It is reported that the full name of Parti is “Pathways Autoregressive Text-to-Image” (Pathways Autoregressive Text-to-Image). As the number of available parameters grows, the output image can also be more realistic.
In this example, Parti has studied 20 billion parameters before generating the final image. In contrast, Imagen is a text-to-image generator designed by Google for diffusion learning.
During its work, it trains a computer model by adding “noise” to the image to initially generate a blurred still image, and the model then learns to try to decode the still image.
As the model improves, the system can gradually turn a series of random points into the lifelike regenerated image we end up seeing.
Finally, apart from Parti and Imagen, we’ve heard of other text-to-image models – like Dall-E, VQ-GAN+CLIP, and Latent Diffusion Models.
