g_rate)
>>> lr_scheduler = get_cosine_schedule_with_warmup(
...     optimizer=optimizer,
...     num_warmup_steps=config.lr_warmup_steps,
...     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:


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>>> from diffusers import DDPMPipeline
>>> import math
>>> import os

