#
EDMDPMSolverMultistepScheduler

`EDMDPMSolverMultistepScheduler` is a Karras formulation of `DPMSolverMultistepScheduler`, a multistep scheduler from DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps and DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu.

DPMSolver (and the improved version DPMSolver++) is a fast dedicated high-order solver for diffusion ODEs with convergence order guarantee. Empirically, DPMSolver sampling with only 20 steps can generate high-quality samples, and it can generate quite good samples even in 10 steps.

##
EDMDPMSolverMultistepScheduler

###
class diffusers.EDMDPMSolverMultistepScheduler

< source >

( sigma_min: float = 0.002 sigma_max: float = 80.0 sigma_data: float = 0.5 sigma_schedule: str = 'karras' num_train_timesteps: int = 1000 prediction_type: str = 'epsilon' rho: float = 7.0 solver_order: int = 2 thresholding: bool = False dynamic_thresholding_ratio: float = 0.995 sample_max_value: float = 1.0 algorithm_type: str = 'dpmsolver++' solver_type: str = 'midpoint' lower_order_final: bool = True euler_at_final: bool = False final_sigmas_type: Optional = 'zero' )

Parameters

- sigma_min (`float`, optional, defaults to 0.002) — Minimum noise magnitude in the sigma schedule. This was set to 0.002 in the EDM paper [1]; a reasonable range is [0, 10].
- sigma_max (`float`, optional, defaults to 80.0) — Maximum noise magnitude in the sigma schedule. This was set to 80.0 in the EDM paper [1]; a reasonable range is [0.2, 80.0].
- sigma_data (`float`, optional, defaults to 0.5) — The standard deviation of the data distribution. This is set to 0.5 in the EDM paper [1].
- sigma_schedule (`str`, optional, defaults to `karras`) — Sigma schedule to compute the `sigmas`. By default, we the schedule introduced in the EDM paper (https://arxiv.org/abs/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` 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 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++` or `sde-dpmsolver++`. The `dpmsolver++` type implements the algorithms in the DPMSolver++ 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 < 15 inference steps. This can stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10.
- euler_at_final (`bool`, defaults to `False`) — Whether to use Euler’s method in the final step. It is a trade-off between numerical stability and detail richness. This can stabilize the sampling of the SDE variant of DPMSolver for small number of inference steps, but sometimes may result in blurring.
- final_sigmas_type (`str`, defaults to `"zero"`) — The final `sigma` value for the noise schedule during the sampling process. If `"sigma_min"`, the final sigma is the same as the last sigma in the training schedule. If `zero`, the final sigma is set to 0.

Implements DPMSolverMultistepScheduler in EDM formulation as presented in Karras et al. 2022 [1]. `EDMDPMSolverMultistepScheduler` is a fast dedicated high-order solver for diffusion ODEs.

[1] Karras, Tero, et al. “Elucidating the Design Space of Diffusion-Based Generative Models.” https://arxiv.org/abs/2206.00364

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.

####
convert_model_output

< source >

( model_output: Tensor sample: Tensor = None ) →
`torch.Tensor`

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.

Convert the model output to the corresponding type the DPMSolver/DPMSolver++ algorithm needs. DPM-Solver is designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to discretize an integral of the data prediction model.

The algorithm and model type are decoupled. You can use either DPMSolver or DPMSolver++ for both noise prediction and data prediction models.

####
dpm_solver_first_order_update

< source >

( model_output: Tensor sample: Tensor = None noise: Optional = None ) →
`torch.Tensor`

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.

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

####
multistep_dpm_solver_second_order_update

< source >

( model_output_list: List sample: Tensor = None noise: Optional = None ) →
`torch.Tensor`

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.

One step for the second-order multistep DPMSolver.

####
multistep_dpm_solver_third_order_update

< source >

( model_output_list: List sample: Tensor = None ) →
`torch.Tensor`

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 diffusion process.

Returns

`torch.Tensor`

The sample tensor at the previous timestep.

One step for the third-order multistep DPMSolver.

####
scale_model_input

< source >

( sample: Tensor timestep: Union ) →
`torch.Tensor`

Parameters

- sample (`torch.Tensor`) — The input sample.
- timestep (`int`, optional) — The current timestep in the diffusion chain.

Returns

`torch.Tensor`

A scaled input sample.

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.

####
set_begin_index

< source >

( begin_index: int = 0 )

Parameters

- begin_index (`int`) — The begin index for the scheduler.

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

####
set_timesteps

< source >

( num_inference_steps: int = None device: Union = None )

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.

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

####
step

< source >

( model_output: Tensor timestep: Union sample: Tensor generator = None return_dict: bool = True ) →
SchedulerOutput or `tuple`

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 or `tuple`.

Returns

SchedulerOutput or `tuple`

If return_dict is `True`, SchedulerOutput is returned, otherwise a tuple is returned where the first element is the sample tensor.

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

##
SchedulerOutput

###
class diffusers.schedulers.scheduling_utils.SchedulerOutput

< source >

( prev_sample: Tensor )

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.

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

< > Update on GitHub