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# Schedulers
- Schedulers are the algorithms to use diffusion models in inference as well as for training. They include the noise schedules and define algorithm-specific diffusion steps.
- Schedulers can be used interchangable between diffusion models in inference to find the preferred tradef-off between speed and generation quality.
- Schedulers can be used interchangable between diffusion models in inference to find the preferred trade-off between speed and generation quality.
- Schedulers are available in numpy, but can easily be transformed into PyTorch.
## API
- Schedulers should provide one or more `def step(...)` functions that should be called iteratively to unroll the diffusion loop during
the forward pass.
- Schedulers should be framework-agonstic, but provide a simple functionality to convert the scheduler into a specific framework, such as PyTorch
- Schedulers should be framework-agnostic, but provide a simple functionality to convert the scheduler into a specific framework, such as PyTorch
with a `set_format(...)` method.
## Examples