CMStochasticIterativeScheduler  
Multistep and onestep sampling for consistency models.  
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.  
Args:
num_train_timesteps (`int`, defaults to 40):
The number of diffusion steps to train the model.
sigma_min (`float`, defaults to 0.002):
Minimum noise magnitude in the sigma schedule. Defaults to 0.002 from the original implementation.
sigma_max (`float`, defaults to 80.0):
Maximum noise magnitude in the sigma schedule. Defaults to 80.0 from the original implementation.
sigma_data (`float`, defaults to 0.5):
The standard deviation of the data distribution from the EDM
[paper](https://huggingface.co/papers/2206.00364). Defaults to 0.5 from the original implementation.
s_noise (`float`, defaults to 1.0):
The amount of additional noise to counteract loss of detail during sampling. A reasonable range is [1.000,
1.011]. Defaults to 1.0 from the original implementation.
rho (`float`, defaults to 7.0):
The parameter for calculating the Karras sigma schedule from the EDM
[paper](https://huggingface.co/papers/2206.00364). Defaults to 7.0 from the original implementation.
clip_denoised (`bool`, defaults to `True`):
Whether to clip the denoised outputs to `(-1, 1)`.
timesteps (`List` or `np.ndarray` or `torch.Tensor`, *optional*):
An explicit timestep schedule that can be optionally specified. The timesteps are expected to be in
increasing order.
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/cm_stochastic_iterative.md