DALL-E AI image generator is a new AI based system from OPEN AI that can take simple text descriptions like, "a cat on the moon" and turn them into realistic images that have never existed before. DALL-E2 can also realistically edit photos based on a simple natural language description, it can fill it in or replace part of an image with AI-generated imagery. In January 2021, Open AI introduced DALL-E AI image generator a system that could generate images from text like "dog driving cycle" etc. It can make any image with any resolution. There are mainly two technologies behind DALL-E2. It is a part which match images to text and uses that match to train the computer to understand concepts in images.

Speak of Elon Musk and in all probability, companies like Twitter, Tesla or SpaceX will come to your mind. But little do people know about Elon Musk's company OpenAI -- an artificial intelligence (AI) research and development firm that is behind the disruptive chatbot ChatGPT. The brainchild of Musk and former Y Combinator president Sam Altman, OpenAI launched ChatGPT in November 2022 and within a week, the application saw a spike of over a million users. Being able to do anything between coding and interacting that mimics human intelligence, ChatGPT has surpassed previous standards of AI capabilities and has introduced a new chapter in AI technologies and machine learning systems. If you are intrigued by artificial intelligence and take an interest in deep learning and how they can benefit humanity, then you must know about the history of OpenAI and the levels AI development has reached.

One of the latest applications of Artificial Intelligence (AI) is to generate images from natural language descriptions. These generators are now becoming available and achieve impressive results that have been used for example in the front cover of magazines. As the input to the generators is in the form of a natural language text, a question that arises immediately is how these models behave when the input is written in different languages. In this paper we perform an initial exploration of how the performance of three popular text-to-image generators depends on the language. The results show that there is a significant performance degradation when using languages other than English, especially for languages that are not widely used. This observation leads us to discuss different alternatives on how text-to-image generators can be improved so that performance is consistent across different languages. This is fundamental to ensure that this new technology can be used by non-native English speakers and to preserve linguistic diversity.

DALL-E2, the AI image tool, generated these images of a giraffe shopping in a grocery store. When the Silicon Valley research lab OpenAI unveiled DALL-E earlier this year, it wowed the internet. The tool is seen as one of the most advanced artificial intelligence systems for creating images in the world. Type a description, and DALL-E instantly produces professional-looking art or hyperrealistic photographs. "It's incredibly powerful," said Hany Farid, a digital forensics expert at the University of California, Berkeley.

Dall-E 2 is the successor to Open AI's Dall-E model. The name Dall-E is the portmanteau of Wall-E (a sci-fi film by Pixar) and Salvador Dalí (a Spanish artist renowned for his surrealistic style in his paintings). The model is used to generate photorealistic images from a given text description. The model is not made available to the public yet but the Open AI team has made a nice demo on their website. As you can see, these images are what an artist/graphical designer will take hours if not days to produce but DALL-E2 does it in a matter of minutes and the images it produces are so impressive.

It is the new SOTA for text-to-image synthesis. Architecturally, it is actually much simpler than DALL-E2. It consists of a cascading DDPM conditioned on text embeddings from a large pretrained T5 model (attention network). It also contains dynamic clipping for improved classifier free guidance, noise level conditioning, and a memory efficient unet design. It appears neither CLIP nor prior network is needed after all.