3j0 SSKrSSKJrJr SSKrSSKrSSKJrJ r SSK J r J r SSK Jr SSKJrJrJr \ "5(aSSKrSS \S \S \S S \R.4Sjjr"SS\\5rg)N)CallableLiteral) ConfigMixinregister_to_config) deprecateis_scipy_available) randn_tensor)KarrasDiffusionSchedulersSchedulerMixinSchedulerOutputnum_diffusion_timestepsmax_betaalpha_transform_type)cosineexplaplacereturnc :US:XaSnO"US:XaSnOUS:XaSnO[SU35e/n[U5H<nXP- nUS-U- nUR[SU"U5U"U5- - U55 M> [R "U[R S 9$) a Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [0,1]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): The number of betas to produce. max_beta (`float`, defaults to `0.999`): The maximum beta to use; use values lower than 1 to avoid numerical instability. alpha_transform_type (`str`, defaults to `"cosine"`): The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`. Returns: `torch.Tensor`: The betas used by the scheduler to step the model outputs. rch[R"US-S- [R-S- 5S-$)NgMb?gT㥛 ?r)mathcospits b/home/wildlama/miniconda3/lib/python3.13/site-packages/diffusers/schedulers/scheduling_sasolver.py alpha_bar_fn)betas_for_alpha_bar..alpha_bar_fn=s-88QY%/$''9A=>!C Crc S[R"SSU- 5-[R"SS[R"SU- 5-- S-5-n[R"U5n[R "USU-- 5$)Ngr ?rgư>)rcopysignlogfabsrsqrt)rlmbsnrs rrrBsmq#'22TXXa!diiPSVWPWFXBX>X[_>_5``C((3-C99SAG_- -r rc4[R"US-5$)Ng()rrrs rrrIs88AI& &r z"Unsupported alpha_transform_type: r dtype) ValueErrorrangeappendmintorchtensorfloat32)rrrrbetasit1t2s rbetas_for_alpha_barr7#s0x' D  * .  & '=>R=STUU E * +  (!e. . S\"- R0@@@(KL, <<U]] 33r c0\rSrSrSr\VVs/sHoR PM snnrSr\ SSSSSS S S SS S S SSS S S S S \ "S5*SSS4S\ S\ S\ S\ S\ R\\ -S-S\ S\ S\ S\S-S\S\ S\ S\ S \S!\S"\S#\S$\S%\ S&\ S'\ S-S(\ S)\ 4.S*jj5r\S+5r\S,5rSYS-\ 4S.jjrSZS/\ S0\ \R0-4S1jjrS2\R4S3\R44S4jrS5rS6rS7\R4S3\R44S8jrS7\R4S/\ S3\R44S9jrS[S7\R4S/\ S:\ S;\ S3\R44 S<jjr SS=.S>\R4S2\R4S3\R44S?jjr!S@r"SAr#SBr$SCr%S>\R4S2\R4SD\R4SE\ SF\R4S3\R44 SGjr&SH\R4SI\R4SJ\R4SK\R4SE\ SF\R4S3\R44SLjr'S\SM\ \R4-SN\R4S-S3\ 4SOjjr(SPr)S]S>\R4SM\ S2\R4SQ\S3\*\+-4 SRjjr,S2\R4S3\R44SSjr-ST\R4SD\R4SU\R\S3\R44SVjr/SWr0SXr1gs snnf)^SASolverSchedulerWu `SASolverScheduler` is a fast dedicated high-order solver for diffusion SDEs. This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic methods the library implements for all schedulers such as loading and saving. Args: num_train_timesteps (`int`, defaults to 1000): The number of diffusion steps to train the model. beta_start (`float`, defaults to 0.0001): The starting `beta` value of inference. beta_end (`float`, defaults to 0.02): The final `beta` value. beta_schedule (`str`, defaults to `"linear"`): 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 (`np.ndarray`, *optional*): Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`. predictor_order (`int`, defaults to 2): The predictor order which can be `1` or `2` or `3` or '4'. It is