Stable Diffusion for Commercial Use

As the demand for unique digital images and other pieces of art continues to rise, experienced and novice artists are looking for effective and quicker methods of generating more pieces for commercial purposes. Fortunately, the use of deep learning and artificial intelligence (AI) art generators has enabled artists to create as many original pieces as possible.

One such tool is Stable Diffusion. This is a fairly new deep learning model of art generation tool, whose release was announced by Stability.ai in August 2022. Stability.ai is a popular community of software builders and engineers who design and implement solutions using integrated intelligence and improved technology.

What Is Stable Diffusion?

Stable Diffusion, which generates images from text descriptions, is a product of the Computer Vision and Learning research group at the Ludwig Maximilian University of Munich. While this text-to-image model is primarily conditioned to generate full images from texts, it can also be used to perform other image-generation tasks like painting, out-painting, and creating image-to-image translations based on a text prompt.

This tool is a covert diffusion model, which is part of the various deep generative neural networks created by the CompVis Group, was developed and released in collaboration with Stability.ai, Runway, EleutherAI, and LAION. Unlike other AI generators whose code and model weights remain a secret, Stable Diffusion’s code and model weights have been made public.

How It Works

The Stable Diffusion AI art generator runs on almost any computer with a modest GPU. These aspects of Stable Diffusion make it stand out from its competitors, such as DALL-E and Midjourney, which are only accessible through cloud services. Stable Diffusion deploys a substitute for the diffusion model (DM) popularly known as the Latent Diffusion Model (LDM).

However, you can get the same or better performance by simply using NightCafe’s stable diffusion generator. All the features with none of the hassle of setting it up to run locally. 

Developed in 2015, these models are designed to remove successive applications of Gaussian noise on training images considered to be a sequence of denoising autoencoders. As one of these diffusion models, Stable Diffusion is made up of three parts: U-Net, variational autoencoder (VAE), and the optional text encoder.

The VAE part is very important because it compresses images from pixel spaces to smaller dimensional latent spaces, taking a somewhat more semantic connotation to the images. Gaussian noise can be applied to the compressed latent images frequently during advancing diffusion.

The U-Net block, which is composed of a ResNet backbone, denoises the yields from forward diffusion back to acquiring latent representation. Then, the VAE decoder produces the ultimate output by translating the image back into pixel space.

Denoising can easily be openly trained on a sequence of text, an image, and other modalities. You can even use Stable Diffusion negative prompts to let this model know what you don't want to see in the generated images.

This model is compatible with other tools to help you write more expressive and detailed prompts. For instance, with the right Stable Diffusion prompt syntax tools, you can spice up your prompts to generate unique images.

Using Stable Diffusion for Commercial Purposes

During the release of Stable Diffusion in August, Stability.ai announced that it collaborated with its partners to ensure the final product is safe and ethical. The developers incorporated data from their beta model tests and communities to create the most reliable and effective art generator.

In collaboration with the vigorous legal, ethics, and technology experts at HuggingFace, as well as an amazing team of engineers from CoreWeave, the developers added several important elements to the final product. For instance, this model operates under the Creative ML OpenRAIL-M licence, which sanctions the commercial and non-commercial use of Stable Diffusion.

The licence focuses on the ethical and legal application of the model. Therefore, it’s your responsibility to ensure that you use the model legally and ethically. Make sure that the licence accompanies the model if you wish to distribute it. You must also make a copy of the licence available to your end users when including this model in your service.   

Whether you’re creating images and art pieces as a hobby or as a profession, you can make money out of your work by converting your pieces into non-fungible tokens (NFTs) and selling them in the crypto market.