,
...     num_training_steps=(len(train_dataloader) * config.num_epochs),
... ) Then, you’ll need a way to evaluate the model. For evaluation, you can use the DDPMPipeline to generate a batch of sample images and save it as a grid:   Copied >>> from diffusers import DDPMPipeline
>>> from diffusers.utils import make_image_grid
>>> import os

>>> def evaluate(config, epoch, pipeline):
...     # Sample some images from random noise (this is the ba