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Google’s PartiGenerator relies on 20 billion inputs to create photorealistic images




Google unveiled Parti’s text-to-image computer model on Thursday, rendering surrealistic images by studying tens of billions of inputs.

Pathways Autoregressive Text-to-Image (Parti) explores a series of images that Google calls “image tokens” and uses them to create new images, search giants say on their research website. The more parameters (tokens and other training materials) you review, the more realistic the Parti image will be. The model explores 20 billion parameters before producing the final image.

Parti is different from Imagen, a text-to-image generator that Google designed to use diffusion learning. This process adds “noise” to the image to train the computer model so that the image is obscured, like a still image on a television screen. The model then learns to decode the static and recreate the original image. As the model improves, you can turn what looks like a series of random dots into an image.

Google does not make Parti or Imagen publicly available due to the risk of bias in AI datasets. Because datasets are created by humans, they can mistakenly lean towards stereotypes or misrepresent certain groups. Google says both Parti and Imagen have a prejudice against Western stereotypes.

When asked to comment on this story, Google referred to the company’s blog post.

Search giants have invested heavily in artificial intelligence as a way to improve services and develop ambient computing. It’s a kind of very intuitive technology and it’s part of the background. At an I / O developer conference in May, CEO Sundar Pichai said Google Translate is using AI to add languages, create 3D images on maps, and summarize documents. Said.

Parti and Imagen are not the only models from surrounding text to images. The Dall-E, VQ-GAN + CLIP, and Latent Diffusion models are other non-Google text-to-image models that have been gaining attention lately. Dall-E Mini is a publicly available open source text-to-image AI, but trained on smaller datasets.




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