We present a time scheduler that selects sampling points based on entropy rather than uniform time spacing, ensuring each point contributes an equal amount of information to the final generation.

We propose EVODiff, a novel information-theoretically grounded framework that optimizes conditional variance in diffusion models' generative process, achieving significant gains in both efficiency and image quality without prior trajectories.