Stability AI released the pre-trained model weights for Stable Diffusion, a text-to-image AI model, to the general public. Given a text input from a user, Stable Diffusion can generate photorealistic 512x512 pixel images depicting the scene described in the prompt.

Female cyborg assimilated by alien fungus, intricate Three-point lighting portrait, by Ching Yeh and Greg Rutkowski, detailed cyberpunk in the style of GitS 1995 (Source)

Introduction πŸ‘‡

Stable Diffusion is a text-based image generation machine learning model released by Stability.AI. It has the ability to generate image from text! The model is open source which means that it is accessible to everyone to play around with it. In this blog post, I explain how you can run your first Stable Diffusion model but also how it works at a high level.

Side Note: β€œOpen Responsible AI Licenses (Open RAIL) are licenses designed to permit free and open access, reuse, and downstream distribution of derivatives of AI artifacts as long as the behavioral-use restrictions always apply (including to derivative works).”

You might be asking......dude what? Here's an alternative explanationπŸ‘‡

You can use the model for non-commercial purposes and commercial under the terms of the license called Creative ML OpenRAIL-M. It's important not to break any laws, discriminate or insult individuals, generating fake news or information. More information can be found under this link.

Prompt: kneeling cat knight, portrait, finely detailed armor, intricate design, silver, silk, cinematic lighting, 4k (Source)

πŸ“Š The Data

Stable Diffusion was trained off three massive datasets collected by LAION, a nonprofit whose compute time was largely funded by Stable Diffusion’s owner, Stability AI.

I could not find any article that says which model OpenAI has trained DALL-E 2 on. Although they say "hundreds of millions of captioned images", they still did not release any information on the datasets used. On the other hand, the Stability AI team has been very honest and open about how their model was trained. Coupled with Stable Diffusion being Open Source, that could explain the massive popularity of this model, while also making it accessible to the general public.

The Stable Diffusion model was trained using the LAION Aesthetics dataset, a subset of the LAION 5B dataset, containing 120 million image-text pairs from the complete set which contains nearly 6 billion image-text pairs.

Out of the 12 million images they sampled, 47% of the total sample size came from 100 domains, with Pinterest yielding 8.5% of the entire dataset. Other top sources included, Blogspot, Flickr, DeviantArt, and Wikimedia.

Stable Diffusion reportedly runs on less than 10 GB of VRAM at inference time, generating 512x512 images in just a few seconds, meaning running on consumer GPUs is an option.

πŸ”Ž Under the Hood of Stable Diffusion

Stable Diffusion is powered by Latent Diffusion, a cutting-edge text-to-image synthesis technique. This method was described in a paper published by AI researchers at the Ludwig Maximilian University of Munich titled β€œHigh-Resolution Image Synthesis with Latent Diffusion Models.”

In a nutshell, latent diffusions are diffusion models (DMs) that achieve state-of-the-art synthesis results on image data and more by breaking down the process of making an image into a series of applications of denoising autoencoders.

The architecture of latent diffusion model. (Image source: Rombach & Blattmann, et al. 2022)

Stability AI puts diffusion models in the latent space of powerful pre-trained autoencoders so that they could be trained with limited computing resources without losing their quality or flexibility.

Latent diffusion model (LDM; Rombach & Blattmann, et al. 2022) runs the diffusion process in the latent space instead of pixel space, making training cost lower and inference speed faster. It is motivated by the observation that most bits of an image contribute to perceptual details and the semantic and conceptual composition still remains after aggressive compression. LDM loosely decomposes the perceptual compression and semantic compression with generative modeling learning by first trimming off pixel-level redundancy with autoencoder and then manipulate/generate semantic concepts with diffusion process on learned latent. (Source)

The Paper πŸ“

High-Resolution Image Synthesis with Latent Diffusion Models

By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. However, since these models typically operate directly in pixel space, optimization of powerful DMs often consumes hundreds of GPU days and inference is expensive due to sequential evaluations.

High-Resolution Image Synthesis with Latent Diffusion Models
High-Resolution Image Synthesis with Latent Diffusion Models

πŸ’» Run Stable Diffusion on MacOS

Prerequisites πŸ’»


You will need one of the following:

  • An NVIDIA-based graphics card with 4 GB or more VRAM memory.
  • An Apple computer with an M1 chip.
  • macOS 12.3 or higher.


