Interrupting encoder training in diffusion models enables faster/ less computationally demanding generative AI

https://techxplore.com/news/2025-09-encoder-diffusion-enables-efficient-generative.html

"mathematically complex/ expensive to train, overcome modifying Schrödinger bridge-type diffusion adding noise to real data through encoder/ reconstructed samples using variational autoencoders with infinite number of latent variables... prior loss/ drift matching objective functions reduce computational cost/ prevent overfitting... connects any 2 probability distributions over finite time using stochastic differential equation supporting more complex noising processes/ higher-quality sample generation"