| # Compares flow matching (FM) conditional flow matching (CFM) and optimal | |
| # transport conditional flow matching on four datasets. twodim is not possible | |
| # for the flow matching algorithm as it has a non-gaussian source distribution. | |
| # FM is therefore only run on three datasets. | |
| python src/train.py -m experiment=cfm \ | |
| model=cfm,otcfm \ | |
| launcher=mila_cpu_cluster \ | |
| model.sigma_min=0.1 \ | |
| datamodule=scurve,moons,twodim,gaussians \ | |
| seed=42,43,44,45,46 & | |
| # Sleep to avoid launching jobs at the same time | |
| sleep 1 | |
| python src/train.py -m experiment=cfm \ | |
| model=fm \ | |
| launcher=mila_cpu_cluster \ | |
| model.sigma_min=0.1 \ | |
| datamodule=scurve,moons,gaussians \ | |
| seed=42,43,44,45,46 & | |