GAWWN
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
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
- University of Michigan,Max Planck Institute for Informatics
- Organisation type
- Academia,Academia
- Country
- United States of America, Germany
- Published
- 8 October 2016
- Authors
- Scott E. Reed, Zeynep Akata, S. Mohan, Samuel Tenka, B. Schiele, Honglak Lee
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image generation
- Approach
- Supervised
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
- 235,760 tokens
directly stated in paper
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
- Closed — provider access only
- Model access
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
https://paperswithcode.com/sota/text-to-image-generation-on-cub I don't see any standard benchmarks where they would claim SOTA results
Sources
Where this record came from and when it was last checked.
- Reference
- Learning What and Where to Draw
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
GAWWN was published by University of Michigan,Max Planck Institute for Informatics, in United States of America, in October 2016. academia,Academia is the category the publisher falls under.
It works in Vision, and is recorded as doing image generation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
The training set ran to roughly 235,760 tokens.
The reason it appears in this catalogue at all is sOTA improvement.
Answers
GAWWN — common questions
When was GAWWN released?
GAWWN was published in October 2016. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is GAWWN used for?
GAWWN works in Vision, and is recorded as handling image generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run GAWWN?
None. GAWWN is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is GAWWN open source?
No. GAWWN has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does GAWWN have?
No parameter count has been published for GAWWN, which is why no memory or speed figure appears on this page.
Who created GAWWN?
GAWWN was published by University of Michigan,Max Planck Institute for Informatics, based in United States of America, categorised as academia,Academia.
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.