Generation of story videos corresponding to user input using generative models

    公开(公告)号:US12299796B2

    公开(公告)日:2025-05-13

    申请号:US18067642

    申请日:2022-12-16

    Applicant: Lemon Inc.

    Abstract: The present disclosure provides systems and methods for video generation corresponding to a user input. Given a user input, a story video with content relevant to the user input can be generated. One aspect includes a computing system comprising a processor and memory. The processor can be configured to execute a program using portions of the memory to receive the user input, generate a story text based on the user input, generate a plurality of story images based on the story text, and output a story including the story text and a story video having content corresponding to the story text, wherein the story video includes the plurality of story images. Additionally or alternatively, the story video can include audio data and a plurality of generated animated videos, each animated video corresponding to a story image in the plurality of story images.

    Generation of image corresponding to input text using dynamic value clipping

    公开(公告)号:US12136141B2

    公开(公告)日:2024-11-05

    申请号:US18052866

    申请日:2022-11-04

    Applicant: Lemon Inc.

    Abstract: Systems and methods are provided that include a processor executing a program to receive input text from a user. The processor is further configured to, for a predetermined number of iterations, input an initial image into a diffusion process to generate a processed image, back-propagate the processed image through a text-image match gradient calculator to calculate a gradient against the input text, and update the initial image with an image generated by applying the calculated gradient to the processed image. The pixel values of the processed image during a first portion of the predetermined number of iterations are value clamped to a first range, and pixel values of the processed image during a second portion of the predetermined number of iterations are value clamped to a second range that is a subset of the first range.

    Generation of image corresponding to input text using multi-text guided image cropping

    公开(公告)号:US12131406B2

    公开(公告)日:2024-10-29

    申请号:US18052870

    申请日:2022-11-04

    Applicant: Lemon Inc.

    CPC classification number: G06T11/00 G06F40/40 G06T5/70

    Abstract: Systems and methods are provided that include a processor executing a program to receive an input from a user, where the input including a first input text and a second input text. The processor is further configured to provide an initial image and, for a predetermined number of iterations, define a first and second regions of the initial image associated with the first and second input texts, respectively, define a plurality of patches of the initial image, input the initial image into a diffusion process to generate a processed image, back-propagate the processed image through a text-image match gradient calculator by generating an image embedding based on the processed image, generating a text embedding based on the region and the input text that are associated with a patch, and calculating a differential between the image embedding and the text embedding.

    Generation of images corresponding to input text using multi-algorithm diffusion sampling

    公开(公告)号:US12079902B2

    公开(公告)日:2024-09-03

    申请号:US18052862

    申请日:2022-11-04

    Applicant: Lemon Inc.

    CPC classification number: G06T11/00 G06F40/40 G06T5/70

    Abstract: Systems and methods are provided that include a processor executing a program to process an initial image through a first diffusion stage to generate a final first stage image, wherein the first diffusion stage includes using a diffusion model, a gradient estimator model smaller than the diffusion model, and a text-image match gradient calculator. The processor further executes the program to process the final first stage image through a second diffusion stage to generate a final second stage image. The second diffusion stage includes, for a second predetermined number of iterations, inputting the final first stage image to through the diffusion model, back-propagate the image through the text-image match gradient calculator to calculate a second stage gradient against the input text, and update the final first stage image by applying the second stage gradient to the final first stage image.

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