recommended to use `predictor_order=2` for guided sampling, and `predictor_order=3` for unconditional sampling. corrector_order (`int`, defaults to 2): The corrector order which can be `1` or `2` or `3` or '4'. It is recommended to use `corrector_order=2` for guided sampling, and `corrector_order=3` for unconditional sampling. prediction_type (`str`, defaults to `epsilon`, *optional*): Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process), `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen Video](https://huggingface.co/papers/2210.02303) paper). tau_func (`Callable`, *optional*): Stochasticity during the sampling. Default in init is `lambda t: 1 if t >= 200 and t <= 800 else 0`. SA-Solver will sample from vanilla diffusion ODE if tau_func is set to `lambda t: 0`. SA-Solver will sample from vanilla diffusion SDE if tau_func is set to `lambda t: 1`. For more details, please check https://huggingface.co/papers/2309.05019 thresholding (`bool`, defaults to `False`): Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such as Stable Diffusion. dynamic_thresholding_ratio (`float`, defaults to 0.995): The ratio for the dynamic thresholding method. Valid only when `thresholding=True`. sample_max_value (`float`, defaults to 1.0): The threshold value for dynamic thresholding. Valid only when `thresholding=True` and `algorithm_type="dpmsolver++"`. algorithm_type (`str`, defaults to `data_prediction`): Algorithm type for the solver; can be `data_prediction` or `noise_prediction`. It is recommended to use `data_prediction` with `solver_order=2` for guided sampling like in Stable Diffusion. lower_order_final (`bool`, defaults to `True`): Whether to use lower-order solvers in the final steps. Default = True. use_karras_sigmas (`bool`, *optional*, defaults to `False`): Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`, the sigmas are determined according to a sequence of noise levels {σi}. use_exponential_sigmas (`bool`, *optional*, defaults to `False`): Whether to use exponential sigmas for step sizes in the noise schedule during the sampling process. use_beta_sigmas (`bool`, *optional*, defaults to `False`): Whether to use beta sigmas for step sizes in the noise schedule during the sampling process. Refer to [Beta Sampling is All You Need](https://huggingface.co/papers/2407.12173) for more information. lambda_min_clipped (`float`, defaults to `-inf`): Clipping threshold for the minimum value of `lambda(t)` for numerical stability. This is critical for the cosine (`squaredcos_cap_v2`) noise schedule. variance_type (`str`, *optional*): Set to "learned" or "learned_range" for diffusion models that predict variance. If set, the model's output contains the predicted Gaussian variance. timestep_spacing (`str`, defaults to `"linspace"`): The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information. steps_offset (`int`, defaults to 0): An offset added to the inference steps, as required by some model families. r ig-C6?g{Gz?linearNrepsilonFgףp= ??data_predictionTinflinspacernum_train_timesteps beta_startbeta_end beta_schedule trained_betaspredictor_ordercorrector_orderprediction_typetau_func thresholdingdynamic_thresholding_ratiosample_max_valuealgorithm_typelower_order_finaluse_karras_sigmasuse_exponential_sigmasuse_beta_sigmasuse_flow_sigmas flow_shiftlambda_min_clipped variance_typetimestep_spacing steps_offsetcURR(a[5(d [S5e[ URRURR URR /5S:a [S5eUb)[R"U[RS9Ul OUS:Xa*[R"X#U[RS9Ul OkUS:Xa4[R"US-US-U[RS9S-Ul O1US :Xa[U5Ul O[US UR35eS UR- Ul[R""UR S S 9Ul[R&"UR$5Ul[R&"SUR$- 5Ul[R,"UR(5[R,"UR*5- UlSUR$- UR$- S-UlS UlU S;a[U S UR35eSUl[6R"S US- U[6RS9SSS2R95n[R:"U5UlS/[?XgS- 5-Ul S/[?XgS- 5-Ul!U c SUl"OXl"U S:HUl#S Ul$SUl%SUl&SUl'UR0RQS5Ulg)Nz:Make sure to install scipy if you want to use beta sigmas.r znOnly one of `config.use_beta_sigmas`, `config.use_exponential_sigmas`, `config.use_karras_sigmas` can be used.r*r; scaled_linearr"rsquaredcos_cap_v2z is not implemented for r=rdim)r>noise_predictionc"US:aUS::aS$S$)Ni r rrs r,SASolverScheduler.__init__..s18Sa&Ga&Gr r>cpu))configrQr ImportErrorsumrPrOr,r0r1r2r3r@r7NotImplementedError __class__alphascumprodalphas_cumprodr&alpha_tsigma_tr$lambda_tsigmasinit_noise_sigmanum_inference_stepsnpcopy from_numpy timestepsmax timestep_list model_outputsrI predict_x0lower_order_nums last_sample _step_index _begin_indexto)selfrArBrCrDrErFrGrHrIrJrKrLrMrNrOrPrQrRrSrTrUrVrWrvs r__init__SASolverScheduler.__init__s6 ;; & &/A/C/CZ[ [  ++T[[-O-OQUQ\Q\QnQno pst tA   $m5==IDJ h & >QY^YfYfgDJ o -OcM'--    J1 1,-@ADJ%7OPTP^P^O_&`a aDJJ& #mmDKKQ?zz$"5"56 zz!d&9&9"9:  $,,/%))DLL2II D///43F3FF3N !$ !H H%(88PQUQ_Q_P`&ab b$( KK#6#:(a[R"SSURR - US-5n S U - n[R"URR@U-SURR@S- U--- 5SSR5nXpRR -R5n[R2"XwSS/5R[R45nO[RB"U[R "S[EU55U5nSUR(S- UR(S- S -n [R2"X{//5R[R45n[RF"U5Ul$[RF"U5RKU[RS 9Ul&[EU5Ul'S/[QURRRURRTS- 5-Ul+SUl,SUl-SUl.SUl/URHRKS 5Ul$gs sn fs sn fs sn f)au Sets the discrete timesteps used for the diffusion chain (to be run before inference). Args: num_inference_steps (`int`): The number of diffusion steps used when generating samples with a pre-trained model. device (`str` or `torch.device`, *optional*): The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. rr@r Nr^leadingtrailingzY is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.r") in_sigmasrrr=)rr+rd)0r0 searchsortedfliprorerTrAnumpyitemrVrsr@roundrtastypeint64arangerWr,arrayrlr$rO_convert_to_karras _sigma_to_t concatenater2rP_convert_to_exponentialrQ_convert_to_betarRrSinterplenrurprrvrrrwrFrGryr{r|r}r~) rrrr clipped_idx last_timesteprv step_ratiorp log_sigmassigmarj