StyleGAN-XL
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
- Max Planck Institute for Intelligent Systems,University of Tübingen
- Organisation type
- Academia,Academia
- Country
- Germany
- Published
- 5 May 2022
- Authors
- Axel Sauer, Katja Schwarz, Andreas Geiger
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
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 1.3 × 10²¹ FLOP
- How it was established
- Hardware
" to match the prior state-of-the-art performance of ADM [Dhariwal and Nichol 2021] at a resolution of 512^2 pixels, training the models on a single NVIDIA Tesla V100 takes 400 days compared to the previously required 1914 V100-days." 125000000000000 FLOP / GPU / sec * 9600 GPU-hours [for a 512 resolution SOTA result] * 3600 sec / hour * 0.3 [assumed utilization] = 1.296e+21 FLOP
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA V100
- Chip-hours
- 9,600
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 license: https://github.com/autonomousvision/stylegan-xl
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
StyleGAN-XL was published by Max Planck Institute for Intelligent Systems,University of Tübingen, in the country recorded as Germany, during May 2022. It comes out of an organisation categorised as academia,Academia.
It works in the domain of Image generation, and is recorded as performing the task of image generation.
The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold.
What went into building it
Training it took a computation budget of roughly 1.3 × 10²¹ FLOP, on hardware recorded as NVIDIA V100. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
StyleGAN-XL — common questions
StyleGAN-XL— 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.
StyleGAN-XL— 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.
StyleGAN-XL— who created it?
It was published by Max Planck Institute for Intelligent Systems,University of Tübingen, based in Germany, an organisation categorised as academia,Academia.
StyleGAN-XL— when was it released?
It was published in May 2022. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
StyleGAN-XL— 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.
StyleGAN-XL— 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.
StyleGAN-XL— how much compute was used to train it?
Training consumed around 1.3 × 10²¹ FLOP, on hardware recorded as NVIDIA V100. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
StyleGAN-XL— 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.
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.