MR Multitasking is an efficient approach for quantification of multiple parametric maps in a single scan. The Bloch equations can be used to derive conventional contrast-weighted images, which are still preferred by clinicians for diagnosis, from quantitative maps. However, due to imperfect modeling and acquisition, these synthetic images often exhibit artifacts. In this study, we developed a deep learning-based method to synthesize contrast-weighted images from Multitasking spatial factors without explicit Bloch modeling. We demonstrated that our method provided synthetic images with higher quality and fidelity than the model-based approach or a similar deep learning method using quantitative maps as input.