sigma_lasts r set_timestepsSASolverScheduler.set_timesteps s((DMMA3)GIgIgh ++99KGNNPVVX  ;; ' ': 5 A}q02E2IJPPRSWUWSWXY\Z\]bbdkklnltltu [[ ) )Y 6&+BCJ1&9A&=>KRRTUYWYUYZ[^\^_ddfmmnpnvnvwI 11 1I [[ ) )Z 788;NNJ -ZK@FFHMMOVVWYW_W_`I NI;;//01JK A 3 33t7J7JJsRSVVF^ ;; ( (WWV_))+F,,v,gFSY!ZSY%"2"25"ESY!Z[aacI^^VBC[$9:AA"**MF [[ / /WWV_))+F11F1lFSY!ZSY%"2"25"ESY!Z[I^^VBC[$9:AA"**MF [[ ( (WWV_))+F**V*eFSY!ZSY%"2"25"ESY!Z[I^^VBC[$9:AA"**MF [[ ( ([[A (G(G$GI\_`I`aF6\FWWT[[33f<T[[E[E[^_E_ciDi@ijklomopuuwF++"A"AAGGII^^VBC[$9:AA"**MFYYy"))As6{*CVLFt221559L9LQ9OOTWWJ^^V\$:;BB2::NF&&v. )))477vU[[7Y#&y>   ++T[[-H-H1-L MN!"  kknnU+ I"[ "[ "[s.] 8]4]samplercXURnURtp4nU[R[R4;aUR 5nUR X4[R"U5-5nUR5n[R"X`RRSS9n[R"USURRS9nURS5n[R"X*U5U- nUR "X4/UQ76nUR!U5nU$)a  Apply dynamic thresholding to the predicted sample. "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing pixels from saturation at each step. We find that dynamic thresholding results in significantly better photorealism as well as better image-text alignment, especially when using very large guidance weights." https://huggingface.co/papers/2205.11487 Args: sample (`torch.Tensor`): The predicted sample to be thresholded. Returns: `torch.Tensor`: The thresholded sample. r r[)r/rw)r+shaper0r2float64floatreshapersprodabsquantilererKclamprL unsqueezer)rrr+ batch_sizechannelsremaining_dims abs_sampless r_threshold_sample#SASolverScheduler._threshold_sample_s( 06 - ~  6 6\\^F rww~7N,NOZZ\ NN:{{'M'MST U KK 1$++66  KKNVR+a/ F~F5! r c[R"[R"US55nX2SS2[R4- n[R"US:SS9R SS9R URSS- S9nUS-nX%nX&nXs- Xx- - n [R "U SS5n SU - U-X--n U RUR5n U $)at Convert sigma values to corresponding timestep values through interpolation. Args: sigma (`np.ndarray`): The sigma value(s) to convert to timestep(s). log_sigmas (`np.ndarray`): The logarithm of the sigma schedule used for interpolation. Returns: `np.ndarray`: The interpolated timestep value(s) corresponding to the input sigma(s). g|=Nr)axisr)rwr ) rsr$maximumnewaxiscumsumargmaxcliprr) rrr log_sigmadistslow_idxhigh_idxlowhighwrs rrSASolverScheduler._sigma_to_tsFF2::eU34 q"**}55))UaZq188a8@EE*JZJZ[\J]`aJaEbQ;!#_ , GGAq! Ug  , IIekk "r cvURR(a SU- nUnX#4$SUS-S-S-- nX-nX#4$)z Convert sigma values to alpha_t and sigma_t values. Args: sigma (`torch.Tensor`): The sigma value(s) to convert. Returns: `tuple[torch.Tensor, torch.Tensor]`: A tuple containing (alpha_t, sigma_t) values. r rr")rerR)rrrmrns r_sigma_to_alpha_sigma_t)SASolverScheduler._sigma_to_alpha_sigma_tsS ;; & &%iGG E1HqLS01GoGr rc[URS5(aURRnOSn[URS5(aURRnOSnUbUOUSR 5nUbUOUSR 5nSn[ R "SSU5nUSU- -nUSU- -nXXx- --U-n U $)a Construct the noise schedule as proposed in [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364). Args: in_sigmas (`torch.Tensor`): The input sigma values to be converted. num_inference_steps (`int`): The number of inference steps to generate the noise schedule for. Returns: `torch.Tensor`: The converted sigma values following the Karras noise schedule. sigma_minN sigma_maxr^rg@r )hasattrrerrrrsr@) rrrrrrrhoramp min_inv_rho max_inv_rhorps rr$SASolverScheduler._convert_to_karrass$ 4;; , , --II 4;; , , --II!