3jr SSKrSSKrSSKJr SSKJr SSKrSSKrSSK J r J r SSK J r Jr SSKJrJr \"5(a SSKrSS KJr \"S S \ 55rSS \S \S\SS\R04Sjjr"SS\\ 5rg)N) dataclass)Literal) ConfigMixinregister_to_config) BaseOutputis_scipy_available)KarrasDiffusionSchedulersSchedulerMixin) integratec`\rSrSr%Sr\R \S'Sr\R S-\S'Sr g)LMSDiscreteSchedulerOutput aM Output class for the scheduler's `step` function output. Args: prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images): Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the denoising loop. pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images): The predicted denoised sample `(x_{0})` based on the model output from the current timestep. `pred_original_sample` can be used to preview progress or for guidance. prev_sampleNpred_original_sample) __name__ __module__ __qualname____firstlineno____doc__torchTensor__annotations__r__static_attributes__rf/home/wildlama/miniconda3/lib/python3.13/site-packages/diffusers/schedulers/scheduling_lms_discrete.pyrr s' 04%,,-4rrnum_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$) a Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [0,1]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): The number of betas to produce. max_beta (`float`, defaults to `0.999`): The maximum beta to use; use values lower than 1 to avoid numerical instability. alpha_transform_type (`str`, defaults to `"cosine"`): The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`. Returns: `torch.Tensor`: The betas used by the scheduler to step the model outputs. r"ch[R"US-S- [R-S- 5S-$)NgMb?gT㥛 ?r)mathcospits r alpha_bar_fn)betas_for_alpha_bar..alpha_bar_fnNs-88QY%/$''9A=>!C Crr$c S[R"SSU- 5-[R"SS[R"SU- 5-- S-5-n[R"U5n[R "USU-- 5$)Ngr ?rgư>)r(copysignlogfabsr#sqrt)r,lmbsnrs rr-r.Ssmq#'22TXXa!diiPSVWPWFXBX>X[_>_5``C((3-C99SAG_- -rr#c4[R"US-5$)Ng()r(r#r+s rr-r.Zs88AI& &rz"Unsupported alpha_transform_type: r dtype) ValueErrorrangeappendminrtensorfloat32)rr r!r-betasit1t2s rbetas_for_alpha_barrD4s0x' D  * .  & '=>R=STUU E * +  (!e. . S\"- R0@@@(KL, <<U]] 33rc\rSrSrSr\VVs/sHoR PM snnrSr\ S8S\ S\ S\ S\ S \ R\\ -S-S \S \S \S \SS\SS\ 4Sjj5r\S\ \R*-4Sj5r\S\ 4Sj5r\S\ 4Sj5rS9S\ SS4SjjrS\R*S\ \R*-S\R*4SjrS\ S\ S\ S\ 4SjrS:S \ S!\ \R8-4S"jjrS:S\ \R*-S#\R*S-S\ 4S$jjrS\ \R*-SS4S%jrS&\ RS'\ RS\ R4S(jr S)\R*S\R*4S*jr!S)\R*S \ S\R*4S+jr"S;S)\R*S \ S,\ S-\ S\R*4 S.jjr#Sr?r@rUrDNotImplementedError __class__alphascumprodalphas_cumprodnparray concatenateastype from_numpysigmasnum_inference_steps set_timesteps derivativesis_scale_input_called _step_index _begin_indexto) selfrHrIrJrKrLrMrNrOrPrTrXrns r__init__LMSDiscreteScheduler.__init__s  ++T[[-O-OQUQ\Q\QnQno pst tA   $m5==IDJ h & >QY^YfYfgDJ o - C3H[chcpcpquvvDJ 1 1,-@ADJ%7OPTP^P^O_&`a aDJJ& #mmDKKQ?A 3 33t7J7JJsRS"u 56==bjjI&&v. $( !2 .5%*" kknnU+ rr%cURRS;aURR5$URR5S-S-S-$)a The standard deviation of the initial noise distribution. Returns: `float` or `torch.Tensor`: The standard deviation of the initial noise distribution, computed based on the maximum sigma value and the timestep spacing configuration. )rUrWrr r0)rcrTrnmaxrvs rinit_noise_sigma%LMSDiscreteScheduler.init_noise_sigmasH ;; ' '+C C;;??