# EDMDPMSolverMultistepScheduler `EDMDPMSolverMultistepScheduler` is a [Karras formulation](https://huggingface.co/papers/2206.00364) of `DPMSolverMultistepScheduler`, a multistep 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. 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[[diffusers.EDMDPMSolverMultistepScheduler]] #### diffusers.EDMDPMSolverMultistepScheduler[[diffusers.EDMDPMSolverMultistepScheduler]] [Source](https://github.com/huggingface/diffusers/blob/v0.37.0/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py#L28) 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://huggingface.co/papers/2206.00364 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.EDMDPMSolverMultistepScheduler.add_noisehttps://github.com/huggingface/diffusers/blob/v0.37.0/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py#L808[{"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 0.002) : 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://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. 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). rho (`float`, *optional*, defaults to 7.0) : The rho parameter in the Karras sigma schedule. This was set to 7.0 in the EDM paper [1]. 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. 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++](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`) : A current instance of a sample created by the diffusion process. **Returns:** ``torch.Tensor`` The converted model output. #### dpm_solver_first_order_update[[diffusers.EDMDPMSolverMultistepScheduler.dpm_solver_first_order_update]] [Source](https://github.com/huggingface/diffusers/blob/v0.37.0/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py#L502) 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. noise (`torch.Tensor`, *optional*) : The noise tensor to add to the original samples. **Returns:** ``torch.Tensor`` The sample tensor at the previous timestep. #### index_for_timestep[[diffusers.EDMDPMSolverMultistepScheduler.index_for_timestep]] [Source](https://github.com/huggingface/diffusers/blob/v0.37.0/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py#L673) 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.EDMDPMSolverMultistepScheduler.multistep_dpm_solver_second_order_update]] [Source](https://github.com/huggingface/diffusers/blob/v0.37.0/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py#L545) 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. noise (`torch.Tensor`, *optional*) : The noise tensor to add to the original samples. **Returns:** ``torch.Tensor`` The sample tensor at the previous timestep. #### multistep_dpm_solver_third_order_update[[diffusers.EDMDPMSolverMultistepScheduler.multistep_dpm_solver_third_order_update]] [Source](https://github.com/huggingface/diffusers/blob/v0.37.0/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py#L618) 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`) : A current instance of a sample created by diffusion process. **Returns:** ``torch.Tensor`` The sample tensor at the previous timestep. #### precondition_inputs[[diffusers.EDMDPMSolverMultistepScheduler.precondition_inputs]] [Source](https://github.com/huggingface/diffusers/blob/v0.37.0/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py#L180) Precondition the input sample by scaling it according to the EDM formulation. **Parameters:** sample (`torch.Tensor`) : The input sample tensor to precondition. sigma (`float` or `torch.Tensor`) : The current sigma (noise level) value. **Returns:** ``torch.Tensor`` The scaled input sample. #### precondition_noise[[diffusers.EDMDPMSolverMultistepScheduler.precondition_noise]] [Source](https://github.com/huggingface/diffusers/blob/v0.37.0/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py#L199) Precondition the noise level by applying a logarithmic transformation. **Parameters:** sigma (`float` or `torch.Tensor`) : The sigma (noise level) value to precondition. **Returns:** ``torch.Tensor`` The preconditioned noise value computed as `0.25 * log(sigma)`. #### precondition_outputs[[diffusers.EDMDPMSolverMultistepScheduler.precondition_outputs]] [Source](https://github.com/huggingface/diffusers/blob/v0.37.0/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py#L219) Precondition the model outputs according to the EDM formulation. **Parameters:** sample (`torch.Tensor`) : The input sample tensor. model_output (`torch.Tensor`) : The direct output from the learned diffusion model. sigma (`float` or `torch.Tensor`) : The current sigma (noise level) value. **Returns:** ``torch.Tensor`` The denoised sample computed by combining the skip connection and output scaling. #### scale_model_input[[diffusers.EDMDPMSolverMultistepScheduler.scale_model_input]] [Source](https://github.com/huggingface/diffusers/blob/v0.37.0/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py#L255) Scale the denoising model input to match the Euler algorithm. Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. **Parameters:** sample (`torch.Tensor`) : The input sample tensor. timestep (`float` or `torch.Tensor`) : The current timestep in the diffusion chain. **Returns:** ``torch.Tensor`` A scaled input sample. #### set_begin_index[[diffusers.EDMDPMSolverMultistepScheduler.set_begin_index]] [Source](https://github.com/huggingface/diffusers/blob/v0.37.0/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py#L169) 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.EDMDPMSolverMultistepScheduler.set_timesteps]] [Source](https://github.com/huggingface/diffusers/blob/v0.37.0/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py#L279) 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.EDMDPMSolverMultistepScheduler.step]] [Source](https://github.com/huggingface/diffusers/blob/v0.37.0/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py#L726) 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/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.