhances diffusion models' parameter robustness to mitigate DP noise effects without compromising privacy guarantees. At its core, RPGen employs adversarial model perturbation (AMP) during public pre-training to build resilience against perturbations, but we identify and tackle the critical issue of robustness transferability across domains. RPGen achieves this through a three-step process: (1) A pre-trained classifier infers labels for private images, aggregated into a class distribution noised with Gaussian mechanism for DP, and public samples are selected to match this privatized distribution for domain alignment; (2) The diffusion model is pre-trained on this curated subset with adversarial model perturbation to foster robustness; (3) The model undergoes fine-tuning on private data using DP-SGD. This synergy of robustness augmentation and transferability optimization yields high-fidelity synthesis. Extensive evaluations on ImageNet for pre-training, with CelebA and CIFAR-10 for synthesis, show RPGen outperforming state-of-the-art baselines across $\varepsilon \in {1, 5, 10}$. On average, it achieves 20.18\% lower FID and 5.45\% higher classification accuracy. Ablations confirm the efficacy of domain curation and modest perturbations, establishing RPGen as a new benchmark for privacy-utility trade-offs in image generation.