r/MachineLearning - [D] Flow-Based Generative Models, Bijective Transforms and Neural Lossless Compression

In order to be able to sample from p(x) all generative models attempt to learn a function from a known prior distribution p(z) to the natural distribution p(x). I don't think this is true. Some generative models are capable of sampling the learned p(x) directly, like autoregressive models which for example might model the joint distribution over all pixels in an image by using the probability product rule (e.g. Many common language models do the same over words or characters. Been meaning to read more about flow-based generative modeling.