Thermal infrared (TIR) images are not influenced by the illumination variations and can be used in total darkness. With these advantages, TIR technology has a wide application in surveillance systems and various defense systems. However, there are not enough TIR images for wide range of application because the equipment for thermal infrared imaging is expensive and demands strict imaging conditions. To address this problem, we propose a sparse generative model based on pix2pix framework to produce synthetic TIR data from optical RGB images. Considering little texture and color information in TIR images, this model uses a U-net architecture but only selects partial low-level and high-level information for symmetric connections. Specially, we integrate intensity and gradient losses into the objective to train models, which assists generation models to learn more infrared images' characteristics. The experiments on public datasets prove that this proposed method can generate TIR data from optical images. Compared with current pix2pix networks, this method achieves increases by over 6.5% and over 1.2% separately on the metrics of SSIM and PSNR based on the public datasets. The SSIM value even gets an increase by 7% for daytime images. Meanwhile the network parameters decent by 13%.