ControlNet (SDv2)
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
- Stability AI
- Organisation type
- Industry
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 26 November 2023
- Authors
- Lvmin Zhang, Anyi Rao, Maneesh Agrawala
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Image generation
- Task
- Image-to-image
- Base model
- Stable Diffusion 2.1
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
200k training samples
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.
- How it was established
- Hardware
- Fine-tuning compute
- 2.1 × 10¹⁹ FLOP
160000000000000 FLOP/GPU/sec [3090Ti reported, bf16 assumed] * 120 hours * 3600 sec / hour * 1 GPU * 0.3 [assumed utilization] = 20736000000000000000 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 GeForce RTX 3090 Ti
- Chips used
- 1
- Wall-clock time
- 120 hours
- Power draw
- 490 W
"one single NVIDIA RTX 3090Ti, and 5 days of training"
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
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
- Adding Conditional Control to Text-to-Image Diffusion Models
- Last updated
- 11 February 2026
What the numbers mean
Where it came from
ControlNet (SDv2) was published by Stability AI, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during November 2023. The publishing organisation is categorised as industry.
It works in the domain of Image generation, and is recorded as performing the task of image-to-image.
Its starting point was an existing base model, Stable Diffusion 2.1. That is why it shares the base model's general shape and size.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
ControlNet (SDv2) — common questions
ControlNet (SDv2)— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
ControlNet (SDv2)— who created it?
It was published by Stability AI, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as industry.
ControlNet (SDv2)— when was it released?
It was published in November 2023. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
ControlNet (SDv2)— what is it used for?
It works in the domain of Image generation, and is recorded as handling the task of image-to-image. 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.
ControlNet (SDv2)— 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.
ControlNet (SDv2)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
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.