Papers › Conditional Image Generation with PixelCNN Decoders
Conditional Image Generation with PixelCNN Decoders
Aaron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, Koray Kavukcuoglu
This work explores conditional image generation with a new image density model based on the PixelCNN architecture. The model can be conditioned on any vector, including descriptive labels or tags, or latent embeddings created by other networks. When conditioned on class labels from the ImageNet database, the model is able to generate diverse, realistic scenes representing distinct animals, objects, landscapes and structures. When conditioned on an embedding produced by a convolutional network given a single image of an unseen face, it generates a variety of new portraits of the same person with different facial expressions, poses and lighting conditions. We also show that conditional PixelCNN can serve as a powerful decoder in an image autoencoder. Additionally, the gated convolutional layers in the proposed model improve the log-likelihood of PixelCNN to match the state-of-the-art performance of PixelRNN on ImageNet, with greatly reduced computational cost.
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Code
Syntology Ran 4 of 14 code samples harvested from 5 repositories linked to this paper; 10 have no recorded run. Of those that ran: 2 ran · honoured contract; 1 ran · our draft was wrong; 1 ran · fixture could not drive it.
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Code Syntology ran Syntology
14 samples harvested; 4 ran; 2 honoured the contract we drafted; 10 have 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 |
|---|---|---|---|---|---|---|---|
| Density Estimation | CIFAR-10 | Pixel CNN | NLL (bits/dim) | 3.03 | #10 of 15 | Archive leaderboard | report |
| Image Generation | ImageNet 32x32 | Gated PixelCNN | bpd | 3.83 | #25 of 35 | Archive leaderboard | report |
| Image Generation | ImageNet 64x64 | Gated PixelCNN (van den Oord et al., [2016c]) | Bits per dim | 3.57 | #50 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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