3j+SSKJr SSKrSSKJr SSKJrJr SSK J r SSK J r J r JrJrJr \ R""\5r\R(R"SS 55r\"S S \55r"S S \\5rg)) dataclassN) ConfigMixinregister_to_config)logging)CommonSchedulerStateFlaxKarrasDiffusionSchedulersFlaxSchedulerMixinFlaxSchedulerOutputbroadcast_to_shape_from_leftc \rSrSr%\\S'\R\S'\R\S'\R\S'Sr\ \S'\ S\S\RS\RS\R4Sj5r S r g) EulerDiscreteSchedulerState"commoninit_noise_sigma timestepssigmasNnum_inference_stepscU"UUUUS9$)Nrrrr)clsrrrrs m/home/wildlama/miniconda3/lib/python3.13/site-packages/diffusers/schedulers/scheduling_euler_discrete_flax.pycreate"EulerDiscreteSchedulerState.create,s-   r) __name__ __module__ __qualname____firstlineno__r __annotations__jnpndarrayrint classmethodr__static_attributes__rrrrr"sw  kk!{{ KK##  $  ++  ;;      rrc \rSrSr%\\S'Srg) FlaxEulerDiscreteSchedulerOutput<staterN)rrr r!rr"r'rrrr)r)<s &&rr)cb\rSrSr%Sr\VVs/sHoR PM snnr\R\ S'\ S5r \ SSSSS S S \R4S \S \S\S\S\R$S -S\S\S\R4Sjj5rS'S\S -S\4SjjrS\S\R$S\S\R$4SjrS(S\S\S\S\4SjjrS)S\S\R$S\S\R$S\S\\-4 S jjrS\S!\R$S"\R$S#\R$S\R$4 S$jrS%rS&rg s snnf)*FlaxEulerDiscreteSchedulerAad Euler scheduler (Algorithm 2) from Karras et al. (2022) https://huggingface.co/papers/2206.00364. . Based on the original k-diffusion implementation by Katherine Crowson: https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L51 [`~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$FlaxEulerDiscreteScheduler.has_statebsrig-C6?g{Gz?linearNepsilonlinspacenum_train_timesteps beta_startbeta_end beta_schedule trained_betasprediction_typetimestep_spacingc :[RS5 Xlg)NzFlax classes are deprecated and will be removed in Diffusers v1.0.0. We recommend migrating to PyTorch classes or pinning your version of Diffusers.)loggerwarningr/) r2r8r9r:r;r<r=r>r/s r__init__#FlaxEulerDiscreteScheduler.__init__fs  [  rrreturnc |Uc[R"U5n[R"SURR 5R 5SSS2nSUR- UR- S-n[R"U[R"S[U55U5n[R"U[R"S/URS9/5nURRS;aUR5nOUR5S-S-S-n[RUUUUS 9$) Nrr?r/r7trailingrr)r rr#arangeconfigr8roundalphas_cumprodinterplen concatenatearrayr/r>maxr)r2rrrrs r create_state'FlaxEulerDiscreteScheduler.create_statexs >)006FJJq$++"A"ABHHJ4R4P v,,,0E0EE#MIszz!S['A6J&#))SE*L!MN ;; ' '+C C%zz|  &  1A 5#= *11- 2  rr+sampletimestepc[R"URU:HSS9unUSnURUnX%S-S-S-- nU$)a Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the Euler algorithm. Args: state (`EulerDiscreteSchedulerState`): the `FlaxEulerDiscreteScheduler` 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 rsizerrrG)r#whererr)r2r+rWrX step_indexsigmas rscale_model_input,FlaxEulerDiscreteScheduler.scale_model_inputsP %//X"=AF ]  Z(1HqLS01 rrshapec URRS:Xa9[R"URRS- SUUR S9nOURRS:XanURRU-n[R "SU5U-R5SSS2R5R[5nUS- nO"[SURR35eSURR- URR- S -n[R"U[R "S[U55U5n[R "U[R""S /UR S9/5nURRS ;aUR%5nOUR%5S -S-S -nUR'UUUUS 9$)aV Sets the timesteps used for the diffusion chain. Supporting function to be run before inference. Args: state (`EulerDiscreteSchedulerState`): the `FlaxEulerDiscreteScheduler` state data class instance. num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. r7rrrIleadingNrFz=timestep_spacing must be one of ['linspace', 'leading'], got rGrHrJr)rrrr)rMr>r#r7r8r/rLrNcopyastypefloat ValueErrorrrOrPrQrRrSrTreplace)r2r+rrar step_ratiorrs r set_timesteps(FlaxEulerDiscreteScheduler.set_timestepss ;; ' ': 5  //!3#jj I [[ ) )Y 688 Je ,'BCKKcfknkvkv4 / */ !/  / % / n! ;V*;Vkk;V ;V  ;V  ;V *E 1;Vz * ++ {{  ;;   /ECsD+r-) dataclassesrflax jax.numpynumpyr#configuration_utilsrrutilsrscheduling_utils_flaxr r r r r get_loggerrr@structrr)r-rrrrs" A   H %   2 '':' '`/!3[`/r