# CosineDPMSolverMultistepScheduler

The [CosineDPMSolverMultistepScheduler](/docs/diffusers/main/en/api/schedulers/cosine_dpm#diffusers.CosineDPMSolverMultistepScheduler) is a variant of [DPMSolverMultistepScheduler](/docs/diffusers/main/en/api/schedulers/multistep_dpm_solver#diffusers.DPMSolverMultistepScheduler) with cosine schedule, proposed by Nichol and Dhariwal (2021).
It is being used in the [Stable Audio Open](https://huggingface.co/papers/2407.14358) paper and the [Stability-AI/stable-audio-tool](https://github.com/Stability-AI/stable-audio-tools) codebase.

This scheduler was contributed by [Yoach Lacombe](https://huggingface.co/ylacombe).

## CosineDPMSolverMultistepScheduler[[diffusers.CosineDPMSolverMultistepScheduler]]
#### diffusers.CosineDPMSolverMultistepScheduler[[diffusers.CosineDPMSolverMultistepScheduler]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_cosine_dpmsolver_multistep.py#L28)

Implements a variant of `DPMSolverMultistepScheduler` with cosine schedule, proposed by Nichol and Dhariwal (2021).
This scheduler was used in Stable Audio Open [1].

[1] Evans, Parker, et al. "Stable Audio Open" https://huggingface.co/papers/2407.14358

This model inherits from [SchedulerMixin](/docs/diffusers/main/en/api/schedulers/overview#diffusers.SchedulerMixin) and [ConfigMixin](/docs/diffusers/main/en/api/configuration#diffusers.ConfigMixin). Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.

add_noisediffusers.CosineDPMSolverMultistepScheduler.add_noisehttps://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_cosine_dpmsolver_multistep.py#L566[{"name": "original_samples", "val": ": Tensor"}, {"name": "noise", "val": ": Tensor"}, {"name": "timesteps", "val": ": Tensor"}]- **original_samples** (`torch.Tensor`) --
  The original samples to which noise will be added.
- **noise** (`torch.Tensor`) --
  The noise tensor to add to the original samples.
- **timesteps** (`torch.Tensor`) --
  The timesteps at which to add noise, determining the noise level from the schedule.0`torch.Tensor`The noisy samples with added noise scaled according to the timestep schedule.

Add noise to the original samples according to the noise schedule at the specified timesteps.

**Parameters:**

sigma_min (`float`, *optional*, defaults to 0.3) : Minimum noise magnitude in the sigma schedule. This was set to 0.3 in Stable Audio Open [1].

sigma_max (`float`, *optional*, defaults to 500) : Maximum noise magnitude in the sigma schedule. This was set to 500 in Stable Audio Open [1].

sigma_data (`float`, *optional*, defaults to 1.0) : The standard deviation of the data distribution. This is set to 1.0 in Stable Audio Open [1].

sigma_schedule (`str`, *optional*, defaults to `exponential`) : Sigma schedule to compute the `sigmas`. By default, we the schedule introduced in the EDM paper (https://huggingface.co/papers/2206.00364). Other acceptable value is "exponential". The exponential schedule was incorporated in this model: https://huggingface.co/stabilityai/cosxl.

num_train_timesteps (`int`, defaults to 1000) : The number of diffusion steps to train the model.

solver_order (`int`, defaults to 2) : The DPMSolver order which can be `1` or `2`. It is recommended to use `solver_order=2`.

prediction_type (`str`, defaults to `v_prediction`, *optional*) : Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process), `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen Video](https://huggingface.co/papers/2210.02303) paper).

solver_type (`str`, defaults to `midpoint`) : Solver type for the second-order solver; can be `midpoint` or `heun`. The solver type slightly affects the sample quality, especially for a small number of steps. It is recommended to use `midpoint` solvers.

lower_order_final (`bool`, defaults to `True`) : Whether to use lower-order solvers in the final steps. Only valid for  [!TIP] > The algorithm and model type are decoupled. You can use either DPMSolver or DPMSolver++ for both
noise > prediction and data prediction models.

**Parameters:**

model_output (`torch.Tensor`) : The direct output from the learned diffusion model.

sample (`torch.Tensor`) : A current instance of a sample created by the diffusion process.

