Latent Space Diffusion Model With Time-Dependent Self-Attention

The patent replaces conventional mechanical neural network structures with a diffusion-based approach that uses stochastic processes and transformer architectures. The diffusion model iteratively denoises images through multiple steps, substituting traditional deterministic neural network forward passes with a probabilistic denoising process that captures temporal dependencies more effectively.

The patent introduces dynamic time-dependent self-attention mechanisms that adapt to different temporal stages of the diffusion process. The attention weights are modified based on time steps, allowing the model to dynamically adjust its focus across different temporal resolutions during the iterative denoising process, thereby improving both quality and efficiency.

The patent segments the temporal dependency capture into discrete time steps within the diffusion process. Each time step has its own self-attention mechanism that processes information at that specific temporal resolution. This segmentation allows the complex temporal dependency problem to be broken down into manageable stages, each handled by a simplified attention mechanism.

The patent implements nested self-attention mechanisms where self-attention layers are nested within the diffusion process steps. The self-attention mechanism operates at multiple temporal scales simultaneously, with inner attention mechanisms processing fine-grained temporal dependencies and outer mechanisms capturing broader temporal patterns, creating a nested structure that efficiently handles complexity.