biX,ddlmZddlmZmZmZddlZddlZddl m Z m Z ddl m Z ddlmZmZddlmZej&eZeGd d eZGd d e e Zy) ) dataclass)OptionalTupleUnionN) ConfigMixinregister_to_config)SchedulerMixin) BaseOutputlogging) randn_tensorcXeZdZUdZej ed<dZeej ed<y)SCMSchedulerOutputaq 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__rr^/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/diffusers/schedulers/scheduling_scm.pyrr!s' 37(5<<07rrc@eZdZdZdZe ddededefdZ e dZ e dZ dd efd Z dd ed ejdeeej"fdedef dZdZddZ d dej*dedej*dej,dedeeeff dZdZy )! SCMSchedulera `SCMScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with non-Markovian guidance. 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. prediction_type (`str`, defaults to `trigflow`): Prediction type of the scheduler function. Currently only supports "trigflow". sigma_data (`float`, defaults to 0.5): The standard deviation of the noise added during multi-step inference. num_train_timestepsprediction_type sigma_datacd|_d|_tjt j d|dddj jtj|_ d|_ d|_ y)a Initialize the SCM scheduler. Args: num_train_timesteps (`int`, defaults to 1000): The number of diffusion steps to train the model. prediction_type (`str`, defaults to `trigflow`): Prediction type of the scheduler function. Currently only supports "trigflow". sigma_data (`float`, defaults to 0.5): The standard deviation of the noise added during multi-step inference. g?Nr) init_noise_sigmanum_inference_stepsr from_numpynparangecopyastypeint64 timesteps _step_index _begin_index)selfrr r!s r__init__zSCMScheduler.__init__Fsh&!$$( ))"))A7J*KDbD*Q*V*V*X*_*_`b`h`h*ij rc|jSN)r-r/s r step_indexzSCMScheduler.step_indexbsrc|jSr2r.r3s r begin_indexzSCMScheduler.begin_indexfs   rr7c||_y)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. Nr6)r/r7s rset_begin_indexzSCMScheduler.set_begin_indexks (rNr%r,device max_timestepsintermediate_timestepsc Z||jjkDr=td|d|jjd|jjd|t||dzk7r td| | td| | td ||d k7r td ||_|t |t r+tj|| j|_ nt |tjr%|j|j|_ nvtd t||.tj||dg| j|_ n/tj|d|dz| j|_ d|_d|_y)a 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. timesteps (`torch.Tensor`, *optional*): Custom timesteps to use for the denoising process. max_timesteps (`float`, defaults to 1.57080): The maximum timestep value used in the SCM scheduler. intermediate_timesteps (`float`, *optional*, defaults to 1.3): The intermediate timestep value used in SCM scheduler (only used when num_inference_steps=2). z`num_inference_steps`: z6 cannot be larger than `self.config.train_timesteps`: zG as the unet model trained with this scheduler can only handle maximal z timesteps.NrzWIf providing custom timesteps, `timesteps` must be of length `num_inference_steps + 1`.zFIf providing custom timesteps, `max_timesteps` should not be provided.z5Should provide either `timesteps` or `max_timesteps`.rzNIntermediate timesteps for SCM is not supported when num_inference_steps != 2.)r:zUnsupported timesteps type: r)configr ValueErrorlenr% isinstancelistrtensorfloatr,rtotypelinspacer-r.)r/r%r,r:r;r<s r set_timestepszSCMScheduler.set_timestepsus* !@!@ @)*=)>?KK3345 KK;;ef f  !6TU U ! -2E2Jmn n#6  )T*!&i!G!M!M!OIu||4!*f!5!;!;!= #?Y?P!QRR # /"\\=:PRS*T]cdjjlDN#^^M1>QTU>U^dekkmDN rc|jVt|tjr%|j |j j }|j||_y|j|_yr2) r7rArrrEr,r:index_for_timestepr-r.)r/timesteps r_init_step_indexzSCMScheduler._init_step_indexsU    #(ELL1#;;t~~'<'<=#66x@D #00D rc| |j}||k(j}t|dkDrdnd}||jS)Nrr)r,nonzeror@item)r/rKschedule_timestepsindicesposs rrJzSCMScheduler.index_for_timestepsL  %!% %1::< wr rcossinr@r shaper:r!r-r) r/rSrKrTrUrVtsparameterizationpred_x0noisers rstepzSCMScheduler.stepsS4  # # +s  ?? "  ! !( + NN4??Q. / NN4?? + ;;66 z )iilV+eiil\.IIG=>N=OPQ Q t~~  "\// 8K8KW`a++(()  ))A,0599Q<%3GGK!K A) )!kPWXXrc.|jjSr2)r>rr3s r__len__zSCMScheduler.__len__s{{...r)irYg?)r)NNgH.!?g?r2)NT)rrrrorderr intstrrDr0propertyr4r7r9rrrr:rHrLrJ FloatTensor Generatorboolrrrbrdrrrrr4sJ  E$() ! !! !!6  !!(3(#'+/&(+ 8! 8!<<8!c5<<'( 8!  8! !& 8!v1 #&&* >Y''>Y>Y!! >Y ?? >Y  >Y !5( )>Y@/rr) dataclassesrtypingrrrnumpyr'rconfiguration_utilsrr schedulers.scheduling_utilsr utilsr r utils.torch_utilsr get_loggerrloggerrrrrrrusa$")) A8',   H % 88 8"T/>;T/r