ESRGAN
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
- Chinese University of Hong Kong (CUHK),Chinese Academy of Sciences,Nanyang Technological University
- Organisation type
- Academia,Academia,Academia
- Country
- Hong Kong, China, Singapore
- Published
- 1 September 2018
- Authors
- Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Chen Change Loy, Yu Qiao, Xiaoou Tang
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
- tokens
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
- Citations
- 4,625
Sources
Where this record came from and when it was last checked.
- Reference
- ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
- Last updated
- 25 May 2026
What the numbers mean
What this model is
ESRGAN was published by Chinese University of Hong Kong (CUHK),Chinese Academy of Sciences,Nanyang Technological University, in Hong Kong, in September 2018. It comes out of academia,Academia,Academia.
It works in Vision, Image generation, and is recorded as doing image super-resolution.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
ESRGAN — common questions
Is ESRGAN open source?
The licensing for ESRGAN was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does ESRGAN have?
No parameter count has been published for ESRGAN, which is why no memory or speed figure appears on this page.
Who created ESRGAN?
ESRGAN was published by Chinese University of Hong Kong (CUHK),Chinese Academy of Sciences,Nanyang Technological University, based in Hong Kong, categorised as academia,Academia,Academia.
When was ESRGAN released?
ESRGAN was published in September 2018. 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 ESRGAN used for?
ESRGAN works in Vision, Image generation, and is recorded as handling image super-resolution. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run ESRGAN?
None. ESRGAN 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.
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.