Diffusion-GAN
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
- UT Austin,Microsoft
- Organisation type
- Academia,Industry
- Country
- United States of America
- Published
- 5 June 2022
- Authors
- Zhendong Wang, Huangjie Zheng, Pengcheng He, Weizhu Chen, Mingyuan Zhou
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Image generation
- Task
- Image generation, Text-to-image
- Numerical format
- FP16
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
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
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
- Hugging Face
- zhendongw
MIT license https://github.com/Zhendong-Wang/Diffusion-GAN https://huggingface.co/zhendongw/diffusion-gan/tree/main
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
- 327
Table 1 "We demonstrate the advantages of Diffusion-GAN over strong GAN baselines on various datasets, showing that it can produce more realistic images with higher stability and data efficiency than state-of-the-art GANs." "Quantitatively, Diffusion StyleGAN2 outperforms all the GAN baselines in generation diversity, as measured by Recall, on all 6 benchmark datasets and outperforms them in FID by a clear margin on 5 out of the 6 benchmark datasets."
Sources
Where this record came from and when it was last checked.
- Reference
- Diffusion-GAN: Training GANs with Diffusion
- Last updated
- 25 May 2026
What the numbers mean
About this model
Diffusion-GAN was published by UT Austin,Microsoft, in United States of America, in June 2022. academia,Industry is the category the publisher falls under.
It works in Image generation, and is recorded as doing image generation, Text-to-image.
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. It is published under the zhendongw organisation on Hugging Face.
Training and provenance
The reason it appears in this catalogue at all is sOTA improvement.
Answers
Diffusion-GAN — common questions
Is Diffusion-GAN open source?
Its weights are published, so Diffusion-GAN 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 Diffusion-GAN have?
No parameter count has been published for Diffusion-GAN, which is why no memory or speed figure appears on this page.
Who created Diffusion-GAN?
Diffusion-GAN was published by UT Austin,Microsoft, based in United States of America, categorised as academia,Industry.
When was Diffusion-GAN released?
Diffusion-GAN was published in June 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.
What is Diffusion-GAN used for?
Diffusion-GAN works in Image generation, and is recorded as handling image generation, Text-to-image. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Where can I download Diffusion-GAN?
Its weights are published under the zhendongw organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.
What GPU do I need to run Diffusion-GAN?
We cannot say. Diffusion-GAN 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.