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How To Generate Any Image From Text: The Wonders of AI in Computer-Generated Imagery


As a technology enthusiast taking a hiatus from writing on cryptocurrencies, I shall indulge myself today in sharing an interesting discovery I made on the Internet (well, it is not exactly a ground-breaking piece of technology that just emerged, but still quite a novel one worthy of mention in my opinion!) But first, please enjoy a photo of a cute dog below.

Appreciate the texture, depth of field and quality of bokeh presented in this interesting shot — a piece of art presumably captured using a decent portrait lens on a DSLR. But what if I told you that this photo was generated by AI from scratch using purely word prompts, in under 2 minutes? Pretty mind-boggling, isn’t it?

The AI generated this photo using the keywords “cute dog grinning at camera”.

To be sure that the AI did not just download the photo from the plethora of cute dog photo galleries available online, I did a reverse Google Image search, and was satisfied to find that the photo generated was indeed unique, as far as Google is concerned.

 

Introducing DALL.E

Dalle E is a “trained neural network” that can generate images merely from text descriptions. According to its site, Dalle E has a diverse set of capabilities, including creating “anthropomorphised versions of animals and objects, combining unrelated concepts in plausible ways, rendering text, and applying transformations to existing images”.

DALL·E is a 12-billion parameter version of GPT-3 trained to generate images from text descriptions, using a dataset of text–image pairs. We’ve found that it has a diverse set of capabilities, including creating anthropomorphized versions of animals and objects, combining unrelated concepts in plausible ways, rendering text, and applying transformations to existing images.

To trace its origins, the original DALL.E model was first developed by OpenAI in January 2021 to generate digital images from natural language descriptions. Its successor, DALL.E2, was announced in April 2022, with an improved ability to generate more realistic images at higher resolutions that can combine concepts, attributes and styles. However, still in its beta stage, DALL.E is not readily accessible to the public yet, as users have to join the waitlist before being able to try it out.

Plus, DALL.E is not free to use!

In this first phase of the beta, users can buy additional DALL·E credits in 115-credit increments (460 images) for $15 on top of their free monthly credits. One credit is applied each time a prompt is entered and a user hits “generate” or “variations.”

For those of us enthusiastic to relish this new piece of technology, Craiyon (formerly DALL-E Mini until a change of name was requested by OpenAI in June 2022) comes to the rescue! Released in 2022, Craiyon is an open-source community-created project that is based on the first version of DALL·E. Trained on unfiltered data from the Internet, Craiyon can generate whatever image you want the AI to produce by only using text captions, however random the scenario presented might be; the only limit is your imagination!

 

How To Generate Images From Text

  • Head over to the Craiyon site here or huggingface.co.
  • Enter a specific caption of the image that you intend to generate. From my limited experience, Craiyon worked best at generating landscapes/ inanimate objects.
  • Click run, and expect results in less than 2 minutes.
  • If the images generated are not up to expectations, press run again to generate a new batch of images.

 

Interesting Results

The quality of images generated range from visually pleasing and impressive to downright grotesque, especially if it involves complex imagery/ faces. However overall, the creative process makes for an interesting time-killer, what with the array of fascinating images generated.

Sometimes the AI may take things too literally!

 

Limitations

The AI presented in DALL.E is a fascinating piece of work that sees a huge potential, but currently still plagued with a few limitations.

  • Faces appear to be distorted, due to the limitations of the current image encoder.
  • Images generated have a low resolution.
  • Because the model was trained on unfiltered data from the Internet, it may generate images that reinforce certain stereotypes.

 

Final Thoughts

Indeed, the concept of ‘text-to-image’ is an impressive technology that would have been inconceivable just a few years back. Its applications know no bounds, especially for content creators.

What are your thoughts on this? Feel free to leave comments in the section below!

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Cryptoindulgence
Cryptoindulgence

I'm an avid investor in stocks and cryptocurrency, keen to share my humble knowledge with the community.


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