FARMER: Flow AutoRegressive Transformer over Pixels
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.
6900348b22d452aac6dd433b
Guangting Zheng
6900348b22d452aac6dd433c
Qinyu Zhao
6900348b22d452aac6dd433d
Tao Yang
6900348b22d452aac6dd433e
Fei Xiao
6900348b22d452aac6dd433f
Zhijie Lin
6900348b22d452aac6dd4340
Jie Wu
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2025-10-28T15:36:31.445Z
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wujie10558@gmail.com
wujie10
user
wujie10
6900348b22d452aac6dd4341
Jiajun Deng
6900348b22d452aac6dd4342
Yanyong Zhang
6900348b22d452aac6dd4343
Rui Zhu
FARMER: Flow AutoRegressive Transformer over Pixels
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.
FARMER, a unified generative framework combining Normalizing Flows and Autoregressive models, achieves competitive image synthesis performance with exact likelihoods and scalable training.
Normalizing Flows Autoregressive models invertible autoregressive flow self-supervised dimension reduction one-step distillation resampling-based classifier-free guidance