Representative Feature Extraction During Diffusion Process for Sketch
  Extraction with One Example
Kwan Yun Youngseo Kim Kwanggyoon Seo Chang Wook Seo Junyong Noh
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.