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Glow is an open source generative model released by OpenAI that demonstrates flow-based generative modeling techniques. Unlike models that rely on approximate inference, Glow uses invertible transformations to directly learn the data distribution, allowing for exact likelihood computation and efficient sampling. The model is capable of producing high-quality synthetic images while maintaining interpretable latent spaces that enable meaningful manipulation of generated outputs. Glow’s architecture is based on reversible layers and efficient flow operations, which allow large-scale training while keeping memory usage manageable. The repository provides training code, pretrained models, and scripts for generating samples or reproducing key results from the original research. Glow is primarily intended for researchers and practitioners exploring generative modeling, likelihood-based training, and interpretable deep learning systems.
Features
- Implements flow-based generative modeling for exact likelihoods
- Provides high-quality image synthesis with interpretable latent spaces
- Supports efficient and scalable training with invertible layers
- Includes pretrained models and sampling scripts
- Enables latent space manipulation for controlled generation
- Serves as a reference for flow-based deep learning research
Project Samples
