bin/ddlmZddlmZmZmZddlZddlZddlm Z ddlm Z ddl m Z mZddlmZmZmZej&jGd d ZeGd d eZGd dee Zy)) dataclass)OptionalTupleUnionN)random) ConfigMixinregister_to_config)FlaxSchedulerMixinFlaxSchedulerOutputbroadcast_to_shape_from_leftceZdZUdZeej ed<dZeej ed<dZ eej ed<e dZ y)ScoreSdeVeSchedulerStateN timestepsdiscrete_sigmassigmasc|SN)clss f/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/diffusers/schedulers/scheduling_sde_ve_flax.pycreatezScoreSdeVeSchedulerState.create$s u ) __name__ __module__ __qualname__rrjnpndarray__annotations__rr classmethodrrrrrrsO(,Ix $+-1OXckk*1$(FHS[[ !(rrcbeZdZUdZeed<ejed<dZe ejed<y)FlaxSdeVeOutputaL Output class for the ScoreSdeVeScheduler's step function output. Args: state (`ScoreSdeVeSchedulerState`): prev_sample (`jnp.ndarray` 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 (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images): Mean averaged `prev_sample`. Same as `prev_sample`, only mean-averaged over previous timesteps. state prev_sampleNprev_sample_mean) rrr__doc__rr rrr&rrrrr#r#)s-  $#.2hs{{+2rr#ceZdZdZedZe ddedededededef d Z d Z dd e d ede dede f dZ dd e d edededede f dZdZ dd e dej"dedej"dej&dedeee ffdZ dd e dej"dej"dej&dedeee ff dZdZy )FlaxScoreSdeVeSchedulera The variance exploding stochastic differential equation (SDE) scheduler. For more information, see the original paper: https://huggingface.co/papers/2011.13456 [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. snr (`float`): coefficient weighting the step from the model_output sample (from the network) to the random noise. sigma_min (`float`): initial noise scale for sigma sequence in sampling procedure. The minimum sigma should mirror the distribution of the data. sigma_max (`float`): maximum value used for the range of continuous timesteps passed into the model. sampling_eps (`float`): the end value of sampling, where timesteps decrease progressively from 1 to epsilon. correct_steps (`int`): number of correction steps performed on a produced sample. cy)NTrselfs r has_statez!FlaxScoreSdeVeScheduler.has_stateTsrnum_train_timestepssnr sigma_min sigma_max sampling_eps correct_stepscyrr)r,r.r/r0r1r2r3s r__init__z FlaxScoreSdeVeScheduler.__init__Xs rctj}|j||jj|jj |jj |jjSr)rr set_sigmasconfigr.r0r1r2)r,r$s r create_statez$FlaxScoreSdeVeScheduler.create_statedsX(//1  KK + + KK ! ! KK ! ! KK $ $   rNr$num_inference_stepsshapereturnc||n|jj}tjd||}|j |S)a  Sets the continuous timesteps used for the diffusion chain. Supporting function to be run before inference. Args: state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` state data class instance. num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. sampling_eps (`float`, optional): final timestep value (overrides value given at Scheduler instantiation). r )r)r8r2rlinspacereplace)r,r$r:r;r2rs r set_timestepsz%FlaxScoreSdeVeScheduler.set_timestepsns>(4'?|T[[E]E] LLL2EF }}y}11rc||n|jj}||n|jj}||n|jj}|j|j |||}t jt jt j|t j||}t j|jDcgc] }|||z |zzc}}|j||Scc}w)au Sets the noise scales used for the diffusion chain. Supporting function to be run before inference. The sigmas control the weight of the `drift` and `diffusion` components of sample update. Args: state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` state data class instance. num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. sigma_min (`float`, optional): initial noise scale value (overrides value given at Scheduler instantiation). sigma_max (`float`, optional): final noise scale value (overrides value given at Scheduler instantiation). sampling_eps (`float`, optional): final timestep value (overrides value given at Scheduler instantiation). )rr) r8r0r1r2rr@rexpr>logarrayr?) r,r$r:r0r1r2rtrs rr7z"FlaxScoreSdeVeScheduler.set_sigmass0"+!6IDKK1(DD^_}}_V}LL_sDc|tj|dk(tj||j|dz S)Nrr )rwhere zeros_liker)r,r$rrEs rget_adjacent_sigmaz*FlaxScoreSdeVeScheduler.get_adjacent_sigmas4yya):E&U):: !159 9;IYafggrcH|j tdtj|d}tj||j }t jj|}t jj|}|jj|z|z dzdz} | t j|j dz} | j} t| |j } || |zz} | | dzdz|zz} |s| |fSt| |S) aY Correct the predicted sample based on the output model_output of the network. This is often run repeatedly after making the prediction for the previous timestep. Args: state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` state data class instance. model_output (`jnp.ndarray`): direct output from learned diffusion model. sample (`jnp.ndarray`): current instance of sample being created by diffusion process. generator: random number generator. return_dict (`bool`): option for returning tuple rather than FlaxSdeVeOutput class Returns: [`FlaxSdeVeOutput`] or `tuple`: [`FlaxSdeVeOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. rPr rRrTrrrQ)r%r$)rrUrrZr[r;rlinalgnormr8r/rVrYrr#) r,r$rJrLrMrNr` grad_norm noise_norm step_sizer&r%s r step_correctz$FlaxScoreSdeVeScheduler.step_corrects 2 ?? "l  ll3A& #V\\:JJOOL1 ZZ__U+ [[__z1I=!CaG a 99 %%' 0FLLI !I $<<&9q=S*@E)II ' ';eDDrc.|jjSr)r8r.r+s r__len__zFlaxScoreSdeVeScheduler.__len__s{{...r)ig333333?g{Gz?g@gh㈵>r )rN)NNN)T)rrrr'propertyr-r intfloatr5r9rrr@r7rIrrjaxArrayboolrr#rarhrjrrrr)r)<s.$(!"                  sw2-2DG2PU2jo2 !2. " !M'!M!!M !M  !M  !M "!MFb!:h':hkk:h :h  :h YY :h:h % &:hD! 2E'2Ekk2E 2E YY 2E  2E % &2Eh/rr)) dataclassesrtypingrrrflaxrn jax.numpynumpyrrconfiguration_utilsr r scheduling_utils_flaxr r rstructrr#r)rrrrysu"")) Ahh 3)3 3$\/0+\/r