Abstract: A method including receiving an input from a user interface of a device, the input indicating a desired characteristic of an image. The method including transmitting a prompt indicating the desired characteristic to a set of servers with a request to generate the image, causing the set of servers to: generate, using a set of encoding models, a prompt encoding based on the prompt; generate, using a first transformer block of a diffusion transformer model, a first prompt embedding and a first image embedding based on the prompt encoding and a noise input; generate, using a second transformer block of the diffusion transformer model, a second image embedding based on the first image embedding and the first prompt embedding; and generate the image based on the second image embedding. The method including receiving the image from the set of servers and presenting the image on a display of the device.

Abstract: A method including receiving a first representation of an image in a first latent space of a first machine learning model. The method further includes generating, by a second machine learning model based at least in part on the first representation, a second representation of the image in a second latent space of the second machine learning model. The method further includes updating, without generating an output image corresponding to the image, a set of weights of the second machine learning model based at least in part on the first representation and the second representation.

Abstract: Apparatuses, systems, and techniques are presented to generate digital content. In at least one embodiment, one or more neural networks are used to generate a three-dimensional voxel representation of a scene based, at least in part, upon a plurality of two-dimensional images of the scene.

Abstract: In various examples, systems and methods are disclosed relating to aligning images into frames of a first video using at least one first temporal attention layer of a neural network model. The first video has a first spatial resolution. A second video having a second spatial resolution is generated by up-sampling the first video using at least one second temporal attention layer of an up-sampler neural network model, wherein the second spatial resolution is higher than the first spatial resolution.

Abstract: A method including receiving a prompt describing a desired characteristic of an image. The method further including generating, using a set of encoding models, a prompt encoding based on the prompt. The method further including generating, using a first transformer block of a diffusion transformer model, a first prompt embedding and a first image embedding based on the prompt encoding and a noise input. The method further including generating, using a second transformer block of the diffusion transformer model, a second image embedding based on the first image embedding and the first prompt embedding. The method further including generating the image based on the second image embedding.

Abstract: In various examples, systems and methods are disclosed relating to aligning images into frames of a first video using at least one first temporal attention layer of a neural network model. The first video has a first spatial resolution. A second video having a second spatial resolution is generated by up-sampling the first video using at least one second temporal attention layer of an up-sampler neural network model, wherein the second spatial resolution is higher than the first spatial resolution.

Abstract: In various examples, systems and methods are disclosed relating to aligning images into frames of a first video using at least one first temporal attention layer of a neural network model. The first video has a first spatial resolution. A second video having a second spatial resolution is generated by up-sampling the first video using at least one second temporal attention layer of an up-sampler neural network model, wherein the second spatial resolution is higher than the first spatial resolution.