XMC-GAN
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
- Google Research
- Organisation type
- Industry
- Country
- United States of America
- Published
- 14 April 2022
- Authors
- Han Zhang, Jing Yu Koh, Jason Baldridge, Honglak Lee, Yinfei Yang
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
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
- Epochs
- 1,000
"Models are trained with a batch size of 256. For reporting results in our paper, models are trained for 1000 epochs, and we report the scores corresponding to the checkpoint with the best FID score on the validation set."
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
- 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
- Unknown
"XMC-GAN also generalizes to the challenging Localized Narratives dataset (which has longer, more detailed descriptions), improving state-of-the-art FID from 48.70 to 14.12"
Sources
Where this record came from and when it was last checked.
- Reference
- Cross-Modal Contrastive Learning for Text-to-Image Generation
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
XMC-GAN was published by Google Research, in United States of America, in April 2022. It comes out of industry.
It works in Image generation, and is recorded as doing image generation, Text-to-image.
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
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
XMC-GAN — common questions
Who created XMC-GAN?
XMC-GAN was published by Google Research, based in United States of America, categorised as industry.
When was XMC-GAN released?
XMC-GAN was published in April 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 XMC-GAN used for?
XMC-GAN works in Image generation, and is recorded as handling image generation, Text-to-image. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run XMC-GAN?
None. XMC-GAN 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 XMC-GAN open source?
No. XMC-GAN has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does XMC-GAN have?
No parameter count has been published for XMC-GAN, 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.