This paper unravels the potential of sketches for diffusion models,
addressing the deceptive promise of direct sketch control in generative AI. We
importantly democratise the process, enabling amateur sketches to generate
precise images, living up to the commitment of "what you sketch is what you
get". A pilot study underscores the necessity, revealing that deformities in
existing models stem from spatial-conditioning. To rectify this, we propose an
abstraction-aware framework, utilising a sketch adapter, adaptive time-step
sampling, and discriminative guidance from a pre-trained fine-grained
sketch-based image retrieval model, working synergistically to reinforce
fine-grained sketch-photo association. Our approach operates seamlessly during
inference without the need for textual prompts; a simple, rough sketch akin to
what you and I can create suffices! We welcome everyone to examine results
presented in the paper and its supplementary. Contributions include
democratising sketch control, introducing an abstraction-aware framework, and
leveraging discriminative guidance, validated through extensive experiments.

通过揭示素描在扩散模型中的潜力，解决了生成人工智能直接素描控制的虚假承诺。我们巧妙地实现了专业素描生成精确图片的民主化过程，证实现有模型变形的空间限制性问题。为了纠正这个问题，我们提出了一种意识到抽象的框架，利用素描适配器，自适应时间步骤采样和经过预训练的精细化素描图像检索模型的辨别引导，协同工作以加强细粒度素描照片关联性。我们的方法在推理过程中无缝运行，无需文本提示；一张简单的草图就像你和我都可以创建的草图一样，就足够了！我们欢迎大家查看论文及其附加资料中展示的结果。贡献包括专业素描生成的民主化控制、引入意识到抽象的框架和利用辨别引导，通过大量实验证实。