Denoising Diffusion Probabilistic Model for Face Sketch-to-Photo Synthesis

The field of face sketch-to-photo synthesis involves generating photographic facial images with enhanced details and a heightened sense of style realism. In recent years, the advancement of deep learning techniques has significantly contributed to the development of methods for synthesizing photographic face images from sketches. Nevertheless, challenges remain in synthesizing facial photographs with richer details and more accurate structural representation. This paper introduces a novel architecture for face sketch-to-photo synthesis, using denoising diffusion probabilistic models (DDPM). Our approach simplifies the complex transformation process into sequential forward and backward denoising steps. We incorporate a pretrained coarse generator to effectively encode sketch information, integrating it into each backward step to guide the generative process toward accurate photo space representation. Furthermore, we design a detail diffusion branch to refine the coarse photo face generated from the coarse generator. By deeply fusing multiscale detail features from this branch with a sophisticated conditional noise predictor, our model effectively captures the correlation between detail and stylistic elements both in sketches and in photographic faces. Extensive experimental evaluations on three datasets show the effectiveness of our model, emphasizing its ability to synthesize facial photographs with remarkable realism and rich detail. The synthesized facial images consistently demonstrate superior face recognition accuracy, surpassing that of state-of-the-art methods.