U-Net GAN (FFHQ)
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
- Bosch Center for Artificial Intelligence,Max Planck Institute for Informatics
- Organisation type
- Industry,Academia
- Country
- Germany
- Published
- 19 March 2021
- Authors
- Edgar Schönfeld, Bernt Schiele, Anna Khoreva
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
AGPL-3.0 license (copyleft) https://github.com/boschresearch/unetgan
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
- A U-Net Based Discriminator for Generative Adversarial Networks
- Last updated
- 11 February 2026
What the numbers mean
About this model
U-Net GAN (FFHQ) was published by Bosch Center for Artificial Intelligence,Max Planck Institute for Informatics, in the country recorded as Germany, during March 2021. The category the publisher falls under is industry,Academia.
It works in the domain of Image generation, and is recorded as performing the task of image generation.
Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded.
Answers
U-Net GAN (FFHQ) — common questions
U-Net GAN (FFHQ)— who created it?
It was published by Bosch Center for Artificial Intelligence,Max Planck Institute for Informatics, based in Germany, an organisation categorised as industry,Academia.
U-Net GAN (FFHQ)— when was it released?
It was published in March 2021. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
U-Net GAN (FFHQ)— what is it used for?
It works in the domain of Image generation, and is recorded as handling the task of image generation. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
U-Net GAN (FFHQ)— where can I download it?
The weights are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
U-Net GAN (FFHQ)— what GPU do I need to run it?
We cannot say. It 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.
U-Net GAN (FFHQ)— is it open source?
Its weights are published, so it 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.
U-Net GAN (FFHQ)— 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.
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.