AI models
GigaGAN
Training compute
3.9×10²² FLOP
Parameters
1B
Published
Jun 19, 2023
GigaGAN is an AI model developed by POSTECH, Carnegie Mellon University (CMU) and Adobe (South Korea and United States), first published in June 2023. It works in the image generation domain, on tasks such as text-to-image and image generation.
Training it took an estimated 3.9×10²² FLOP of compute (estimation method: hardware). The model has 1,000,000,000 parameters. It was trained on roughly 2B datapoints.
Access: Unreleased. Its weights are not openly released. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- POSTECH, Carnegie Mellon University (CMU), Adobe
- Country of organization
- South Korea, United States
- Domain
- Image generation
- Task
- Text-to-image, Image generation
- Training compute
- 3.9×10²² FLOP
- Compute estimation method
- Hardware
- Parameters
- 1,000,000,000
- Dataset size
- 2B
- Model accessibility
- Unreleased
- Open weights
- No
- Epoch confidence
- Confident
SourceEpoch AI, 'AI Models'. Published online at epoch.ai. Retrieved 2026-07-29 from https://epoch.ai/data/ai-models. Licensed under CC BY 4.0.
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