By default, the experiment name is the config name. Experiments are saved in save/ with corresponding names. Append -n to manually set a name.

After filling in the information in load/wandb.yaml, append -w to log to Wandb.

Dataset format is image folders. To set up data, fill in root_path for configs in datasets/. For example, train and val can be ImageNet training and validation set (as image folders for different classes), eval_ae/eval_zdm can be a smaller validation subset (image folders, in the paper it is 5K samples in total) that is used to evaluate FID for reconstruction or generation.

The commands below are for DiTo-XL, the configs can be changed accordingly (in configs/experiments/) for other scales or to enable noise synchronization. If the GPU memory is not sufficient, a multi-node training with torchrun is needed (or the batch size can be reduced in configs/datasets/*.yaml). In the paper, DiTo at B, L, XL were trained on 1, 2, 4 nodes with 8 A100 per node, and latent diffusion models were trained on 2 nodes.