PG-SWGAN
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
- ETH Zurich
- Organisation type
- Academia
- Country
- Switzerland
- Published
- 15 June 2019
- Authors
- Jiqing Wu, Zhiwu Huang, Dinesh Acharya, Wen Li, Janine Thoma, Danda Pani Paudel, Luc Van Gool
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Image generation
- Task
- Image generation
- Numerical format
- FP32
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
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Open (non-commercial)
looks like code but no weights, no license specified: https://github.com/musikisomorphie/swd
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
- Unknown
- Citations
- 136
"For fair comparison, we equip the same progressive growing architecture with our proposed SWGAN objective and its dual SWD blocks (PG-SWGAN). As shown in Fig. 3 (Right) and Fig. 5, our PG-SWGAN can outperform PG-WGAN in terms of both qualitative and quantitative comparison on the CelebA-HQ and LSUN datasets"
Sources
Where this record came from and when it was last checked.
- Reference
- Sliced Wasserstein Generative Models
- Last updated
- 1 December 2025
What the numbers mean
About this model
PG-SWGAN was published by ETH Zurich, in the country recorded as Switzerland, during June 2019. It comes out of an organisation categorised as academia.
It works in the domain of Image generation, and is recorded as performing the task of image generation.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
Its inclusion criterion: sOTA improvement.
Answers
PG-SWGAN — common questions
PG-SWGAN— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
PG-SWGAN— who created it?
It was published by ETH Zurich, based in Switzerland, an organisation categorised as academia.
PG-SWGAN— when was it released?
It was published in June 2019. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
PG-SWGAN— what is it used for?
It works in the domain of Image generation, and is recorded as handling the task of image generation. These are the areas it was designed around; they describe intent rather than a hard boundary.
PG-SWGAN— what GPU do I need to run it?
None. This 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.
PG-SWGAN— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
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.