 random noises, a channel axis corresponding to the number of input channels, and a sample_size axis for the height and width of the image:


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>>> import torch

>>> torch.manual_seed(0)

>>> noisy_sample = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
>>> noisy_sample.shape
torch.Size([1, 3, 256, 256])
For inference, pass the noisy image to the model and a timestep. The timestep indicates ho