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公开(公告)号：US20250054199A1
公开(公告)日：2025-02-13
申请号：US18923108
申请日：2024-10-22
Applicant: Snap Inc.
Inventor： Jian Ren , Menglei Chai , Sergey Tulyakov , Qing Jin
Abstract: System and methods for compressing image-to-image models. Generative Adversarial Networks (GANs) have achieved success in generating high-fidelity images. An image compression system and method adds a novel variant to class-dependent parameters (CLADE), referred to as CLADE-Avg, which recovers the image quality without introducing extra computational cost. An extra layer of average smoothing is performed between the parameter and normalization layers. Compared to CLADE, this image compression system and method smooths abrupt boundaries, and introduces more possible values for the scaling and shift. In addition, the kernel size for the average smoothing can be selected as a hyperparameter, such as a 3×3 kernel size. This method does not introduce extra multiplications but only addition, and thus does not introduce much computational overhead, as the division can be absorbed into the parameters after training.
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公开(公告)号：US20240395028A1
公开(公告)日：2024-11-28
申请号：US18400677
申请日：2023-12-29
Applicant: Snap Inc.
Inventor： Pavlo Chemerys , Colin Eles , Ju Hu , Qing Jin , Yanyu Li , Ergeta Muca , Jian Ren , Dhritiman Sagar , Aleksei Stoliar , Sergey Tulyakov , Huan Wang
IPC: G06V10/82 , G06N3/0455
Abstract: Described is a system for improving machine learning models. In some cases, the system improves such models by identifying an autoencoder for a latent diffusion machine learning model, the latent diffusion machine learning model is trained to receive text as input and output an image based on the received text. The system identifies a number of channels in a decoder of the autoencoder, the decoder being configured to receive latent features as input and output images. The system further identifies a performance characteristic of the decoder and changes the node topology of the decoder based on the performance characteristic to generate an updated decoder. The system retrains the latent diffusion machine learning model using the updated decoder by inputting latent features to the updated decoder, receiving an outputted image from the updated decoder, and updating one or more weights of the decoder based on an assessment of the outputted image.
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Patent Agency Ranking
公开(公告)号：US20240394932A1
公开(公告)日：2024-11-28
申请号：US18400873
申请日：2023-12-29
Applicant: Snap Inc.
Inventor： Pavlo Chemerys , Colin Eles , Ju Hu , Qing Jin , Yanyu Li , Ergeta Muca , Jian Ren , Dhgritiman Sagar , Aleksei Stoliar , Sergey Tulyakov , Huan Wang
Abstract: Described is a system for improving machine learning models. In some cases, the system improves such models by identifying a performance characteristic for machine learning model blocks in an iterative denoising process of a machine learning model, connecting a prior machine learning model block with a subsequent machine learning model block of the machine learning model blocks within the machine learning model based on the identified performance characteristic, identifying a prompt of a user, the prompt indicative of an intent of the user for generative images, and analyzing data corresponding to the prompt using the machine learning model to generate one or more images, the machine learning model trained to generate images based on data corresponding to prompts.
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公开(公告)号：US20240394843A1
公开(公告)日：2024-11-28
申请号：US18434411
申请日：2024-02-06
Applicant: Snap Inc.
Inventor： Pavlo Chemerys , Colin Eles , Ju Hu , Qing Jin , Yanyu Li , Ergeta Muca , Jian Ren , Dhritiman Sagar , Aleksei Stoliar , Sergey Tulyakov , Huan Wang
Abstract: Described is a system for improving machine learning models by accessing a first latent diffusion machine learning model, the first latent diffusion machine learning model trained to perform a first number of denoising steps, accessing a second latent diffusion machine learning model that was derived from the first latent diffusion machine learning model, the second latent diffusion machine learning model trained to perform a second number of denoising steps, generating noise data, processing the noise data via the first latent diffusion machine learning model to generate one or more first images, processing the noise data via the second latent diffusion machine learning model to generate one or more second images, and modify a parameter of the second latent diffusion machine learning model based on a comparison of the one or more first images with the one or more second images.
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公开(公告)号：US12154303B2
公开(公告)日：2024-11-26
申请号：US18238979
申请日：2023-08-28
Applicant: Snap Inc.
Inventor： Jian Ren , Menglei Chai , Sergey Tulyakov , Qing Jin
Abstract: System and methods for compressing image-to-image models. Generative Adversarial Networks (GANs) have achieved success in generating high-fidelity images. An image compression system and method adds a novel variant to class-dependent parameters (CLADE), referred to as CLADE-Avg, which recovers the image quality without introducing extra computational cost. An extra layer of average smoothing is performed between the parameter and normalization layers. Compared to CLADE, this image compression system and method smooths abrupt boundaries, and introduces more possible values for the scaling and shift. In addition, the kernel size for the average smoothing can be selected as a hyperparameter, such as a 3×3 kernel size. This method does not introduce extra multiplications but only addition, and thus does not introduce much computational overhead, as the division can be absorbed into the parameters after training.
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公开(公告)号：US20240394933A1
公开(公告)日：2024-11-28
申请号：US18596452
申请日：2024-03-05
Applicant: Snap Inc.
Inventor： Pavlo Chemerys , Colin Eles , Ju Hu , Qing Jin , Yanyu Li , Ergeta Muca , Jian Ren , Dhritiman Sagar , Aleksei Stoliar , Sergey Tulyakov , Huan Wang
IPC: G06T11/00
Abstract: Described is a system for improving machine learning models by accessing a first latent diffusion machine learning model, accessing a second latent diffusion machine learning model that was derived from the first latent diffusion machine learning model, the second latent diffusion machine learning model trained to perform a second number of denoising steps, generating noise data, processing the noise data via the first latent diffusion machine learning model to generate one or more first latent features, processing the noise data via the second latent diffusion machine learning model to generate one or more second latent features, and inputting the one or more first latent features and the one or more second latent features into a loss function. The system then modifies a parameter of the second latent diffusion machine learning model based on the output of the loss function.
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公开(公告)号：US20220207329A1
公开(公告)日：2022-06-30
申请号：US17558327
申请日：2021-12-21
Applicant: Snap Inc.
Inventor： Jian Ren , Oliver Woodford , Sergey Tulyakov , Jiazhuo Wang , Qing Jin
Abstract: Systems and methods herein describe an image compression system. The image compression system generates a first generative adversarial network (GAN), identifies a threshold, based on the threshold, generates a second GAN by pruning channels of the first GAN, trains the second GAN using similarity-based knowledge distillation from the first GAN, and stores the trained second GAN.
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