*!6IIbM [!TIP] > The algorithm and model type are decoupled. You can use either data_prediction or noise_prediction for both > noise prediction and data prediction models. Args: model_output (`torch.Tensor`): The direct output from the learned diffusion model. 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 argumentrv1.0.0zPassing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`)r>r<)learned learned_ranger v_predictionflow_predictionzprediction_type given as zd must be one of `epsilon`, `sample`, `v_prediction`, or `flow_prediction` for the SASolverScheduler.)r]zQ must be one of `epsilon`, `sample`, or `v_prediction` for the SASolverScheduler.)rpopr,rrprrrerMrHrUrJrrmrn) rrrargskwargsrrrmrnx0_predr<s rconvert_model_output&SASolverScheduler.convert_model_output?s2"$i!m47J1M >4y1}a !RSS   Z   DOO,77> ;; % %)< <{{**i7;;,,0LL#/2A2#6L!l$::gE,,8&,,>!*W-CC,,0AA++doo6 \#99 / 0K0K/LMVV {{''009N[[ ' '+? ?{{**i7;;,,0LL*1bqb51G*G,,8!l$::gE,,>!073CC / 0K0K/LMAA {{''#'<<#94<<;Q!g$55@009!g$55@N/@r cUS;dS5eUS:Xa3[R"U*5[R"X2- 5S- -$US:Xa<[R"U*5US-[R"X2- 5-US-- -$US:XaN[R"U*5US-SU--S-[R"X2- 5-US-SU--S-- -$US:Xa`[R"U*5US-SUS---SU--S-[R"X2- 5-US-SUS---SU--S-- -$g) zT Calculate the integral of exp(-x) * x^order dx from interval_start to interval_end rr rr)order is only supported for 0, 1, 2 and 3rr rrNr0r)rorderinterval_start interval_ends r%get_coefficients_exponential_negative7SASolverScheduler.get_coefficients_exponential_negativesz $Q&QQ$ A:99l]+uyy9V/WZ[/[\ \ aZ99l]+!#uyy1N'OOS_bcScd aZ99l]+"Q%77!;uyyIf?gg?Q%559; aZ99l]+"Q):%::Q=OORSS))L9:;?Qq%881|;KKaOQ r cUS;dS5eSUS--U-nSUS--U-nUS:Xa<[R"U5S[R"XV- *5- -SUS--- $US:XaH[R"U5US- US- [R"XV- *5-- -SUS--S-- $US:XaZ[R"U5US-SU-- S-US-SU-- S-[R"XV- *5-- -SUS--S-- $US:Xal[R"U5US-SUS--- SU--S- US-SUS--- SU--S- [R"XV- *5-- -SUS--S-- $g ) z\ Calculate the integral of exp(x(1+tau^2)) * x^order dx from interval_start to interval_end rrr rrrrNr)rrrrtauinterval_end_covinterval_start_covs r%get_coefficients_exponential_positive7SASolverScheduler.get_coefficients_exponential_positives $Q&QQ$QJ,6#q&jN: A: *+q599?O?d=e3f/fgklortuoukuv aZ *+%))A-=M=b;c1dde QJ1$ & aZ *+%q(1/?+??!C)1,q3E/EEIii"2"G HIJJ QJ1$ & aZ *+%q(1/?