Stable Diffusion Versus DALL-E
When we take a look at text-to-image tools that utilise Artificial Intelligence (AI), there are numerous options available. However, out of all of them, the main two, which have showcased exceptional results, are Stable Diffusion and DALL-E.
Both of these solutions and tools feature their own unique strengths as well as weaknesses, so let’s dive into the question of which tools are better for specific use cases.
AI Image Generation and How It's Impacting the Industry
There are people in favour of unrestricted image generation due to the fact that it opens opportunities for the ideas of many more people to come to fruition; these people would otherwise have to spend thousands of hours perfecting a skill or an application to accomplish the same feat. However, others are of the opinion that it could potentially do a lot more harm in the long term.
Despite all of that, there are numerous teams of developers constantly aiming to bring a better solution to the demanding market of AI-generated art, and the two main competitors are Stable Diffusion AI art and DALL-E.
DALL-E is essentially a set of machine learning models which was specifically developed by OpenAI as a means of generating digital images from natural language descriptions. In other words, it is an AI image generator where users can create just about anything that they can come up with, including some of the most beautiful artworks out there–or something truly horrifying, as there is no limit.
DALL-E uses a twelve-billion parameter training version of the GPT-3 transformer model as a means of interpreting the natural language inputs, after which it can generate an image that corresponds to them.
Stable Diffusion Explained
Stable Diffusion, on the other hand, is a model for generating visual creations through the utilisation of AI and what makes it differentiate itself from other models is the fact that its creators open-sourced it, meaning that anyone can view its code and understand it.
Stable Diffusion employs a frozen CLIP ViT-L/14 text encoder, through which it can condition the model on text prompts. It operates the image generation process within a "diffusion" process at runtime, and while it only starts with noise, it gradually improves upon an image until the point in time when there is no noise at all, which brings it much closer to a provided text description.
Stable Diffusion Versus DALL-E: How They Stack Up
DALL-E can generate many more images within the span of seconds when compared to Stable Diffusion, which can take up to over a minute in order to generate results. However, DALL-E is also widely available, and there is a limited number of prompt generations free every month for each user, after which there is a cost associated with generating images beyond the monthly fee allocation.
On the other hand, Stable Diffusion is fully free and open-source, which means that it is openly available to everyone. It is, however, resource and system intensive, so users might need a lot of solid hardware in terms of Graphics Processing Units (GPUs) to get the most out of it.
Moving Forward With the Future of AI Images
Both of the solutions presented here feature their own specific strengths as well as their own weaknesses. In terms of quality, DALL-E is more capable when compared to Stable DIffusion in most cases, although Stable Diffusion is much more permissive with the text prompts due to the fact that it can generate people much better.
Stable Diffusion mainly has the advantage due to the fact that it is far more cost-efficient to use, but it is also important to remember that both tools are in the early stages of development. Both of these solutions have their place in the marketplace, and it is likely that we will see a much higher level of competition going forward.Looking for a tutorial of Stable Diffusion or wondering about the pros and cons when it comes to Stable Diffusion versus Imagen? We’ve got you covered–check out our latest articles.