+hfSSKrSSKJr SSKJrJrJrJr SSKr SSK r SSK J r J r SSKJrJr SSKJrJr \"5(aSSKr\"S S \55rSS jr"S S \\ 5rg)N) dataclass)ListOptionalTupleUnion) ConfigMixinregister_to_config) BaseOutputis_scipy_available)KarrasDiffusionSchedulersSchedulerMixinc`\rSrSr%Sr\R \S'Sr\ \R \S'Sr g)KDPM2DiscreteSchedulerOutputaM 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__rr__static_attributes__rj/home/james-whalen/.local/lib/python3.13/site-packages/diffusers/schedulers/scheduling_k_dpm_2_discrete.pyrrs' 37(5<<07rrc &US:XaSnOUS:XaSnO[SU35e/n[U5H<nXP- nUS-U- nUR[SU"U5U"U5- - U55 M> [R "U[R S9$)a Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [0,1]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar. Choose from `cosine` or `exp` Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs cosinech[R"US-S- [R-S- 5S-$)NgMb?gT㥛 ?r)mathcospits r alpha_bar_fn)betas_for_alpha_bar..alpha_bar_fnLs-88QY%/$''9A=>!C Crexpc4[R"US-5$)Ng()r$r+r's r r)r*Qs88AI& &rz"Unsupported alpha_transform_type: r dtype) ValueErrorrangeappendminrtensorfloat32)num_diffusion_timestepsmax_betaalpha_transform_typer)betasit1t2s r betas_for_alpha_barr<3s.x' D  & '=>R=STUU E * +  (!e. . S\"- R0@@@(KL, <<U]] 33rc\rSrSrSr\VVs/sHoR PM snnrSr\ S0S\ S\ S\ S\ S \ \\R \\ 4S \ \S \ \S \ \S \ S\ S\ 4Sjj5r\S5r\S5r\S5rS1S\ 4SjjrS\R4S\\ \R44S\R44SjrS2S\ S\\ \R84S\ \ 4Sjjr\S5rS3SjrSr S r!S!\R4S\R44S"jr"S!\R4S\ S\R44S#jr#S4S!\R4S\ S$\ S%\ S\R44 S&jjr$S5S'\\R4\R 4S\\ \R44S\\R4\R 4S(\S\\%\&44 S)jjr'S*\R4S+\R4S,\R4S\R44S-jr(S.r)S/r*gs snnf)6KDPM2DiscreteScheduler_u KDPM2DiscreteScheduler is inspired by the DPMSolver2 and Algorithm 2 from the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper. 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 0.00085): The starting `beta` value of inference. beta_end (`float`, defaults to 0.012): 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` or `scaled_linear`. trained_betas (`np.ndarray`, *optional*): Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`. 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. 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://imagen.research.google/video/paper.pdf) paper). 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. rNnum_train_timesteps beta_startbeta_end beta_schedule trained_betasuse_karras_sigmasuse_exponential_sigmasuse_beta_sigmasprediction_typetimestep_spacing steps_offsetc URR(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 9UlUR'USU5 SUlSUlUR,R/S5Ulg)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-linear scaled_linear?rsquaredcos_cap_v2z is not implemented for g?rdimcpu)configrGr ImportErrorsumrFrEr/rr3r4r8linspacer<NotImplementedError __class__alphascumprodalphas_cumprod set_timesteps _step_index _begin_indexsigmasto) selfr@rArBrCrDrErFrGrHrIrJs r __init__KDPM2DiscreteScheduler.