o Gii0@sddlmZddlZddlZddlmZddlmZddlm Z m Z ddl m Z ddl mZmZmZe eZejjGd d d ZeGd d d eZGd ddee ZdS)) dataclassN)random) ConfigMixinregister_to_config)logging)FlaxSchedulerMixinFlaxSchedulerOutputbroadcast_to_shape_from_leftc@sPeZdZUdZejdBed<dZejdBed<dZejdBed<e ddZ dS)ScoreSdeVeSchedulerStateN timestepsdiscrete_sigmassigmascCs|SN)clsrr_/home/ubuntu/.local/lib/python3.10/site-packages/diffusers/schedulers/scheduling_sde_ve_flax.pycreate+szScoreSdeVeSchedulerState.create) __name__ __module__ __qualname__r jnpndarray__annotations__rr classmethodrrrrrr $s r c@s6eZdZUdZeed<ejed<dZejdBed<dS)FlaxSdeVeOutputaL Output class for the ScoreSdeVeScheduler's step function output. Args: state (`ScoreSdeVeSchedulerState`): prev_sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images): Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the denoising loop. prev_sample_mean (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images): Mean averaged `prev_sample`. Same as `prev_sample`, only mean-averaged over previous timesteps. state prev_sampleNprev_sample_mean) rrr__doc__r rrrrrrrrr0s  rc@seZdZdZeddZe      d,d ed ed ed ededef ddZ ddZ  d-de dede dede f ddZ   d.de ded ed edede f ddZddZ d/de d!ejd"ed#ejd$ejd%edee Bfd&d'Z d/de d!ejd#ejd$ejd%edee Bf d(d)Zd*d+ZdS)0FlaxScoreSdeVeSchedulera The variance exploding stochastic differential equation (SDE) scheduler. For more information, see the original paper: https://huggingface.co/papers/2011.13456 [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. snr (`float`): coefficient weighting the step from the model_output sample (from the network) to the random noise. sigma_min (`float`): initial noise scale for sigma sequence in sampling procedure. The minimum sigma should mirror the distribution of the data. sigma_max (`float`): maximum value used for the range of continuous timesteps passed into the model. sampling_eps (`float`): the end value of sampling, where timesteps decrease progressively from 1 to epsilon. correct_steps (`int`): number of correction steps performed on a produced sample. cCsdS)NTrselfrrr has_state[sz!FlaxScoreSdeVeScheduler.has_state333333?{Gz?@h㈵>rnum_train_timestepssnr sigma_min sigma_max sampling_eps correct_stepscCstddS)NzFlax classes are deprecated and will be removed in Diffusers v1.0.0. We recommend migrating to PyTorch classes or pinning your version of Diffusers.)loggerwarning)r#r*r+r,r-r.r/rrr__init___s z FlaxScoreSdeVeScheduler.__init__cCs*t}|||jj|jj|jj|jjSr)r r set_sigmasconfigr*r,r-r.)r#rrrr create_statensz$FlaxScoreSdeVeScheduler.create_staterNrnum_inference_stepsshapereturncCs.|dur|n|jj}td||}|j|dS)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). Nr)r )r4r.rlinspacereplace)r#rr6r7r.r rrr set_timestepsxs z%FlaxScoreSdeVeScheduler.set_timestepscsdurn|jjdurn|jj|dur|n|jj}|jdur*||||}ttt t |}t fdd|jD}|j ||dS)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). Ncsg|] }|qSrr).0tr-r,rr sz6FlaxScoreSdeVeScheduler.set_sigmas..)rr) r4r,r-r.r r;rexpr9logarrayr:)r#rr6r,r-r.rrrr>rr3s  z"FlaxScoreSdeVeScheduler.set_sigmascCs"t|dkt||j|dS)Nrr)rwhere zeros_liker)r#rr r=rrrget_adjacent_sigmas"z*FlaxScoreSdeVeScheduler.get_adjacent_sigmaT model_outputtimestepsamplekey return_dictcCs|jdur td|t|jd}|t|jd}|j|}||||} t |} |d| dd} | } t | |j} | | d|} t j |dd}t j||jd} || } | | | }|sk|| |fSt|| |d S) a Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion process from the learned model outputs (most often the predicted noise). Args: state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` state data class instance. model_output (`jnp.ndarray`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. 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. NZ`state.timesteps` is not set, you need to run 'set_timesteps' after creating the schedulerrrr?numrIr7)rrr)r ValueErrorronesr7lenlongrrErDflattenr rsplitnormalr)r#rrFrGrHrIrJr sigmaadjacent_sigmadrift diffusionnoiserrrrr step_preds,      z!FlaxScoreSdeVeScheduler.step_predc Cs|jdur tdtj|dd}tj||jd}tj|}tj|}|j j ||dd} | t |jd} | } t | |j} || |} | | dd|} |sZ| |fSt| |d 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. NrKrrMrOrrrL)rr)r rPrrUrVr7rlinalgnormr4r+rQrTr r) r#rrFrHrIrJr[ grad_norm noise_norm step_sizerrrrr step_corrects"      z$FlaxScoreSdeVeScheduler.step_correctcCs|jjSr)r4r*r"rrr__len__%szFlaxScoreSdeVeScheduler.__len__)r%r&r'r(r)r)rN)NNN)T)rrrr propertyr$rintfloatr2r5r tupler;r3rErrjaxArrayboolrr\rbrcrrrrr!Cs    #  B 4r!) dataclassesrflaxrh jax.numpynumpyrrconfiguration_utilsrrutilsrscheduling_utils_flaxr r r get_loggerrr0structr rr!rrrrs