AI models
Denoising Diffusion Probabilistic Models (LSUN Bedroom)
Training compute
7.8×10¹⁹ FLOP
Parameters
256M
Published
Jun 11, 2021
Denoising Diffusion Probabilistic Models (LSUN Bedroom) is an AI model developed by University of California (UC) Berkeley (United States), first published in June 2021. It works in the vision domain, on tasks such as image generation.
Training it took an estimated 7.8×10¹⁹ FLOP of compute (estimation method: hardware). The model has 256,000,000 parameters. It was trained on roughly 596.3B datapoints. Training ran on Google TPU v3. The compute alone is estimated at $436 in 2023 dollars.
Access: Open weights (unrestricted). Its weights are openly available. The reference paper has 30,642 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- University of California (UC) Berkeley
- Country of organization
- United States
- Domain
- Vision
- Task
- Image generation
- Training compute
- 7.8×10¹⁹ FLOP
- Compute estimation method
- Hardware
- Parameters
- 256,000,000
- Dataset size
- 596.3B
- Training hardware
- Google TPU v3
- Training cost (2023 USD)
- $436
- Model accessibility
- Open weights (unrestricted)
- Open weights
- Yes
- Citations
- 30,642
- Epoch confidence
- Confident
More from University of California (UC) Berkeley
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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