公开(公告)号：US20250077842A1

公开(公告)日：2025-03-06

申请号：US18459186

申请日：2023-08-31

Applicant: Adobe Inc.

Inventor： Hareesh Ravi ,  Sachin Kelkar ,  Ajinkya Gorakhnath Kale

IPC: G06N3/045 ,  G06N3/0475

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for selectively conditioning layers of a neural network and utilizing the neural network to generate a digital image. In particular, in some embodiments, the disclosed systems condition an upsampling layer of a neural network with an image vector representation of an image prompt. Additionally, in some embodiments, the disclosed systems condition an additional upsampling layer of the neural network with a text vector representation of a text prompt without the image vector representation of the image prompt. Moreover, in some embodiments, the disclosed systems generate, utilizing the neural network, a digital image from the image vector representation and the text vector representation.

公开(公告)号：US10713432B2

公开(公告)日：2020-07-14

申请号：US15476640

申请日：2017-03-31

Applicant: Adobe Inc.

Inventor： Tanya Goyal ,  Sachin Kelkar ,  Natwar Modani ,  Manas Agarwal ,  Jeenu Grover

IPC: G06F17/00 ,  G06F40/194 ,  G06F40/117 ,  G06F40/166 ,  G06F40/197

Abstract: This disclosure generally covers systems and methods that identify and differentiate types of changes made from one version of a document to another version of the document. In particular, the disclosed systems and methods identify changes between different document versions as factual changes or paraphrasing changes or (in some embodiments) as changes of a more specific revision category. Moreover, in some embodiments, the disclosed systems and methods also generate a comparison of the first and second versions that identifies changes as factual changes or paraphrasing changes or (in some embodiments) as changes of a more specific revision category. The disclosed systems and methods, in some embodiments, further rank sentences that include changes made between different document versions or group similar (or the same) type of changes within a comparison of document versions.

公开(公告)号：US20240020954A1

公开(公告)日：2024-01-18

申请号：US17812596

申请日：2022-07-14

Applicant: ADOBE INC.

Inventor： Sachin Kelkar ,  Ajinkya Gorakhnath Kale ,  Midhun Harikumar

IPC: G06V10/774 ,  G06T5/00 ,  G06T7/194 ,  G06V10/771 ,  G06V10/776 ,  G06V10/26 ,  G06V10/75 ,  G06F16/532

CPC classification number: G06V10/774 ,  G06T5/005 ,  G06T7/194 ,  G06V10/771 ,  G06V10/776 ,  G06V10/267 ,  G06V10/759 ,  G06F16/532 ,  G06T2207/20081 ,  G06V2201/10

Abstract: Systems and methods for image processing, and specifically for generating object-agnostic image representations, are described. Embodiments of the present disclosure receive a training image including a foreground object and a background, remove the foreground object from the training image to obtain a modified training image, inpaint a portion of the modified training image corresponding to the foreground object to obtain an inpainted training image, encode the training image and the inpainted training image using a machine learning model to obtain an encoded training image and an encoded inpainted training image, and update parameters of the machine learning model based on the encoded training image and the encoded inpainted training image.

公开(公告)号：US20250095114A1

公开(公告)日：2025-03-20

申请号：US18470240

申请日：2023-09-19

Applicant: Adobe Inc.

Inventor： Hareesh Ravi ,  Sachin Kelkar ,  Ajinkya Gorakhnath Kale

IPC: G06T5/00 ,  G06T5/50

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating digital images by conditioning a diffusion neural network with input prompts. In particular, in one or more embodiments, the disclosed systems generate, utilizing a reverse diffusion model, an image noise representation from a first image prompt. Additionally, in some embodiments, the disclosed systems generate, utilizing a diffusion neural network conditioned with a first vector representation of the first image prompt, a first denoised image representation from the image noise representation. Moreover, in some embodiments, the disclosed systems generate, utilizing the diffusion neural network conditioned with a second vector representation of a second image prompt, a second denoised image representation from the image noise representation. Furthermore, in some embodiments, the disclosed systems combine the first denoised image representation and the second denoised image representation to generate a digital image.

公开(公告)号：US20240362842A1

公开(公告)日：2024-10-31

申请号：US18308017

申请日：2023-04-27

Applicant: Adobe Inc.

Inventor： Hareesh Ravi ,  Sachin Kelkar ,  Midhun Harikumar ,  Ajinkya Gorakhnath Kale

IPC: G06T11/60 ,  G06T5/00

CPC classification number: G06T11/60 ,  G06T5/70 ,  G06T2200/24 ,  G06T2207/20084

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for utilizing a diffusion prior neural network for text guided digital image editing. For example, in one or more embodiments the disclosed systems utilize a text-image encoder to generate a base image embedding from the base digital image and an edit text embedding from edit text. Moreover, the disclosed systems utilize a diffusion prior neural network to generate a text-image embedding. In particular, the disclosed systems inject the base image embedding at a conceptual editing step of the diffusion prior neural network and condition a set of steps of the diffusion prior neural network after the conceptual editing step utilizing the edit text embedding. Furthermore, the disclosed systems utilize a diffusion neural network to create a modified digital image from the text-edited image embedding and the base image embedding.