This paper replaces standard diffusion denoising with conditional normalizing flows to get four-step image generation without giving up exact likelihood training. The self-distillation angle makes it especially relevant for teams trying to cut sampling cost while preserving trainability.
Normalizing Trajectory Models replace standard diffusion denoising steps with conditional normalizing flows, enabling four-step image generation while retaining exact likelihood training and supporting self-distillation.