# DPMSolverMultistepInverse

`DPMSolverMultistepInverse` is the inverted scheduler from [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://huggingface.co/papers/2206.00927) and [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://huggingface.co/papers/2211.01095) by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu.

The implementation is mostly based on the DDIM inversion definition of [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://huggingface.co/papers/2211.09794) and notebook implementation of the `DiffEdit` latent inversion from [Xiang-cd/DiffEdit-stable-diffusion](https://github.com/Xiang-cd/DiffEdit-stable-diffusion/blob/main/diffedit.ipynb).

## Tips

Dynamic thresholding from [Imagen](https://huggingface.co/papers/2205.11487) is supported, and for pixel-space
diffusion models, you can set both `algorithm_type="dpmsolver++"` and `thresholding=True` to use the dynamic
thresholding. This thresholding method is unsuitable for latent-space diffusion models such as
Stable Diffusion.

## DPMSolverMultistepInverseScheduler[[diffusers.DPMSolverMultistepInverseScheduler]]
#### diffusers.DPMSolverMultistepInverseScheduler[[diffusers.DPMSolverMultistepInverseScheduler]]

[Source](https://github.com/huggingface/diffusers/blob/v0.37.0/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py#L86)

`DPMSolverMultistepInverseScheduler` is the reverse scheduler of [DPMSolverMultistepScheduler](/docs/diffusers/v0.37.0/en/api/schedulers/multistep_dpm_solver#diffusers.DPMSolverMultistepScheduler).

This model inherits from [SchedulerMixin](/docs/diffusers/v0.37.0/en/api/schedulers/overview#diffusers.SchedulerMixin) and [ConfigMixin](/docs/diffusers/v0.37.0/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.DPMSolverMultistepInverseScheduler.add_noisehttps://github.com/huggingface/diffusers/blob/v0.37.0/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py#L1115[{"name": "original_samples", "val": ": Tensor"}, {"name": "noise", "val": ": Tensor"}, {"name": "timesteps", "val": ": IntTensor"}]- **original_samples** (`torch.Tensor`) --
  The original samples to add noise to.
- **noise** (`torch.Tensor`) --
  The noise tensor.
- **timesteps** (`torch.IntTensor`) --
  The timesteps at which to add noise.0`torch.Tensor`The noisy samples.

Add noise to the clean `original_samples` using the scheduler's equivalent function.

**Parameters:**

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

beta_start (`float`, defaults to 0.0001) : The starting `beta` value of inference.

beta_end (`float`, defaults to 0.02) : The final `beta` value.

beta_schedule (`str`, defaults to `"linear"`) : The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear`, `scaled_linear`, or `squaredcos_cap_v2`.

trained_betas (`np.ndarray`, *optional*) : Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.

solver_order (`int`, defaults to 2) : The DPMSolver order which can be `1` or `2` or `3`. It is recommended to use `solver_order=2` for guided sampling, and `solver_order=3` for unconditional sampling.

prediction_type (`str`, defaults to `epsilon`, *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).

thresholding (`bool`, defaults to `False`) : Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such as Stable Diffusion.

dynamic_thresholding_ratio (`float`, defaults to 0.995) : The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.

sample_max_value (`float`, defaults to 1.0) : The threshold value for dynamic thresholding. Valid only when `thresholding=True` and `algorithm_type="dpmsolver++"`.

algorithm_type (`str`, defaults to `dpmsolver++`) : Algorithm type for the solver; can be `dpmsolver`, `dpmsolver++`, `sde-dpmsolver` or `sde-dpmsolver++`. The `dpmsolver` type implements the algorithms in the [DPMSolver](https://huggingface.co/papers/2206.00927) paper, and the `dpmsolver++` type implements the algorithms in the [DPMSolver++](https://huggingface.co/papers/2211.01095) paper. It is recommended to use `dpmsolver++` or `sde-dpmsolver++` with `solver_order=2` for guided sampling like in Stable Diffusion.

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`, *optional*) : A current instance of a sample created by the diffusion process.

**Returns:**

``torch.Tensor``

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

[Source](https://github.com/huggingface/diffusers/blob/v0.37.0/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py#L699)

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`, *optional*) : A current instance of a sample created by the diffusion process.

noise (`torch.Tensor`, *optional*) : The noise tensor.

**Returns:**

``torch.Tensor``

The sample tensor at the previous timestep.
#### multistep_dpm_solver_second_order_update[[diffusers.DPMSolverMultistepInverseScheduler.multistep_dpm_solver_second_order_update]]

[Source](https://github.com/huggingface/diffusers/blob/v0.37.0/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py#L774)

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`, *optional*) : A current instance of a sample created by the diffusion process.

**Returns:**

``torch.Tensor``

The sample tensor at the previous timestep.
#### multistep_dpm_solver_third_order_update[[diffusers.DPMSolverMultistepInverseScheduler.multistep_dpm_solver_third_order_update]]

[Source](https://github.com/huggingface/diffusers/blob/v0.37.0/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py#L898)

One step for the third-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`, *optional*) : A current instance of a sample created by diffusion process.

noise (`torch.Tensor`, *optional*) : The noise tensor.

**Returns:**

``torch.Tensor``

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

[Source](https://github.com/huggingface/diffusers/blob/v0.37.0/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py#L1100)

Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep.

**Parameters:**

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

**Returns:**

``torch.Tensor``

A scaled input sample.
#### set_timesteps[[diffusers.DPMSolverMultistepInverseScheduler.set_timesteps]]

[Source](https://github.com/huggingface/diffusers/blob/v0.37.0/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py#L283)

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.DPMSolverMultistepInverseScheduler.step]]

[Source](https://github.com/huggingface/diffusers/blob/v0.37.0/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py#L1014)

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.

variance_noise (`torch.Tensor`) : Alternative to generating noise with `generator` by directly providing the noise for the variance itself. Useful for methods such as `CycleDiffusion`.

return_dict (`bool`) : Whether or not to return a [SchedulerOutput](/docs/diffusers/v0.37.0/en/api/schedulers/overview#diffusers.schedulers.scheduling_utils.SchedulerOutput) or `tuple`.

**Returns:**

`[SchedulerOutput](/docs/diffusers/v0.37.0/en/api/schedulers/overview#diffusers.schedulers.scheduling_utils.SchedulerOutput) or `tuple``

If return_dict is `True`, [SchedulerOutput](/docs/diffusers/v0.37.0/en/api/schedulers/overview#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/v0.37.0/src/diffusers/schedulers/scheduling_utils.py#L61)

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.