(tisddlZddlmZddlmZddlZddlZddlm Z m Z ddl m Z ddl mZmZeGdd eZGd d e e Zy) N) dataclass)Literal) ConfigMixinregister_to_config)SchedulerMixin) BaseOutput deprecateceZdZUejed<dZejdzed<dZejdzed<dZe dzed<y)HeliosSchedulerOutput prev_sampleN model_outputs last_sample this_order) __name__ __module__ __qualname__torch FloatTensor__annotations__rrrintu/Volumes/fast/ai/experiments/MLX_z-image/.venv/lib/python3.12/site-packages/diffusers/schedulers/scheduling_helios.pyr r sF""".2M5$$t+2,0K""T)0!Jd !rr c%~eZdZgZdZedddgddddd d d d gd d d ddfdededededede de dede de de deede de de de de d f"d!Z d"Zd#Zed$Zed%ZdUd&efd'Zd(Z dVd)ed*ed zd+e ej,zd,e d zd-e d zd.e f d/Zd-ed0ed1ej0fd2Zd3Zd4ZdWd5Zd6Z dXd7ej<d8eej<zd9ej<d:ej>d zd0ej<d zd;ej<d zdZ"d?Z#d d d@d7ej0d9ej0d0ej0d=ej0fdAZ$d d d d dBd7ej0d9ej0dCed0ej0d;ej0d=ej0f dDZ%d d d d d dEdFej0dGej0dHej0dCedIej0d0ej0d=ej0fdJZ& dYd7ej0d8eej0zd9ej0dd zdr?)rNstage_distancer'training_stepsr(i_s start_indice end_indice start_sigma end_sigma ori_sigmar)corrected_sigma tot_distance start_ratio end_ratiotimestep_ratio timestep_max timestep_minrY stage_sigmass rrCz*HeliosScheduler.init_sigmas_for_each_stagels ##88kk-- =C{3/.@AL|Q/L[q1NBCJZ8J++l388:K:D~:U J/446[^I)4D ! !# &ax O  ))#$ !q5y/(Ba)m(TW`(`#aen"n/1  ! !+ "9 :%0D  c "#,DOOC '!,>* =Cax! !.#"67,F fqj . yq 9:\I )4i(@D  %!=C!11#6Nt~~c.2Cn2T.UVX[\L>>#c.2Cn2T.UWehiWi*jkL L,QR@RSI",Y "E #25K[K[\efigi\jKk  $ $S );;ua!1CDL).)9)9,s:K)LD ! !# &!rc|jS)zg The index counter for current timestep. It will increase 1 after each scheduler step. )rLrNs r step_indexzHeliosScheduler.step_indexs rc|jS)zq The index for the first timestep. It should be set from pipeline with `set_begin_index` method. rMrxs r begin_indexzHeliosScheduler.begin_indexs    rr|c||_y)z Sets the begin index for the scheduler. This function should be run from pipeline before the inference. Args: begin_index (`int`): The begin index for the scheduler. Nr{)rNr|s rset_begin_indexzHeliosScheduler.set_begin_indexs (rc4||jjzSNrRr%)rNsigmas r _sigma_to_tzHeliosScheduler._sigma_to_tst{{6666rnum_inference_steps stage_indexdevicerDmuis_amplify_first_chunkc|jjdk(r|r |dzdz}n|dz}||_|j|jjdk(r|t j dd|jjz |dzddjt j}|jjdk7r?|jjrJ|j|jjd|}||jjzj}tj|}n|j |}t j |dj#|dj#|}|j$|} t j | dj#| dj#|} tj| }tj|j'||_tj*|tj,dgj'||_d|_|j3|jjdk(rJ|j(dd|_tj*|j.dd |j.ddg|_|jjr|jjdk(sJ|j|d|j.|_|jjdk(r,|j.dd|jjz|_y|j |j5|j.dd|j |j7|j |j5z zz|_yy) zA Setting the timesteps and sigmas for each stage dmdrrNr8rrr)rRr3rr\r'rSrTr%astypefloat32r&r4 time_shiftrVrrWr>rEr?rZrYcatzerosrDrLreset_scheduler_historyr`r_) rNrrrrDrrrYstage_timestepsrvratioss r set_timestepszHeliosScheduler.set_timestepss. ;; % % .%&9A&=&A#&9A&=##6   ;;   "~QDKK,K,K(KM`cdMdefigijqqJJ;;$$+#{{????!__T[[->->VLF$++"A"AAGGII%%f-F"66{CO "'')#((*#I  00=L[[a!5!5!7b9I9N9N9PRefF%%f-F)))477v7FiiQ 89< !