Feng-Xiang Yang

Conditional information in diffusion models becomes significantly more impactful after a specific point in the generation trajectory, not uniformly throughout.

Fixed guidance weights in diffusion models are suboptimal: C$^2$FG offers a training-free, theoretically grounded approach to dynamically adjust guidance strength, improving performance across diverse generative tasks.