US 12,236,559 B2
Augmented diffusion inversion using latent trajectory optimization
Jiaxin Zhang, Mountain View, CA (US); Kamalika Das, Saratoga, CA (US); and Sricharan Kallur Palli Kumar, Fremont, CA (US)
Assigned to Intuit Inc., Mountain View, CA (US)
Filed by INTUIT INC., Mountain View, CA (US)
Filed on Nov. 14, 2023, as Appl. No. 18/508,762.
Application 18/508,762 is a continuation of application No. 18/309,514, filed on Apr. 28, 2023, granted, now 11,893,713.
Prior Publication US 2024/0362756 A1, Oct. 31, 2024
Int. Cl. G06T 5/70 (2024.01); G06T 9/00 (2006.01)
CPC G06T 5/70 (2024.01) [G06T 9/00 (2013.01); G06T 2207/20076 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A method of automated image processing, comprising:
receiving an image and a diffusion model of the image;
generating a noise vector for the diffusion model by applying noise to the image;
encoding the image with a bias correction variable by using a conditional textual embedding of the diffusion model to generate an augmented diffusion model;
generating an augmented noise vector using the noise vector, the augmented diffusion model, and a latent trajectory optimization process, wherein generating the augmented noise vector comprises determining a plurality of diffusion trajectories at a plurality of steps between the image and the noise vector by minimizing a difference between the plurality of diffusion trajectories at the plurality of steps and one or more initial trajectories between the image and the noise vector; and
generating an output based on the augmented noise vector.