wi 4ddlZddlmZddlmZmZmZddlZddlm Z m Z ddl m Z ddl mZdd lmZmZeGd d e ZGd d ee ZdS)N) dataclass)OptionalTupleUnion) ConfigMixinregister_to_config) BaseOutput) randn_tensor)SchedulerMixinSchedulerOutputc<eZdZUdZejed<ejed<dS) 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__v/root/.openclaw/workspace/chatterbox_venv_py311/lib/python3.11/site-packages/diffusers/schedulers/scheduling_sde_ve.pyrrs:  l"""""rrceZdZdZdZe d$ded ed ed ed ed ef dZd%de j de ede j fdZ d&ded ede ee jffdZ d'ded ed ed efdZdZ d(de j dede j de e jdede eeff dZ d(de j de j de e jdede eeff dZde j d e j d!e j de j fd"Zd#ZdS))ScoreSdeVeSchedulera `ScoreSdeVeScheduler` is a variance exploding stochastic differential equation (SDE) scheduler. This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic methods the library implements for all schedulers such as loading and saving. Args: num_train_timesteps (`int`, defaults to 1000): The number of diffusion steps to train the model. snr (`float`, defaults to 0.15): A coefficient weighting the step from the `model_output` sample (from the network) to the random noise. sigma_min (`float`, defaults to 0.01): The initial noise scale for the sigma sequence in the sampling procedure. The minimum sigma should mirror the distribution of the data. sigma_max (`float`, defaults to 1348.0): The maximum value used for the range of continuous timesteps passed into the model. sampling_eps (`float`, defaults to 1e-5): The end value of sampling where timesteps decrease progressively from 1 to epsilon. correct_steps (`int`, defaults to 1): The number of correction steps performed on a produced sample. r 333333?{Gz?@h㈵>num_train_timestepssnr sigma_min sigma_max sampling_eps correct_stepscR||_d|_|||||dSN)init_noise_sigma timesteps set_sigmas)selfr$r%r&r'r(r)s r__init__zScoreSdeVeScheduler.__init__Gs4!* +Y <PPPPPrNsampletimestepreturnc|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/r1r2s rscale_model_inputz%ScoreSdeVeScheduler.scale_model_inputYs  rnum_inference_stepsdevicec`||n |jj}tjd||||_dS)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 r7)configr(rlinspacer-)r/r6r(r7s r set_timestepsz!ScoreSdeVeScheduler.set_timestepsjs7(4'?||T[E] <9LU[\\\rcn |jjn |jj||n |jj}|j|||z |j|z zz|_tjtj tj tj ||_ tj fd|jD|_dS)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). Nc&g|] }z |zzSrr).0tr'r&s r z2ScoreSdeVeScheduler.set_sigmas..s)#e#e#eQRIY1F10L$L#e#e#er)r:r&r'r(r-r<sigmasrexpr;mathlogdiscrete_sigmastensor)r/r6r&r'r(s `` rr.zScoreSdeVeScheduler.set_sigmas}s$"+!6IIDK !   2L A A A9y#8dn|>["\\ $y8K8KTXV_M`M`bu)v)vwwl#e#e#e#e#eVZVd#e#e#eff rctj|dktj||j|j|dz |jS)Nrr )rwhere zeros_liketor7rF)r/r-r@s rget_adjacent_sigmaz&ScoreSdeVeScheduler.get_adjacent_sigmasZ{ N  QTT)"233 4 4  Q / 2 293C D D   rT model_output generator return_dictc|jtd|tj|jd|jz}|t |jdz z}||j j}|j ||j}| |||j}tj |} |dz|dzz dz} | } t | jt |jkr?| d} t | jt |jk?| | 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. NY`self.timesteps` is not set, you need to run 'set_timesteps' after creating the schedulerrr9r r?)layoutrNr7dtype)rr)r- ValueErrorronesshaper7lenlongrKrFrLrJflatten unsqueezer rTrUr)r/rMr2r1rNrOr-sigmaadjacent_sigmadrift diffusionnoiserrs r step_predzScoreSdeVeScheduler.step_preds< > !k ej LOFM    T^!4!4q!89??AA LL!5!<== $Y/226=AA00HEEHHWW ((AX 11c9 %%'' )/""S%6%666!++B//I)/""S%6%666 1 |33 L)FMagam   "E>&U)::  3!12 2{EUVVVVrc|jtdt|j|j||j}tj| |jddd }tj| |jddd }|j j |z|z dzdz}|tj |jd|jz}|}t|jt|jkr?|d}t|jt|jk?|||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. NrQ)rTrNrrS)dimrrR)r)r-rVr rXrTrKr7rnormreshapemeanr:r%rWr[rYr\r) r/rMr1rNrOra grad_norm noise_norm step_sizerrs r step_correctz ScoreSdeVeScheduler.step_corrects6 > !k  V\&-9UUUXXY_YfggJ|33L4Fq4I2NNTVWWW\\^^ Z ek!nb A ArJJJOOQQ [_z1I=!CaG  6<? ; ; > >v} M MM %%'' )/""S%6%666!++B//I)/""S%6%666!I $<<&9q=S*@E)II  "> !;7777roriginal_samplesrar-c||j}|j|j|}|||dddddfzn"tj||dddddfz}||z}|Sr+)rKr7rFr randn_like)r/rlrar-rB noisy_sampless r add_noisezScoreSdeVeScheduler.add_noisesLL!1!899 %(()9)@AA)L  F111dD$./ / /!"233fQQQdD=P6QQ   00 rc|jjSr+)r:r$)r/s r__len__zScoreSdeVeScheduler.__len__,s {..r)rr r!r"r#r r+)NN)NNN)NT)rrrrorderr intfloatr0rrrr5rstrr7r<r.rL Generatorboolrrrbrrkrprrrrrrr.s, E$(!"QQ QQ Q  Q  QQQQQ"  Y^Ye$hl]]#&]6;]LQRUW\WcRcLd]]]](qugg#&g38gLQghmgggg8   04 BWBWlBWBW BW EO, BW  BW {E! "BWBWBWBWP04 5858l58 58EO, 58  58 % & 58585858n,|<   "/////rr)rD dataclassesrtypingrrrrconfiguration_utilsrr utilsr utils.torch_utilsr scheduling_utilsr rrrrrrrs" !!!!!!)))))))))) 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