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We introduce DiffSketch, a method for generating a variety of stylizedsketches from images. Our approach focuses on selecting representative featuresfrom 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 trainedgenerator into a streamlined extractor. We select denoising diffusion featuresthrough analysis and integrate these selected features with VAE features toproduce sketches. Additionally, we propose a sampling scheme for trainingmodels using a conditional generative approach. Through a series ofcomparisons, we verify that distilled DiffSketch not only outperforms existingstate-of-the-art sketch extraction methods but also surpasses diffusion-basedstylization methods in the task of extracting sketches.

Representative Feature Extraction During Diffusion Process for Sketch Extraction with One Example