3j> SSKrSSKJr SSKJr SSKrSSKrSSKJ r J r SSK J r SSK Jr SS KJr \"S S \ 55rSS \S \S\SS\R(4Sjjr"SS\\ 5rg)N) dataclass)Literal) ConfigMixinregister_to_config) BaseOutput) randn_tensor)SchedulerMixincV\rSrSr%Sr\R \S'\R \S'Srg)RePaintSchedulerOutputaH 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_samplepred_original_sampleN) __name__ __module__ __qualname____firstlineno____doc__torchTensor__annotations____static_attributes__ra/home/wildlama/miniconda3/lib/python3.13/site-packages/diffusers/schedulers/scheduling_repaint.pyr r s ,,&rr num_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_fnIs-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,Nsmq#'22TXXa!diiPSVWPWFXBX>X[_>_5``C((3-C99SAG_- -rr!c4[R"US-5$)Ng()r&r!r)s rr+r,Us88AI& &rz"Unsupported alpha_transform_type: r dtype) ValueErrorrangeappendminrtensorfloat32)rrrr+betasit1t2s rbetas_for_alpha_barrB/s0x' D  * .  & '=>R=STUU E * +  (!e. . S\"- R0@@@(KL, <<U]] 33rc \rSrSrSrSr\S$S\S\S\S\ S \S \ RS-S \ 4S jj5r S%S \RS\S-S\R4SjjrS&S\S\S\S\ \R"-4SjjrSrS'S\RS\S \RS\RS\RS\R(S-S\ S\\-4SjjrS%SjrS\RS\RS \R2S\R4S!jrS"rS#rg)(RePaintSchedulerca `RePaintScheduler` is a scheduler for DDPM inpainting inside a given mask. 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`, `squaredcos_cap_v2`, or `sigmoid`. eta (`float`): The weight of noise for added noise in diffusion step. If its value is between 0.0 and 1.0 it corresponds to the DDIM scheduler, and if its value is between -0.0 and 1.0 it corresponds to the DDPM scheduler. trained_betas (`np.ndarray`, *optional*): Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`. clip_sample (`bool`, defaults to `True`): Clip the predicted sample between -1 and 1 for numerical stability. r Nnum_train_timesteps beta_startbeta_end beta_scheduleeta trained_betas clip_samplecUb[R"U5UlOUS:Xa*[R"X#U[RS9UlOUS:Xa4[R"US-US-U[RS9S-UlOsUS:Xa[ U5UlO\US:Xa<[R"SS U5n[R "U5X2- -U-UlO[US UR35eS UR- Ul [R"URS S 9Ul [R"S 5Ul [R"S 5UlS UlSUl[R"["R$"S U5SSS2R'55UlXPlg)Nlinearr6 scaled_linearr.rsquaredcos_cap_v2sigmoidiz is not implemented for ?r)dim)r from_numpyr>linspacer=rBrQNotImplementedError __class__alphascumprodalphas_cumprodr<onefinal_alpha_cumprodinit_noise_sigmanum_inference_stepsnparangecopy timestepsrJ) selfrFrGrHrIrJrKrLr>s r__init__RePaintScheduler.__init__sr  $))-8DJ h & >QY^YfYfgDJ o - C3H[chcpcpquvvDJ 1 1,-@ADJ i 'NN2q*=>Eu-1FG*TDJ%7OPTP^P^O_&`a aDJJ& #mmDKKQ?<<$#(<<#4 !$$( ))"))A7J*KDbD*Q*V*V*XYrsampletimestepr#cU$)aN Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.Tensor`): The input sample. timestep (`int`, *optional*): The current timestep in the diffusion chain. Returns: `torch.Tensor`: A scaled input sample. r)rerhris rscale_model_input"RePaintScheduler.scale_model_inputs  rr` jump_length jump_n_sampledevicec4[URRU5nXl/n0n[ SX- U5H nUS- Xg'M UnUS:aeUS- nUR U5 UR US5S:a1XhS- Xh'[ U5Hn US-nUR U5 M US:aMe[R"U5URRUR--n[R"U5RU5Ul g)u7 Sets the discrete timesteps used for the diffusion chain (to be run before inference). Args: num_inference_steps (`int`): The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` must be `None`. jump_length (`int`, defaults to 10): The number of steps taken forward in time before going backward in time for a single jump (“j” in RePaint paper). Take a look at Figure 9 and 10 in the paper. jump_n_sample (`int`, defaults to 10): The number of times to make a forward time jump for a given chosen time sample. Take a look at Figure 9 and 10 in the paper. device (`str` or `torch.device`, *optional*): The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. rr N) r;configrFr`r9r:getraarrayrrVtord) rer`rmrnrordjumpsjr*_s r set_timestepsRePaintScheduler.set_timestepss 0"$++"A"ACVW#6  q-;[IA$q(EHJ 1fAA   Q yyA" 8a<{+AAA$$Q', 1fHHY'4;;+J+JdNfNf+fg )))477?rcXRRUR-- nURUnUS:aURUO URnSU- nSU- nXe- SX4- - -nU$)Nrr )rqrFr`r\r^)rer* prev_timestep alpha_prod_talpha_prod_t_prev beta_prod_tbeta_prod_t_prevvariances r _get_varianceRePaintScheduler._get_variancesKK;;t?W?WWW **1- BOSTBTD// >Z^ZrZr,& 00%2q<;[7[\r model_outputoriginal_imagemask generator return_dictcUnX RRUR-- n URUn U S:aURU O URn SU - n XZ^ZrZr,& !'c)9L)H HLZ]L]] ;; " "#(;;/CR#K $$\//9[g[m[mnHHt11(;sBB  q5TXX\ 5(H"#%6!6A!E# MP\ \.s25IILaadll-c1^CL]H]beGeinFnn /1S4ZCT4TT $  &2BYmnnrcURRUR-n[U5HnURX%-nUR R S:Xa;[URURUS9nURUR 5nO)[URX1R URS9nSU- S-U-US-U--nM U$)Nmps)r7rrr r.) rqrFr`r9r>rotyper rr7rt)rerhrirnr?betars r undo_stepRePaintScheduler.undo_step[s KK + +t/G/G GqA::hl+D}}!!U*$V\\QZ[/$V\\Y}}djdpdpq$h3&/$)e2CCF roriginal_samplesrrdc[S5e)NzCUse `DDPMScheduler.add_noise()` to train for sampling with RePaint.)rX)rerrrds r add_noiseRePaintScheduler.add_noisels ""ghhrc.URR$N)rqrF)res r__len__RePaintScheduler.__len__ts{{...r) rZr\r>rJr^r_r`r]rd)ig-C6?g{Gz?rNgNTr) rN)NT)rrrrrorderrintfloatstrrandarrayboolrfrrrkrorxr Generatorr tuplerr IntTensorrrrrrrrDrDcs4 E$("%+/ ( (( (  (  (zzD((((T d V[VbVb(%) -@ -@-@ -@ ell" -@^4-1 [oll[o[o [o  [o ll [o??T)[o[o % '[oz"i,,i||i?? i  i/rrD)g+?r )r& dataclassesrtypingrnumpyrarconfiguration_utilsrrutilsrutils.torch_utilsr scheduling_utilsr r rrrrBrDrrrrs ! 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