**Returns:**

``torch.Tensor``

The converted model output.
#### dpm_solver_first_order_update[[diffusers.CosineDPMSolverMultistepScheduler.dpm_solver_first_order_update]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_cosine_dpmsolver_multistep.py#L338)

One step for the first-order DPMSolver (equivalent to DDIM).

**Parameters:**

model_output (`torch.Tensor`) : The direct output from the learned diffusion model.

sample (`torch.Tensor`) : A current instance of a sample created by the diffusion process.

**Returns:**

``torch.Tensor``

The sample tensor at the previous timestep.
#### index_for_timestep[[diffusers.CosineDPMSolverMultistepScheduler.index_for_timestep]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_cosine_dpmsolver_multistep.py#L432)

Find the index for a given timestep in the schedule.

**Parameters:**

timestep (`int` or `torch.Tensor`) : The timestep for which to find the index.

schedule_timesteps (`torch.Tensor`, *optional*) : The timestep schedule to search in. If `None`, uses `self.timesteps`.

**Returns:**

``int``

The index of the timestep in the schedule.
#### multistep_dpm_solver_second_order_update[[diffusers.CosineDPMSolverMultistepScheduler.multistep_dpm_solver_second_order_update]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_cosine_dpmsolver_multistep.py#L373)

One step for the second-order multistep DPMSolver.

**Parameters:**

model_output_list (`List[torch.Tensor]`) : The direct outputs from learned diffusion model at current and latter timesteps.

sample (`torch.Tensor`) : A current instance of a sample created by the diffusion process.

**Returns:**

``torch.Tensor``

The sample tensor at the previous timestep.
#### scale_model_input[[diffusers.CosineDPMSolverMultistepScheduler.scale_model_input]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_cosine_dpmsolver_multistep.py#L174)

Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep. Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the Euler algorithm.

**Parameters:**

sample (`torch.Tensor`) : The input sample.

timestep (`int`, *optional*) : The current timestep in the diffusion chain.

**Returns:**

``torch.Tensor``

A scaled input sample.
#### set_begin_index[[diffusers.CosineDPMSolverMultistepScheduler.set_begin_index]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_cosine_dpmsolver_multistep.py#L135)

Sets the begin index for the scheduler. This function should be run from pipeline before the inference.

**Parameters:**

begin_index (`int`, defaults to `0`) : The begin index for the scheduler.
#### set_timesteps[[diffusers.CosineDPMSolverMultistepScheduler.set_timesteps]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_cosine_dpmsolver_multistep.py#L198)

Sets the discrete timesteps used for the diffusion chain (to be run before inference).

**Parameters:**

num_inference_steps (`int`) : The number of diffusion steps used when generating samples with a pre-trained model.

device (`str` or `torch.device`, *optional*) : The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
#### step[[diffusers.CosineDPMSolverMultistepScheduler.step]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_cosine_dpmsolver_multistep.py#L483)

Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with
the multistep DPMSolver.

**Parameters:**

model_output (`torch.Tensor`) : The direct output from learned diffusion model.

timestep (`int`) : The current discrete timestep in the diffusion chain.

sample (`torch.Tensor`) : A current instance of a sample created by the diffusion process.

generator (`torch.Generator`, *optional*) : A random number generator.

return_dict (`bool`) : Whether or not to return a [SchedulerOutput](/docs/diffusers/main/en/api/schedulers/pndm#diffusers.schedulers.scheduling_utils.SchedulerOutput) or `tuple`.

**Returns:**

`[SchedulerOutput](/docs/diffusers/main/en/api/schedulers/pndm#diffusers.schedulers.scheduling_utils.SchedulerOutput) or `tuple``

If return_dict is `True`, [SchedulerOutput](/docs/diffusers/main/en/api/schedulers/pndm#diffusers.schedulers.scheduling_utils.SchedulerOutput) is returned, otherwise a
tuple is returned where the first element is the sample tensor.

## SchedulerOutput[[diffusers.schedulers.scheduling_utils.SchedulerOutput]]
#### diffusers.schedulers.scheduling_utils.SchedulerOutput[[diffusers.schedulers.scheduling_utils.SchedulerOutput]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_utils.py#L62)

Base class for the output of a scheduler's `step` function.

**Parameters:**

prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images) : Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the denoising loop.

