biZ3ddlmZddlmZmZmZddlZddlmZ ddl m Z m Z ddl mZmZmZmZmZmZej(jGdd ZeGd d eZGd d ee Zy)) dataclass)OptionalTupleUnionN) ConfigMixinregister_to_config)CommonSchedulerStateFlaxKarrasDiffusionSchedulersFlaxSchedulerMixinFlaxSchedulerOutputadd_noise_commonget_velocity_commonc eZdZUeed<ej ed<ej ed<ej ed<dZee ed<e dedej dej dej fdZ y)DDIMSchedulerStatecommonfinal_alpha_cumprodinit_noise_sigma timestepsNnum_inference_stepsc|||||S)Nrrrr)clsrrrrs d/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/diffusers/schedulers/scheduling_ddim_flax.pycreatezDDIMSchedulerState.create-s 3-   ) __name__ __module__ __qualname__r __annotations__jnpndarrayrrint classmethodrrrrrr#s{  $kk!{{)-#-  $  ![[  ++  ;;    rrceZdZUeed<y)FlaxDDIMSchedulerOutputstateN)rr r!rr"rrrr(r(=s rr(ceZdZUdZeDcgc]}|j c}}Zeje d<e dZ e dddddd d d d d ejf d ededededeej$dedededededejfdZd-deedefdZ d-dedej$deedej$fdZ d.deded edefd!Zdefd"Z d/ded#ej$dedej$d$ed%edeeeffd&Zded'ej$d(ej$d)ej$dej$f d*Zdedej$d(ej$d)ej$dej$f d+Z d,Z!ycc}}w)0FlaxDDIMSchedulera~ Denoising diffusion implicit models is a scheduler that extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with non-Markovian guidance. [`~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. For more details, see the original paper: https://huggingface.co/papers/2010.02502 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`, `scaled_linear`, or `squaredcos_cap_v2`. trained_betas (`jnp.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. clip_sample (`bool`, default `True`): option to clip predicted sample between for numerical stability. The clip range is determined by `clip_sample_range`. clip_sample_range (`float`, default `1.0`): the maximum magnitude for sample clipping. Valid only when `clip_sample=True`. set_alpha_to_one (`bool`, default `True`): each diffusion step uses the value of alphas product at that step and at the previous one. For the final step there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`, otherwise it uses the value of alpha at step 0. steps_offset (`int`, default `0`): An offset added to the inference steps, as required by some model families. prediction_type (`str`, default `epsilon`): indicates whether the model predicts the noise (epsilon), or the samples. One of `epsilon`, `sample`. `v-prediction` is not supported for this scheduler. dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`): the `dtype` used for params and computation. dtypecy)NTrselfs r has_statezFlaxDDIMScheduler.has_statemsrig-C6?g{Gz?linearNT?repsilonnum_train_timesteps beta_startbeta_end beta_schedule trained_betas clip_sampleclip_sample_rangeset_alpha_to_one steps_offsetprediction_typec | |_yNr,) r/r4r5r6r7r8r9r:r;r<r=r,s r__init__zFlaxDDIMScheduler.__init__qs  rrreturnc|tj|}|jjr!t j d|j n|jd}t j d|j }t jd|jjjddd}tj||||S)Nr2r@rr) r rconfigr;r#arrayr,alphas_cumprodaranger4roundr)r/rrrrs r create_statezFlaxDDIMScheduler.create_states >)006F15 0L0LCIIc ,RXRgRghiRj  99S ;JJq$++"A"ABHHJ4R4P !(( 3- )  rr)sampletimestepc|S)a Args: state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` state data class instance. sample (`jnp.ndarray`): input sample timestep (`int`, optional): current timestep Returns: `jnp.ndarray`: scaled input sample r)r/r)rKrLs rscale_model_inputz#FlaxDDIMScheduler.scale_model_inputs  rrshapec|jj|z}tjd||zj ddd|jj z}|j ||S)a Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference. Args: state (`DDIMSchedulerState`): the `FlaxDDIMScheduler` state data class instance. num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. rNrD)rr)rEr4r#rHrIr<replace)r/r)rrO step_ratiors r set_timestepszFlaxDDIMScheduler.set_timestepssr[[448KK ZZ#67*DKKMdPRdSVZVaVaVnVnn }} 3  rc|jj|}tj|dk\|jj||j}d|z }d|z }||z d||z z z}|S)Nrr )rrGr#wherer) r/r)rL prev_timestep alpha_prod_talpha_prod_t_prev beta_prod_tbeta_prod_t_prevvariances r _get_variancezFlaxDDIMScheduler._get_variances}||228< II Q  ; ;M JELeLe ,& 00${2qyQQrc2t|j|||Sr?)rr)r/r)rKrkrs r get_velocityzFlaxDDIMScheduler.get_velocity0s#5<< JJrc.|jjSr?)rEr4r.s r__len__zFlaxDDIMScheduler.__len__9s{{...rr?)r)gT)"rr r!__doc__r name _compatiblesr#r,r"propertyr0r float32r%floatstrrr$boolrAr rrJrNrrSr\rr(rirmrorq).0es00rr+r+Bs$L%BBqAFFBL 99 $("%/3 #&!%(;;       , !yy  8,@#A M_ 2Y] ' 14 HPQT    SU ' >A JO  , #5 $ [M![Mkk[M [M  [M  [M[M &- .[MzR!R++R{{ R ;; R  RK!K K{{ K ;; K  K/aCsE*r+) dataclassesrtypingrrrflax jax.numpynumpyr#configuration_utilsrr scheduling_utils_flaxr r r rrrstructrr(r+rrrrss$")) A   2 1 x/*Kx/r