FARMER: Flow AutoRegressive Transformer over Pixels
Authors: name: Guangting Zheng, name: Qinyu Zhao, name: Tao Yang, name: Fei Xiao, name: Zhijie Lin, name: Jie Wu, name: Jiajun Deng, name: Yanyong Zhang, name: Rui Zhu

Directly modeling the explicit likelihood of the raw data distribution is key
topic in the machine learning area, which achieves the scaling successes in
Large Language Models by autoregressive modeling. However, continuous AR
modeling over visual pixel data suffer from extremely long sequences and
high-dimensional spaces. In this paper, we present FARMER, a novel end-to-end
generative framework that unifies Normalizing Flows (NF) and Autoregressive
(AR) models for tractable likelihood estimation and high-quality image
synthesis directly from raw pixels. FARMER employs an invertible autoregressive
flow to transform images into latent sequences, whose distribution is modeled
implicitly by an autoregressive model. To address the redundancy and complexity
in pixel-level modeling, we propose a self-supervised dimension reduction
scheme that partitions NF latent channels into informative and redundant
groups, enabling more effective and efficient AR modeling. Furthermore, we
design a one-step distillation scheme to significantly accelerate inference
speed and introduce a resampling-based classifier-free guidance algorithm to
boost image generation quality. Extensive experiments demonstrate that FARMER
achieves competitive performance compared to existing pixel-based generative
models while providing exact likelihoods and scalable training.