wi;ddlZddlmZddlmZmZmZddlZddl Z ddl m Z m Z ddl mZddlmZdd lmZeGd d eZ ddZGddee ZdS)N) dataclass)OptionalTupleUnion) ConfigMixinregister_to_config) BaseOutput) randn_tensor)SchedulerMixinc<eZdZUdZejed<ejed<dS)RePaintSchedulerOutputal 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. pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images): The predicted denoised sample (x_{0}) based on the model output from the current timestep. `pred_original_sample` can be used to preview progress or for guidance. prev_samplepred_original_sampleN)__name__ __module__ __qualname____doc__torchTensor__annotations__w/root/.openclaw/workspace/chatterbox_venv_py311/lib/python3.11/site-packages/diffusers/schedulers/scheduling_repaint.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)NgMb?gT㥛 ?r)mathcospits r alpha_bar_fnz)betas_for_alpha_bar..alpha_bar_fnHs,8QY%/$'9A=>>!C Crexpc0tj|dzS)Ng()r r&r#s rr%z)betas_for_alpha_bar..alpha_bar_fnMs8AI&& &rz"Unsupported alpha_transform_type: r dtype) ValueErrorrangeappendminrtensorfloat32)num_diffusion_timestepsmax_betaalpha_transform_typer%betasit1t2s rbetas_for_alpha_barr7/s.x'' D D D D  & & ' ' ' 'T>RTTUUU E * + +MM ( (!e. . S\\"-- R0@0@@@(KKLLLL <U] 3 3 33rceZdZdZdZe d)d ed ed ed edede e j de fdZ d*dejde edejfdZ d+dedededeeejffdZdZ d,dejdedejdejdejd e ejd!e deeeffd"Zd*d#Zd$ejd%ejd&ejdejfd'Zd(ZdS)-RePaintSchedulerah `RePaintScheduler` is a scheduler for DDPM inpainting inside a given mask. This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic methods the library implements for all schedulers such as loading and saving. Args: num_train_timesteps (`int`, defaults to 1000): The number of diffusion steps to train the model. beta_start (`float`, defaults to 0.0001): The starting `beta` value of inference. beta_end (`float`, defaults to 0.02): The final `beta` value. beta_schedule (`str`, defaults to `"linear"`): The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear`, `scaled_linear`, `squaredcos_cap_v2`, or `sigmoid`. eta (`float`): The weight of noise for added noise in diffusion step. If its value is between 0.0 and 1.0 it corresponds to the DDIM scheduler, and if its value is between -0.0 and 1.0 it corresponds to the DDPM scheduler. trained_betas (`np.ndarray`, *optional*): Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`. clip_sample (`bool`, defaults to `True`): Clip the predicted sample between -1 and 1 for numerical stability. r -C6?{Gz?linearNTnum_train_timesteps beta_startbeta_end beta_scheduleeta trained_betas clip_samplech|tj||_n|dkr(tj|||tj|_n|dkr1tj|dz|dz|tjdz|_ns|dkrt ||_nX|dkr9tjdd |}tj|||z z|z|_nt|d |jd |jz |_ tj |j d |_ tj d |_ tj d |_d |_d|_tjt#jd |ddd|_||_dS)Nr=r( scaled_linear?rsquaredcos_cap_v2sigmoidiz is not implemented for ?r)dim)r from_numpyr3linspacer/r7rJNotImplementedError __class__alphascumprodalphas_cumprodr.onefinal_alpha_cumprodinit_noise_sigmanum_inference_stepsnparangecopy timestepsrC) selfr?r@rArBrCrDrEr3s r__init__zRePaintScheduler.__init__xs  $)-88DJJ h & & H>QY^YfgggDJJ o - - C3H[chcpqqquvvDJJ 1 1 1,-@AADJJ i ' 'N2q*=>>Eu--J1FG*TDJJ%&`&`PTP^&`&`aa aDJ& #mDKQ???<$$#(<#4#4 !$$( )")A7J*K*KDDbD*Q*V*V*X*XYYrsampletimestepreturnc|S)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. r)r^r`ras rscale_model_inputz"RePaintScheduler.scale_model_inputs  r rY jump_length jump_n_sampledevicec0t|jj|}||_g}i}t d||z |D] }|dz ||< |}|dkrt|dz }||||ddkr:||dz ||<t |D]} |dz}|||dkttj||jj|jzz}tj | ||_ dS)u Sets the discrete timesteps used for the diffusion chain (to be run before inference). Args: num_inference_steps (`int`): The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` must be `None`. jump_length (`int`, defaults to 10): The number of steps taken forward in time before going backward in time for a single jump (“j” in RePaint paper). Take a look at Figure 9 and 10 in the paper. jump_n_sample (`int`, defaults to 10): The number of times to make a forward time jump for a given chosen time sample. Take a look at Figure 9 and 10 in the paper. device (`str` or `torch.device`, *optional*): The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. rr N) r-configr?rYr+r,getrZarrayrrOtor]) r^rYrfrgrhr]jumpsjr$_s r set_timestepszRePaintScheduler.set_timestepss90"$+"ACVWW#6  q- ;[II ) )A$q(E!HH 1ffAA   Q   yyA"" 8a<a{++((AAA$$Q''''1ffHY''4;+JdNf+fg ))4477??rc||jj|jzz }|j|}|dkr |j|n|j}d|z }d|z }||z d||z z z}|S)Nrr )rjr?rYrUrW)r^r$ prev_timestep alpha_prod_talpha_prod_t_prev beta_prod_tbeta_prod_t_prevvariances r _get_variancezRePaintScheduler._get_variancesDK;t?WWW *1- BOSTBTBTD/ >>Z^Zr,& 00%{2q>Z^Zr,& !'c)9L)H HLZ]L]] ; " L#(;/CR#K#K $\/9V[g[mnnnHt11(;;sBB  q55TX\\ 5(H"#%6!6A!E# MP\ \.s25IILaadll-c1^CL]H]beGeinFnn /1S4ZCT4TT  $  &2BYmnnnnrcl|jj|jz}t|D]}|j||z}|jjdkr7t|j|j |}| |j}n"t|j||j|j }d|z dz|z|dz|zz}|S)Nmps)r)r}rr rH) rjr?rYr+r3rhtyper rr)rm)r^r`rar}nr4betars r undo_stepzRePaintScheduler.undo_stepOs K +t/G Gq D DA:hl+D}!U**$V\QZ[[[//$V\Yv}djdpqqq$h3&/$)e2CCFF roriginal_samplesrr]c td)NzCUse `DDPMScheduler.add_noise()` to train for sampling with RePaint.)rQ)r^rrr]s r add_noisezRePaintScheduler.add_noise`s ""ghhhrc|jjSN)rjr?)r^s r__len__zRePaintScheduler.__len__hs {..r)r:r;r<r=r>NTr)rereN)NT)rrrrorderr intfloatstrrrZndarrayboolr_rrrdrrhrqry Generatorrrrr IntTensorrrrrrr9r9[sd4 E$("%.2 (( (( (  (  (  +(((((T  Y^Ye(+/ -@-@ -@-@ -@ c5<'( -@-@-@-@^404 WoWolWoWo Wo  Wo l WoEO,WoWo %u, -WoWoWoWor"i,i|i? i  iiii/////rr9)rr)r dataclassesrtypingrrrnumpyrZrconfiguration_utilsrr utilsr utils.torch_utilsr scheduling_utilsr rr7r9rrrrs6 !!!!!!)))))))))) 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