SOTA)
transformer architecture combined with latent space sampling and can be trained
on a consumer workstation. We empirically demonstrate that LeDiFlow remarkably
outperforms the respective FM baselines. For instance, when operating directly
on pixels, our model accelerates inference by up to 3.75x compared to the
corresponding pixel-space baseline. Simultaneously, our latent FM model
enhances image quality on average by 1.32x in CLIP Maximum Mean Discrepancy
(CMMD) metric against its respective baseline.