ERNIE-ViLG
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
- Baidu
- Organisation type
- Industry
- Country
- China
- Published
- 31 December 2021
- Authors
- Han Zhang, Weichong Yin, Yewei Fang, Lanxin Li, Boqiang Duan, Zhihua Wu, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Multimodal, Image generation, Vision, Language
- Task
- Vision-language generation, Image generation, Text-to-image, Image captioning, Language modeling/generation, Visual question answering
- Approach
- Self-supervised learning
- 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.
- Parameters
- 10B
- Training data
- tokens
"To explore the landscape of large-scale pre-training for bidirectional text-image generation, we pre-train a 10-billion parameter model on a large-scale dataset of 145 million high-quality Chinese image-text pairs."
To explore the landscape of large-scale pre-training for bidirectional text-image generation, we pre-train a 10-billion parameter model on a large-scale dataset of 145 million high-quality Chinese image-text pairs.
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
it seems that the model was completely substituted by its successor ViLG 2.0 - I haven't found niether model weights nor code or API.
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Foundation model
- Yes
- Why it is tracked
- SOTA improvement
- Citations
- 70
"we train a 10-billion parameter ERNIE-ViLG model on a large-scale dataset of 145 million (Chinese) image-text pairs which achieves state-of-the-art performance for both text-to-image and image-to-text tasks" "chieves state-of-the-art performance for both text-to-image and image-to-text tasks, obtaining an FID of 7.9 on MS-COCO for text-to-image synthesis and best results on COCO-CN and AIC-ICC for image captioning"
Sources
Where this record came from and when it was last checked.
- Reference
- ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language Generation
- Last updated
- 25 May 2026
What the numbers mean
Background
ERNIE-ViLG was published by Baidu, in the country recorded as China, during December 2021. The publishing organisation is categorised as industry.
It works in the domain of Multimodal, Image generation, Vision, Language, and is recorded as performing the task of vision-language generation, Image generation, Text-to-image, Image captioning, Language modeling/generation, Visual question answering.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
The reason it appears in this catalogue at all: sOTA improvement.
Answers
ERNIE-ViLG — common questions
ERNIE-ViLG— who created it?
It was published by Baidu, based in China, an organisation categorised as industry.
ERNIE-ViLG— when was it released?
It was published in December 2021. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
ERNIE-ViLG— what is it used for?
It works in the domain of Multimodal, Image generation, Vision, Language, and is recorded as handling the task of vision-language generation, Image generation, Text-to-image, Image captioning, Language modeling/generation, Visual question answering. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
ERNIE-ViLG— 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.
ERNIE-ViLG— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
ERNIE-ViLG— how many parameters does it have?
It has a parameter count of 10B. "To explore the landscape of large-scale pre-training for bidirectional text-image generation, we pre-train a 10-billion parameter model on a large-scale dataset of 145 million high-quality Chinese image-text pairs.". That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
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.