US 12,406,418 B2
Personalized text-to-image generation
Jing Shi, San Jose, CA (US); Wei Xiong, San Jose, CA (US); Zhe Lin, Clyde Hill, WA (US); and Hyun Joon Jung, Monte Sereno, CA (US)
Assigned to ADOBE INC., San Jose, CA (US)
Filed by ADOBE INC., San Jose, CA (US)
Filed on Sep. 28, 2023, as Appl. No. 18/476,504.
Claims priority of provisional application 63/497,353, filed on Apr. 20, 2023.
Prior Publication US 2024/0355022 A1, Oct. 24, 2024
Int. Cl. G06T 11/60 (2006.01); G06T 7/194 (2017.01); G06T 9/00 (2006.01)
CPC G06T 11/60 (2013.01) [G06T 7/194 (2017.01); G06T 9/00 (2013.01); G06T 2207/20081 (2013.01)] 7 Claims
OG exemplary drawing
 
1. A method of generating an image, comprising:
obtaining an input description and an input image depicting a subject;
encoding the input description using a text encoder of an image generation model to obtain a text embedding;
encoding the input image using a subject encoder of the image generation model to obtain a subject embedding;
generating a guidance embedding by combining the subject embedding and the text embedding; and
generating an output image based on the guidance embedding using a diffusion model of the image generation model, wherein the output image depicts one or more aspects of the input image and the input description.