Lightweight Adapter for Diffusion Model Latent Vector Prediction

The patent divides the denoising process into multiple stages with different numbers of operations. Early timesteps use a higher number of denoising operations to establish fundamental structure, while later timesteps use fewer operations for refinement. This segmentation allows the system to achieve good output quality without uniformly applying high computational cost across all timesteps, thereby resolving the contradiction between manufacturing precision and productivity.

The patent performs extensive training in advance to learn the noise distribution characteristics at different timesteps. This preliminary action stores the learned patterns in the model weights, allowing the system to achieve effective denoising during inference without performing extensive computational operations in real-time. The training phase consumes computational resources upfront, but this enables efficient operation during actual content generation, resolving the contradiction between manufacturing precision and energy loss.

The patent dynamically adjusts the number of denoising operations based on the timestep. Instead of using a fixed high number of operations for all timesteps, the system adaptively reduces the number of operations as the diffusion process progresses. This dynamic approach maintains output quality by using sufficient operations when needed (early timesteps) while reducing computational overhead in later stages, thereby resolving the contradiction between manufacturing precision and time loss.