  • At least 12 GB Main Memory RAM. 8GB of RAM also works, but it is annoyingly slow.


  • At least 6 GB of free disk space for the machine learning model, Python, and all its dependencies.

Conda Installation 🐍

I generally recommend that you install Conda. Pip is a package manager, and Virtualenv is an environment manager. Conda is both!

So Conda is a packaging tool and installer that aims to do more than what pip does; handle library dependencies outside of the Python packages as well as the Python packages themselves. Conda also creates a virtual environment, like virtualenv does.

Create an Environment 🌱

Use the following command to create your own environment that is specifically set up for Stable Diffusion.

conda create --name myenv

In your case, it could be

conda create --name StableDiffusion

...or use a shorter name! πŸ™‚ Once that's done, activate the environment to use it.

conda activate StableDiffusion

Python Version 🐍

First let's check in the terminal what Python version you have installed. At the time of writing this article, I did not have the required version 3.10 installed...

$ python3 -V                                                            
Python 3.9.6

Important: You need at least Python 3.10 to run Stable Diffusion. To upgrade your current Python version, use the following (considering you have Homebrew installed)πŸ‘‡

brew update && brew install python

To check your python version, use the following command.

python3 --version

Getting Started – Clone the Fork of Stable Diffusion Β πŸ’»

Let's start by creating a new folder on your Desktop and switch into that folder. Type in the following commands into the terminal.

mkdir StableDiffusion
cd StableDiffusion

Run the following to clone the fork of the Stable Diffusion repository

git clone -b apple-silicon-mps-support
cd stable-diffusion
mkdir -p models/ldm/stable-diffusion-v1/

Let's set up a virtual environment (virtualenv) to install needed dependencies:

python3 -m pip install virtualenv
python3 -m virtualenv venv

Activate the virtualenv

source venv/bin/activate

If Β you see "ERROR: Failed building wheel for onnx" you might need to install these packages:

ERROR: Failed building wheel for onnx
ERROR: Failed building wheel for onnx

To fix this issue, run the following command

brew install Cmake protobuf rust

Download the Weights 🌎

Go to the Hugging Face repository. Read the license and agree with its terms, then click "Access repository".

Download "sd-v1-4.ckpt" (~4 GB) on that page and save it as models/ldm/stable-diffusion-v1/model.ckpt in the directory you created above.

Run it! πŸš€

Now, you can run Stable Diffusion:

python scripts/ \
  --prompt "a black cat chilling on the street" \
  --n_samples 1 --n_iter 1 --plms

Your output's in "outputs/txt2img-samples/".

Stable Diffusion Output - A Black Cat Chilling on the Street

If that is not working, you should consider downgrading protobuf

pip install protobuf==3.19.4

For me that caused another problem that I could not resolve first. You need to rename the downloaded weights from sd-v1-4.ckpt to model.ckpt and move it inside the stable-diffusion-v1 folder.

Check out the Stable Diffusion repo to get more information.

GitHub - magnusviri/stable-diffusion: Go to lstein/stable-diffusion for all the best stuff and a stable release. This repository is my testing ground and it’s very likely that I’ve done something that will break it.
Go to lstein/stable-diffusion for all the best stuff and a stable release. This repository is my testing ground and it's very likely that I've done something that will break it. - GitHub - ...

1-Click Solution πŸ–±οΈ

CHARL-E packages Stable Diffusion into a simple app. No complex setup, dependencies, or internet required β€” just download and say what you want to see.

Charlie Holtz has released CHARL-E – A 1-click installer for Mac (M1 & M2) users
Charlie Holtz has released CHARL-E – A 1-click installer for Mac (M1 & M2) users

Installation on Windows πŸ–₯️

Windows Installation
Stable Diffusion web UI

πŸ“ Stable Diffusion Prompting Cheatsheet

Moritz wrote a great blog that summarises a few prompts that you can use once you run Stable Diffusion

Stable Diffusion prompting cheatsheet | Moritz’ Blog
A handy list of words for making better Stable Diffusion prompts.
Stable Diffusion Prompting Cheatsheet

Links from the official Stability AI announcement blog post.

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Keep engineering your mind! ❀️