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We present CoGS, a novel method for the style-conditioned, sketch-drivensynthesis of images. CoGS enables exploration of diverse appearancepossibilities for a given sketched object, enabling decoupled control over thestructure and the appearance of the output. Coarse-grained control over objectstructure and appearance are enabled via an input sketch and an exemplar"style" conditioning image to a transformer-based sketch and style encoder togenerate a discrete codebook representation. We map the codebook representationinto a metric space, enabling fine-grained control over selection andinterpolation between multiple synthesis options before generating the imagevia a vector quantized GAN (VQGAN) decoder. Our framework thereby unifiessearch and synthesis tasks, in that a sketch and style pair may be used to runan initial synthesis which may be refined via combination with similar resultsin a search corpus to produce an image more closely matching the user's intent.We show that our model, trained on the 125 object classes of our newly createdPseudosketches dataset, is capable of producing a diverse gamut of semanticcontent and appearance styles.

CoGS: Controllable Generation and Search from Sketch and Style