FARMER is a novel generative framework that integrates Normalizing Flows and Autoregressive models for effective likelihood estimation and high-quality image synthesis directly from raw pixel data. It incorporates an invertible autoregressive flow to convert images into latent sequences and employs a self-supervised dimension reduction method to optimize the modeling process. Experimental results show that FARMER achieves competitive performance compared to existing models while ensuring exact likelihoods and scalable training.