/B+BBQIYEYY\]])1,q3Eq3H/HH1OaKaadeeii"2"G HIJJ QJ1$ & r c US;deU[U5S- :XdeUS:XaS//$US:Xa@SUSUS- - US*USUS- - /SUSUS- - US*USUS- - //$US:XaUSUS- USUS- -nUSUS- USUS- -nUSUS- USUS- -nSU- US*US- U- USUS-U- /SU- US*US- U- USUS-U- /SU- US*US- U- USUS-U- //$US:XGaUSUS- USUS- -USUS- -nUSUS- USUS- -USUS- -nUSUS- USUS- -USUS- -nUSUS- USUS- -USUS- -nSU- US*US- US- U- USUS-USUS--USUS--U- US*US-US-U- /SU- US*US- US- U- USUS-USUS--USUS--U- US*US-US-U- /SU- US*US- US- U- USUS-USUS--USUS--U- US*US-US-U- /SU- US*US- US- U- USUS-USUS--USUS--U- US*US-US-U- //$g)z2 Calculate the coefficient of lagrange polynomial rr rrrN)r)rr lambda_list denominator1 denominator2 denominator3 denominator4s rlagrange_polynomial_coefficient1SASolverScheduler.lagrange_polynomial_coefficientsa  $$$K(1,,,, A:C5L aZQ+a.89 ^O{1~ A'FG Q+a.89 ^O{1~ A'FG  aZ'N[^; AQ\]^Q_@_`L'N[^; AQ\]^Q_@_`L'N[^; AQ\]^Q_@_`L $!!n_{1~5EN[^3lB  $!!n_{1~5EN[^3lB  $!!n_{1~5EN[^3lB "aZQ+a.0q>KN24q>KN24  Q+a.0q>KN24q>KN24  Q+a.0q>KN24q>KN24  Q+a.0q>KN24q>KN24  $!!n_{1~5 AF,V#AQ7%a.;q>9:%a.;q>9:# # "!n_{1~5 AF,V  $!!n_{1~5 AF,V#AQ7%a.;q>9:%a.;q>9:# # "!n_{1~5 AF,V  $!!n_{1~5 AF,V#AQ7%a.;q>9:%a.;q>9:# # "!n_{1~5 AF,V  $!!n_{1~5 AF,V#AQ7%a.;q>9:%a.;q>9:# # "!n_{1~5 AF,V E-- +r c US;deU[U5:XdS5e/nURUS- U5n[U5HnSn [U5H]n UR(a&XUU UR US- U - X#U5-- n M:XUU UR US- U - X#5-- n M_ UR U 5 M [U5U:XdS5eU$)N)r rrrz4the length of lambda list must be equal to the orderr rz3the length of coefficients does not match the order)rr r-rzrrr.) rrrrrr coefficientslagrange_coefficientr4 coefficientjs rget_coefficients_fn%SASolverScheduler.get_coefficients_fnKs  $$$K((`*``( #CCEAI{[uAK5\??#:1#=@j@j A ~SA$K #:1#=@j@j A ~A$K "    ,< E)`+``)r noiserrc  [U5S:aUSOURSS5nUc [U5S:aUSnO [S5eUc [U5S:aUSnO [S5eUc [U5S:aUSnO [S 5eUc [U5S :aUS nO [S 5eUb [SS S 5 URn UR UR S-UR UR pURU 5upURU 5up[R"U 5[R"U 5- n[R"U 5[R"U 5- n[R"U5nX- n/n[U5HqnUR U- nURUR U5unn[R"U5[R"U5- nURU5 Ms URXOUUU5nUnUR(GaTUS:XGaMUR UR S- nURU5unn[R"U5[R"U5- nUS==S[R"SUS--U-5-US-S- USUS---S- [R"SUS--U*-5-SUS--S-- - -UU- - - ss'US==S[R"SUS--U-5-US-S- USUS---S- [R"SUS--U*-5-SUS--S-- - -UU- - -ss'[U5HqnUR(a>USUS--U -[R"US-*U-5-UU-U US-*-- nMRUSUS--*U -UU-U US-*-- nMs UR(a=U [R "S[R"SUS--U-5- 5-U-nO8XZ-[R "[R"SU-5S- 5-U-nUR(a,[R"US-*U-5X- -U-U-U-nO X- U-U-U-nUR#UR$5nU$)a One step for the SA-Predictor. 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 SA-Predictor at this timestep. Returns: `torch.Tensor`: The sample tensor at the previous timestep. r prev_timestepNr rrz.missing `noise` as a required keyword argumentr.missing `order` as a required keyword argumentr,missing `tau` 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`r=rrr,rryrprrr0r$ zeros_liker-r.rrzrr&rr+) rrrrrrrrrmodel_output_listrnsigma_s0rmalpha_s0ro lambda_s0 gradient_parthrr4sialpha_sisigma_si lambda_sigradient_coefficientsx temp_sigma temp_alpha_s temp_sigma_s temp_lambda_s noise_partx_ts r!stochastic_adams_bashforth_update3SASolverScheduler.stochastic_adams_bashforth_update_s!6$'t9q=QfjjRV6W >4y1}a !RSS =4y1}Q !QRR =4y1}Q !QRR ;4y1}1g !OPP  $ ^  !.. KK!