o ۷i/@sddlmZddlZddlZddlmZmZddlm Z ddl m Z m Z ddl mZe eZeGdd d e ZGd d d e eZdS) ) dataclassN) ConfigMixinregister_to_config)SchedulerMixin) BaseOutputlogging) randn_tensorc@s.eZdZUdZejed<dZejdBed<dS)SCMSchedulerOutputaq Output class for the scheduler's `step` function output. Args: 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. pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images): The predicted denoised sample `(x_{0})` based on the model output from the current timestep. `pred_original_sample` can be used to preview progress or for guidance. prev_sampleNpred_original_sample)__name__ __module__ __qualname____doc__torchTensor__annotations__r rrY/home/ubuntu/vllm_env/lib/python3.10/site-packages/diffusers/schedulers/scheduling_scm.pyr s  r c@seZdZdZdZe   d-dededefd d Z e d d Z e d dZ d.defddZ    d/dedejdeejBdedef ddZdeejBddfdd Z d0deejBd!ejdBdefd"d#Z  $d1d%ejded&ejd'ejd(edeeBf d)d*Zd+d,ZdS)2 SCMSchedulera `SCMScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with non-Markovian guidance. 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. Args: num_train_timesteps (`int`, defaults to 1000): The number of diffusion steps to train the model. prediction_type (`str`, defaults to `trigflow`): Prediction type of the scheduler function. Currently only supports "trigflow". sigma_data (`float`, defaults to 0.5): The standard deviation of the noise added during multi-step inference. trigflow?num_train_timestepsprediction_type sigma_datacCsFd|_d|_ttd|dddtj|_ d|_ d|_ dS)a Initialize the SCM scheduler. Args: num_train_timesteps (`int`, defaults to 1000): The number of diffusion steps to train the model. prediction_type (`str`, defaults to `trigflow`): Prediction type of the scheduler function. Currently only supports "trigflow". sigma_data (`float`, defaults to 0.5): The standard deviation of the noise added during multi-step inference. g?Nr) init_noise_sigmanum_inference_stepsr from_numpynparangecopyastypeint64 timesteps _step_index _begin_index)selfrrrrrr__init__Es * zSCMScheduler.__init__cC|jSN)r(r*rrr step_indexazSCMScheduler.step_indexcCr,r-r)r.rrr begin_indexer0zSCMScheduler.begin_indexrr2cCs ||_dS)z Sets the begin index for the scheduler. This function should be run from pipeline before the inference. Args: begin_index (`int`, defaults to `0`): The begin index for the scheduler. Nr1)r*r2rrrset_begin_indexjs zSCMScheduler.set_begin_indexNH.!??r r'device max_timestepsintermediate_timestepscCsF||jjkrtd|d|jjd|jjd|dur(t||dkr(td|dur4|dur4td|dur@|dur@td |durL|d krLtd ||_|dur{t|trctj||d  |_ n8t|tj rr| | |_ n)td t ||durtj||dg|d  |_ ntj|d|d|d  |_ d|_d|_dS)a Sets the discrete timesteps used for the diffusion chain (to be run before inference). Args: num_inference_steps (`int`): The number of diffusion steps used when generating samples with a pre-trained model. timesteps (`torch.Tensor`, *optional*): Custom timesteps to use for the denoising process. max_timesteps (`float`, defaults to 1.57080): The maximum timestep value used in the SCM scheduler. intermediate_timesteps (`float`, *optional*, defaults to 1.3): The intermediate timestep value used in SCM scheduler (only used when num_inference_steps=2). z`num_inference_steps`: z6 cannot be larger than `self.config.train_timesteps`: zG as the unet model trained with this scheduler can only handle maximal z timesteps.NrzWIf providing custom timesteps, `timesteps` must be of length `num_inference_steps + 1`.zFIf providing custom timesteps, `max_timesteps` should not be provided.z5Should provide either `timesteps` or `max_timesteps`.rzNIntermediate timesteps for SCM is not supported when num_inference_steps != 2.)r6zUnsupported timesteps type: r)configr ValueErrorlenr isinstancelistrtensorfloatr'rtotypelinspacer(r))r*r r'r6r7r8rrr set_timestepsts8    zSCMScheduler.set_timestepstimestepreturncCs@|jdurt|tjr||jj}|||_dS|j |_dS)z Initialize the step index for the scheduler based on the given timestep. Args: timestep (`float` or `torch.Tensor`): The current timestep to initialize the step index from. N) r2r<rrr@r'r6index_for_timestepr(r))r*rDrrr_init_step_indexs   zSCMScheduler._init_step_indexschedule_timestepscCs:|dur|j}||k}t|dkrdnd}||S)ak Find the index of a given timestep in the timestep schedule. Args: 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). Nrr)r'nonzeror;item)r*rDrHindicesposrrrrFs   zSCMScheduler.index_for_timestepT model_outputsample generator return_dictc Cs|jdur td|jdur|||j|jd}|j|j}|jj}|dkr8t||t ||} ntd|t |jdkrbt |j |j |d|jj} t|| t || } n| } |jd7_|sq| | fSt| | dS)a 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). Args: model_output (`torch.FloatTensor`): The direct output from learned diffusion model. timestep (`float`): The current discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): A current instance of a sample created by the diffusion process. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~schedulers.scheduling_scm.SCMSchedulerOutput`] or `tuple`. Returns: [`~schedulers.scheduling_utils.SCMSchedulerOutput`] or `tuple`: If return_dict is `True`, [`~schedulers.scheduling_scm.SCMSchedulerOutput`] is returned, otherwise a tuple is returned where the first element is the sample tensor. NzaNumber of inference steps is 'None', you need to run 'set_timesteps' after creating the schedulerrrzUnsupported parameterization: )r6rO)r r )r r:r/rGr'r9rrcossinr;r shaper6rr(r ) r*rMrDrNrOrPtsparameterizationpred_x0noiser rrrsteps.     zSCMScheduler.stepcCs|jjSr-)r9rr.rrr__len__szSCMScheduler.__len__)rrr)r)NNr4r5r-)NT)r rrrorderrintstrr?r+propertyr/r2r3rrr6rCrGrF FloatTensor Generatorboolr tuplerYrZrrrrr3sv     ; # @r) dataclassesrnumpyr"rconfiguration_utilsrrschedulers.scheduling_utilsrutilsrrutils.torch_utilsr get_loggerr loggerr rrrrrs