USING STABLE DIFFUSION TO GENERATE SEAMLESS CONTENT TILE SETS IN CONTENT GENERATION SYSTEMS AND APPLICATIONS

Approaches presented herein can utilize a network that learns to generate a set of content tiles that represent a type of content (e.g., texture) and satisfy a set of rules or boundary conditions. The network can be a diffusion network that learns or adapts to the boundary conditions over several iterations. An indication of a type of content, along with a set of noisy prior images, can then be provided as input to the trained diffusion network, which can generate a set of content images. The content images can then be placed using a random (or other) selection process, as long as each selection satisfies the respective boundary conditions. Such an approach enables a small number of content tiles to be used for a texture region with a repeatability or pattern that may not be obviously detectable by a typical human viewer.