o piE+@sddlmZddlmZmZmZddlZddlmZ ddl m Z ddl m Z mZddlmZmZmZmZmZejjGd d d ZeGd d d eZGd ddee ZdS)) dataclass)OptionalTupleUnionN) integrate) ConfigMixinregister_to_config)CommonSchedulerStateFlaxKarrasDiffusionSchedulersFlaxSchedulerMixinFlaxSchedulerOutputbroadcast_to_shape_from_leftc @szeZdZUeed<ejed<ejed<ejed<dZee ed<dZ eejed<e dedejdejdejfdd Z dS) LMSDiscreteSchedulerStatecommoninit_noise_sigma timestepssigmasNnum_inference_steps derivativescCs|||||dS)Nrrrr)clsrrrrrro/home/ubuntu/SoloSpeech/.venv/lib/python3.10/site-packages/diffusers/schedulers/scheduling_lms_discrete_flax.pycreate-sz LMSDiscreteSchedulerState.create) __name__ __module__ __qualname__r __annotations__jnpndarrayrrintr classmethodrrrrrr s"    rc@seZdZUeed<dS)FlaxLMSSchedulerOutputstateN)rrrrrrrrrr$4s r$c@sBeZdZUdZddeDZejed<e ddZ e ddd d d d ej fd e dedededeejdedejfddZd3deedefddZdedejde dejfddZdefddZ d4ded!e d"edefd#d$Z % &d5ded'ejde dejd(e d)edeeeffd*d+Zded,ejd-ejd.ejdejf d/d0Zd1d2Zd S)6FlaxLMSDiscreteSchedulera Linear Multistep Scheduler for discrete beta schedules. Based on the original k-diffusion implementation by Katherine Crowson: https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181 [`~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. beta_start (`float`): the starting `beta` value of inference. beta_end (`float`): the final `beta` value. beta_schedule (`str`): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear` or `scaled_linear`. trained_betas (`jnp.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. prediction_type (`str`, default `epsilon`, optional): prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4 https://imagen.research.google/video/paper.pdf) dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`): the `dtype` used for params and computation. cCsg|]}|jqSr)name).0errr Usz#FlaxLMSDiscreteScheduler.dtypecCsdS)NTrselfrrr has_stateYsz"FlaxLMSDiscreteScheduler.has_statei-C6?g{Gz?linearNepsilonnum_train_timesteps beta_startbeta_end beta_schedule trained_betasprediction_typecCs ||_dSNr+)r-r2r3r4r5r6r7r+rrr__init__]s z!FlaxLMSDiscreteScheduler.__init__rreturncCs^|dur t|}td|jjddd}d|j|jd}|}t j||||dS)Nrr ?r) r rr arangeconfigr2roundalphas_cumprodmaxr)r-rrrrrrr create_statejs z%FlaxLMSDiscreteScheduler.create_stater%sampletimestepcCs@tj|j|kdd\}|d}|j|}||ddd}|S)a  Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the K-LMS algorithm. Args: state (`LMSDiscreteSchedulerState`): the `FlaxLMSDiscreteScheduler` state data class instance. sample (`jnp.ndarray`): current instance of sample being created by diffusion process. timestep (`int`): current discrete timestep in the diffusion chain. Returns: `jnp.ndarray`: scaled input sample r )sizerrr=)r whererr)r-r%rDrE step_indexsigmarrrscale_model_input{s  z*FlaxLMSDiscreteScheduler.scale_model_inputcs<fdd}tj|jjdddd}|S)z Compute a linear multistep coefficient. Args: order (TODO): t (TODO): current_order (TODO): csRd}tD] }|kr q||j|jj|9}q|S)N?)ranger)tauprodk current_orderorderr%trrlms_derivatives  4zDFlaxLMSDiscreteScheduler.get_lms_coefficient..lms_derivativer r/)epsrelr)rquadr)r-r%rRrSrQrTintegrated_coeffrrPrget_lms_coefficients &z,FlaxLMSDiscreteScheduler.get_lms_coefficientrrshapec Cstj|jjdd||jd}t|tj}t|tj}t |d}d|j j |j j d}d||||||}t |tj dg|jdg}|tj}tjd||jd} |j|||| dS) a Sets the timesteps used for the diffusion chain. Supporting function to be run before inference. Args: state (`LMSDiscreteSchedulerState`): the `FlaxLMSDiscreteScheduler` state data class instance. num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. r rr9rKr=g)r)rrrr)r linspacer?r2r+floorastypeint32ceilmodrrA concatenatearrayzerosreplace) r-r%rrYrlow_idxhigh_idxfracrrrrr set_timestepss   z&FlaxLMSDiscreteScheduler.set_timestepsT model_outputrR return_dictc s,jdur tdj}jjdkr|||}n&jjdkr7|| |ddd||dd}n tdjjd |||} jtj| d t jkrdjt jd d t dfd d t D} |t ddt| tjD} |s| fSt| dS)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 (`LMSDiscreteSchedulerState`): the `FlaxLMSDiscreteScheduler` 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. order: coefficient for multi-step inference. return_dict (`bool`): option for returning tuple rather than FlaxLMSSchedulerOutput class Returns: [`FlaxLMSSchedulerOutput`] or `tuple`: [`FlaxLMSSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. NzaNumber of inference steps is 'None', you need to run 'set_timesteps' after creating the schedulerr1 v_predictionrr r=zprediction_type given as z, must be one of `epsilon`, or `v_prediction`)rrcsg|] }|qSr)rX)r( curr_orderrRr-r%rErrr*sz1FlaxLMSDiscreteScheduler.step..css|] \}}||VqdSr8r)r(coeff derivativerrr s  z0FlaxLMSDiscreteScheduler.step..) prev_sampler%)r ValueErrorrr?r7rcr appendrlendeleteminrLsumzipreversedr$) r-r%rirErDrRrjrIpred_original_samplero lms_coeffsrqrrmrsteps0    ,    zFlaxLMSDiscreteScheduler.steporiginal_samplesnoisercCs*|j|}t||j}|||}|Sr8)rflattenrrY)r-r%r}r~rrI noisy_samplesrrr add_noise s  z"FlaxLMSDiscreteScheduler.add_noisecCs|jjSr8)r?r2r,rrr__len__sz FlaxLMSDiscreteScheduler.__len__r8)r)rhT) rrr__doc__r _compatiblesr r+rpropertyr.r float32r"floatstrrr!r:r rrCrJrXrrgboolrr$r|rrrrrrr&9s      *  A r&) dataclassesrtypingrrrflax jax.numpynumpyr scipyrconfiguration_utilsrr scheduling_utils_flaxr r r rrstructrr$r&rrrrs