e into a data sample.
beta_schedule: the type of noise schedule to use for inference and training.
beta_start and beta_end: the start and end noise values for the noise schedule.
To predict a slightly less noisy image, pass the following to the scheduler’s step() method: model output, timestep, and current sample.


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>>> less_noisy_sample = scheduler.step(model_output=noisy_residual, timestep=2, sample=noisy_sample).prev_sample
>>> less_