SRGAN
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
- Organisation type
- Industry
- Country
- United States of America
- Published
- 25 May 2017
- Authors
- Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision, Image generation
- Task
- Image super-resolution
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
- 700,000 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
- 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
- Highly cited
- Record confidence
- Unknown
- Citations
- 11,592
Sources
Where this record came from and when it was last checked.
- Reference
- Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
- Last updated
- 1 January 2026
What the numbers mean
What this model is
SRGAN was published by Twitter, in the country recorded as United States of America, during May 2017. The category the publisher falls under is industry.
It works in the domain of Vision, Image generation, and is recorded as performing the task of image super-resolution.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
It was trained on a corpus of about 700,000 tokens of text.
The reason it appears in this catalogue at all: highly cited.
Answers
SRGAN — common questions
SRGAN— what is it used for?
It works in the domain of Vision, Image generation, and is recorded as handling the task of image super-resolution. These are the areas it was designed around; they describe intent rather than a hard boundary.
SRGAN— 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.
SRGAN— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
SRGAN— 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.
SRGAN— who created it?
It was published by Twitter, based in United States of America, an organisation categorised as industry.
SRGAN— when was it released?
It was published in May 2017. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
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.