bi 4ddlZddlmZddlmZmZmZddlZddlm Z m Z ddl m Z ddl mZdd lmZmZeGd d e ZGd d ee Zy)N) dataclass)OptionalTupleUnion) ConfigMixinregister_to_config) BaseOutput) randn_tensor)SchedulerMixinSchedulerOutputcNeZdZUdZej ed<ej ed<y) SdeVeOutputa 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. prev_sample_mean (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images): Mean averaged `prev_sample` over previous timesteps. prev_sampleprev_sample_meanN)__name__ __module__ __qualname____doc__torchTensor__annotations__a/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/diffusers/schedulers/scheduling_sde_ve.pyrrs ll"rrc6eZdZdZdZe ddedededededef d Zdd e jd e ed e jfdZ d dedede ee jffdZ d!dedededefdZdZ d"de jd ed e jde e j&ded e eeff dZ d"de jd e jde e j&ded e eeff dZde jde jde jd e jfdZdZy )#ScoreSdeVeSchedulera `ScoreSdeVeScheduler` is a variance exploding stochastic differential equation (SDE) scheduler. 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. snr (`float`, defaults to 0.15): A coefficient weighting the step from the `model_output` sample (from the network) to the random noise. sigma_min (`float`, defaults to 0.01): The initial noise scale for the sigma sequence in the sampling procedure. The minimum sigma should mirror the distribution of the data. sigma_max (`float`, defaults to 1348.0): The maximum value used for the range of continuous timesteps passed into the model. sampling_eps (`float`, defaults to 1e-5): The end value of sampling where timesteps decrease progressively from 1 to epsilon. correct_steps (`int`, defaults to 1): The number of correction steps performed on a produced sample. r num_train_timestepssnr sigma_min sigma_max sampling_eps correct_stepscH||_d|_|j||||yN)init_noise_sigma timesteps set_sigmas)selfrr r!r"r#r$s r__init__zScoreSdeVeScheduler.__init__Gs(!* +Y <PrNsampletimestepreturnc|S)a Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.Tensor`): The input sample. timestep (`int`, *optional*): The current timestep in the diffusion chain. Returns: `torch.Tensor`: A scaled input sample. r)r*r,r-s rscale_model_inputz%ScoreSdeVeScheduler.scale_model_inputYs  rnum_inference_stepsdevicect||n|jj}tjd||||_y)aA Sets the continuous timesteps used for the diffusion chain (to be run before inference). Args: num_inference_steps (`int`): The number of diffusion steps used when generating samples with a pre-trained model. sampling_eps (`float`, *optional*): The final timestep value (overrides value given during scheduler instantiation). device (`str` or `torch.device`, *optional*): The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. Nr r2)configr#rlinspacer()r*r1r#r2s r set_timestepsz!ScoreSdeVeScheduler.set_timestepsjs2(4'?|T[[E]E] <9LU[\rc@||n|jj}||n|jj}||n|jj}|j|j |||||z |j|z zz|_tjtjtj|tj|||_ tj|jDcgc] }|||z |zzc}|_ycc}w)a Sets the noise scales used for the diffusion chain (to be run before inference). The sigmas control the weight of the `drift` and `diffusion` components of the sample update. Args: num_inference_steps (`int`): The number of diffusion steps used when generating samples with a pre-trained model. sigma_min (`float`, optional): The initial noise scale value (overrides value given during scheduler instantiation). sigma_max (`float`, optional): The final noise scale value (overrides value given during scheduler instantiation). sampling_eps (`float`, optional): The final timestep value (overrides value given during scheduler instantiation). N)r5r!r"r#r(r7sigmasrexpr6mathlogdiscrete_sigmastensor)r*r1r!r"r#ts rr)zScoreSdeVeScheduler.set_sigmas}s$"+!6IDKK> !   