pecific point in the diffusion process and applies noise to the image according to a noise schedule and an update rule.
Let’s take a look at the DDPMScheduler and use the add_noise method to add some random noise to the sample_image from before:


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>>> import torch
>>> from PIL import Image
>>> from diffusers import DDPMScheduler

>>> noise_scheduler = DDPMScheduler(num_train_timesteps=1000)
>>> noise = torch.randn(sample_image.shape)
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