biddlZddlmZddlmZmZmZddlZddlm Z m Z ddl m Z ddl mZdd lmZ dd ZeGd d e ZGd dee Zy)N) dataclass)OptionalTupleUnion) ConfigMixinregister_to_config) BaseOutput) randn_tensor)SchedulerMixinc $|dk(rd}n|dk(rd}ntd|g}t|D]<}||z }|dz|z }|jtd||||z z |>t j |tj S)a Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [0,1]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar. Choose from `cosine` or `exp` Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs cosinecftj|dzdz tjzdz dzS)NgMb?gT㥛 ?r)mathcospits n/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/diffusers/schedulers/scheduling_consistency_decoder.py alpha_bar_fnz)betas_for_alpha_bar..alpha_bar_fn's-88QY%/$''9A=>!C Cexpc2tj|dzS)Ng()rrrs rrz)betas_for_alpha_bar..alpha_bar_fn,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 * +M ( (!e. . S\"- R0@@@(KLM <<U]] 33rc0eZdZUdZej ed<y)!ConsistencyDecoderSchedulerOutputa> 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____doc__r Tensor__annotations__rrr,r,:srr,cNeZdZdZe ddedefdZ ddeede e e jffdZ ed Zdd e j d eed e j fd Z dde j d e ee j fd e j dee j$ded e eeff dZy)ConsistencyDecoderSchedulerr num_train_timesteps sigma_datact|}d|z }tj|d}tj||_tjd|z |_tjd|z dz }tjd|z }||dzz|dz|dzzz |_||z|dz|dzzdzz |_||dz|dzzdzz |_y)Ng?r)dimr r?) r*r cumprodsqrtsqrt_alphas_cumprodsqrt_one_minus_alphas_cumprodc_skipc_outc_in)selfr7r8r&alphasalphas_cumprodsigmassqrt_recip_alphas_cumprods r__init__z$ConsistencyDecoderScheduler.__init__Ks $$78uv15#(::n#= -2ZZn8L-M*C.0145$)JJs^/C$D!/*a-?619z[\}C\] j(FAI A ,E#+MM -Z]1Js0RR rNnum_inference_stepsdevicec|dk7r tdtjddgtj||_|j j ||_|jj ||_|jj ||_|jj ||_ |jj ||_ y)Nrz8Currently more than 2 inference steps are not supported.ii)rrJ) rr r!long timestepsr>tor?r@rArB)rCrIrJs r set_timestepsz)ConsistencyDecoderScheduler.set_timestepsas ! #WX XtSkFS#'#;#;#>#>v#F -1-O-O-R-RSY-Z*kknnV, ZZ]]6* IILL( rc:|j|jdS)Nr)r?rM)rCs rinit_noise_sigmaz,ConsistencyDecoderScheduler.init_noise_sigmaps11$..2CDDrsampletimestepreturnc&||j|zS)a Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.Tensor`): The input sample. timestep (`int`, *optional*): The current timestep in the diffusion chain. Returns: `torch.Tensor`: A scaled input sample. )rB)rCrRrSs rscale_model_inputz-ConsistencyDecoderScheduler.scale_model_inputts (+++r model_output generator return_dictc<|j||z|j||zz}tj|j|k(d}|t |jdz k(r|}nt |j||j|j} |j|j|dzj|j|z|j|j|dzj|j| zz}|s|fSt|S)a Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion process from the learned model outputs (most often the predicted noise). Args: model_output (`torch.Tensor`): The direct output from the learned diffusion model. timestep (`float`): The current timestep in the diffusion chain. sample (`torch.Tensor`): A current instance of a sample created by the diffusion process. generator (`torch.Generator`, *optional*): A random number generator. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~schedulers.scheduling_consistency_models.ConsistencyDecoderSchedulerOutput`] or `tuple`. Returns: [`~schedulers.scheduling_consistency_models.ConsistencyDecoderSchedulerOutput`] or `tuple`: If return_dict is `True`, [`~schedulers.scheduling_consistency_models.ConsistencyDecoderSchedulerOutput`] is returned, otherwise a tuple is returned where the first element is the sample tensor. rr )rXrrJ)r-)rAr@r whererMlenr shaperrJr>rNr?r,) rCrWrSrRrXrYx_0 timestep_idxr-noises rstepz ConsistencyDecoderScheduler.steps >jj"\1DKK4IF4RR{{4>>X#=>qA 3t~~.2 2K isyyY\YcYcdE(( q8H)IJMMciiX[^^44T^^LSTDT5UVYYZ]ZcZcdgllm  > !0[IIr)ir;)NN)N)NT)r.r/r0orderr intfloatrHrrstrr rJrOpropertyrQr2rV Generatorboolr,rrar4rrr6r6Hs E$(S SSS..2+/ )%c] )c5<<'( )EE, , ,Y^YeYe,,04 /Jll/Ju||+,/J /J EOO, /J  /J 0%7 8/Jrr6)g+?r)r dataclassesrtypingrrrr configuration_utilsrr utilsr utils.torch_utilsr scheduling_utilsr r*r,r6r4rrros[ !)) A,, !)4X     lJ.+lJr