Papers › Glow: Generative Flow with Invertible 1x1 Convolutions

Glow: Generative Flow with Invertible 1x1 Convolutions

9 Jul 2018NeurIPS 2018 12arXiv:1807.03039archive 2025-07-28

Diederik P. Kingma, Prafulla Dhariwal

Flow-based generative models (Dinh et al., 2014) are conceptually attractive due to tractability of the exact log-likelihood, tractability of exact latent-variable inference, and parallelizability of both training and synthesis. In this paper we propose Glow, a simple type of generative flow using an invertible 1x1 convolution. Using our method we demonstrate a significant improvement in log-likelihood on standard benchmarks. Perhaps most strikingly, we demonstrate that a generative model optimized towards the plain log-likelihood objective is capable of efficient realistic-looking synthesis and manipulation of large images. The code for our model is available at https://github.com/openai/glow

Code

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Tasks

Results from the paper archive 2025-07-28

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Methods

Introduced by this paper: Activation Normalization, Invertible 1x1 Convolution

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