What Was Dall-E 2 Trained On?

A Glimpse Into Dall-E 2's Training Data

What Was Dall-E 2 Trained On

Last Updated on

Dall-E 2 is a powerful artificial intelligence language model that can generate images from text descriptions. You might wonder what Dall-E 2 was trained on to accomplish such a feat. The answer lies in the massive amount of data the model was trained on.

So, What OpenAI Has Trained it On?

OpenAI has trained Dall-E 2 on a massive dataset containing millions of images and corresponding text descriptions. OpenAI created this entire dataset by crawling the internet and collecting images from various sources, including social media, search engines, and image-hosting websites.

The images in the dataset cover a wide range of subjects, from animals and plants to objects and scenes. The text descriptions are diverse, ranging from simple phrases to more complex sentences and paragraphs.

Essential AI Tools

Editor's pick
EXCLUSIVE DEAL 10,000 free bonus credits

Jasper AI

On-brand AI content wherever you create. 100,000+ customers creating real content with Jasper. One AI tool, all the best models.
Editor's pick
Only $0.01 per 100 words

Originality AI detector

Originality.AI Is The Most Accurate AI Detection.Across a testing data set of 1200 data samples it achieved an accuracy of 96% while its closest competitor achieved only 35%. Useful Chrome extension. Detects across emails, Google Docs, and websites.
Editor's pick


Experience the full power of an AI content generator that delivers premium results in seconds. 8 million users enjoy writing blogs 10x faster, effortlessly creating higher converting social media posts or writing more engaging emails. Sign up for a free trial.
Editor's pick
Recommended SEO Content tool

Jasper AI

The best tool for SEO AI content. No. 1 SEO tool. Starts at $29/month
*Prices are subject to change. PC Guide is reader-supported. When you buy through links on our site, we may earn an affiliate commission. Learn more

The dataset was carefully curated to ensure it represented the real world. It includes images and descriptions of common objects and scenes people encounter daily. It includes images and descriptions of unusual and exotic subjects, such as rare animals and obscure landmarks.

What is the Training Process?

To train Dall-E 2, the dataset was fed into the model in batches. OpenAI then trained the model to generate images from the text descriptions using supervised learning.

During the training process, Dall-E 2 learned to recognize patterns in the data and use them to generate new images. The model learned from its mistakes and adjusted its parameters to improve performance. The massive amount of data gave the model a wealth of information to draw from when generating new images.

The Benefits of Dall-E 2

Dall-E 2 has many practical applications in a variety of fields, including design, marketing, and entertainment. For example, it can generate images for websites and advertising campaigns or create illustrations for books and magazines.

Dall-E 2 can generate images for virtual reality and video games, where realistic graphics are essential for creating an immersive experience. Additionally, designers can generate images for scientific research, such as creating simulations of complex systems or visualizing data.

Dall-E 2 can help people with disabilities, such as people with visual impairment. By generating images from text descriptions, Dall-E 2 can visually represent the world that might otherwise be inaccessible to people with certain disabilities.


OpenAI trained Dall-E 2 on a massive dataset of images and text descriptions. The company carefully curated this dataset to ensure it represented the real world. And the company trained the model using supervised learning to generate images from text descriptions.

The dataset size had a crucial role in making Dall-E 2 so powerful, and it has many practical applications in various fields. From design and marketing to scientific research and entertainment, Dall-E 2 has the potential to revolutionize the way we create and interact with visual media.