#ImageNet

'Oxford researchers introduce FOP, an optimizer that leverages intra-batch gradient variance to achieve up to 7.5× faster convergence and drastically lower GPU costs.'

Decoupled Diffusion Transformers (DDT) separate semantic encoding and detail decoding to speed up training and improve image generation quality, setting new benchmarks on ImageNet datasets.