Wrong order of dataset scale and shift in model training

[jhbastek]

In train_ddpm/runners/diffusion_tub.py the loss is computed as

loss = loss_registry[config.model.type](model, x, t, e, b, x_offset.item(), x_scale.item()).

This calls def conditional_noise_estimation_loss(model, x0: torch.Tensor, t: torch.LongTensor, e: torch.Tensor, b: torch.Tensor, x_scale, x_offset, keepdim=False, p=0.1) in train_ddpm/functions/losses.py and thus swaps x_scale with x_offset. It would be much appreciated if the authors could double check if they used a correct version for the results in the paper or these results suffer from this mismatch. Thank you in advance.