# Glow: Generative Flow with Invertible 1x1 Convolutions

arXiv: https://arxiv.org/abs/1807.03039 · HTML: https://mlsanity.com/paper/1807.03039/

## Key facts

- Cited by 1,545 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2018.

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## Abstract (arXiv)

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