Stable Diffusion Versus Latent Diffusion

Within the world of art, there has been a trend in which many creatives are utilising Artificial Intelligence (AI) as well as Machine Learning (ML) to bring their ideas to life. 

Many people do not have the time or the skill to draw out art manually, and the artists that do possess the talent do require motivation and inspiration. It is due to these reasons that AI-generated art from programs that achieve this goal can be extremely useful.

In this article, we’ll dive into Stable Diffusion text-to-image versus Latent Diffusion to see which one provides these better overall results.

What Is Stable Diffusion?

Stable Diffusion is a model that is utilised for the generation of virtual creations through the utilisation of AI, and it differentiates itself from other models due to the fact that its creators have open-sourced it. What this means is that just about anyone can gain access to its code and analyse it. Stable Diffusion employs a frozen CLIP ViT-L/14 text encoder, allowing it to generate images based on text prompts.

It operates by a process known as “diffusion,” during which it starts only with noise and gradually improves upon an image until the point in time when there is no noise at all, bringing the image much closer to a provided text description.

What Is Latent Diffusion?

When it comes to databases used to train an AI algorithm, this text-to-image model was created by CompVis and trained on the LAION-100M dataset. Latent Diffusion produces impressive images based on text prompts, but it is clear that the model outputs content that reinforces or exacerbates societal biases. According to the Latent Diffusion paper, the deep learning modules will typically produce or exacerbate biases that are already present within the data. 

Its LAION-100M dataset scraped non-curated image text pairs from the internet–with the exception being the removal of illegal content–and is meant to be utilised for research purposes. 

How Do They Differ?

Latent Diffusion works by decomposing the image formation process within a sequential application of denoising autoencoders; diffusion models (DMs) achieve state-of-the-art synthesis results on the image data and beyond.

On the other end of the spectrum, Stable Diffusion generates general coherency much better and has the result of generating more complex images. It is also much quicker, as when coupled with a Graphics Processing Unit (GPU) with eight gigabytes of VRAM, for example, it can create an image within seconds. It also does best when it comes to realistic images in the form of oil paintings, which means that it specialises in the creation of artworks that have their own distinctive styles. 

The Future of AI-Generated Art

When we look at AI-generated art as a whole, there are people that are in favour of unrestricted access to image generation due to the fact that this provides each creative with the opportunity to let their imagination run wild and create artworks that would otherwise be nearly impossible for them to pull off with their given skill level. However, others argue that AI could also harm the art industry as generators are becoming so good that it is almost impossible to differentiate a real, hand-drawn digital artwork from one generated by AI. 

In any case, many of these models are limited in terms of the maximum resolution that they can actually output, although developers behind these models are constantly pushing toward providing a better solution to the demanding market of AI-generated art. 

Looking for other Stable Diffusion comparisons? Check out our latest article on Stable Diffusion versus Disco Diffusion.