Chitwan Saharia Profile picture
Jun 2, 2022 6 tweets 4 min read Read on X
A small thread on analysing text rendering capability of #imagen.

Starting with a simple example, Imagen is able to reliably render text without any need for rigorous cherrypicking. e.g. these are non-cherry picked examples for writing "Text-to-Image" on a storefront. Image
Imagen is also capable of writing text in a variety of
interesting settings. Here are some examples (1 sample picked out of 8 for each prompt). ImageImageImageImage
Some more examples of interesting settings... ImageImageImageImage
As the text gets longer, it naturally becomes more and more difficult to render correctly, but Imagen is pretty good for complex text prompts.
"Text", "Text-to-Image", "Text-to-Image Diffusion", "Text-to-Image Diffusion Models" ImageImageImageImage
The images get worse with very long text renderings, e.g. "Photorealistic Text-to-Image Diffusion Models"
We are still able to find good samples within 8 random generations. Image
Interestingly, the model is able to write non meaningful combination of letters pretty reliably too. E.g. "magie-xett-ot" (shuffled text-to-image) Image

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More from @Chitwan_Saharia

May 24, 2022
We are thrilled to announce Imagen, a text-to-image model with unprecedented photorealism and deep language understanding. Explore imagen.research.google and Imagen!

A large rusted ship stuck in a frozen lake. Snowy mountains and beautiful sunset in the background. #imagen Image
A plush toy koala bear relaxing on a lounge chair and working on a laptop. The chair is beside a rose flower pot. There is a window on the wall beside the flower pot with a view of snowy mountains. #imagen Image
Imagen uses a large pre-trained language model (T5-XXL) as a text encoder, and a cascade of diffusion models for 1024x1024 image generation. Imagen outperforms all existing techniques on MS-COCO benchmark by a considerable margin. Image
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