bi=ddlZddlmZddlmZmZmZddlZddl Z ddl m Z m Z ddl mZddlmZdd lmZeGd d eZ dd ZGd dee Zy)N) dataclass)OptionalTupleUnion) ConfigMixinregister_to_config) BaseOutput) randn_tensor)SchedulerMixincNeZdZUdZej ed<ej ed<y)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__b/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/diffusers/schedulers/scheduling_repaint.pyrrs ,,&rrc $|dk(rd}n|dk(rd}ntd|g}t|D]<}||z }|dz|z }|jtd||||z z |>t j |tj S)a Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [0,1]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar. Choose from `cosine` or `exp` Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs cosinecftj|dzdz tjzdz dzS)NgMb?gT㥛 ?r)mathcospits r alpha_bar_fnz)betas_for_alpha_bar..alpha_bar_fnHs-88QY%/$''9A=>!C Crexpc2tj|dzS)Ng()rr%r"s rr$z)betas_for_alpha_bar..alpha_bar_fnMs88AI& &rz"Unsupported alpha_transform_type: r dtype) ValueErrorrangeappendminrtensorfloat32)num_diffusion_timestepsmax_betaalpha_transform_typer$betasit1t2s rbetas_for_alpha_barr6/s.x' D  & '=>R=STUU E * +M ( (!e. . S\"- R0@@@(KLM <<U]] 33rc eZdZdZdZe d"dededededed e e jd e fd Z d#d ejd e edejfdZ d$dedededeeej$ffdZdZ d%dejd ed ejdejdejde ej*de deeeffdZd#dZdejdejdej4dejfd Zd!Zy)&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 Nnum_train_timesteps beta_startbeta_end beta_scheduleeta trained_betas clip_samplec|tj||_n|dk(r-tj|||tj|_n|dk(r6tj|dz|dz|tjdz|_np|dk(rt ||_nZ|dk(r;tjdd |}tj |||z z|z|_nt|d |jd |jz |_ tj|jd |_ tjd |_ tjd |_d |_d|_tjt#j$d |dddj'|_||_y)Nlinearr' scaled_linear?rsquaredcos_cap_v2sigmoidiz is not implemented for ?r)dim)r from_numpyr2linspacer.r6rENotImplementedError __class__alphascumprodalphas_cumprodr-onefinal_alpha_cumprodinit_noise_sigmanum_inference_stepsnparangecopy timestepsr=) selfr9r:r;r<r=r>r?r2s r__init__zRePaintScheduler.__init__xsx  $))-8DJ h & H>QY^YfYfgDJ o - C3H[chcpcpquvvDJ 1 1,-@ADJ i 'NN2q*=>Eu-J1FG*TDJ%7OPTP^P^O_&`a aDJJ& #mmDKKQ?<<$#(<<#4 !$$( ))"))A7J*KDbD*Q*V*V*XYrsampletimestepreturnc|S)a Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.Tensor`): The input sample. timestep (`int`, *optional*): The current timestep in the diffusion chain. Returns: `torch.Tensor`: A scaled input sample. r)rYr[r\s rscale_model_inputz"RePaintScheduler.scale_model_inputs  rrT jump_length jump_n_sampledevicec*t|jj|}||_g}i}t d||z |D] }|dz ||< |}|dk\rb|dz }|j ||j |ddkDr1||dz ||<t |D]} |dz}|j ||dk\rbtj||jj|jzz}tj|j||_ y)u Sets the discrete timesteps used for the diffusion chain (to be run before inference). Args: num_inference_steps (`int`): The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` must be `None`. jump_length (`int`, defaults to 10): The number of steps taken forward in time before going backward in time for a single jump (“j” in RePaint paper). Take a look at Figure 9 and 10 in the paper. jump_n_sample (`int`, defaults to 10): The number of times to make a forward time jump for a given chosen time sample. Take a look at Figure 9 and 10 in the paper. device (`str` or `torch.device`, *optional*): The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. rr N) r,configr9rTr*r+getrUarrayrrJtorX) rYrTr`rarbrXjumpsjr#_s r set_timestepszRePaintScheduler.set_timestepss0"$++"A"ACVW#6  q- ;[I )A$q(E!H ) 1fAA   Q yyA" 8a<a{+(AAA$$Q'( 1fHHY'4;;+J+JdNfNf+fg )))477?rc||jj|jzz }|j|}|dk\r|j|n |j}d|z }d|z }||z d||z z z}|S)Nrr )rdr9rTrPrR)rYr# prev_timestep alpha_prod_talpha_prod_t_prev beta_prod_tbeta_prod_t_prevvariances r _get_variancezRePaintScheduler._get_variancesDKK;;t?W?WWW **1- BOSTBTD// >Z^ZrZr,& 00%{2qZ^ZrZr,& !'c)9L)H HLZ]L]] ;; " "#(;;/CR#K $$\//9V[g[m[mnHHt11(;sBB  q5TXX\ 5(H"#%6!6A!E# MP\ \.s25IILaadll-c1^CL]H]beGeinFnn /1S4ZCT4TT $  &2BYmnnrc|jj|jz}t|D]}|j||z}|j j dk(r>t|j|j|}|j|j }n-t|j||j |j}d|z dz|z|dz|zz}|S)Nmps)r(rwrzr rC) rdr9rTr*r2rbtyper r|r(rg)rYr[r\rwnr3betar}s r undo_stepzRePaintScheduler.undo_stepSs KK + +t/G/G Gq DA::hl+D}}!!U*$V\\QZ[/$V\\Yv}}djdpdpq$h3&/$)e2CCF D roriginal_samplesr}rXctd)NzCUse `DDPMScheduler.add_noise()` to train for sampling with RePaint.)rL)rYrr}rXs r add_noisezRePaintScheduler.add_noiseds ""ghhrc.|jjSN)rdr9)rYs r__len__zRePaintScheduler.__len__ls{{...r)ig-C6?g{Gz?rAgNTr) rN)NT)rrrrorderr intfloatstrrrUndarrayboolrZrrr_rrbrkrs Generatorrrrr IntTensorrrrrrr8r8[s4 E$("%.2 ( (( (  (  (  +(((T  Y^YeYe(+/ -@ -@-@ -@ c5<<'( -@^404 [oll[o[o [o  [o ll [oEOO,[o[o %u, -[oz"i,,i||i?? i  i/rr8)g+?r)r dataclassesrtypingrrrnumpyrUrconfiguration_utilsrr utilsr utils.torch_utilsr scheduling_utilsr rr6r8rrrrs^ !)) 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