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Conditional generative models of high-dimensional images have manyapplications, but supervision signals from conditions to images can beexpensive to acquire. This paper describes Diffusion-Decoding models withContrastive representations (D2C), a paradigm for training unconditionalvariational autoencoders (VAEs) for few-shot conditional image generation. D2Cuses a learned diffusion-based prior over the latent representations to improvegeneration and contrastive self-supervised learning to improve representationquality. D2C can adapt to novel generation tasks conditioned on labels ormanipulation constraints, by learning from as few as 100 labeled examples. Onconditional generation from new labels, D2C achieves superior performance overstate-of-the-art VAEs and diffusion models. On conditional image manipulation,D2C generations are two orders of magnitude faster to produce over StyleGAN2ones and are preferred by 50% - 60% of the human evaluators in a double-blindstudy.

D2C: Diffusion-Denoising Models for Few-shot Conditional Generation