2L A9y#8dnn|>["\\ $yy8KTXXV_M`bu)vwllVZVdVd#eQRIY1F10L$L#ef #es<Dctj|dk(tj|j|j|j |dz j|jS)Nrr )rwhere zeros_liketor2r=)r*r(r?s rget_adjacent_sigmaz&ScoreSdeVeScheduler.get_adjacent_sigmasZ{{ N   QTT)"2"23 4  Q / 2 293C3C D  r model_output generator return_dictc|j td|tj|jd|j z}|t |jdz zj}|j|jj }|j|j|j }|j||j|j }tj|} |dz|dzz dz} | j} t | jt |jkr=| jd} t | jt |jkr=| | dz|zz } t|j|j||j |j } || z } | | | zz} |s| | fSt#| | S) aD 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.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`, *optional*, defaults to `True`): Whether or not to return a [`~schedulers.scheduling_sde_ve.SdeVeOutput`] or `tuple`. Returns: [`~schedulers.scheduling_sde_ve.SdeVeOutput`] or `tuple`: If return_dict is `True`, [`~schedulers.scheduling_sde_ve.SdeVeOutput`] is returned, otherwise a tuple is returned where the first element is the sample tensor. Y`self.timesteps` is not set, you need to run 'set_timesteps' after creating the schedulerrr4r r?)layoutrFr2dtype)rr)r( ValueErrorronesshaper2lenlongrCr=rDrBflatten unsqueezer rLrMr)r*rEr-r,rFrGr(sigmaadjacent_sigmadrift diffusionnoiserrs r step_predzScoreSdeVeScheduler.step_preds< >> !k ejj LLOFMM  T^^!4q!89??A LL!5!5!&U):: !12 2{EUVVrc|j tdt|j|j|j |j }tj|j|jdddj}tj|j|jdddj}|jj|z|z dzdz}|tj|jdj |j z}|j}t|jt|jkr=|j!d}t|jt|jkr=|||zz} | |dzdz|zz} |s| fSt#| S) a Correct the predicted sample based on the `model_output` of the network. This is often run repeatedly after making the prediction for the previous timestep. Args: model_output (`torch.Tensor`): The direct output from learned diffusion model. 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`, *optional*, defaults to `True`): Whether or not to return a [`~schedulers.scheduling_sde_ve.SdeVeOutput`] or `tuple`. Returns: [`~schedulers.scheduling_sde_ve.SdeVeOutput`] or `tuple`: If return_dict is `True`, [`~schedulers.scheduling_sde_ve.SdeVeOutput`] is returned, otherwise a tuple is returned where the first element is the sample tensor. rI)rLrFrrK)dimrrJ)r)r(rNr rPrLrCr2rnormreshapemeanr5r rOrSrQrTr) r*rEr,rFrGrY grad_norm noise_norm step_sizerrs r step_correctz ScoreSdeVeScheduler.step_corrects6 >> !k  V\\&--9UXXY_YfYfgJJ|33L4F4Fq4I2NTVW\\^ ZZ ekk!nb ArJOOQ [[__z1I=!CaG  6<<? ; > >v}} MM %%' )//"S%66!++B/I)//"S%66!I $<<&9q=S*@E)II > !;77roriginal_samplesrYr(c|j|j}|jj|j|}|||dddddfzn tj||dddddfz}||z}|Sr&)rCr2r=r randn_like)r*rdrYr(r9 noisy_sampless r add_noisezScoreSdeVeScheduler.add_noisesLL!1!8!89 %%(()9)@)@A)L  F1dD$./ /!!"23fQdD=P6QQ   00 rc.|jjSr&)r5r)r*s r__len__zScoreSdeVeScheduler.__len__,s{{...r)ig333333?g{Gz?g@gh㈵>r r&)NN)NNN)NT)rrrrorderr intfloatr+rrrr0rstrr2r7r)rD GeneratorboolrrrZrrcrhrjrrrrr.s, E$(!"Q QQ Q  Q  QQQ"  Y^YeYe$hl]#&]6;]LQRUW\WcWcRcLd](qug#&g38gLQghmg8 04 BWllBWBW BW EOO, BW  BW {E! "BWP04 58ll58 58EOO, 58  58 % & 58n,,||<<   "/rr)r; dataclassesrtypingrrrrconfiguration_utilsrr utilsr utils.torch_utilsr scheduling_utilsr rrrrrrrwsM" !)) A,=  #* #  # /.+/r