Stable Diffusion 1.2
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
- Ludwig Maximilian University of Munich
- Organisation type
- Academia
- Country
- Germany
- Published
- 22 August 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
- Base model
- Stable Diffusion 1.1
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
515k steps at 512x512 on "laion-improved-aesthetics". Batch: 32 x 8 x 2 x 4 = 2048
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 PCIe
- Chips used
- 256
- Power draw
- 153.9 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 (restricted use)
- Training code
- Open (restricted use)
- Hugging Face
- CompVis
Open RAIL-M license https://huggingface.co/CompVis/stable-diffusion-v1-2 Open RAIL-M license https://github.com/CompVis/stable-diffusion
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 v1-2 Model Card
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Stable Diffusion 1.2 was published by Ludwig Maximilian University of Munich, in the country recorded as Germany, during August 2022. The publishing organisation is categorised as academia.
It works in the domain of Image generation, and is recorded as performing the task of image generation, Text-to-image.
Rather than being trained from scratch, it is derived from Stable Diffusion 1.1. That is why it shares the base model's general shape and size.
Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded. On Hugging Face it is published under the organisation CompVis.
Answers
Stable Diffusion 1.2 — common questions
Stable Diffusion 1.2— 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.
Stable Diffusion 1.2— who created it?
It was published by Ludwig Maximilian University of Munich, based in Germany, an organisation categorised as academia.
Stable Diffusion 1.2— when was it released?
It was published in August 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.
Stable Diffusion 1.2— 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. These are the areas it was designed around; they describe intent rather than a hard boundary.
Stable Diffusion 1.2— where can I download it?
Its weights are published on Hugging Face, under the organisation CompVis. We do not host model files — this site calculates what hardware is needed to run them.
Stable Diffusion 1.2— what GPU do I need to run it?
We cannot say. It 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.
Stable Diffusion 1.2— is it open source?
Its weights are published, so it 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.