i  ddlZddlmZddlmZmZmZmZddlZddl m Z m Z ddl m Z ddlmZdd lmZ dd ed ed ed dej(fdZeGdde ZGddee Zy)N) dataclass)LiteralOptionalTupleUnion) ConfigMixinregister_to_config) BaseOutput) randn_tensor)SchedulerMixinnum_diffusion_timestepsmax_betaalpha_transform_type)cosineexpreturnc $|dk(rd}n|dk(rd}ntd|g}t|D]<}||z }|dz|z }|jtd||||z z |>t j |tj S)aB Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [0,1]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): The number of betas to produce. max_beta (`float`, defaults to `0.999`): The maximum beta to use; use values lower than 1 to avoid numerical instability. alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`): The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`. Returns: `torch.Tensor`: The betas used by the scheduler to step the model outputs. rcftj|dzdz tjzdz dzS)NgMb?gT㥛 ?r)mathcospits }/home/obispo/Crisostomo_bridge/mision_env/lib/python3.12/site-packages/diffusers/schedulers/scheduling_consistency_decoder.py alpha_bar_fnz)betas_for_alpha_bar..alpha_bar_fn(s-88QY%/$''9A=>!C Crc2tj|dzS)Ng()rrrs rrz)betas_for_alpha_bar..alpha_bar_fn-s88AI& &rz"Unsupported alpha_transform_type: r )dtype) ValueErrorrangeappendmintorchtensorfloat32)rrrrbetasit1t2s rbetas_for_alpha_barr,s0x' 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$ded e eeff dZy)ConsistencyDecoderSchedulerr num_train_timesteps sigma_datact|}d|z }tj|d}tj||_tjd|z |_tjd|z dz }tjd|z }||dzz|dz|dzzz |_||z|dz|dzzdzz |_||dz|dzzdzz |_y)Ng?r)dimr r?) r,r%cumprodsqrtsqrt_alphas_cumprodsqrt_one_minus_alphas_cumprodc_skipc_outc_in)selfr9r:r(alphasalphas_cumprodsigmassqrt_recip_alphas_cumprods r__init__z$ConsistencyDecoderScheduler.__init__Ls $$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)r rL) r!r%r&long timestepsr@torArBrCrD)rErKrLs r set_timestepsz)ConsistencyDecoderScheduler.set_timestepsbs ! #WX XtSkFS#'#;#;#>#>v#F -1-O-O-R-RSY-Z*kknnV, ZZ]]6* IILL( rc:|j|jdS)Nr)rArO)rEs rinit_noise_sigmaz,ConsistencyDecoderScheduler.init_noise_sigmaqs11$..2CDDrsampletimesteprc&||j|zS)a Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.Tensor`): The input sample. timestep (`int`, *optional*): The current timestep in the diffusion chain. Returns: `torch.Tensor`: A scaled input sample. )rD)rErTrUs rscale_model_inputz-ConsistencyDecoderScheduler.scale_model_inputus (+++r model_output generator return_dictc<|j||z|j||zz}tj|j|k(d}|t |jdz k(r|}nt |j||j|j} |j|j|dzj|j|z|j|j|dzj|j| zz}|s|fSt|S)a Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion process from the learned model outputs (most often the predicted noise). Args: model_output (`torch.Tensor`): The direct output from the learned diffusion model. timestep (`float`): The current timestep in the diffusion chain. sample (`torch.Tensor`): A current instance of a sample created by the diffusion process. generator (`torch.Generator`, *optional*): A random number generator. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~schedulers.scheduling_consistency_models.ConsistencyDecoderSchedulerOutput`] or `tuple`. Returns: [`~schedulers.scheduling_consistency_models.ConsistencyDecoderSchedulerOutput`] or `tuple`: If return_dict is `True`, [`~schedulers.scheduling_consistency_models.ConsistencyDecoderSchedulerOutput`] is returned, otherwise a tuple is returned where the first element is the sample tensor. rr )rYr rL)r/)rCrBr%whererOlenr shaper rLr@rPrAr.) rErXrUrTrYrZx_0 timestep_idxr/noises rstepz ConsistencyDecoderScheduler.steps >jj"\1DKK4IF4RR{{4>>X#=>qA 3t~~.2 2K isyyY\YcYcdE(( q8H)IJMMciiX[^^44T^^LSTDT5UVYYZ]ZcZcdgllm  > !0[IIr)ir=)NN)N)NT)r0r1r2orderr intfloatrJrrstrr%rLrQpropertyrSr4rW Generatorboolr.rrbr6rrr8r8Is E$(S SSS..2+/ )%c] )c5<<'( )EE, , ,Y^YeYe,,04 /Jll/Ju||+,/J /J EOO, /J  /J 0%7 8/Jrr8)g+?r)r dataclassesrtypingrrrrr%configuration_utilsr r utilsr utils.torch_utilsr scheduling_utilsrrdrer4r,r.r8r6rrrps !22 A,, 5=*4 *4*4"/2*4 \\ *4Z     lJ.+lJr