arguments, and the number of processes (you can change this value to the number of GPUs available to you) to use for training:


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>>> from accelerate import notebook_launcher

>>> args = (config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler)

>>> notebook_launcher(train_loop, args, num_processes=1)
Once training is complete, take a look at the final 🦋 images 🦋 generated by your diffusion model!


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>>> import g