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Normalizing Flows are Capable Generative Models

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arxiv 2412.06329 v3 pith:CLO3JRF6 submitted 2024-12-09 cs.CV cs.LG

Shuangfei Zhai , Ruixiang Zhang , Preetum Nakkiran , David Berthelot , Jiatao Gu , Huangjie Zheng , Tianrong Chen , Miguel Angel Bautista
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Navdeep Jaitly Josh Susskind
This is my paper
classification cs.CVcs.LG
keywords modelstarflowflowsautoregressivecapableestimationgenerativemodeling
verification ladder T0 review T1 audit T2 compute T3 formal
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Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. In this work, we demonstrate that NFs are more powerful than previously believed. We present TarFlow: a simple and scalable architecture that enables highly performant NF models. TarFlow can be thought of as a Transformer-based variant of Masked Autoregressive Flows (MAFs): it consists of a stack of autoregressive Transformer blocks on image patches, alternating the autoregression direction between layers. TarFlow is straightforward to train end-to-end, and capable of directly modeling and generating pixels. We also propose three key techniques to improve sample quality: Gaussian noise augmentation during training, a post training denoising procedure, and an effective guidance method for both class-conditional and unconditional settings. Putting these together, TarFlow sets new state-of-the-art results on likelihood estimation for images, beating the previous best methods by a large margin, and generates samples with quality and diversity comparable to diffusion models, for the first time with a stand-alone NF model. We make our code available at https://github.com/apple/ml-tarflow.

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Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Amortized Moment Matching for Visual Generation

    cs.LG 2026-07 accept novelty 6.0 of 10

    Amortized Fréchet Distance uses neural nets to match conditional means and covariances, yielding stronger one-step visual generators than explicit FD-loss or multi-step teachers.

  2. Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series

    cs.LG 2026-07 conditional novelty 6.0 of 10

    Across 14 sensor generation settings, flow-matching models are the strongest overall baseline, while demographic conditioning, time-frequency modeling, and moderate synthetic augmentation improve hard regimes and down...

  3. MIMFlow: Integrating Masked Image Modeling with Normalizing Flows for End-to-End Image Generation

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    MIMFlow uses a VAE on masked images to feed semantic latents to a normalizing flow while a decoder handles high-frequency details, reporting FID 2.50 and 71.3% linear probing on ImageNet 256x256 with 128 tokens.

  4. SRC-Flow: Compact Semantic Representations Enable Normalizing Flows for Image Generation

    cs.CV 2026-05 unverdicted novelty 6.0 of 10

    SRC-Flow compresses RAE features via a Semantic Representation Compressor into a low-dimensional space, enabling normalizing flows to reach gFID 1.65 on ImageNet 256x256 and 2.07 on 512x512 while retaining exact likelihoods.

  5. PixNerd: Pixel Neural Field Diffusion

    cs.CV 2025-07 conditional novelty 6.0 of 10

    PixNerd is a single-stage pixel-space diffusion transformer that uses predicted neural field weights to decode large patches, reaching 2.15 FID on ImageNet 256 without a VAE.

  6. Guided Unconditional and Conditional Generative Models for Super-Resolution and Inference of Quasi-Geostrophic Turbulence

    physics.flu-dyn 2025-07 conditional novelty 6.0 of 10

    Conditional diffusion models outperform guided unconditional models for super-resolving quasi-geostrophic turbulence, especially from sparse and gappy observations.

  7. Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows

    cs.LG 2025-07 conditional novelty 6.0 of 10

    TarFlowLM models language in a continuous latent space with transformer-based autoregressive normalizing flows, using mixture-CDF and Rosenblatt couplings, and reports competitive NELBO on TEXT8 and OpenWebText.

  8. STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A latent-space transformer autoregressive flow with one deep block plus shallow refiners, tuned noise injection, and score-based guidance reaches competitive FID in high-resolution image synthesis, the first at this s...

  9. A Generative Model-Free Form Deformation Approach for the Generation of Mesh Motions with Applications to PDE

    math.NA 2026-07 conditional novelty 5.0 of 10

    A control-point ODE flow with a Bernstein-polynomial drift can match genus-0 meshes of different topology, and a POD+autoencoder+normalizing-flow generative model on the control trajectories yields lower-dimensional s...

  10. Normalizing Flows are Capable Models for Continuous Control

    cs.LG 2025-05 conditional novelty 5.0 of 10

    A simple normalizing flow policy matches or outperforms diffusion and autoregressive baselines across imitation learning, offline RL, goal-conditioned RL, and unsupervised RL on 82 tasks.

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