__init__s{ ;; & &/A/C/CZ[ [  ++T[[-O-OQUQ\Q\QnQno pst tA   $m5==IDJ h & >QY^YfYfgDJ o - C3H[chcpcpquvvDJ 1 1,-@ADJ%7OPTP^P^O_&`a aDJJ& #mmDKKQ? .6IJ kknnU+ rcURRS;aURR5$URR5S-S-S-$)N)rVtrailingrr rN)rSrIr_maxras r init_noise_sigma'KDPM2DiscreteScheduler.init_noise_sigmasH ;; ' '+C C;;??$ $ !Q&*s22rcUR$)zW The index counter for current timestep. It will increase 1 after each scheduler step. )r]rgs r step_index!KDPM2DiscreteScheduler.step_indexs rcUR$)za The index for the first timestep. It should be set from pipeline with `set_begin_index` method. r^rgs r begin_index"KDPM2DiscreteScheduler.begin_indexs    rrocXlg)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. Nrn)raros r set_begin_index&KDPM2DiscreteScheduler.set_begin_indexs (rsampletimestepreturncURcURU5 UR(aURURnOURURnXS-S-S-- nU$)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. rr rN)rk_init_step_indexstate_in_first_orderr_sigmas_interpol)rartrusigmas r scale_model_input(KDPM2DiscreteScheduler.scale_model_inputse& ?? "  ! !( +  $ $KK0E((9E1HqLS01 rnum_inference_stepsdevicec  XlU=(d URRnURRS:Xa>[R "SUS- U[R S9SSS2R5nGO@S[][e[efgkikgklqqsI [[ ) )Y 6,0H0HHJ1&9:ZGNNPQUSUQUV[[]ddegeoeopI 11 1I [[ ) )Z 7,/G/GGJ#6J;GNNPUUW^^_a_i_ijI NI;;//01JK A 3 33t7J7JJsRSVVF^ 9bii3v;&?H ;; ( (,,v,gFSY!ZSY%"2"25"ESY!Z[aacI [[ / /11F1lFSY!ZSY%"2"25"ESY!Z[I [[ ( (**V*eFSY!ZSY%"2"25"ESY!Z[I**:6999H#077 C!!&),,F,;!**,++FKKN,>,>,@#FJJLiiVABZ-I-I!-LfUWUXk Z[ $yy Ra /!""5"G"G"JO\^\_L` a $$Y/226: *--/__((* XXP_ `P_n  n 9P_ ` #--.@ADDVSbSbDc % -?"d -KYWXWY[_W_M`,agi j r r tIbqM3H#IJ  kknnU+ K"["["[( as7W%WW"W'cURSL$N)rtrgs r ry+KDPM2DiscreteScheduler.state_in_first_orderHs{{d""rcUc URnX!:HR5n[U5S:aSOSnX4R5$)Nr r)rnonzeroritem)raruschedule_timestepsindicesposs r index_for_timestep)KDPM2DiscreteScheduler.index_for_timestepMsH  %!% %1::< wTUCUZ]B]3]#^q.1,-$ [[ ( (H 4%&ST T+DKK,G,G+HHtu   $ $ 79DJ+B!K!7>IJ'B[[FDK AB. $  , oororiginal_samplesnoisercURRURURS9nURRS:Xav[ R "U5(a[URRUR[ RS9nURUR[ RS9nO@URRUR5nURUR5nURc!UVs/sHo`RXe5PM nnOHURbUR/URS-nOUR/URS-nXGR5n[UR5[UR5:a?URS5n[UR5[UR5:aM?XU--n U $s snf)N)rr.mpsr-rr)r_r`rr.typeris_floating_pointrr4rorrkrrr unsqueeze) rarrrr_rr( step_indicesr{ noisy_sampless r add_noise KDPM2DiscreteScheduler.add_noise+s'7'>'>FVF\F\]  " " ' '5 0U5L5LY5W5W!%!2!23C3J3JRWR_R_!2!` ! %5%<%r>_s$L%>>$=qFF$=>L E$(#%BF,116*/( *), ),), ),  ),  bjj$u+&= >? ),$D>),!)),"$),),),),),V33  !!(3( u||+,  B,0-1 R, R,c5<<'(R,&c] R,h## #10ELLRWR^R^4TW\a\h\h.dg)g+?r")r$ dataclassesrtypingrrrrnumpyrrconfiguration_utilsr r utilsr r scheduling_utilsrr scipy.statsrrr<r>rrr rsi !// A2G 8:8 8(!)4Xn/^[n/r