+ , KK ( 77@!99(C99W% '(::IIh'%))H*== ((0   uA1$B!%!=!=dkk"o!N Hh (+eii.AAI   y )  !% 8 88U`be f  ??? "[[1)<= -1-I-I*-U* l % , 7%))L:Q Q %a(iiS!Vx 789!tax1CF #3a#7%))QaZUVTVDW:X#X^_beghbh^hmn]n"ooq!=02( &a(iiS!Vx 789!tax1CF #3a#7%))QaZUVTVDW:X#X^_beghbh^hmn]n"ooq!=02(uAaZii#q& H 456,A./(!a%1 2 1sAv:!8;PQR;S!SVgjknojohpVq!qq  ?? 5::a%))BaK!O2L.L#MMPUUJEIIa!e4Dq4H)IIEQJ ??))c1fIM*g.@AAE UXbbC%*]:ZGCffQWWo r this_model_outputr| last_noise this_samplec  [U5S:aUSOURSS5n Uc [U5S:aUSnO [S5eUc [U5S:aUSnO [S5eUc [U5S:aUSnO [S 5eUc [U5S :aUS nO [S 5eUc [U5S :aUS nO [S 5eU b [SSS5 URn UR UR UR UR S- pURU 5upURU 5up[R"U 5[R"U 5- n[R"U5[R"U 5- n[R"U5nUU- n/n[U5HqnUR U- nURUR U5unn[R"U5[R"U5- nURU5 Ms X/-nURUUUUU5nUnUR(aUS:XaUS==S[R"SUS--U-5-US- USUS---S- [R"SUS--U*-5-SUS--S-U-- - -- ss'US==S[R"SUS--U-5-US- USUS---S- [R"SUS--U*-5-SUS--S-U-- - --ss'[U5HqnUR(a>USUS--U -[R"US-*U-5-UU-UUS-*-- nMRUSUS--*U -UU-UUS-*-- nMs UR(a=U [R "S[R"SUS--U-5- 5-U-nO8Xk-[R "[R"SU-5S- 5-U-nUR(a,[R"US-*U-5X- -U-U-U-nO X- U-U-U-nUR#UR$5nU$)a One step for the SA-Corrector. 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 order of SA-Corrector at this step. Returns: `torch.Tensor`: The corrected sample tensor at the current timestep. r this_timestepNr z4missing `last_sample` as a required keyword argumentrz3missing `last_noise` as a required keyword argumentrz4missing `this_sample` as a required keyword argumentrrrrzPassing `this_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`r=rr)rr.r|r/r0rrrrr2rrnrrmrrorrrrr4r r!r"r#model_prev_listr$r%r*r+s rstochastic_adams_moulton_update1SASolverScheduler.stochastic_adams_moulton_updates>$'t9q=QfjjRV6W  4y1}"1g  !WXX  4y1}!!W  !VWW  4y1}"1g  !WXX =4y1}Q !QRR ;4y1}1g !OPP  $ ^  !.. KK ( KK!+ , 77@!99(C99W% '(::IIh'%))H*== ((5 y  uA1$B!%!=!=dkk"o!N Hh (+eii.AAI   y )  ,.AA $ 8 8 8U`be f  ?? &a(iiS!Vx 7891uQaZ 01 4uyy!c1f*RSQSAT7U U[\_bde_e[ejkZknoZoppr( &a(iiS!Vx 7891uQaZ 01 4uyy!c1f*RSQSAT7U U[\_bde_e[ejkZknoZoppr( uAaZii#q& H 456,A./&Ah/ 0 1sAv:!8;PQR;S!SVehilmhmfnVo!oo  ?? 5::a%))BaK!O2L.L#MMPZZJEIIa!e4Dq4H)IIJVJ ??))c1fIM*g.@AAE UXbbC%*]:ZGCffQWWo r rschedule_timestepscUc URnX!:HR5n[U5S:Xa[UR5S- nU$[U5S:aUSR5nU$USR5nU$)ab Find the index for a given timestep in the schedule. Args: timestep (`int` or `torch.Tensor`): The timestep for which to find the index. schedule_timesteps (`torch.Tensor`, *optional*): The timestep schedule to search in. If `None`, uses `self.timesteps`. Returns: `int`: The index of the timestep in the schedule. rr )rvnonzerorr)rrr7index_candidatesrs rindex_for_timestep$SASolverScheduler.index_for_timestepds$  %!