bi3TddlmZddlmZmZmZddlZddlZddlm Z ddl m Z m Z ddlmZmZmZmZmZej(jGdd ZeGd d eZGd d ee Zy)) dataclass)OptionalTupleUnionN) ConfigMixinregister_to_config)CommonSchedulerStateFlaxKarrasDiffusionSchedulersFlaxSchedulerMixinFlaxSchedulerOutputadd_noise_commonc eZdZUeed<ej ed<ej ed<ej ed<dZee ed<dZ eej ed<dZ eej ed<dZ eej ed <dZ eejed <dZeej ed <dZeej ed <ededej dej dej fd Zy)PNDMSchedulerStatecommonfinal_alpha_cumprodinit_noise_sigma timestepsNnum_inference_steps prk_timestepsplms_timestepscur_model_outputcounter cur_sampleetsc|||||S)Nrrrr)clsrrrrs d/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/diffusers/schedulers/scheduling_pndm_flax.pycreatezPNDMSchedulerState.create4s 3-   )__name__ __module__ __qualname__r __annotations__jnpndarrayrrintrrrrint32rr classmethodr"rr#r!rr"s  $kk!{{)-#-+/M8CKK(/,0NHS[[)0/3hs{{+2#'GXcii '(,J%,!%C#++ %  $  ![[  ++  ;;    r#rceZdZUeed<y)FlaxPNDMSchedulerOutputstateN)r$r%r&rr'rr#r!r.r.Ds r#r.c eZdZUdZeDcgc]}|j c}}Zeje d<e e d<e dZ e ddddd d d d d ejf d e dedededeej$dedede dedejfdZd,deedefdZdede dedefdZ d,dedej$dee dej$fd Z d-ded!ej$de dej$d"edeeeff d#Zded!ej$de dej$deeeff d$Zded!ej$de dej$deeeff d%Zdefd&Z ded'ej$d(ej$d)ej$dej$f d*Z!d+Z"y cc}}w).FlaxPNDMSchedulerab Pseudo numerical methods for diffusion models (PNDM) proposes using more advanced ODE integration techniques, namely Runge-Kutta method and a linear multi-step method. [`~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/2202.09778 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. skip_prk_steps (`bool`): allows the scheduler to skip the Runge-Kutta steps that are defined in the original paper as being required before plms steps; defaults to `False`. set_alpha_to_one (`bool`, default `False`): 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`, 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. dtype pndm_ordercy)NTrselfs r! has_statezFlaxPNDMScheduler.has_statetsr#ig-C6?g{Gz?linearNFrepsilonnum_train_timesteps beta_startbeta_end beta_schedule trained_betasskip_prk_stepsset_alpha_to_one steps_offsetprediction_typec  | |_d|_y)N)r2r3) r6r:r;r<r=r>r?r@rArBr2s r!__init__zFlaxPNDMScheduler.__init__xs r#rreturnc|tj|}|jjr!t j d|j n|jd}t j d|j }t jd|jjjddd}tj||||S)Ng?r2rr) r r"configr@r(arrayr2alphas_cumprodaranger:roundr)r6rrrrs r! create_statezFlaxPNDMScheduler.create_states >)006F15 0L0LCIIc ,RXRgRghiRj  99S ;JJq$++"A"ABHHJ4R4P !(( 3- )  r#r/rshapec |jj|z}tjd||zj |jj z}|jj rMtjgtj}tj|dd|dd|ddgddd}n||j djdtjtjd|jj|zdzgtj|jz}|ddjdddddd}|ddddd}tj||g}tj||j} tjd} tj||j} tjd |z|j} |j||||| | | | S) a Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference. Args: state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` state data class instance. num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. shape (`Tuple`): the shape of the samples to be generated. rrHNrIrr )rD)rrrrrrrr)rJr:r(rMrNrAr?rKr+ concatenater3repeattilezerosr2replace) r6r/rrP step_ratio _timestepsrrrrrrrs r! set_timestepszFlaxPNDMScheduler.set_timestepss[[448KK jj$78:ELLNQUQ\Q\QiQii ;; % %  IIb :M __j"oz"R?PR\]_]`Ra-bcdhfhdhiN''7'9:AA!Dsxx 1dkk==ATTXYYZbebkbklHM +3B/66q9!B?2FM'_TrT2NOO]N$CD 99U$**=))A,YYuDJJ7 iiu DJJ7}} 3')-!  r#sampletimestepc|S)a Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. 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)r6r/r\r]s r!scale_model_inputz#FlaxPNDMScheduler.scale_model_inputs  r# model_output return_dictc X|j td|jjr|j ||||\}}nR|j ||||\}}|j ||||\} } |j t|jk} tjj| || }|jtjj| |j| jtjj| |j| jtjj| |j| jtjj| |j | j }|s||fSt!