Generating realistic network traffic for digital twin systems is difficult because traffic packets exhibit temporal dependence， periodic variation， and scenario-driven uncertainty. To address this problem， this paper proposes FlowDiff， a diffusion-based packet generation algorithm， and TDGM， a temporal diffusion generation model. FlowDiff formulates packet generation as a conditioned reverse-diffusion process that progressively denoises latent traffic features. TDGM integrates temporal-aware embedding， periodic features， convolutional neural network （CNN） and Transformer modules， and cross-attention to capture local， global， and long-range traffic patterns. Experiments on real network traffic datasets show that the generated samples are close to real traffic in MSE， KL divergence， FID， and statistical features， demonstrating the effectiveness of the method for digital twin network simulation.