Papers › Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images
Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images
Rewon Child
We present a hierarchical VAE that, for the first time, generates samples quickly while outperforming the PixelCNN in log-likelihood on all natural image benchmarks. We begin by observing that, in theory, VAEs can actually represent autoregressive models, as well as faster, better models if they exist, when made sufficiently deep. Despite this, autoregressive models have historically outperformed VAEs in log-likelihood. We test if insufficient depth explains why by scaling a VAE to greater stochastic depth than previously explored and evaluating it CIFAR-10, ImageNet, and FFHQ. In comparison to the PixelCNN, these very deep VAEs achieve higher likelihoods, use fewer parameters, generate samples thousands of times faster, and are more easily applied to high-resolution images. Qualitative studies suggest this is because the VAE learns efficient hierarchical visual representations. We release our source code and models at https://github.com/openai/vdvae.
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Code
Syntology Ran 8 of 9 code samples harvested from 4 repositories linked to this paper; 1 has no recorded run. Of those that ran: 2 ran · honoured contract; 3 ran · our draft was wrong; 3 ran · fixture could not drive it.
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Code Syntology ran Syntology
9 samples harvested; 8 ran; 2 honoured the contract we drafted; 1 has no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Generation | FFHQ 1024 x 1024 | Very Deep VAE | bits/dimension | 2.42 | #20 of 20 | Archive leaderboard | report |
| Image Generation | FFHQ 256 x 256 | Very Deep VAE | bits/dimension | 0.61 | #50 of 51 | Archive leaderboard | report |
| Image Generation | ImageNet 32x32 | Very Deep VAE | bpd | 3.8 | #24 of 35 | Archive leaderboard | report |
| Image Generation | ImageNet 64x64 | Very Deep VAE | Bits per dim | 3.52 | #48 of 65 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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