Introduction
Imagine if you could type a sentence of text and have an image generated for you within seconds. That would be pretty cool right? But that sounds like something years or maybe even decades away. This however is anything but true, you can do it, and right now. One of the most popular sites for this exact task is DALL·E mini. It is a part of the open AI venture and has – so far – been a huge success. But how does it work? How can it take any sentence you can think of and generate multiple pictures on the subject, within a matter of seconds? Well, this is exactly what an article by Mateusz Bednarski titled “How DALL-E Mini Works” explains (the article can be found here).
Summary of Article
In the article, Bednarski talks about 3 main “building blocks” (as Bednarski calls them) that are at the core of DALL-E Mini. They are VQGAN, BART, and CLIP. Let’s break each of these down 1 by 1.
| Building block | Description |
| VQGAN | Generates the image through the use of CNNs and the Transformer |
| BART | An autoencoder that is based on the transformer architecture |
| Transformer | Used in image synthesis for long-term relationships |
| CLIP | Can take text and image embeddings and inform the user on how much they match |
My Take
Overall, I liked this article. I liked how the article broke down each “building block” and went into detail about what it does. The article had images that were helpful in understanding the content – especially some of the more technical content. I also really liked how the content, both technical and non-technical, was explained. The author did a really good job of creating the article in such a way that everyone can understand it and it flows logically. In addition, the content of the article was really interesting. It showed me how something that has taken the world by storm – especially in the last couple of months – works and how it goes from text to multiple images, with the use of multiple “building blocks”. The article for me pulled back the curtain on how these things work and showed me how different algorithms can interlock with each other, and how an application can use the benefits of one algorithm and couple it with another to minimize, and in some cases even get rid of, the first algorithms defects.
Conclusion
All in all, this article was a good one. I liked how it flowed, its visuals, and the content was really interesting. It hit home for me as I have been using this tool in my life as well. I recommend you read it(the article can be found here)!