Normalizing Flows are Capable Generative Models
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.
675c831d9e17c3619871d3e6
Shuangfei Zhai
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2024-12-16T11:03:10.096Z
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Shuangfei Zhai
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Ruixiang Zhang
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2024-12-16T11:02:43.750Z
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Ruixiang Zhang
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onlogn
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Preetum Nakkiran
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David Berthelot
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Jiatao Gu
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2024-12-16T11:02:21.807Z
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Jiatao Gu
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Huangjie Zheng
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2024-12-16T11:02:15.470Z
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Huangjie Zheng
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Tianrong Chen
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2024-12-16T11:02:08.979Z
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chentianrong
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Miguel Angel Bautista
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2024-12-16T11:01:56.375Z
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Miguel Angel Bautista
user
FlippyCode
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Navdeep Jaitly
admin_assigned
2024-12-16T11:01:49.180Z
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Navdeep Jaitly
user
ndjaitly
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Josh Susskind
Normalizing Flows are Capable Generative Models
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.
TarFlow, a Transformer-based Normalizing Flow model, achieves state-of-the-art likelihood estimation and high-quality image generation, marking the first time a standalone NF model competes with diffusion models.
Normalizing Flows likelihood-based models density estimation generative modeling TarFlow Transformer-based Masked Autoregressive Flows autoregressive Transformer blocks Gaussian noise augmentation post training denoising guidance method likelihood estimation image generation diffusion models