PixelCNN++
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- OpenAI
- Organisation type
- Industry
- Country
- United States of America
- Published
- 19 January 2017
- Authors
- Tim Salimans, Andrej Karpathy, Xi Chen, Diederik P. Kingma
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Image generation
- Task
- Image generation
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Open — downloadable
- Model access
- Open weights (unrestricted)
- Training code
- Open source
MIT + Apache 2.0 https://github.com/openai/pixel-cnn?tab=readme-ov-file
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications
- Last updated
- 28 November 2025
What the numbers mean
What this model is
PixelCNN++ was published by OpenAI, in United States of America, in January 2017. industry is the category the publisher falls under.
It works in Image generation, and is recorded as doing image generation.
Published weights mean the model runs on your machine rather than someone else's, which is what makes the hardware question below answerable at all.
Answers
PixelCNN++ — common questions
What is PixelCNN++ used for?
PixelCNN++ works in Image generation, and is recorded as handling image generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Where can I download PixelCNN++?
The weights for PixelCNN++ are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
What GPU do I need to run PixelCNN++?
We cannot say. PixelCNN++ has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
Is PixelCNN++ open source?
Its weights are published, so PixelCNN++ can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
How many parameters does PixelCNN++ have?
No parameter count has been published for PixelCNN++, which is why no memory or speed figure appears on this page.
Who created PixelCNN++?
PixelCNN++ was published by OpenAI, based in United States of America, categorised as industry.
When was PixelCNN++ released?
PixelCNN++ was published in January 2017. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.