The facial sketch synthesis (FSS) model, capable of generating sketch portraits from given facial photographs, holds profound implications across multiple domains, encompassing cross-modal face recognition, entertainment, art, media, among others. However, the production of high-quality sketches remains a formidable task, primarily due to the challenges and flaws associated with three key factors: (1) the scarcity of artist-drawn data, (2) the constraints imposed by limited style types, and (3) the deficiencies of processing input information in existing models. To address these difficulties, we propose a lightweight end-to-end synthesis model that efficiently converts images to corresponding multi-stylized sketches, obviating the necessity for any supplementary inputs (\eg, 3D geometry). In this study, we overcome the issue of data insufficiency by incorporating semi-supervised learning into the training process. Additionally, we employ a feature extraction module and style embeddings to proficiently steer the generative transformer during the iterative prediction of masked image tokens, thus achieving a continuous stylized output that retains facial features accurately in sketches. The extensive experiments demonstrate that our method consistently outperforms previous algorithms across multiple benchmarks, exhibiting a discernible disparity.
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