Stable Diffusion 2.1
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
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
- Stability AI
- Organisation type
- Industry
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 7 December 2022
- Authors
- Robin Rombach, Patrick Esser
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Image generation
- Task
- Image generation, Text-to-image
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
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 6.7 × 10²² FLOP
- How it was established
- Hardware
312000000000000 FLOP / GPU / sec [A100 reported, bf16 assumed] * 200000 GPU-hours * 3600 sec / hour * 0.3 [assumed utilization] = 6.7392e+22 FLOP
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA A100 SXM4 40 GB
- Chips used
- 256
- Chip-hours
- 200,000
- Power draw
- 204.7 kW
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
- Open — downloadable
- Model access
- Open weights (unrestricted)
- Training code
- Unreleased
- Hugging Face
- stabilityai
openrail++ license https://huggingface.co/stabilityai/stable-diffusion-2-1 https://github.com/Stability-AI/stablediffusion?tab=readme-ov-file
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Stable Diffusion v2.1 and DreamStudio Updates 7-Dec 22
- Last updated
- 28 November 2025
What the numbers mean
Background
Stable Diffusion 2.1 was published by Stability AI, in United Kingdom of Great Britain and Northern Ireland, in December 2022. industry is the category the publisher falls under.
It works in Image generation, and is recorded as doing image generation, Text-to-image.
Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded. It is published under the stabilityai organisation on Hugging Face.
Training and provenance
The training run consumed about 6.7 × 10²² FLOP, on NVIDIA A100 SXM4 40 GB. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Answers
Stable Diffusion 2.1 — common questions
How many parameters does Stable Diffusion 2.1 have?
No parameter count has been published for Stable Diffusion 2.1, which is why no memory or speed figure appears on this page.
Who created Stable Diffusion 2.1?
Stable Diffusion 2.1 was published by Stability AI, based in United Kingdom of Great Britain and Northern Ireland, categorised as industry.
When was Stable Diffusion 2.1 released?
Stable Diffusion 2.1 was published in December 2022. 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 Stable Diffusion 2.1 used for?
Stable Diffusion 2.1 works in Image generation, and is recorded as handling image generation, Text-to-image. These are the areas it was designed around; they describe intent rather than a hard boundary.
Where can I download Stable Diffusion 2.1?
Its weights are published under the stabilityai organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.
How much compute was used to train Stable Diffusion 2.1?
Around 6.7 × 10²² FLOP, on NVIDIA A100 SXM4 40 GB. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
What GPU do I need to run Stable Diffusion 2.1?
We cannot say. Stable Diffusion 2.1 has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
Is Stable Diffusion 2.1 open source?
Its weights are published, so Stable Diffusion 2.1 can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
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.