widdlZddlmZddlmZmZmZddlZddlm Z m Z ddl m Z ddl mZdd lmZ dd ZeGd de ZGddee ZdS)N) dataclass)OptionalTupleUnion) ConfigMixinregister_to_config) BaseOutput) randn_tensor)SchedulerMixin+?cosinec F|dkrd}n|dkrd}ntd|g}t|D]J}||z }|dz|z }|td||||z z |Kt j|tjS)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 rc\tj|dzdz tjzdz dzS)NgMb?gT㥛 ?r)mathcospits /root/.openclaw/workspace/chatterbox_venv_py311/lib/python3.11/site-packages/diffusers/schedulers/scheduling_consistency_decoder.py alpha_bar_fnz)betas_for_alpha_bar..alpha_bar_fn's,8QY%/$'9A=>>!C Cexpc0tj|dzS)Ng()rrrs rrz)betas_for_alpha_bar..alpha_bar_fn,s8AI&& &rz"Unsupported alpha_transform_type: r )dtype) ValueErrorrangeappendmintorchtensorfloat32)num_diffusion_timestepsmax_betaalpha_transform_typerbetasit1t2s rbetas_for_alpha_barr+s.x'' D D D D  & & ' ' ' 'T>RTTUUU E * + +MM ( (!e. . S\\"-- R0@0@@@(KKLLLL <U] 3 3 33rc(eZdZUdZejed<dS)!ConsistencyDecoderSchedulerOutputa> Output class for the scheduler's `step` function. Args: prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images): Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the denoising loop. prev_sampleN)__name__ __module__ __qualname____doc__r!Tensor__annotations__rrr-r-:s,rr-c:eZdZdZe ddedefdZ ddeed e e e j ffd Z ed Zdd e jd eede jfdZ dde jd e ee jfd e jdee jdede eeff dZdS)ConsistencyDecoderSchedulerr ?num_train_timesteps sigma_datact|}d|z }tj|d}tj||_tjd|z |_tjd|z dz }tjd|z }||dzz|dz|dzzz |_||z|dz|dzzdzz |_||dz|dzzdzz |_dS)Ng?r)dimr rr9) r+r!cumprodsqrtsqrt_alphas_cumprodsqrt_one_minus_alphas_cumprodc_skipc_outc_in)selfr:r;r'alphasalphas_cumprodsigmassqrt_recip_alphas_cumprods r__init__z$ConsistencyDecoderScheduler.__init__Ks $$788uv1555#(:n#=#= -2Zn8L-M-M*C.01455$)Js^/C$D$D!/*a-?619z[\}C\] j(FAI A ,E#+MM -Z]1Js0RR rNnum_inference_stepsdevicec|dkrtdtjddgtj||_|j||_|j||_|j||_|j ||_ |j ||_ dS)Nrz8Currently more than 2 inference steps are not supported.ii)rrL) rr!r"long timestepsr@torArBrCrD)rErKrLs r set_timestepsz)ConsistencyDecoderScheduler.set_timestepsas ! # #WXX XtSkFSSS#'#;#>#>v#F#F -1-O-R-RSY-Z-Z*knnV,, Z]]6** ILL(( rc2|j|jdS)Nr)rArO)rEs rinit_noise_sigmaz,ConsistencyDecoderScheduler.init_noise_sigmaps1$.2CDDrsampletimestepreturnc"||j|zS)a Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.Tensor`): The input sample. timestep (`int`, *optional*): The current timestep in the diffusion chain. Returns: `torch.Tensor`: A scaled input sample. )rD)rErTrUs rscale_model_inputz-ConsistencyDecoderScheduler.scale_model_inputts (+++rT model_output generator return_dictc|j||z|j||zz}tj|j|kd}|t |jdz kr|}nt |j||j|j } |j |j|dz |j|z|j |j|dz |j| zz}|s|fSt|S)a Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion process from the learned model outputs (most often the predicted noise). Args: model_output (`torch.Tensor`): The direct output from the learned diffusion model. timestep (`float`): The current timestep in the diffusion chain. sample (`torch.Tensor`): A current instance of a sample created by the diffusion process. generator (`torch.Generator`, *optional*): A random number generator. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~schedulers.scheduling_consistency_models.ConsistencyDecoderSchedulerOutput`] or `tuple`. Returns: [`~schedulers.scheduling_consistency_models.ConsistencyDecoderSchedulerOutput`] or `tuple`: If return_dict is `True`, [`~schedulers.scheduling_consistency_models.ConsistencyDecoderSchedulerOutput`] is returned, otherwise a tuple is returned where the first element is the sample tensor. rr )rZrrL)r.)rCrBr!whererOlenr shaperrLr@rPrAr-) rErYrUrTrZr[x_0 timestep_idxr.noises rstepz ConsistencyDecoderScheduler.steps >j"\1DK4IF4RR{4>X#=>>qA 3t~..2 2 2KK isyY\YcdddE( q8H)IJMMciXX[^^4T^LSTDT5UVYYZ]Zcddgllm   "> !0[IIIIr)r8r9)NN)N)NT)r/r0r1orderr intfloatrJrrstrr!rLrQpropertyrSr3rX Generatorboolr-rrcr5rrr7r7Hs E$(SS SSSSS..2+/ ) )%c] )c5<'( ) ) ) )EEXE,, , ,Y^Ye,,,,,04 /J/Jl/Ju|+,/J /J EO, /J  /J 0%7 8/J/J/J/J/J/Jrr7)rr)r dataclassesrtypingrrrr!configuration_utilsrr utilsr utils.torch_utilsr scheduling_utilsr r+r-r7r5rrrqs2 !!!!!!)))))))))) AAAAAAAA,,,,,,,,,,,, !)4)4)4)4X          lJlJlJlJlJ.+lJlJlJlJlJr