$ $ !Q&*s22rcUR$)z The index counter for current timestep. It will increase by 1 after each scheduler step. Returns: `int` or `None`: The current step index, or `None` if not initialized. )rsr{s r step_indexLMSDiscreteScheduler.step_indexsrcUR$)z The index for the first timestep. It should be set from pipeline with `set_begin_index` method. Returns: `int` or `None`: The begin index for the scheduler, or `None` if not set. rtr{s r begin_index LMSDiscreteScheduler.begin_indexs   rrcXlg)z Sets the begin index for the scheduler. This function should be run from pipeline before the inference. Args: begin_index (`int`, defaults to `0`): The begin index for the scheduler. Nr)rvrs rset_begin_index$LMSDiscreteScheduler.set_begin_indexs (rrRtimestepcURcURU5 URURnXS-S-S-- nSUlU$)aV Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.Tensor`): The input sample. timestep (`float` or `torch.Tensor`): The current timestep in the diffusion chain. Returns: `torch.Tensor`: A scaled input sample. rr r0T)r_init_step_indexrnrr)rvrRrsigmas rscale_model_input&LMSDiscreteScheduler.scale_model_inputsP ?? "  ! !( + DOO,1HqLS01%)" rorderr, current_orderc^^^^UUUU4Sjn[R"UTRTTRTS-SS9SnU$)aO Compute the linear multistep coefficient. Args: order (`int`): The order of the linear multistep method. t (`int`): The current timestep index. current_order (`int`): The current order for which to compute the coefficient. Returns: `float`: The computed linear multistep coefficient. c>Sn[T5HHnTU:XaM XTRTU- - TRTT- TRTU- - - -nMJ U$)Nr])r;rn)tauprodkrrrvr,s rlms_derivative@LMSDiscreteScheduler.get_lms_coefficient..lms_derivativesmD5\ A%t{{1q511dkk!mBS6TW[WbWbcdghchWi6ijj"Krr -C6?)epsrelr)r quadrn)rvrr,rrintegrated_coeffs```` rget_lms_coefficient(LMSDiscreteScheduler.get_lms_coefficient sI"  %>>.$++a.$++VWZ[V[J\eijklmrrodevicec XlURRS:XaR[R"SURR S- U[R S9SSS2R5nGOzURRS:XaURR UR-n[R"SU5U-R5SSS2R5R[R 5nX0RR- nOURRS:XaURR UR- n[R"URR SU*5R5R5R[R 5nUS-nO"[URRS 35e[R"SUR- UR- S -5n[R"U5n[R "U[R"S[#U55U5nURR$(aEUR'US 9n[R"UVs/sHopR)Xv5PM sn5nOURR*(aEUR-XQS 9n[R"UVs/sHopR)Xv5PM sn5nO_URR.(aDUR1XQS 9n[R"UVs/sHopR)Xv5PM sn5n[R2"US //5R[R 5n[4R6"U5R9US9Ul[4R6"U5R9U[4R S9UlSUlSUl UR:R9S5Ul/Ul!gs snfs snfs snf)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. rUrr r8Nr_rVrWzY is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.r0) in_sigmas)rror`)rrr9ra)"rorcrTrirUrHr?copyarangeroundrlrXr:rjrhr2interplenrM_convert_to_karras _sigma_to_trN_convert_to_exponentialrO_convert_to_betarkrrmrurn timestepsrsrtrq)rvrorr step_ratiorn log_sigmasrs rrp"LMSDiscreteScheduler.set_timesteps(sd$7  ;; ' ': 5 At{{'F'F'JL_gigqgqr"df [[ ) )Y 688DFSY!ZSY%"2"25"ESY!Z[I [[ / /11F1lFSY!ZSY%"2"25"ESY!Z[I [[ ( (**V*eFSY!ZSY%"2"25"ESY!Z[I#077 C&&v.111@ )))477vU]]7[ kknnU+ !"["["[s*Q Q*Q schedule_timestepscUc URnX!:HR5n[U5S:aSOSnX4R5$)a Find the index of a given timestep in the timestep schedule. Args: timestep (`float` or `torch.Tensor`): The timestep value to find in the schedule. 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. For the very first step, returns the second index if multiple matches exist to avoid skipping a sigma when starting mid-schedule (e.g., for image-to-image). r r)rnonzeroritem)rvrrindicesposs rindex_for_timestep'LMSDiscreteScheduler.index_for_timestepcsJ"  %!% %1::< w,LMSDiscreteScheduler.step..bs# 8c#45E 8cs)rr)rrwarningswarnrrrnrcrPr:rqr<rpopr=r;rrbzipreversedrsr) rvrrrRrrrrr curr_order lms_coeffsrs rstepLMSDiscreteScheduler.step!s<)) MME  ?? "  ! !( + DOO, ;; & &) 3#)L,@#@ [[ ( (N :#/6UAX\c'>FVF\F\]  " " ' '5 0U5L5LY5W5W!%!2!23C3J3JRWR_R_!2!` ! %5%<%>$=qFF$=>L E$("%9="'', %HQGQ/, /,/, /,  /, zzDK/$6 /, /,!%/,/,!!DE/,""CD/,/,/,b 3%%,,"6 3 3 C  !S!!(3(t(  @TY^YeYe0   S U :88cELL>P8xY]# ,#BG,,QUBU# #> 1)= 1$ 1 """ "HELLU\\2TW\a\h\hFdg..rs ! A2G 55 5(@H14 1414""<=14 \\ 14h{/>;{/r