wi"ddlZddlmZddlmZmZmZmZddlZddl m Z m Z ddl m Z ddlmZdd lmZeGd d e Z ddZGddee ZdS)N) dataclass)ListOptionalTupleUnion) ConfigMixinregister_to_config) BaseOutput) randn_tensor)SchedulerMixinc(eZdZUdZejed<dS)DDPMWuerstchenSchedulerOutputaA 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. prev_sampleN)__name__ __module__ __qualname____doc__torchTensor__annotations__/root/.openclaw/workspace/chatterbox_venv_py311/lib/python3.11/site-packages/diffusers/schedulers/scheduling_ddpm_wuerstchen.pyrrs,rr+?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)NMb?gT㥛 ?r)mathcospits r alpha_bar_fnz)betas_for_alpha_bar..alpha_bar_fnEs,8QY%/$'9A=>>!C Crexpc0tj|dzS)Ng()r!r'r$s rr&z)betas_for_alpha_bar..alpha_bar_fnJs8AI&& &rz"Unsupported alpha_transform_type: r dtype) ValueErrorrangeappendminrtensorfloat32)num_diffusion_timestepsmax_betaalpha_transform_typer&betasit1t2s rbetas_for_alpha_barr8,s.x'' D D D D  & & ' ' ' 'T>RTTUUU E * + +MM ( (!e. . S\\"-- R0@0@@@(KKLLLL <U] 3 3 33rc TeZdZdZe ddedefdZdZdd ej d e e d ej fd Z dd e de e e deeejffdZ ddej d e d ej ded eeeff dZdej dej dej d ej fdZdZdZdS)DDPMWuerstchenSchedulera Denoising diffusion probabilistic models (DDPMs) explores the connections between denoising score matching and Langevin dynamics sampling. [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. For more details, see the original paper: https://arxiv.org/abs/2006.11239 Args: scaler (`float`): .... s (`float`): .... ?r scalersc||_tj|g|_tj|jd|jzz tjzdzdz|_d|_dS)Nr ?rr;)r<rr/r=r"r#_init_alpha_cumprodinit_noise_sigma)selfr<r=s r__init__z DDPMWuerstchenScheduler.__init__is`  qc""#(9TVq46z-BUX-MPS-S#T#TXY#Y !$rc|jdkrdd|z |jzz }n|jdkr ||jz}tj||j|zd|j|zz tjzdzdz|j|z }|ddS)Nr r?rg-C6?gH.?)r<rr"r=tor#r@clamp)rBr%device alpha_cumprods r_alpha_cumprodz&DDPMWuerstchenScheduler._alpha_cumprodvs ;??QUt{**AA [1__4;A 6"" "q4699V+<+<'< = H3 N  ),,V445 ""66222rNsampletimestepreturnc|S)aP Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.Tensor`): input sample timestep (`int`, optional): current timestep Returns: `torch.Tensor`: scaled input sample r)rBrJrKs rscale_model_inputz)DDPMWuerstchenScheduler.scale_model_inputs  rnum_inference_steps timestepsrGc|tjdd|dz|}t|tjs'tj||}||_dS)a Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference. Args: num_inference_steps (`Dict[float, int]`): the number of diffusion steps used when generating samples with a pre-trained model. If passed, then `timesteps` must be `None`. device (`str` or `torch.device`, optional): the device to which the timesteps are moved to. {2 / 3: 20, 0.0: 10} Nr;gr rG)rlinspace isinstancerrErP)rBrOrPrGs r set_timestepsz%DDPMWuerstchenScheduler.set_timestepsse  sC1Dq1HQWXXXI)U\22 ; Y//226::I"rT model_output return_dictc |j}|j}|}||} |||j|dgd|jddDR} || |j| dgd|jddDR} | | z } d| z |d| z |zd| z z z z} t| j||j|j}d| z d| z zd| z z |z}| || dk j| dgd|jddDRzz}|s| |fSt| | S) a~ Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion process from the learned model outputs (most often the predicted noise). Args: model_output (`torch.Tensor`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`torch.Tensor`): current instance of sample being created by diffusion process. generator: random number generator. return_dict (`bool`): option for returning tuple rather than DDPMWuerstchenSchedulerOutput class Returns: [`DDPMWuerstchenSchedulerOutput`] or `tuple`: [`DDPMWuerstchenSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. rcg|]}dSr r.0_s r z0DDPMWuerstchenScheduler.step..sHeHeHeqHeHeHerr Ncg|]}dSrZrr[s rr^z0DDPMWuerstchenScheduler.step..sWtWtWt^_XYWtWtWtrr;) generatorrGr*cg|]}dSrZrr[s rr^z0DDPMWuerstchenScheduler.step..sFcFcFcQqFcFcFcr)r) r*rGprevious_timesteprIviewsizeshapesqrtr floatrEr)rBrVrKrJr`rWr*rGr%prev_trHalpha_cumprod_prevalphamu std_noisestdpreds rstepzDDPMWuerstchenScheduler.steps2"$ ''**;++Av66;AFF1IIfHeHeTZT`abacacTdHeHeHefff ET00@@EfkkRSnnuWtWtcicopqprprcsWtWtWtuuu 22Ek   ! !Vq5yL.HAP]L]KcKcKeKe.e%e f Y|GZbnbtuuu E c$6673;NOUUWWZccC46Q;--//4V[[^^dFcFcRXR^_`_a_aRbFcFcFcddddd %GGENN$ $,HHHHroriginal_samplesnoisecH|j}|j}|||j|dgd|jddDR}||zd|z |zz}||S)NrRrcg|]}dSrZrr[s rr^z5DDPMWuerstchenScheduler.add_noise..s G G Gq G G Grr r))rGr*rIrcrdrerfrE)rBrprqrPrGr*rH noisy_sampless r add_noisez!DDPMWuerstchenScheduler.add_noises "( &J++If+EEJ NN1   G G,<,B122,F G G G   &**,,/??1}CTBZBZB\B\_dBdd e,,,rc|jjSN)confignum_train_timesteps)rBs r__len__zDDPMWuerstchenScheduler.__len__s {..rc|j|dz }|j|dzd|jd}|S)Nrr )rPabsargminitemexpandre)rBrKindexrhs rrbz)DDPMWuerstchenScheduler.previous_timestepse(1+-2244;;==BBDD *4077q8IJJ r)r;r rw)NNN)NT)rrrrr rgrCrIrrrintrNrrstrrGrUboolrrrorurzrbrrrr:r:Xs  $ $ $  $ $ $ $333    Y^Ye     $()-+/ ## #DI&#c5<'( ####6 ,I,Il,I,I ,I  ,I ,e3 4,I,I,I,I\ -, -| -< -  - - - -///rr:)rr)r! dataclassesrtypingrrrrrconfiguration_utilsr r utilsr utils.torch_utilsr scheduling_utilsrrr8r:rrrrs0$ !!!!!!//////////// AAAAAAAA,,,,,,,,,,,,      J    !)4)4)4)4XNNNNNnkNNNNNr