% .:CCE  A %T^^,q0J ! "Q &)!,113J*!,113Jr cURc[[U[R5(a%UR UR R 5nURU5UlgURUlg)z Initialize the step_index counter for the scheduler. Args: timestep (`int` or `torch.Tensor`): The current timestep for which to initialize the step index. N) r isinstancer0Tensorrrvrr;r}r~)rrs r_init_step_index"SASolverScheduler._init_step_indexsZ    #(ELL11#;;t~~'<'<=#66x@D #00D r return_dictc URc [S5eURcURU5 URS:=(a URSLnUR XS9nU(aPUR URS5nURUURURUURUS9n[[URRURRS- 5S- 5HAn UR U S-UR U 'URU S-URU 'MC XpR S'X RS'[#UR$UUR&UR(S9n URR*(a[-URR[/UR05UR- 5n [-URR[/UR05UR- S-5n O,URRn URRn [-XR2S-5Ul[-XR2S -5Ul UR4S:deURS:deX0lXl UR URS5nUR7UUU UR4US 9n UR2[URRURRS- 5:aU=R2S- slU=R8S- slU(dU 4$[;U S 9$) ae Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with the SA-Solver. Args: model_output (`torch.Tensor`): The direct output from 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. generator (`torch.Generator`, *optional*): A random number generator. return_dict (`bool`): Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`. Returns: [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`: If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a tuple is returned where the first element is the sample tensor. NzaNumber of inference steps is 'None', you need to run 'set_timesteps' after creating the schedulerrrr^)r.r|r/r0rrr ) generatorrr+r)rrrrr) prev_sample)rrr,rr@r|rrIrxr5r/this_corrector_orderr-rwrerFrGryr rrr+rNr/rrvr{this_predictor_orderr,r}r)rrrrrDrB use_correctormodel_output_convert current_taur4rrGrFrEs rstepSASolverScheduler.steps<  # # +s  ?? "  ! !( +!+L0@0@0L #888U --(:(:2(>?K99"6 ,,??"// :Fs4;;66 8S8SVW8WX[\\]A$($6$6q1u$=D  q !$($6$6q1u$=D  q !^"62!)2   &&$$   ;; ( (#&t{{'B'BCDWZ^ZiZiDi#j #&t{{'B'BCDWZ^ZiZiDilmDm#n #';;#>#> #';;#>#> $'(<>S>SVW>W$X!$'(<>S>SVW>W$X!((1,,,((1,,,!mmD$6$6r$:; <<-++ =   3t{{'B'BDKKD_D_bcDc#d d  ! !Q & ! A> !;77r cU$)z Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.Tensor`): The input sample. Returns: `torch.Tensor`: A scaled input sample. ra)rrrrs rscale_model_input#SASolverScheduler.scale_model_inputs  r original_samplesrvcURRURS9UlURRURS9nURUR5nXCS-nUR 5n[ UR 5[ UR 5:a?URS5n[ UR 5[ UR 5:aM?SXC- S-nUR 5n[ UR 5[ UR 5:a?URS5n[ UR 5[ UR 5:aM?XQ-Xb--nU$)a Add noise to the original samples according to the noise magnitude at each timestep (this is the forward diffusion process). Args: original_samples (`torch.Tensor`): The original samples to which noise will be added. noise (`torch.Tensor`): The noise to add to the samples. timesteps (`torch.IntTensor`): The timesteps indicating the noise level for each sample. Returns: `torch.Tensor`: The noisy samples. )rr*r"r^r )rlrrr+flattenrrr)rrPrrvrlsqrt_alpha_prodsqrt_one_minus_alpha_prod noisy_sampless r add_noiseSASolverScheduler.add_noisesW2#1144>$=qFF$=>L E$("%9=  ($(",1"%/"&"'', % %%*5\M$( *1S, S,S, S,  S, zzDK/$6 S,S,S,S,T/S,S,%*S, S,S, S,  !S,"!%#S,$%S,&'S,()S,*"+S,,Tz-S,./S,01S,S,j  !!(3(O,O,S5<rus|$ $ A1,XX @H14 1414""<=14 \\ 14hc/ c/r