  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.
