Web: http://arxiv.org/abs/2209.06970

Sept. 16, 2022, 1:14 a.m. | Chen Henry Wu, Saman Motamed, Shaunak Srivastava, Fernando De la Torre

cs.CV updates on arXiv.org arxiv.org

Generative models (e.g., GANs and diffusion models) learn the underlying data
distribution in an unsupervised manner. However, many applications of interest
require sampling from a specific region of the generative model's output space
or evenly over a range of characteristics. To allow efficient sampling in these
scenarios, we propose Generative Visual Prompt (PromptGen), a framework for
distributional control over pre-trained generative models by incorporating
knowledge of arbitrary off-the-shelf models. PromptGen defines control as an
energy-based model (EBM) and samples images …

arxiv generative models

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