o Gis@sddlZddlmZddlmZddlZddlZddlm Z m Z ddl m Z ddl mZmZeGdd d eZGd d d e e ZdS) N) dataclass)Literal) ConfigMixinregister_to_config)SchedulerMixin) BaseOutput deprecatec@sLeZdZUejed<dZejdBed<dZejdBed<dZe dBed<dS)HeliosSchedulerOutput prev_sampleN model_outputs last_sample this_order) __name__ __module__ __qualname__torch FloatTensor__annotations__r r rintrrZ/home/ubuntu/.local/lib/python3.10/site-packages/diffusers/schedulers/scheduling_helios.pyr s r c%@seZdZgZdZedddgddddd d d d gd d d ddfdededededede de dede de de deede de de de de d f"d!d"Z d#d$Zd%d&Zed'd(Zed)d*Zdld,efd-d.Zd/d0Z dmd1ed2ed Bd3e ejBd4e d Bd5e d Bd6e f d7d8Zd5ed9ed:ejfd;d<Zd=d>Zd?d@ZdndAdBZdCdDZ dodEejdFeejBdGejdHejd Bd9ejd BdIejd BdJe dKe e!BfdLdMZ"dNdOZ#d d dPdEejdGejd9ejdKejfdQdRZ$d d d d dSdEejdGejdTed9ejdIejdKejf dUdVZ%d d d d d dWdXejdYejdZejdTed[ejd9ejdKejfd\d]Z& dpdEejdFeejBdGejdJe d^ed_ed[ejd9ejdIejd`edaedbedcejdKe e!BfdddeZ' dqdEejdFeejBdGejdHejd BdJe dKe e!Bf dfdgZ(dhdiZ)djdkZ*d S)rHeliosScheduleri?)rUUUUUU?gUUUUUU?rrFflow_predictionrTbh2Nunipc exponentialnum_train_timestepsshiftstages stage_rangegamma thresholdingprediction_type solver_order predict_x0 solver_typelower_order_finaldisable_correctorsolver_puse_flow_sigmasscheduler_typeuse_dynamic_shiftingtime_shift_type)r linearcCsi|_i|_i|_i|_i|_i|_||jd|_ |jd|_ ||_ | dvrB| dvr8|j ddn t | d|j| |_dg||_dg||_d|_| |_| |_d|_d|_d|_dS)Nr)bh1r)midpointheunlogrhor)r*z is not implemented for )timestep_ratiostimesteps_per_stagesigmas_per_stage start_sigmas end_sigmasori_start_sigmasinit_sigmas_for_each_stagesigmasitem sigma_min sigma_maxr%rNotImplementedError __class__r)r timestep_listlower_order_numsr,r-r _step_index _begin_index)selfr!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1rrr__init__'s.   zHeliosScheduler.__init__cCs|jj}|jj}tdd||d}d|}t||d|d|dd}t|}|| }d|_ d|_ ||_ | d|_dS)z< initialize the global timesteps and sigmas rrNr3cpu)configr!r"nplinspaceflipcopyr from_numpyclonerGrH timestepstor?)rIr!r"alphasr?rSrrr init_sigmasZs*  zHeliosScheduler.init_sigmascCs8|g}|jj}|jj}|jj}t|D]k}t|||}t|d}t||d|}t||}|j | }||krF|j | nd} ||j |<|dkrpd|} |jj } dt dd| d| | | } d| }||| ||j|<| |j|<qt|} t|D]/}|dkrd}n t|d|| }||dkrd}n t|d|d| }||f|j|<qt|D][}|j|}t|jt|d|d}|jtt|d||d}t|||d}t|tjr|ddnt|dd|j|<tdd|d}t|dd|j|<qdS) z3 Init the timesteps for each stage rrgNg?ir3g+?)rVrLr#r!r$rangermaxminr?r@r=r%mathsqrtappendr;r<sumr8rSrMrN isinstancerTensorrQr9r:)rIstage_distancer#training_stepsr$i_s start_indice end_indice start_sigma end_sigma ori_sigmar%corrected_sigma tot_distance start_ratio end_ratiotimestep_ratio timestep_max timestep_minrS stage_sigmasrrrr>lsP    &       (z*HeliosScheduler.init_sigmas_for_each_stagecC|jS)zg The index counter for current timestep. It will increase 1 after each scheduler step. )rGrIrrr step_indexzHeliosScheduler.step_indexcCrp)zq The index for the first timestep. It should be set from pipeline with `set_begin_index` method. rHrqrrr begin_indexrszHeliosScheduler.begin_indexrrucCs ||_dS)z Sets the begin index for the scheduler. This function should be run from pipeline before the inference. Args: begin_index (`int`): The begin index for the scheduler. Nrt)rIrurrrset_begin_indexs zHeliosScheduler.set_begin_indexcCs ||jjSNrLr!)rIsigmarrr _sigma_to_ts zHeliosScheduler._sigma_to_tnum_inference_steps stage_indexdevicer?muis_amplify_first_chunkc Cs(|jjdkr|r|dd}n|d}||_||jjdkr\|durNtdd|jj|dddtj }|jj dkrN|jj rEJ| |jj d|}||jj }t|}n-|j|}t|d|d|}|j|} t| d| d|} t| }t|j|d|_t|tdgj|d|_d|_||jjdkr|jdd|_t|jdd |jddg|_|jj r|jj dksJ| |d|j|_|jjdkr|jdd|jj|_dS|j||jdd|j||j||_dSdS) zA Setting the timesteps and sigmas for each stage dmdrrNr3rrr})rLr/r{rVr#rMrNr!astypefloat32r"r0 time_shiftrPrrQr9r@r:rTrScatzerosr?rGreset_scheduler_historyrYrX) rIr{r|r}r?r~rrSstage_timestepsroratiosrrr set_timestepssR  "         $   zHeliosScheduler.set_timestepsrytcCs8|jjdkr ||||S|jjdkr||||SdS)a Apply time shifting to the sigmas. Args: mu (`float`): The mu parameter for the time shift. sigma (`float`): The sigma parameter for the time shift. t (`torch.Tensor`): The input timesteps. Returns: `torch.Tensor`: The time-shifted timesteps. r r2N)rLr1_time_shift_exponential_time_shift_linearrIr~ryrrrrrs  zHeliosScheduler.time_shiftcCs$t|t|d|d|SNr)rZexprrrrrs$z'HeliosScheduler._time_shift_exponentialcCs||d|d|Srrrrrrrsz"HeliosScheduler._time_shift_linearcCs:|dur|j}||k}t|dkrdnd}||S)Nrr)rSnonzerolenr@)rItimestepschedule_timestepsindicesposrrrindex_for_timestep!s   z"HeliosScheduler.index_for_timestepcCs@|jdurt|tjr||jj}|||_dS|j |_dSrw) rur^rr_rTrSr}rrGrH)rIrrrr_init_step_index/s   z HeliosScheduler._init_step_index model_outputrsample generator sigma_next return_dictreturnc Cs|du|duks Jd|dur)|dur)t|ts%t|tjs%t|tjr)td|jdur1d|_|tj }|durM|durM|j |j}|j |jd}||||}||j }|jd7_|sg|fSt |dS)Nz:sigma and sigma_next must both be None or both be not NonezPassing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to `EulerDiscreteScheduler.step()` is not supported. Make sure to pass one of the `scheduler.timesteps` as a timestep.rr)r ) r^rr IntTensor LongTensor ValueErrorrrrGrTrr?dtyper ) rIrrrrryrrr rrr step_euler7s.        zHeliosScheduler.step_eulercCsJ|jjrd|}tj|dd}||fSd|ddd}||}||fS)Nrg:0yE>)rYr?)rLr.rclamp)rIryalpha_tsigma_trrr_sigma_to_alpha_sigma_tisz'HeliosScheduler._sigma_to_alpha_sigma_trryc Ost|dkr |dn|dd}|dur#t|dkr|d}ntd|dur-tdddd }|dur;d }|j|j}||\}} |jr|jj d krT|| ||} n:|jj d kr]|} n1|jj d krl||| |} n"|jj dkr|r{|j|j} n|} || |} n td|jj d|jj r| | } | S|jj d kr|S|jj d kr|||| } | S|jj d kr||| |} | Std|jj d)a Convert the model output to the corresponding type the UniPC algorithm needs. Args: model_output (`torch.Tensor`): The direct output from the learned diffusion model. timestep (`int`): The current discrete timestep in the diffusion chain. sample (`torch.Tensor`): A current instance of a sample created by the diffusion process. Returns: `torch.Tensor`: The converted model output. rrNr/missing `sample` as a required keyword argumentrS1.0.0zPassing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`FTepsilonr v_predictionrzprediction_type given as zj must be one of `epsilon`, `sample`, `v_prediction`, or `flow_prediction` for the UniPCMultistepScheduler.zW must be one of `epsilon`, `sample`, or `v_prediction` for the UniPCMultistepScheduler.) rpoprr r?rrrr)rLr'r&_threshold_sample) rIrrryargskwargsrflagrrx0_predrrrrconvert_model_outputssZ            z$HeliosScheduler.convert_model_output)rorderryrrc)Ost|dkr |dn|dd}|dur#t|dkr|d}ntd|dur6t|dkr2|d}ntd|dur@tddd |j} |jd } | d } |} |jr\|j|| | j} | S|durt|durt|j |j d|j |j }}n||}}| |\}}| |\}}t |t |}t |t |}||}|j}g}g}td|D]6}|j |}| |d }| |j |\}}t |t |}|||}||||| |q|d t j||d }g}g} |jr| n|}!t |!}"|"|!d}#d}$|jjd kr|!}%n|jjdkrt |!}%nttd|dD]%}|t ||d| |#|$|%|$|d9}$|#|!d|$}#q(t |}t j| |d } t|dkrt j|dd}|dkrxt jdg| j|d}&nt j|dd dd f| dd || j}&nd}|jr||| ||"| }'|durt d|&|}(nd}(|'||%|(} n#||| ||"| }'|durt d|&|}(nd}(|'||%|(} | | j} | S)a One step for the UniP (B(h) version). Alternatively, `self.solver_p` is used if is specified. Args: model_output (`torch.Tensor`): The direct output from the learned diffusion model at the current timestep. prev_timestep (`int`): The previous discrete timestep in the diffusion chain. sample (`torch.Tensor`): A current instance of a sample created by the diffusion process. order (`int`): The order of UniP at this timestep (corresponds to the *p* in UniPC-p). Returns: `torch.Tensor`: The sample tensor at the previous timestep. r prev_timestepNrrr.missing `order` as a required keyword argumentrzPassing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`r3rrr4rdimrrr}k,bkc...->bc...)rrrr r rEr-stepr r?rrrrlogr}rWr\tensorr)expm1rLr*rCpowstackrlinalgsolverTeinsum))rIrrrryrrrrmodel_output_lists0m0xx_trsigma_s0ralpha_s0lambda_t lambda_s0hr}rksD1sisimialpha_sisigma_si lambda_sirkRbhhh_phi_1h_phi_k factorial_iB_hrhos_px_t_pred_resrrrmultistep_uni_p_bh_updates                  6   z)HeliosScheduler.multistep_uni_p_bh_update)r this_sampler sigma_beforerythis_model_outputr rrc+Ost|dkr |dn|dd} |dur#t|dkr|d}ntd|dur6t|dkr2|d}ntd|durIt|dkrE|d}ntd | durStdd d |j} | d } |} |} |}|durx|durx|j|j|j|jd}}n||}}||\}}||\}}t |t |}t |t |}||}|j }g}g}t d|D]8}|j|d}| |d }||j|\}}t |t |}|||}| || || |q| d tj ||d}g} g}!