,
    140, 120, 100,  80,  60,  40,  20,   0]) Create some random noise with the same shape as the desired output:   Copied >>> import torch

>>> sample_size = model.config.sample_size
>>> noise = torch.randn((1, 3, sample_size, sample_size), device="cuda") Now write a loop to iterate over the timesteps. At each timestep, the model does a UNet2DModel.forward() pass and returns the noisy residual. The scheduler’s step() method takes the noisy re