$==rc|jjr d|z }tj|d}||fSd|dzdzdzz }||z}||fS)Nrg:0yE>)r`r?)rRr2rclamp)rNralpha_tsigma_ts r_sigma_to_alpha_sigma_tz'HeliosScheduler._sigma_to_alpha_sigma_tisb ;; & &%iGkk%T2G E1HqLS01GgoGrrrc"t|dkDr|dn|jdd}|t|dkDr|d}n td| tdddd }|d }|j|j }|j |\}} |jr|jjd k(r || |zz |z } n|jjd k(r|} n|jjd k(r ||z| |zz } nc|jjdk(r'|r|j|j } n|} || |zz } n#td|jjd|jjr|j| } | S|jjd k(r|S|jjd k(r |||zz | z } | S|jjd k(r ||z| |zz} | Std|jjd)a Convert the model output to the corresponding type the UniPC algorithm needs. Args: model_output (`torch.Tensor`): The direct output from the learned diffusion model. timestep (`int`): The current discrete timestep in the diffusion chain. sample (`torch.Tensor`): A current instance of a sample created by the diffusion process. Returns: `torch.Tensor`: The converted model output. rrNr/missing `sample` as a required keyword argumentrY1.0.0zPassing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`FTepsilonr v_predictionr!zprediction_type given as zj must be one of `epsilon`, `sample`, `v_prediction`, or `flow_prediction` for the UniPCMultistepScheduler.zW must be one of `epsilon`, `sample`, or `v_prediction` for the UniPCMultistepScheduler.) rpoprr rDryrr-rRr+r*_threshold_sample) rNrrrargskwargsrflagrrx0_predrs rconvert_model_outputz$HeliosScheduler.convert_model_outputss+."$i!m47J1M >4y1}a !RSS   Z   =DKK0E77> ??{{**i7!Gl$::gE,,8&,,>!F*W|-CC,,0AA"kk$//:G#G 7\#99 / 0K0K/LM\\ {{''009N{{**i7##,,8!Gl$::gE,,>!L07V3CC / 0K0K/LMGGr)rorderrrrc t|dkDr|dn|jdd}|t|dkDr|d}n td|t|dkDr|d}n td| tddd |j} |j d } | d } |} |j r)|j j|| | j} | S|8|6|j|jdz|j|j}}n||}}|j|\}}|j|\}}tj|tj|z }tj|tj|z }||z }|j}g}g}td|D]}|j|z }| |dz }|j|j|\}}tj|tj|z }||z |z }|j!||j!|| z |z |j!d tj"|| }g}g} |j$r| n|}!tj&|!}"|"|!z dz }#d}$|j(j*d k(r|!}%n9|j(j*dk(rtj&|!}%n t-td|dzD]T}|j!tj.||dz | j!|#|$z|%z |$|dzz}$|#|!z d|$z z }#Vtj0|}tj"| | } t|dkDrtj0|d}|dk(r$tj"dg| j2|}&nWtj4j7|dd dd f| dd j9|j9| j2}&nd}|j$r9||z | z||"z| zz }'|tj:d&|}(nd}(|'||%z|(zz } n8||z | z||"z| zz }'|tj:d&|}(nd}(|'||%z|(zz } | j9| j2} | S)a One step for the UniP (B(h) version). Alternatively, `self.solver_p` is used if is specified. Args: model_output (`torch.Tensor`): The direct output from the learned diffusion model at the current timestep. prev_timestep (`int`): The previous discrete timestep in the diffusion chain. sample (`torch.Tensor`): A current instance of a sample created by the diffusion process. order (`int`): The order of UniP at this timestep (corresponds to the *p* in UniPC-p). Returns: `torch.Tensor`: The sample tensor at the previous timestep. r prev_timestepNrrr.missing `order` as a required keyword argumentrzPassing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`r8rrr9r"dimrrrk,bkc...->bc...)rrrr rrJr1stepr rDryrrlogrr^rctensorr-expm1rRr.rHpowstackrlinalgsolverZeinsum))rNrrrrrrrrmodel_output_lists0m0xx_trsigma_s0ralpha_s0lambda_t lambda_s0hrrksD1sisimialpha_sisigma_si lambda_sirkRbhhh_phi_1h_phi_k factorial_iB_hrhos_px_t_pred_ress) rmultistep_uni_p_bh_updatez)HeliosScheduler.multistep_uni_p_bh_updates6$'t9q=QfjjRV6W >4y1}a !RSS =4y1}Q !QRR  $ ^  !..    # r "  ==--$$\2q9EECJ  %- $ DOOa,? @$++dooB^XG *EXG77@!99(C(99W% '(::IIh'%))H*== y q%A1$B"QU8,B!%!=!=dkk"o!N Hh (+eii.AAIi'1,B JJrN JJR2~ &! 3ll3v.  ??aR++b/B," ;; " "e +C [[ $ $ -++b/C%' 'q%!)$A HHUYYsAE* + HHW{*S0 1 1q5 KlQ_4G % KKN LL6 * s8a<++cq)Czse1776J++Acrc3B3hK3B@CCFKNNqwwWC ??X%)Gg,=,BBD <<(963G311CX%)Gg,=,BBD <<(963G311CffQWWo r)r this_sampler sigma_beforerthis_model_outputrrrc t|dkDr|dn|jdd} |t|dkDr|d}n td|t|dkDr|d}n td|t|dkDr|d}n td | tdd d |j} | d } |} |} |}|8|6|j |j |j |j dz }}n||}}|j|\}}|j|\}}tj|tj|z }tj|tj|z }||z }|j}g}g}td|D]}|j |dzz }| |dz }|j|j |\}}tj|tj|z }||z |z }|j||j|| z |z |jd tj||}g} g}!|jr| n|}"tj|"}#|#|"z dz }$d}%|j j"dk(r|"}&n9|j j"dk(rtj|"}&n t%td|dzD]T}| jtj&||dz |!j|$|%z|&z |%|dzz}%|$|"z d|%z z }$Vtj(| } tj|!|}!t|dkDrtj(|d}nd}|dk(r$tjdg| j*|}'nHtj,j/| |!j1|j1| j*}'|jrJ||z | z||#z| zz }(|tj2d|'dd |})nd})|| z }*|(||&z|)|'d |*zzzz } nI||z | z||#z| zz }(|tj2d|'dd |})nd})|| z }*|(||&z|)|'d |*zzzz } | j1| j*} | S)a One step for the UniC (B(h) version). Args: this_model_output (`torch.Tensor`): The model outputs at `x_t`. this_timestep (`int`): The current timestep `t`. last_sample (`torch.Tensor`): The generated sample before the last predictor `x_{t-1}`. this_sample (`torch.Tensor`): The generated sample after the last predictor `x_{t}`. order (`int`): The `p` of UniC-p at this step. The effective order of accuracy should be `order + 1`. Returns: `torch.Tensor`: The corrected sample tensor at the current timestep. r this_timestepNrz4missing `last_sample` as a required keyword argumentrz4missing `this_sample` as a required keyword argumentrrrzPassing `this_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`r8rrr9r"rrrr)rrrr rrDryrrrrr^rcrr-rrRr.rHrrrrrrZr)+rNrrrrrrrrrrrrrmodel_trrrrrrrrrrrrrrrrrrrrrrrrrhos_crcorr_resD1_ts+ rmultistep_uni_c_bh_updatez)HeliosScheduler.multistep_uni_c_bh_updateIs<$'t9q=QfjjRV6W  4y1}"1g  !WXX  4y1}"1g  !WXX =4y1}Q !QRR  $ ^  !.. r " #  EM $ DOO ^XG %|XG77@!99(C(99W% '(::IIh'%))H*== y ##q%AAE*B"QU8,B!%!=!=dkk"o!N Hh (+eii.AAIi'1,B JJrN JJR2~ &! 3ll3v.  ??aR++b/B," ;; " "e +C [[ $ $ -++b/C%' 'q%!)$A HHUYYsAE* + HHW{*S0 1 1q5 KlQ_4G % KKN LL6 * s8a<++cq)CC A:\\3%qwwvFF\\''1-008;;AGGDF ??X%)Gg,=,BBD <<(96#2;LR>7JT__7\]J11Jj$*?*?!*CD"""!44%//! 5   &$$t{{'?'??%%*%  !    ! 0@0@$//R R$#'((   rc|jjdk(r|j|||||S|jjdk(r|j||||St)Neuler)rrrrrr#)rrrr)rRr3rrrH)rNrrrrrs rrzHeliosScheduler.step>sy ;; % % 0??)!#' # [[ ' '7 2??)!' # & %rc$dg|jjz|_dg|jjz|_d|_|jj |_|jj |_d|_d|_d|_ y)Nr) rRr,rrJrKr0r1rrLrMrxs rrz'HeliosScheduler.reset_scheduler_historyXsw"Vdkk&>&>>"Vdkk&>&>> !!%!>!> ,,  rc.|jjSrrrxs r__len__zHeliosScheduler.__len__bs{{...r)r)NNNNFr)NNNNNT) NNTNNNNNNNNN)NNNT)+rrr _compatiblesrrrfloatlistboolstrrrrOr\rCpropertyryr|r~rrrrrfrrrrrr Generatorr tuplerrrrrrrrr rrrrr#s L E$(0"0 "&')#' $%%*''/>%+++/>!! /> ??T) />   4' />%%,/>/>  &/>d  $" NllN N || N Nh $"#'DllD D  D || DLLD DT%)$(%)"L <<L\\ L \\ L  LllL||L Lb(,# ""%)"#'"#'"(,d lld  $d  d  d  d d lld ||d LLd d !d d d   &d T/3$(,0 &''&%+++&!! & ??T) &  &  &&4!/rr)ra dataclassesrtypingrnumpyrSrconfiguration_utilsrrschedulers.scheduling_utilsrutilsr r r rrrrrsJ ! 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