# DPMSolverSDEScheduler The `DPMSolverSDEScheduler` is inspired by the stochastic sampler from the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper, and the scheduler is ported from and created by [Katherine Crowson](https://github.com/crowsonkb/). ## DPMSolverSDEScheduler[[diffusers.DPMSolverSDEScheduler]] #### diffusers.DPMSolverSDEScheduler[[diffusers.DPMSolverSDEScheduler]] [Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_sde.py#L195) DPMSolverSDEScheduler implements the stochastic sampler from the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper. 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.DPMSolverSDEScheduler.add_noisehttps://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_sde.py#L776[{"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:** num_train_timesteps (`int`, defaults to 1000) : The number of diffusion steps to train the model. beta_start (`float`, defaults to 0.00085) : The starting `beta` value of inference. beta_end (`float`, defaults to 0.012) : 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` or `scaled_linear`. trained_betas (`np.ndarray`, *optional*) : Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`. 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). use_karras_sigmas (`bool`, *optional*, defaults to `False`) : Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`, the sigmas are determined according to a sequence of noise levels {σi}. use_exponential_sigmas (`bool`, *optional*, defaults to `False`) : Whether to use exponential sigmas for step sizes in the noise schedule during the sampling process. use_beta_sigmas (`bool`, *optional*, defaults to `False`) : Whether to use beta sigmas for step sizes in the noise schedule during the sampling process. Refer to [Beta Sampling is All You Need](https://huggingface.co/papers/2407.12173) for more information. noise_sampler_seed (`int`, *optional*, defaults to `None`) : The random seed to use for the noise sampler. If `None`, a random seed is generated. timestep_spacing (`str`, defaults to `"linspace"`) : The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information. steps_offset (`int`, defaults to 0) : An offset added to the inference steps, as required by some model families. **Returns:** ``torch.Tensor`` The noisy samples with added noise scaled according to the timestep schedule. #### index_for_timestep[[diffusers.DPMSolverSDEScheduler.index_for_timestep]] [Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_sde.py#L304) Find the index of a given timestep in the timestep schedule. **Parameters:** timestep (`float` or `torch.Tensor`) : The timestep value to find in the schedule. 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. For the very first step, returns the second index if multiple matches exist to avoid skipping a sigma when starting mid-schedule (e.g., for image-to-image). #### scale_model_input[[diffusers.DPMSolverSDEScheduler.scale_model_input]] [Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_sde.py#L383) Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. **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.DPMSolverSDEScheduler.set_begin_index]] [Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_sde.py#L373) 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.DPMSolverSDEScheduler.set_timesteps]] [Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_sde.py#L410) 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. num_train_timesteps (`int`, *optional*) : The number of train timesteps. If `None`, uses `self.config.num_train_timesteps`. #### step[[diffusers.DPMSolverSDEScheduler.step]] [Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_sde.py#L655) Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion process from the learned model outputs (most often the predicted noise). **Parameters:** model_output (`torch.Tensor`) : The direct output from learned diffusion model. timestep (`float` or `torch.Tensor`) : The current discrete timestep in the diffusion chain. sample (`torch.Tensor`) : A current instance of a sample created by the diffusion process. return_dict (`bool`) : Whether or not to return a `DPMSolverSDESchedulerOutput` or tuple. s_noise (`float`, *optional*, defaults to 1.0) : Scaling factor for noise added to the sample. **Returns:** ``DPMSolverSDESchedulerOutput` or `tuple`` If return_dict is `True`, `DPMSolverSDESchedulerOutput` 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#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.