Abstract: In various examples, one or more processors can include one or more circuits to obtain image data for a current image being generated, in accordance with one or more ray tracing operations. The image data can correspond to a current image prior to or during reconstruction of that image. The image can be grainy, include artifacts, etc. To reduce these affects, the one or more circuits can execute one or more attention operations to determine an update to the current image based at least on a plurality of patches of the current image and windowed attention regions corresponding to each patch of the plurality of patches. In some examples, the windowed attention regions can extend at least in part beyond boundaries of each patch, allowing for information from neighboring patches to be used to update the image when reconstructing the image.

Abstract: Apparatuses, systems, and techniques to implement transformer-based neural network image upscaling are disclosed. In at least one embodiment, one or more neural networks may implement a transformer-based neural network to generate high resolution image data based, at least in part, on one or more mis-aligned self-attention windows to be applied to one or more lower resolution versions of the higher resolution image data.

Abstract: Apparatuses, systems, and techniques are presented to reconstruct one or more images. In at least one embodiment, one or more neural networks are used to determine one or more second colors of one or more pixels based, at least in part, on one or more spatial variations and/or temporal variations of a first color of the one or more pixels.

Abstract: Apparatuses, systems, and techniques are presented to generate images with one or more visual effects applied. In at least one embodiment, one or more visual effects are applied to one or more images having a resolution that is less than a first resolution and those visual effects approximated for one or more images having a resolution that is greater than or equal to the first resolution.

Abstract: Apparatuses, systems, and techniques are presented to reconstruct one or more images. In at least one embodiment, one or more objects in an image are caused to be generated based, at least in part, on applying one or more offsets to a motion of the one or more objects relative to one or more prior images.

Abstract: Apparatuses, systems, and techniques are presented to reconstruct one or more images. In at least one embodiment, one or more objects in an image are caused to be generated based, at least in part, on a motion of the one or more objects between two or more frames of the image.

Abstract: Apparatuses, systems, and techniques are presented to generate images. In at least one embodiment, one or more neural networks are used to generate one or more images using one or more pixel weights determined based, at least in part, on one or more sub-pixel offset values.

Abstract: Various examples, systems, and methods are disclosed relating to feature cache-based generative video editing for dynamic frame generation. A system can apply a first frame as input to a machine learning model to retrieve, from the machine learning model, a first embedding of the first frame. The system can store the first embedding in a cache, wherein the cache includes a second embedding of a second frame. The system can generate a third frame using the machine learning model based at least on the cache, wherein the third frame is associated with the first frame. The system can output the third frame to a video stream comprising a fourth frame.

Abstract: Apparatuses, systems, and techniques are presented to reconstruct one or more images. In at least one embodiment, one or more neural networks are used to determine one or more second colors of one or more pixels based, at least in part, on one or more spatial variations and/or temporal variations of a first color of the one or more pixels.

Abstract: Apparatuses, systems, and techniques are presented to generate images. In at least one embodiment, one or more neural networks are used to generate one or more images using one or more pixel weights determined based, at least in part, on one or more sub-pixel offset values.

Abstract: Apparatuses, systems, and techniques are presented to generate images with one or more visual effects applied. In at least one embodiment, one or more visual effects are applied to one or more images having a resolution that is less than a first resolution and those visual effects approximated for one or more images having a resolution that is greater than or equal to the first resolution.

Abstract: Apparatuses, systems, and techniques are presented to generate images with one or more visual effects applied. In at least one embodiment, one or more visual effects are applied to one or more images having a resolution that is less than a first resolution and those visual effects approximated for one or more images having a resolution that is greater than or equal to the first resolution.

Abstract: Apparatuses, systems, and techniques are presented to generate images with one or more visual effects applied. In at least one embodiment, one or more visual effects are applied to one or more images having a resolution that is less than a first resolution and those visual effects approximated for one or more images having a resolution that is greater than or equal to the first resolution.

Abstract: Apparatuses, systems, and techniques are presented to generate images with one or more visual effects applied. In at least one embodiment, one or more visual effects are applied to one or more images having a resolution that is less than a first resolution and those visual effects approximated for one or more images having a resolution that is greater than or equal to the first resolution.

Abstract: Apparatuses, systems, and techniques are presented to reconstruct one or more images. In at least one embodiment, one or more circuits are to use one or more neural networks to adjust one or more pixel blending weights.

Abstract: Apparatuses, systems, and techniques are presented to reconstruct one or more images. In at least one embodiment, one or more objects in an image are caused to be generated based, at least in part, on applying one or more offsets to a motion of the one or more objects relative to one or more prior images.

Abstract: Apparatuses, systems, and techniques are presented to generate images. In at least one embodiment, one or more neural networks are used to generate one or more images using one or more pixel weights determined based, at least in part, on one or more sub-pixel offset values.

Abstract: Apparatuses, systems, and techniques are presented to generate images with one or more visual effects applied. In at least one embodiment, one or more visual effects are applied to one or more images having a resolution that is less than a first resolution and those visual effects approximated for one or more images having a resolution that is greater than or equal to the first resolution.

Abstract: Apparatuses, systems, and techniques are presented to generate images. In at least one embodiment, one or more neural networks are used to generate one or more images using one or more pixel weights determined based, at least in part, on one or more sub-pixel offset values.

Abstract: Apparatuses, systems, and techniques are presented to generate images. In at least one embodiment, one or more neural networks are used to generate one or more images using one or more pixel weights.