|jr| n|}"t|"}#|#|"d}$d}%|jjdkr|"}&n|jjdkr$t|"}&ntt d|dD]%}| t||d|! |$|%|&|%|d9}%|$|"d|%}$q.t| } tj |!|d}!t|dkrotj|dd}nd}|dkrtj dg| j|d}'ntj| |!|| j}'|jr||| ||#| }(|durtd|'dd |})nd})|| }*|(||&|)|'d |*} n1||| ||#| }(|durtd|'dd |})nd})|| }*|(||&|)|'d |*} | | j} | S)a One step for the UniC (B(h) version). Args: this_model_output (`torch.Tensor`): The model outputs at `x_t`. this_timestep (`int`): The current timestep `t`. last_sample (`torch.Tensor`): The generated sample before the last predictor `x_{t-1}`. this_sample (`torch.Tensor`): The generated sample after the last predictor `x_{t}`. order (`int`): The `p` of UniC-p at this step. The effective order of accuracy should be `order + 1`. Returns: `torch.Tensor`: The corrected sample tensor at the current timestep. r this_timestepNrz4missing `last_sample` as a required keyword argumentrz4missing `this_sample` as a required keyword argumentrrrzPassing `this_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`r3rrr4rrrrr)rrrr r r?rrrrrr}rWr\rr)rrLr*rCrrrrrrTr)+rIrr rrrryrrrrrrrmodel_trrrrrrrr}rrrrrrrrrrrrrrrrrhos_crcorr_resD1_trrrmultistep_uni_c_bh_updateIs                     z)HeliosScheduler.multistep_uni_c_bh_updater rEcus_step_indexcus_lower_order_numcus_this_ordercus_last_samplecCs0|jdur td| dur|jdurd|_n| |_| dur | |_| dur'| |_| dur.| |_|jdko?|jd|jvo?|jdu}|j|||d}|dur^|dur^|dd|_ |dd|_ |rm|j ||j||j||d}|dur|dur||d<|dd|_ |dd|_ n)t |j jdD]}|j |d|j |<|j |d|j |<q||j d<||j d<|j jrt|j jt|j|j}n|j j}t||jd|_|jdksJ||_|j|||j|| d}| dur|j|j jkr|jd7_| dur|jd7_|s|||j|jfSt|||j|jdS) NzaNumber of inference steps is 'None', you need to run 'set_timesteps' after creating the schedulerrrrr3)rr rrrry)rrrryr)r r r r)r{rrrrGrFrr r,rr rErrWrLr(r+rYrrSrr )rIrrrrr rErryrrrrr use_correctormodel_output_convertrrr rrr step_unipcs  "    zHeliosScheduler.step_unipccCsB|jjdkr|j|||||dS|jjdkr|j||||dSt)Neuler)rrrrrr)rrrr)rLr/rrrC)rIrrrrrrrrr>s  zHeliosScheduler.stepcCsPdg|jj|_dg|jj|_d|_|jj|_|jj|_d|_d|_d|_ dS)Nr) rLr(r rErFr,r-r rGrHrqrrrrXs   z'HeliosScheduler.reset_scheduler_historycCs|jjSrwrxrqrrr__len__bszHeliosScheduler.__len__)r)NNNNFrw)NNNNNT) NNTNNNNNNNNN)NNNT)+rrr _compatiblesrrrfloatlistboolstrrrrJrVr>propertyrrrurvrzrr}rr_rrrrrr Generatorr tuplerrrrrrrrrrrrrr#s      2<    @   2 T         j  r)rZ dataclassesrtypingrnumpyrMrconfiguration_utilsrrschedulers.scheduling_utilsrutilsrr r rrrrrs