We introduce DiffSketch, a method for generating a variety of stylized sketches from images. Our approach focuses on selecting representative features from the rich semantics of deep features within a pretrained diffusion model. This novel sketch generation method can be trained with one manual drawing. Furthermore, efficient sketch extraction is ensured by distilling a trained generator into a streamlined extractor. We select denoising diffusion features through analysis and integrate these selected features with VAE features to produce sketches. Additionally, we propose a sampling scheme for training models using a conditional generative approach. Through a series of comparisons, we verify that distilled DiffSketch not only outperforms existing state-of-the-art sketch extraction methods but also surpasses diffusion-based stylization methods in the task of extracting sketches.

翻译：我们提出DiffSketch方法，用于从图像生成多种风格化素描。该方法专注于从预训练扩散模型的深层特征中选取代表性语义特征。这种新型素描生成方法可通过单张手绘草图完成训练。此外，通过将训练好的生成器蒸馏为精简提取器，确保了高效的素描提取。我们通过分析筛选去噪扩散特征，并将其与VAE特征融合以生成素描。同时提出使用条件生成方法训练模型的采样方案。系列对比实验证实，蒸馏后的DiffSketch不仅在素描提取任务中优于现有最优方法，更胜于基于扩散的风格化方法。