This paper introduces Real NVP (Real-valued Non-volume Preserving) transformations as a powerful invertible and learnable method for density estimation. The approach allows for exact log-likelihood computation, exact sampling, and exact inference of latent variables, enabling the modeling of complex data distributions. The method is based on a change of variable formula, which enables tractable density evaluation and inference. The paper demonstrates the effectiveness of Real NVP in modeling natural images across four datasets, showing its ability to generate realistic samples, evaluate log-likelihoods, and manipulate latent variables. The model is built using coupling layers, which are simple invertible transformations that allow for efficient computation of Jacobian determinants. These layers are combined in a multi-scale architecture, which enables the model to handle high-dimensional data efficiently. The model also incorporates batch normalization and deep residual networks to improve training and performance. Experiments on natural image datasets such as CIFAR-10, Imagenet, LSUN, and CelebA show that Real NVP achieves competitive performance in terms of sample quality and log-likelihood. The model is able to generate high-quality samples and maintain spatial smoothness, even when extrapolating to larger image sizes. Additionally, the model's latent space is shown to have semantic meaning, with the ability to generate manifolds that reflect higher-level concepts. The paper also discusses the limitations of the model, including the challenge of capturing higher-level semantic concepts in the latent space. Despite these limitations, Real NVP provides a flexible and tractable approach to density estimation, with potential applications in various domains beyond image modeling.This paper introduces Real NVP (Real-valued Non-volume Preserving) transformations as a powerful invertible and learnable method for density estimation. The approach allows for exact log-likelihood computation, exact sampling, and exact inference of latent variables, enabling the modeling of complex data distributions. The method is based on a change of variable formula, which enables tractable density evaluation and inference. The paper demonstrates the effectiveness of Real NVP in modeling natural images across four datasets, showing its ability to generate realistic samples, evaluate log-likelihoods, and manipulate latent variables. The model is built using coupling layers, which are simple invertible transformations that allow for efficient computation of Jacobian determinants. These layers are combined in a multi-scale architecture, which enables the model to handle high-dimensional data efficiently. The model also incorporates batch normalization and deep residual networks to improve training and performance. Experiments on natural image datasets such as CIFAR-10, Imagenet, LSUN, and CelebA show that Real NVP achieves competitive performance in terms of sample quality and log-likelihood. The model is able to generate high-quality samples and maintain spatial smoothness, even when extrapolating to larger image sizes. Additionally, the model's latent space is shown to have semantic meaning, with the ability to generate manifolds that reflect higher-level concepts. The paper also discusses the limitations of the model, including the challenge of capturing higher-level semantic concepts in the latent space. Despite these limitations, Real NVP provides a flexible and tractable approach to density estimation, with potential applications in various domains beyond image modeling.