Facial sketches are both a concise way of showing the identity of a person
and a means to express artistic intention. While a few techniques have recently
emerged that allow sketches to be extracted in different styles, they typically
rely on a large amount of data that is difficult to obtain. Here, we propose
StyleSketch, a method for extracting high-resolution stylized sketches from a
face image. Using the rich semantics of the deep features from a pretrained
StyleGAN, we are able to train a sketch generator with 16 pairs of face and the
corresponding sketch images. The sketch generator utilizes part-based losses
with two-stage learning for fast convergence during training for high-quality
sketch extraction. Through a set of comparisons, we show that StyleSketch
outperforms existing state-of-the-art sketch extraction methods and few-shot
image adaptation methods for the task of extracting high-resolution abstract
face sketches. We further demonstrate the versatility of StyleSketch by
extending its use to other domains and explore the possibility of semantic
editing. The project page can be found in
this https URL

通过预训练的 StyleGAN 模型以及基于部分损失的两阶段学习技术，提出了 StyleSketch 方法，通过 16 对面部图像和相应的素描图像，训练了一个能够提取高分辨率风格化素描的生成器，并通过比较表明 StyleSketch 在提取高分辨率抽象人脸素描方面优于现有的最先进的素描提取方法和少量样本图像适应方法，同时还探索了其在其他领域的可用性和语义编辑的可能性。

基于有限数据的生成先验进行风格化面部素描提取

Stylized Face Sketch Extraction via Generative Prior with Limited Data

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.

DiffSketch 是一种从图像中生成各种风格化素描的方法，通过从预先训练好的扩散模型中选择代表性特征，我们的方法专注于从深层特征的丰富语义中选择。这种新颖的素描生成方法可以通过一个手绘图进行训练，通过将经过训练的生成器提炼成简化的提取器，确保了高效的素描提取。我们通过分析选择去噪扩散特征，并将这些选定的特征与 VAE 特征结合起来生成素描。此外，我们提出了一种使用条件生成方法来训练模型的采样方案。通过一系列比较，我们验证了提炼的 DiffSketch 不仅优于现有最先进的素描提取方法，而且在提取素描的任务中超过了基于扩散的风格化方法。