3j, SSKJr SSKrSSKJr SSKJr SSKJ r J r SSK J r SSK JrJrJrJrJr \ R&"\5r\R,R"S S 55r\"S S \55r"S S\\ 5rg)) dataclassN) integrate) ConfigMixinregister_to_config)logging)CommonSchedulerStateFlaxKarrasDiffusionSchedulersFlaxSchedulerMixinFlaxSchedulerOutputbroadcast_to_shape_from_leftc \rSrSr%\\S'\R\S'\R\S'\R\S'Sr\ \S'Sr \RS-\S'\ S\S\RS\RS\R4S j5r S r g) LMSDiscreteSchedulerState#commoninit_noise_sigma timestepssigmasNnum_inference_steps derivativescU"UUUUS9$)Nrrrr)clsrrrrs k/home/wildlama/miniconda3/lib/python3.13/site-packages/diffusers/schedulers/scheduling_lms_discrete_flax.pycreate LMSDiscreteSchedulerState.create0s-   r)__name__ __module__ __qualname____firstlineno__r __annotations__jnpndarrayrintr classmethodr__static_attributes__rrrrr#s  kk!{{ KK##'+Kt#*  $  ++  ;;      rrc \rSrSr%\\S'Srg)FlaxLMSSchedulerOutput@staterN)r r!r"r#rr$r)rrrr+r+@s $$rr+cp\rSrSr%Sr\VVs/sHoR PM snnr\R\ S'\ S5r \ SSSSS S \R4S \S \S \S\S\R$S -S\S\R4Sjj5rS'S\S -S\4SjjrS\S\R$S\S\R$4SjrS\4SjrS(S\S\S\S\4SjjrS)S\S\R$S\S\R$S\S\S\\-4S jjrS\S!\R$S"\R$S#\R$S\R$4 S$jrS%rS&r g s snnf)*FlaxLMSDiscreteSchedulerEa8 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://huggingface.co/papers/2210.02303) dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`): the `dtype` used for params and computation. dtypecg)NTrselfs r has_state"FlaxLMSDiscreteScheduler.has_stateesri-C6?g{Gz?linearNepsilonnum_train_timesteps beta_startbeta_end beta_schedule trained_betasprediction_typec:[RS5 Xplg)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.)loggerwarningr1)r4r:r;r<r=r>r?r1s r__init__!FlaxLMSDiscreteScheduler.__init__is  [  rrreturnc<Uc[R"U5n[R"SURR 5R 5SSS2nSUR- UR- S-nUR5n[RUUUUS9$)Nrr ?r) r rr%arangeconfigr:roundalphas_cumprodmaxr)r4rrrrs r create_state%FlaxLMSDiscreteScheduler.create_statezs >)006FJJq$++"A"ABHHJ4R4P v,,,0E0EE#M"::<(//- 0  rr-sampletimestepc[R"URU:HSS9unUSnURUnX%S-S-S-- nU$)a Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the K-LMS algorithm. Args: state (`LMSDiscreteSchedulerState`): the `FlaxLMSDiscreteScheduler` state data class instance. sample (`jnp.ndarray`): current instance of sample being created by diffusion process. timestep (`int`): current discrete timestep in the diffusion chain. Returns: `jnp.ndarray`: scaled input sample r )sizerrrH)r%whererr)r4r-rPrQ step_indexsigmas rscale_model_input*FlaxLMSDiscreteScheduler.scale_model_inputsP %//X"=AF ]  Z(1HqLS01 rc^^^^UUUU4Sjn[R"UTRTTRTS-SS9SnU$)zj Compute a linear multistep coefficient. Args: order (TODO): t (TODO): current_order (TODO): c>Sn[T5HHnTU:XaM XTRTU- - TRTT- TRTU- - - -nMJ U$)N?)ranger)tauprodk current_orderorderr-ts rlms_derivativeDFlaxLMSDiscreteScheduler.get_lms_coefficient..lms_derivativesmD5\ A%u||AE22u||A DU7VY^YeYefgjkfkYl7lmm"Krr r7)epsrelr)rquadr)r4r-rarbr`rcintegrated_coeffs ```` rget_lms_coefficient,FlaxLMSDiscreteScheduler.get_lms_coefficientsI  %>>.%,,q/5<0FlaxLMSDiscreteScheduler.step..s# 8d#45E 8ds) prev_sampler-)r ValueErrorrrJr?rvr%appendrlendeleteminr\rhsumzipreversedr+) r4r-r|rQrPrar}rVpred_original_sampler curr_order lms_coeffsrs rstepFlaxLMSDiscreteScheduler.steps6  $ $ ,s  X& ;; & &) 3#)L,@#@ [[ ( (N :#/6UAX\c Jc "'@#++adilitit, )B 4 * (* !*  * # * d ?L(?Lkk?L ?L  ?L  ?L?L % '?LB ( ++ {{  ;;   /cCsD2r/) dataclassesrflax jax.numpynumpyr%scipyrconfiguration_utilsrrutilsrscheduling_utils_flaxr r r r r get_loggerr rAstructrr+r/rrrrs" A   H %   8 %0% %n/1;n/r