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)SchedulerMixincXeZdZUdZej ed<dZeej ed<y)UnCLIPSchedulerOutputaq 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_sampleNpred_original_sample) __name__ __module__ __qualname____doc__torchTensor__annotations__rra/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/diffusers/schedulers/scheduling_unclip.pyrrs' 37(5<<07rrc $|dk(rd}n|dk(rd}ntd|g}t|D]<}||z }|dz|z }|jtd||||z z |>t j |tj S)a Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [0,1]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar. Choose from `cosine` or `exp` Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs cosinecftj|dzdz tjzdz dzS)NgMb?gT㥛 ?r)mathcospits r alpha_bar_fnz)betas_for_alpha_bar..alpha_bar_fnIs-88QY%/$''9A=>!C Crexpc2tj|dzS)Ng()rr%r"s rr$z)betas_for_alpha_bar..alpha_bar_fnNs88AI& &rz"Unsupported alpha_transform_type: r dtype) ValueErrorrangeappendminrtensorfloat32)num_diffusion_timestepsmax_betaalpha_transform_typer$betasit1t2s rbetas_for_alpha_barr60s.x' D  & '=>R=STUU E * +M ( (!e. . S\"- R0@@@(KLM <<U]] 33rceZdZdZe ddedededee dedef dZ dd e jd eed e jfd Z ddedeee jffdZddZ dde jd ed e jdeeded eeeff dZde jde jde j*d e jfdZy )UnCLIPSchedulera NOTE: do not use this scheduler. The DDPM scheduler has been updated to support the changes made here. This scheduler will be removed and replaced with DDPM. This is a modified DDPM Scheduler specifically for the karlo unCLIP model. This scheduler has some minor variations in how it calculates the learned range variance and dynamically re-calculates betas based off the timesteps it is skipping. The scheduler also uses a slightly different step ratio when computing timesteps to use for inference. See [`~DDPMScheduler`] for more information on DDPM scheduling Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. variance_type (`str`): options to clip the variance used when adding noise to the denoised sample. Choose from `fixed_small_log` or `learned_range`. clip_sample (`bool`, default `True`): option to clip predicted sample between `-clip_sample_range` and `clip_sample_range` for numerical stability. clip_sample_range (`float`, default `1.0`): The range to clip the sample between. See `clip_sample`. prediction_type (`str`, default `epsilon`, optional): prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion process) or `sample` (directly predicting the noisy sample`) num_train_timesteps variance_type clip_sampleclip_sample_rangeprediction_type beta_schedulec|dk7r tdt||_d|jz |_t j |jd|_t jd|_d|_ d|_ t jtjd|dddj|_||_y)Nsquaredcos_cap_v2zBUnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'?rdim)r)r6r2alphasrcumprodalphas_cumprodr-oneinit_noise_sigmanum_inference_steps from_numpynparangecopy timestepsr:)selfr9r:r;r<r=r>s r__init__zUnCLIPScheduler.__init__ys / /ab b()<= DJJ& #mmDKKQ?<<$!$$( ))"))A7J*KDbD*Q*V*V*XY*rNsampletimestepreturnc|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)rPrRrSs rscale_model_inputz!UnCLIPScheduler.scale_model_inputs  rrJdevicecf||_|jjdz |jdz z }tjd||zj dddj jtj}tj|j||_ y)a Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference. Note that this scheduler uses a slightly different step ratio than the other diffusers schedulers. The different step ratio is to mimic the original karlo implementation and does not affect the quality or accuracy of the results. Args: num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. r rNrD) rJconfigr9rLrMroundrNastypeint64rrKtorO)rPrJrW step_ratiorOs r set_timestepszUnCLIPScheduler.set_timestepss$7 kk559d>V>VYZ>Z[ YYq"56CJJLTrTRWWY``acaiaij )))477?rc||dz }|j|}|dk\r|j|n |j}d|z }d|z }||dz k(r|j|} nd||z z } ||z | z} ||jj}|dk(rDt j t j| d} t jd| z} | S|dk(r6| j } | j } |dzdz } | | zd| z | zz} | S) Nr rfixed_small_logg#B ;)r,? learned_ranger) rGrHr2rYr:rlogclampr%)rPr# prev_timesteppredicted_variancer: alpha_prod_talpha_prod_t_prev beta_prod_tbeta_prod_t_prevbetavariancemin_logmax_logfracs r _get_variancezUnCLIPScheduler._get_variances0  EM**1- BOSTBTD// >Z^ZbZb,& 00 AE !::a=D|&777D $k1D8   KK55M - -yyX5!ABHyyx0Ho -llnGhhjG&*a/Dg~TW(< must be one of `epsilon` or `sample` for the UnCLIPScheduler.)r( generatorrW)rgrfrazvariance_type given as zM must be one of `fixed_small_log` or `learned_range` for the UnCLIPScheduler.)rr)shaper:rsplitrGrHr2rErYr=r)r;rer<r r(rWrqr%r)rPrrrSrRrfrvrsr#rgrhrirjrkrlalpharpred_original_sample_coeffcurrent_sample_coeffpred_prev_samplermvariance_noises rstepzUnCLIPScheduler.steps:    a FLLOa$7 7DZ^ZbZb,& 00 AE !::a=DKKNE|&777DHE ;; & &) 3$*[S-AL-P$PT`ehTi#i [[ ( (H 4#/ +DKK,G,G+HI,,  ;; " "#(;;$t{{'D'D&DdkkFcFc$ '8C&@4&G;%V"$~0@@;N68LLOcflOll q5)"",*<*< ZfZmZmN))#5+*H !!%66###6(N//1 -d.@.@-AB00  .0H+h6 $  %1AXlmmroriginal_samplesnoiserOc|jj|j|_|jj|j}|j|j}||dz}|j }t |j t |j kr=|jd}t |j t |j kr=d||z dz}|j }t |j t |j kr=|jd}t |j t |j kr=||z||zz}|S)N)rWr'rbrDr )rGr]rWr(flattenlenrw unsqueeze)rPrrrOrGsqrt_alpha_prodsqrt_one_minus_alpha_prod noisy_sampless r add_noisezUnCLIPScheduler.add_noiseKsa#1144V@""R(, pnllpnpn pn  } pnpn $e+ ,pnf,,||??   rr8)g+?r)r dataclassesrtypingrrrnumpyrLrconfiguration_utilsrr utilsr utils.torch_utilsr scheduling_utilsr rr6r8rrrrs^ !)) 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