||S)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). This function calls `step_prk()` or `step_plms()` depending on the internal variable `counter`. Args: state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` 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. return_dict (`bool`): option for returning tuple rather than FlaxPNDMSchedulerOutput class Returns: [`FlaxPNDMSchedulerOutput`] or `tuple`: [`FlaxPNDMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. aNumber of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler)rrrr) prev_sampler/)r ValueErrorrJr? step_plmsstep_prkrlenrjaxlaxselectrXrrrr.) r6r/r`r]r\rardprk_prev_sample prk_stateplms_prev_sample plms_stateconds r!stepzFlaxPNDMScheduler.stepsZ8  $ $ ,s  ;; % %!%|Xv!V K)-ulHV\)] &OY+/>>%xY_+` ( j==3u':':#;;D''..@PQKMM!$i6P6PR\RmRm!nGGNN4 G77>>$ 0D0DjF[F[\tY->-> @R@RS "E' '&;eLLr#c |j tdtj|jdzd|j j |jzdz}||z }|j|jdzdz}tjj|jdzdk7||jd|zz}|jtjj|jdz|jd|zz|jd|zz|jd|zztj|jtjj|jdzdk(|jj ddj#|jddj dj#||jtjj|jdzdk(||j$ }|j$}|j'|||||}|j|jdz }||fS) ay Step function propagating the sample with the Runge-Kutta method. RK takes 4 forward passes to approximate the solution to the differential equation. Args: state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` state data class instance. model_output (`jnp.ndarray`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`jnp.ndarray`): current instance of sample being created by diffusion process. return_dict (`bool`): option for returning tuple rather than FlaxPNDMSchedulerOutput class Returns: [`FlaxPNDMSchedulerOutput`] or `tuple`: [`FlaxPNDMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. rcrrrDgUUUUUU?gUUUUUU?r )rrrr)rrer(whererrJr:rrirjrkrrXselect_n zeros_likeratsetr_get_prev_sample) r6r/r`r]r\ diff_to_prev prev_timesteprrds r!rgzFlaxPNDMScheduler.step_prk$s2  $ $ ,s yy MMA q$++"A"AUE^E^"^bc"c !</ &&u}}'9A'=>ww~~ ]]Q 1 $   " "U\%9 9   WW-- !&&)==&&)==&&)==u556 "q( Qq!%%eii!n588;?? M  ww~~"q(   (%% ++E:xXde  emma&7 8U##r#c|j td||jj|jzz }t j |dkD|d}t j |j dk(||}t j |j dk(||jj|jzz|}|jtjj|j dk7|jjddj|jddjdj||jtjj|j dk7||j}|jtjjt j |j dd|||jdzdz d|jdz|jd z dz d |jdzd |jd zz d |jd zzdz dd|jdzd|jd zz d|jd zzd|jdzz z}|j}|j"}|j%|||||}|j|j dz}||fS)av Step function propagating the sample with the linear multi-step method. This has one forward pass with multiple times to approximate the solution. Args: state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` 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. return_dict (`bool`): option for returning tuple rather than FlaxPNDMSchedulerOutput class Returns: [`FlaxPNDMSchedulerOutput`] or `tuple`: [`FlaxPNDMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. rcrr rsrD)rrrIrrRrS gUUUUUU?7;% )rrt)rrerJr:r(rurrXrirjrkrrxryrrvcliprrz)r6r/r`r]r\r|rds r!rfzFlaxPNDMScheduler.step_plmshs2  $ $ ,s  !4;;#B#BeF_F_#__  -!"3]AF  %--1"4h N 99 MMQ 4;;+J+JeNgNg+g giq &  " Qq!%%eii!n588;?? M  ww~~ "       WW--1- " -2UYYr]"UYYr]2a7eiim#b599R=&881uyy};LLPRR " %UYYr](::R%))B-=OORSV[V_V_`bVcRcce   !!-- ++E68]T`a  emma&7 8U##r#c|jj|}tj|dk\|jj||j}d|z }d|z } |j j dk(r|dz|z|dz|zz}n<|j j dk7r#td|j j d||z dz} || dzz||z|zdzz} | |z||z |z| z z } | S)Nrr v_predictiong?r9zprediction_type given as z+ must be one of `epsilon` or `v_prediction`)rrLr(rurrJrBre) r6r/r\r]r|r` alpha_prod_talpha_prod_t_prev beta_prod_tbeta_prod_t_prev sample_coeffmodel_output_denom_coeffrds r!rzz"FlaxPNDMScheduler._get_prev_samples5||228< II Q  ; ;M JELeLe ,& 00 ;; & &. 8(#-=cAQU[@[ [[ ( (I 5+DKK,G,G+HHst *L8cB $02Bs2K#K ; &): :O$ 6 !%6%E$UXp$p p r#original_samplesnoiserc2t|j|||SN)rr)r6r/rrrs r! add_noisezFlaxPNDMScheduler.add_noises  .>yQQr#c.|jjSr)rJr:r5s r!__len__zFlaxPNDMScheduler.__len__s{{...r#r)T)#r$r%r&__doc__r name _compatiblesr(r2r'r*propertyr7r float32floatstrrr)boolrEr rrOrr[r_rr.rqrgrfrzrr).0es00r!r1r1Is#J%BBqAFFBL 99O $("%/3$!&(;;       , yy( 8,@#A M_ 05 #55 C5 X]5 bt5 pY]'14HPQT  .! 5M!5Mkk5M 5M  5M  5M &- .5MnB$!B$kkB$ B$  B$ &- . B$H\$!\$kk\$ \$  \$ &- . \$|+&8+ZR!R++R{{ R ;; R  R/[ CsE?r1) dataclassesrtypingrrrflaxri jax.numpynumpyr(configuration_utilsrr scheduling_utils_flaxr r r rrstructrr.r1rr#r!rsw"")) A   B 1 t/*Kt/r#