+hSSKrSSKJr SSKJrJrJr SSKrSSKJ r J r SSK J r SSK Jr SS KJr SS jr\"S S \ 55r"S S\\ 5rg)N) dataclass)OptionalTupleUnion) ConfigMixinregister_to_config) BaseOutput) randn_tensor)SchedulerMixinc &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 m/home/james-whalen/.local/lib/python3.13/site-packages/diffusers/schedulers/scheduling_consistency_decoder.py alpha_bar_fn)betas_for_alpha_bar..alpha_bar_fn's-88QY%/$''9A=>!C Cexpc4[R"US-5$)Ng()rrrs rrr,s88AI& &rz"Unsupported alpha_transform_type: r )dtype) ValueErrorrangeappendmintorchtensorfloat32)num_diffusion_timestepsmax_betaalpha_transform_typerbetasit1t2s rbetas_for_alpha_barr+s.x' D  & '=>R=STUU E * +  (!e. . S\"- R0@@@(KL, <<U]] 33rc8\rSrSr%Sr\R \S'Srg)!ConsistencyDecoderSchedulerOutput:a& Output class for the scheduler's `step` function. 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. prev_sampleN) __name__ __module__ __qualname____firstlineno____doc__r!Tensor__annotations____static_attributes__r0rrr-r-:srr-cf\rSrSrSr\SS\S\4Sjj5rSS\ \S\ \ \ R44S jjr\S 5rSS \ R"S \ \S \ R"4SjjrSS\ R"S \ \\ R"4S \ R"S\ \ R&S\S \ \\44 SjjrSrg)ConsistencyDecoderSchedulerHr num_train_timesteps sigma_datac[U5nSU- n[R"USS9n[R"U5Ul[R"SU- 5Ul[R"SU- S- 5n[R"SU- 5nXrS--US-US--- UlXb-US-US--S-- UlXvS-US--S-- Ulg)Ng?r)dimr r?) r+r!cumprodsqrtsqrt_alphas_cumprodsqrt_one_minus_alphas_cumprodc_skipc_outc_in)selfr<r=r'alphasalphas_cumprodsigmassqrt_recip_alphas_cumprods r__init__$ConsistencyDecoderScheduler.__init__Ks $$78uv15#(::n#= -2ZZn8L-M*C.0145$)JJs^/C$D!/a-?619z[\}C\] (FAI A ,E#+MM -Z]1Js0RR rNnum_inference_stepsdevicecUS:wa [S5e[R"SS/[RUS9UlUR R U5UlURR U5UlURR U5UlURR U5Ul URR U5Ul g)Nrz8Currently more than 2 inference steps are not supported.ii)rrP) rr!r"long timestepsrCtorDrErFrG)rHrOrPs r set_timesteps)ConsistencyDecoderScheduler.set_timestepsas ! #WX XtSkFS#'#;#;#>#>v#F -1-O-O-R-RSY-Z*kknnV, ZZ]]6* IILL( rc:URURS$)Nr)rDrS)rHs rinit_noise_sigma,ConsistencyDecoderScheduler.init_noise_sigmaps11$..2CDDrsampletimestepreturnc$XRU-$)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. )rG)rHrZr[s rscale_model_input-ConsistencyDecoderScheduler.scale_model_inputts (+++r model_output generator return_dictc<URUU-URUU--n[R"URU:H5SnU[ UR5S- :XaUnO[ URXFRURS9n URURUS-RUR5U-URURUS-RUR5U --nU(dU4$[US9$)a& Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion process from the learned model outputs (most often the predicted noise). Args: model_output (`torch.Tensor`): The direct output from the learned diffusion model. timestep (`float`): The current 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`, *optional*, defaults to `True`): Whether or not to return a [`~schedulers.scheduling_consistency_models.ConsistencyDecoderSchedulerOutput`] or `tuple`. Returns: [`~schedulers.scheduling_consistency_models.ConsistencyDecoderSchedulerOutput`] or `tuple`: If return_dict is `True`, [`~schedulers.scheduling_consistency_models.ConsistencyDecoderSchedulerOutput`] is returned, otherwise a tuple is returned where the first element is the sample tensor. rr )rarrP)r/)rFrEr!whererSlenr shaperrPrCrTrDr-) rHr`r[rZrarbx_0 timestep_idxr/noises rstep ConsistencyDecoderScheduler.steps>jj"\1DKK4IF4RR{{4>>X#=>qA 3t~~.2 2K iyyY\YcYcdE(( q8H)IJMMciiX[^^44T^^LSTDT5UVYYZ]ZcZcdgllm  > !0[IIr)rGrFrErCrDrS)ir@)NN)N)NT)r1r2r3r4orderr intfloatrMrrstrr!rPrUpropertyrXr6r^ Generatorboolr-rrjr8r0rrr:r:Hs% E$(S SSS..2+/ )%c] )c5<<'( )EE, , ,Y^YeYe,,04 /Jll/Ju||+,/J /J EOO, /J  /J 0%7 8/J/Jrr:)g+?r)r dataclassesrtypingrrrr!configuration_utilsrr utilsr utils.torch_utilsr scheduling_utilsr r+r-r:r0rrrys[ !)) A,, !)4X     lJ.+lJr