+hE+SSKJr SSKJrJrJr SSKrSSKJr SSK J r SSK J r Jr SSKJrJrJrJrJr \R*R"S S 55r\"S S \55r"S S\\ 5rg)) dataclass)OptionalTupleUnionN) integrate) ConfigMixinregister_to_config)CommonSchedulerStateFlaxKarrasDiffusionSchedulersFlaxSchedulerMixinFlaxSchedulerOutputbroadcast_to_shape_from_leftc \rSrSr%\\S'\R\S'\R\S'\R\S'Sr\ \ \S'Sr \ \R\S'\ S\S\RS\RS\R4S j5r S rg) LMSDiscreteSchedulerState commoninit_noise_sigma timestepssigmasNnum_inference_steps derivativescU"XX4S9$)Nrrrr)clsrrrrs k/home/james-whalen/.local/lib/python3.13/site-packages/diffusers/schedulers/scheduling_lms_discrete_flax.pycreate LMSDiscreteSchedulerState.create-s&yhhr)__name__ __module__ __qualname____firstlineno__r __annotations__jnpndarrayrrintr classmethodr__static_attributes__rr!rrr s  kk!{{ KK)-#-*.K#++&-i)i=@[[iUXU`U`ijmjujuiir!rc \rSrSr%\\S'Srg)FlaxLMSSchedulerOutput4staterN)r"r#r$r%rr&r+rr!rr-r-4s $$r!r-ct\rSrSr%Sr\VVs/sHoR PM snnr\R\ S'\ S5r \ SSSSS S \R4S \S \S \S\S\\R&S\S\R4Sjj5rS'S\\S\4SjjrS\S\R&S\S\R&4SjrS\4SjrS(S\S\S\S\4SjjrS)S\S\R&S\S\R&S\S\S\\\44S jjrS\S!\R&S"\R&S#\R&S\R&4 S$jr S%r!S&r"g s snnf)*FlaxLMSDiscreteScheduler9a> Linear Multistep Scheduler for discrete beta schedules. Based on the original k-diffusion implementation by Katherine Crowson: https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181 [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. beta_start (`float`): the starting `beta` value of inference. beta_end (`float`): the final `beta` value. beta_schedule (`str`): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear` or `scaled_linear`. trained_betas (`jnp.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. prediction_type (`str`, default `epsilon`, optional): prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4 https://imagen.research.google/video/paper.pdf) dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`): the `dtype` used for params and computation. dtypecg)NTrselfs r has_state"FlaxLMSDiscreteScheduler.has_stateYsr!i-C6?g{Gz?linearNepsilonnum_train_timesteps beta_startbeta_end beta_schedule trained_betasprediction_typecXplgNr3)r6r<r=r>r?r@rAr3s r__init__!FlaxLMSDiscreteScheduler.__init__]s  r!rreturnc<Uc[R"U5n[R"SURR 5R 5SSS2nSUR- UR- S-nUR5n[RUUUUS9$)Nrr ?r) r rr'arangeconfigr<roundalphas_cumprodmaxr)r6rrrrs r create_state%FlaxLMSDiscreteScheduler.create_statejs >)006FJJq$++"A"ABHHJ4R4P v,,,0E0EE#M"::<(//- 0  r!r/sampletimestepc[R"URU:HSS9unUSnURUnX%S-S-S-- nU$)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 )sizerrrJ)r'whererr)r6r/rRrS step_indexsigmas rscale_model_input*FlaxLMSDiscreteScheduler.scale_model_input{sP %//X"=AF ]  Z(1HqLS01 r!c^^^^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"Kr!r r9)epsrelr)rquadr)r6r/rcrdrbreintegrated_coeffs ```` rget_lms_coefficient,FlaxLMSDiscreteScheduler.get_lms_coefficientsI  %>>.%,,q/5<0FlaxLMSDiscreteScheduler.step..s# 8d#45E 8ds) prev_sampler/)r ValueErrorrrLrArwr'appendrlendeleteminr^rjsumzipreversedr-) r6r/r}rSrRrcr~rXpred_original_sampler curr_order lms_coeffsrs rstepFlaxLMSDiscreteScheduler.steps6  $ $ ,s  X& ;; & &) 3#)L,@#@ [[ ( (N :#/6UAX\crsy")) Aiii& %0% %b/1;b/r!