What is Stable Diffusion?

Stable Diffusion is an AI model that produces images using text-based instructions. The tool is known for its ability to create very detailed, realistic-looking content. It is mostly used to create images but can also be used to edit images and design user interfaces.

What is Stable Diffusion?

Stable Diffusion is a generative AI model that creates unique, realistic images from text. It works primarily using special instructions that are entered in text form, called prompts. However, recognising voice commands is now also a feature of Stable Diffusion. Newer versions of the tool can also create short videos and animations (when used with extensions like Deforum).

Stable Diffusion is based on deep learning, meaning it uses artificial neural networks to process information. That makes it possible for the model to independently learn from data. It was trained with millions of text-image pairs, enabling it to recognise patterns in datasets and generate relevant content.

The AI tool has its origins in a research project at LMU Munich and Heidelberg University in Germany. The model has been continuously improved on since the first version was released in 2022. It now supports up to 8 billion parameters, which helps the artificial intelligence better recognise the intention behind prompts and generate better results. Since Stable Diffusion is run as open-source software, its source code is freely accessible.

What are the features of Stable Diffusion?

Stable Diffusion has a number of characteristics and features that make it interesting for both private individuals and companies. In fact, many consider it one of the best AI image generators on the market. Some of Stable Diffusion’s features include:

• Open source: Any person can download and use the Stable Diffusion source code for their individual projects. Thanks to the tool’s active community, there are plenty of tutorials and documentation available.
• First-class results: Stable Diffusion delivers detailed, realistic content, even when given complex prompts. That’s due in part to the architecture of the AI tool and in part to its training with the extensive LAION dataset.
• Platform independence: Stable Diffusion can be run on powerful servers as well as standard consumer hardware, meaning that you can use it on standard PCs and laptops. This scalability allows a wide range of users to access the software for creative and professional projects, without the need for expensive cloud services.
• High flexibility: With the right knowledge, you can adapt the AI tool to your individual creative needs or build applications based on specific workflows.

How does Stable Diffusion work?

Unlike most other AI image generators, Stable Diffusion is a diffusion model. Diffusion is an innovative approach that involves converting images from the training data into visual noise. When an image is created, the process is reversed. In training, the model learns how to generate meaningful images from the noise by continuously checking the difference between a generated image and real images. Stable Diffusion’s architecture has four central components:

• Variational autoencoder (VAE): The VAE consists of an encoder and a decoder. The encoder compresses an image to make it easier to manipulate and determines its semantic meaning. The decoder is responsible for image output.
• Diffusion processes: Forward diffusion gradually adds Gaussian noise to the image until only random noise remains. Reverse diffusion later reverses this process iteratively, creating a unique image from the noise.
• Noise predictor: The noise predictor predicts the amount of noise in the latent space and subtracts it from the image. It repeats the process a specific number of times to reduce the noise more and more. Up until version 3.0, a U-Net model (convolutional neural network) was used for that. Newer versions use the rectified flow transformer.
• Text conditioning: A tokeniser translates text input into units the AI model can understand, in order to capture the user’s intention and interpret it precisely. The prompt is then passed on to the noise predictor.

What is Stable Diffusion used for?

Stable Diffusion is mainly used for generating images. What those images are in turn used for will vary widely. While creatives and designers use the AI image generator to implement ideas, advertising agencies use it to make digital designs for campaigns and projects.

Stable Diffusion can also be used for editing images. You can, for example, remove specific objects from an image, paint over them or change their colour, replace the background with another one and change the lighting.

Finally, the AI model can also help you design user interfaces. Using text prompts, you can generate entire user interfaces or individual elements like buttons, icons and backgrounds. That allows designers to quickly and easily test concepts and, in the best case, to improve user experience design.

What are the limitations of Stable Diffusion?

Even though Stable Diffusion has many features and an impressive range of ability, it does come with limitations. Some of its most notable limitations include:

• Image errors: Stable Diffusion is capable of generating detailed images, but it does produce inaccuracies, especially with abstract concepts. Inexperienced users in particular might find it hard to get the results they are looking for.
• Unknown use cases: Stable Diffusion only has access to the examples in the training dataset. If there isn’t any data in that dataset for a prompt, the model won’t be able to generate a satisfying image, or only to a very limited extent.
• Copyright problems: The data used to train the AI were used without the express permission of its creators. That has already led to legal disputes in several cases where the creators took issue with the unauthorised use of their works.
• Bias and stereotypes: As with other AI models, Stable Diffusion also runs the risk that prejudices from its training data are reproduced in the images it generates. That can result in discriminatory or stereotypical depictions with regard to race, gender, culture and age.
• Hardware requirements: Stable Diffusion requires considerable computing resources, in particular a powerful graphics card (GPU) with sufficient VRAM (video random access memory). This can be a hurdle for users with standard hardware. Loading times and speed for image generation on that kind of system are very limited.