o ۷i@sNddlZddlZddlmZmZddlmZddlmZGdddeeZ dS) N) ConfigMixinregister_to_config) randn_tensor)SchedulerMixinc@sJeZdZdZdZedddZdd eej Bfd d Z dd dZ ddZ d S)ScoreSdeVpSchedulera `ScoreSdeVpScheduler` is a variance preserving stochastic differential equation (SDE) scheduler. 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 2000): The number of diffusion steps to train the model. beta_min (`int`, defaults to 0.1): beta_max (`int`, defaults to 20): sampling_eps (`int`, defaults to 1e-3): The end value of sampling where timesteps decrease progressively from 1 to epsilon. 皙?MbP?cCsd|_d|_d|_dSN)sigmasdiscrete_sigmas timesteps)selfnum_train_timestepsbeta_minbeta_max sampling_epsrg/home/ubuntu/vllm_env/lib/python3.10/site-packages/diffusers/schedulers/deprecated/scheduling_sde_vp.py__init__,s zScoreSdeVpScheduler.__init__NdevicecCstjd|jj||d|_dS)a Sets the continuous 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. device (`str` or `torch.device`, *optional*): The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. r )rN)torchlinspaceconfigrr)rnum_inference_stepsrrrr set_timesteps2s z!ScoreSdeVpScheduler.set_timestepsc Csj|jdur tdd|d|jj|jjd||jj}tdtd|}|}t |j t |j krH| d}t |j t |j ks9| |}d t |j}|jj||jj|jj}|}t |j t |j kr| d}t |j t |j kspd ||} t|} | | d|} || |} t |j |j ||j|jd } | | t| | }|| fS) ax Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion process from the learned model outputs (most often the predicted noise). Args: score (): x (): t (): generator (`torch.Generator`, *optional*): A random number generator. NzY`self.timesteps` is not set, you need to run 'set_timesteps' after creating the schedulergпrg?g?g@gg)layout generatorrdtype)r ValueErrorrrrrsqrtexpflattenlenshape unsqueezerr!rr#math) rscorextr"log_mean_coeffstddtbeta_tdrift diffusionx_meannoiserrr step_pred>s0 ,      zScoreSdeVpScheduler.step_predcCs|jjSr)rr)rrrr__len__kszScoreSdeVpScheduler.__len__)r r r r r) __name__ __module__ __qualname____doc__orderrrstrrrrr7r8rrrrrs  -r) r+rconfiguration_utilsrrutils.torch_utilsrscheduling_utilsrrrrrrs