Learning the Integral of a Diffusion Model

Diffusion models generate images by taking many small steps from noise to data, but this process is slow and costly. Flow maps offer a faster alternative by training neural networks to predict any point along these paths directly, rather than following them step by step. This can reduce sampling to a single step. However, building accurate flow maps is complex and typically requires a pre-trained diffusion model as a starting point.