What is DALL-E: Turning Text Into Images in 2023

What is DALL-E: Turning Text Into Images in 2023

The concept of speaking something into existence is frowned upon with a ton of skepticism in certain circles.

We often write things as “wishful thinking” or “pipe dreams.”

But there’s something to be said about the power of manifestation – especially regarding technology.

In early 2021, OpenAI released a new artificial intelligence model called Dall-E.

Dall-E is a 12-billion parameter training version of the GPT-3 transformer model.

Leading to the rise of various AI art generators, DALL-E has been deemed the “Picasso of AI” by some.

In this article, I’ll explore what DALL-E is, how it works, and what the future of this technology holds.

Let’s get right into it.

What is DALL-E?

Trained on a neural network and able to take text captions as input, DALL-E generates corresponding images.

In other words, this AI art tool turns text into images.

This is a significant achievement because, up until now, artificial intelligence models have had difficulty understanding and generating images from text descriptions.

DALL-E can generate a wide variety of images from anthropomorphized versions of animals and objects to surrealist images and completely novel creations.

A transformer language model like GPT-3, DALL-E is trained on a large dataset of text and images.

This training enables its algorithms and models to learn the relationships between words and concepts and how to map those concepts onto visual representations.

One can use specific artist names like Salvador Dali and Pablo Picasso as image inputs.

There’s also the option to create art that resembles something straight out of Pixar’s Wall-E.

Whatever the input method for generating new images, this AI tool is genuinely remarkable.

DALL-E AI can also regenerate any rectangular region, or “crop,” of an image it has already generated.

These image variations are created by running the generated CLIP image embeddings and using a Diffusion decoder to modify the image.

If you’re not satisfied with a particular aspect of an existing image, you can ask DALL-E to generate a new one.

When it comes to pixels, DALL-E can generate a 1024×1024 resolution, which is higher than many of the other tools available on the market.

Past Technology

Generative adversarial networks (GAN) used to be the best method for creating images from textual descriptions.

However, GANs have several limitations.

First, they require a lot of data to function correctly.

They also tend to produce images that are low quality and lack detail.

While GAN had been around for quite some time, many believe that the release of DALL-E meant the end of GAN’s reign.

DALL-E is also much more efficient than GANs, as it can generate realistic images much better, of much better quality, and in a fraction of the time.

DALL-e mini

In addition to the full DALL-E AI model, OpenAI has also released a miniature version called DALL-E mini.

Despite its fewer capabilities, DALL-E mini can still generate high-quality images.

DALL-E mini by Craiyon.com is more accessible to those who do not have access to large amounts of computing resources.

DALL-E mini is also an open-source version of its predecessor and is available for anyone.

DALL-E Capabilities

DALL-E can modify several of an object’s attributes.

This leads to unique and exciting results, all of which are based on the text description given to DALL-E.

It also means that this platform can control the number of times an object appears in an image and its size, shape, and color.

DALL-E can also create images composed of entire scenes from scratch, not just individual objects.

This opens up even more possibilities for the type of image generated.

DALL-E is capable of drawing multiple objects, as well as forming relationships between them.

The ability to generate complex scenes is a significant step forward in artificial intelligence.

On their website, DALL-E provides the example of “a hedgehog wearing a red hat, yellow gloves, blue shirt, and green pants.”

With the hat having a specific color attribute, it’s not enough for the tool to recognize and create the said hat, but it must also be able to place it on the hedgehog’s head correctly.

The same applies to the gloves, shirt, and pants mentioned in the description.

This is a significant achievement and paves the way for even more complex images to be generated in the future.

With this concept known as variable binding, DALL-E can generate images that contain multiple objects and scenes.

Considering Three-Dimensions

DALL-E is not just restricted to two-dimensional images.

The platform is also able to generate three-dimensional models of objects.

DALL-E is also able to generate three-dimensional models of objects from different angles.

During various testing phases, developers wanted to draw the head of a model from multiple angles, and they found that DALL-E could create a smooth 3D model that they could view from any angle.

Image credit: https://openai.com/

The Unspoken Words

The words someone uses to describe an object rarely contain all the necessary information to generate an accurate image.

DALL-E can consider the words that are not written but still implied.

This allows for a complete understanding of the object being described.

For example, if someone were to describe a tree, they might not mention the leaves, the shadow, or the surrounding environment.

However, DALL-E can consider these unspoken words and generate an image containing all of these elements.

While 3D rendering engines would be able to get close after several attempts, the fact that one doesn’t need to specify every detail explicitly is a powerful demonstration of what artificial intelligence can be capable of.

Image credit: https://openai.com/

The Real vs. The Imagery

Combining worlds composed of authentic images and those based on DALL-E’s artificial intelligence can create some Intriguing results.

The ability to synthesize objects and scenes that look identical to the real world opens up a whole new range of possibilities for what can be created.

DALL-E gives a few examples of this situation:

  • taking qualities associated with random objects and moving them to animals
  • making connections that were never made before through unrelated inspiration

For example, the text prompt “a snail with the texture of a harp”, results in an image that mixes the real world and DALL-E’s imagination.

Image credit: https://openai.com/

The result does not exist in the real world but can yield some interesting outputs.

Geographic Knowledge

DALL-E appears to have a fair amount of knowledge about geographical details, landmarks, and communities.

Consider a text prompt like:

  • a photo of the food in China

Image credit: https://openai.com/

These prompts allow DALL-E to generate pretty accurate images representative of the real thing.


On September 28, 2022, DALL-E 2 was officially opened to the public.

While previously, it was available on an invite-only basis, with a waitlist of those interested, it was then opened to anyone that wanted to explore more.

The new version came with several new features and improvements, the most notable of which was the training data sets used to train the artificial intelligence.

In terms of pricing, in July 2022, OpenAI began charging credits for art generation on the DALLE-2 platform after two months of being free to use.

To get started, all users receive a free credit bonus.

After that, they are given 15 credits each month.

For those that want more, they can buy $15 for 115 credits, which should technically be able to generate as many as 450+ DALL-E images.

The Future

While the technology is still in its relatively early days, the potential applications for DALL-E 2 is vast.

In the future, we could see DALL-E being used to generate illustrations, product designs, and even works of art.

This AI image generator could also create photorealistic images for movies and video games.

The possibilities are endless.

What’s certain is that DALL-E represents a significant step forward in artificial intelligence.

As this technology continues to develop, we can only imagine how it will change our world.

DALL-E will also help researchers study the impacts of technological change on society, like economic inequality or bias in machine learning.

In addition, the ethical challenges that come with new technology will be further considered, ensuring that DALL-E-powered applications consider the safety and responsibility of their users.

Wrap Up.

As far as text-to-image generation using natural language goes, Openai’s DALL-E is one of the first AI models that led the way to show just how well a machine can understand the complexities of our world.

From creating original images to adopting existing ones, creating a high-quality professional illustration of anything you can dream of, and working with digital art to create new experiences, this AI system generates images like a real artist.

Its ability to consider unspoken, implied ideas as part of a given context and create unique yet coherent images that have never been seen before is quite mind-boggling.

This means that the generated images can be used for anything from social media to product design to creating new worlds for video games and movies.

Major brands and companies are now using image generation models to create realistic images of their products for marketing and advertising, which will only increase in the future.

Further reading on AdamEnfroy.com: AI technology is now found in many aspects of a business.

From using an AI system to write words, create books, and develop marketing material to using AI marketing tools for analyzing data and segmenting audiences, the benefits of AI for business are many.

AI video generators are also being used to create realistic and high-quality video material, and this trend is only set to continue.

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