Stable Diffusion 3
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
- 22 February 2024
- Authors
- Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion English, Kyle Lacey, Alex Goodwin, Yannik Marek, Robin Rombach
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.
- Parameters
- 8B
- 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
- 5 × 10²² FLOP
- How it was established
- Reported
Finally, we performed a scaling study of this combination up to a model size of 8B parameters and 5 × 1022 training FLOPs.
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
- API access
- Training code
- Unreleased
Apr 2014 We have partnered with Fireworks AI, the fastest and most reliable API platform in the market, to deliver Stable Diffusion 3 and Stable Diffusion 3 Turbo. In keeping with our commitment to open generative AI, we aim to make the model weights available for self-hosting with a Stability AI Membership in the near future.
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
"Our largest models outperform state-of-the art open models such as SDXL (Podell et al., 2023), SDXL-Turbo (Sauer et al., 2023), Pixart-α (Chen et al., 2023), and closed-source models such as DALL-E 3 (Betker et al., 2023) both in quantitative evaluation (Ghosh et al., 2023) of prompt understanding and human preference ratings.:
Sources
Where this record came from and when it was last checked.
- Reference
- Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
- Last updated
- 28 November 2025
What the numbers mean
About this model
Stable Diffusion 3 was published by Stability AI, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during February 2024. The publishing organisation is categorised as industry.
It works in the domain of Image generation, and is recorded as performing the task of image generation, Text-to-image.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Producing it required arithmetic totalling around 5 × 10²² FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The reason it appears in this catalogue at all: sOTA improvement.
Answers
Stable Diffusion 3 — common questions
Stable Diffusion 3— how many parameters does it have?
It has a parameter count of 8B. 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.
Stable Diffusion 3— who created it?
It was published by Stability AI, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as industry.
Stable Diffusion 3— when was it released?
It was published in February 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Stable Diffusion 3— what is it used for?
It works in the domain of Image generation, and is recorded as handling the task of image generation, Text-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.
Stable Diffusion 3— how much compute was used to train it?
Training consumed around 5 × 10²² FLOP. 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.
Stable Diffusion 3— 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.
Stable Diffusion 3— 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.