Dynamically predicting and updating conditioning embeddings at each generation step improves diffusion model efficiency and quality—you don't need to reuse the same embedding throughout the entire denoising process.
This paper proposes using predicted embeddings as dynamic conditioning signals in diffusion transformers instead of static embeddings. A separate transformer (NEPA) predicts image embeddings at each denoising step, allowing the conditioning to adapt to the current noise level. The approach achieves competitive image generation quality on ImageNet with significantly less training compute.