In order to overcome the limitations of traditional digital mural restoration methods that primarily rely on image prior information and are inadequate for restoring large areas of damage, a novel mural restoration framework was proposed. Comprehensive restoration was achieved by constructing a diffusion probabilistic model guided by contour prior information. In the framework, contour and damage information were employed as guiding signals, and the missing portions were gradually diffused and reconstructed. In order to address the issue of color misalignment, a latent variable diffusion probabilistic model was employed, in which global color information was integrated to dynamically adjust and calibrate colors during the restoration process. As a result, the restoration outcomes were ensured to retain the original style while avoiding local color deviations. This restoration framework was capable of precisely restoring large damaged areas and effectively recovering the mural's color and texture details. The effectiveness of the restoration framework was evaluated using a mural dataset from the Mogao Caves, with qualitative and quantitative experimental assessments of restoration results for murals with varying degrees of damage. The results show that significant advantages in mural restoration tasks are demonstrated by the proposed method. It is concluded that this approach provides an effective solution for mural preservation and digital restoration.