o pi#I@sddlmZddlmZmZmZmZddlZddl Z ddl m Z m Z ddl mZmZddlmZdd lmZeeZeGd d d eZGd d d ee ZdS)) dataclass)ListOptionalTupleUnionN) ConfigMixinregister_to_config) BaseOutputlogging) randn_tensor)SchedulerMixinc@seZdZUdZejed<dS)$CMStochasticIterativeSchedulerOutputa> Output class for the scheduler's `step` function. Args: prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images): Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the denoising loop. prev_sampleN)__name__ __module__ __qualname____doc__torchTensor__annotations__rrp/home/ubuntu/SoloSpeech/.venv/lib/python3.10/site-packages/diffusers/schedulers/scheduling_consistency_models.pyrs  rc@s~eZdZdZdZe       d>d ed ed ed edededefddZ e ddZ e ddZ d?defddZ dejdeeejfdejfddZd eeejffd!d"Z # # #d@d$eed%eeejfd&eeefd'd(Zd)d*Zd+d,Zd-d.ZdAd/d0Zd1d2Z # dBd3ejdeeejfdejd4eejd5edee e!ff d6d7Z"d8ejd9ejd&ejdejfd:d;Z#d)r7r?rrrset_begin_indexws z.CMStochasticIterativeScheduler.set_begin_indexsampletimestepreturncCsF|jdur |||j|j}||d|jjdd}d|_|S)av Scales the consistency model input by `(sigma**2 + sigma_data**2) ** 0.5`. Args: sample (`torch.Tensor`): The input sample. timestep (`float` or `torch.Tensor`): The current timestep in the diffusion chain. Returns: `torch.Tensor`: A scaled input sample. NrrT)r<_init_step_indexr0configr$r3)r7rArBsigmarrrscale_model_inputs   z0CMStochasticIterativeScheduler.scale_model_inputr0cCs2t|tjstj|tjd}dt|d}|S)ab Gets scaled timesteps from the Karras sigmas for input to the consistency model. Args: sigmas (`float` or `np.ndarray`): A single Karras sigma or an array of Karras sigmas. Returns: `float` or `np.ndarray`: A scaled input timestep or scaled input timestep array. dtypeg@o@ggE6) isinstancer*ndarrayarrayfloat64log)r7r0r1rrrr-s z)CMStochasticIterativeScheduler.sigma_to_tNr.devicer1c Cs|dur |dur td|dur|durtd|durRtdt|D]}||||dkr3tdq#|d|jjkrFtd|jjdtj|tjd }d |_n:||jjkrjtd |d |jjd |jjd||_ |jj|j }t d|| ddd tj}d|_|jj}|ddd }||d}||}||}t||jjgg tj}t|j|d|_t|drt|j|tjd |_n t|j|d|_d|_d|_|jd|_dS)a Sets the 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. timesteps (`List[int]`, *optional*): Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default timestep spacing strategy of equal spacing between timesteps is used. If `timesteps` is passed, `num_inference_steps` must be `None`. NzEExactly one of `num_inference_steps` or `timesteps` must be supplied.z:Can only pass one of `num_inference_steps` or `timesteps`.r z(`timesteps` must be in descending order.rz=`timesteps` must start before `self.config.train_timesteps`: .rHTz`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.F)rOmpsr() ValueErrorrangelenrEr!r*rLint64r2r.arangeroundcopyastyper,r- concatenater"float32rr/r6r0str startswithr1r4r5) r7r.rOr1i step_ratior!r8r0rrr set_timestepssT *   z,CMStochasticIterativeScheduler.set_timestepscCsH|jj}|jj}|jj}|d|}|d|}|||||}|S)z6Constructs the noise schedule of Karras et al. (2022).r )rEr"r#r&)r7r8r"r#r& min_inv_rho max_inv_rhor0rrrr,s  z1CMStochasticIterativeScheduler._convert_to_karrascCsD|jj}|d|d|d}|||d|dd}||fS)Nrr)rEr$)r7rFr$c_skipc_outrrr get_scalingssz+CMStochasticIterativeScheduler.get_scalingscCsT|jj}|jj}|d||d|d}||||d|dd}||fS)a Gets the scalings used in the consistency model parameterization (from Appendix C of the [paper](https://huggingface.co/papers/2303.01469)) to enforce boundary condition. `epsilon` in the equations for `c_skip` and `c_out` is set to `sigma_min`. Args: sigma (`torch.Tensor`): The current sigma in the Karras sigma schedule. Returns: `tuple`: A two-element tuple where `c_skip` (which weights the current sample) is the first element and `c_out` (which weights the consistency model output) is the second element. rr)rEr"r$)r7rFr"r$rdrerrr#get_scalings_for_boundary_condition s  zBCMStochasticIterativeScheduler.get_scalings_for_boundary_conditioncCs:|dur|j}||k}t|dkrdnd}||S)Nr r)r1nonzerorUitem)r7rBschedule_timestepsindicesposrrrindex_for_timestep's   z1CMStochasticIterativeScheduler.index_for_timestepcCs@|jdurt|tjr||jj}|||_dS|j |_dSN) r?rJrrr6r1rOrmr4r5)r7rBrrrrD6s   z/CMStochasticIterativeScheduler._init_step_index model_output generator return_dictcCsNt|ttjtjfrtd|jd|jst d|j j }|j j }|j dur-|||j|j }|j d|j jkrE|j|j d} n|jd} ||\} } | || |} |j jrc| dd} t|jdkrvt|j|j|j|d} nt|} | |j j}| j||d}| ||d |d d }|jd7_|s|fSt|d S) a 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: model_output (`torch.Tensor`): The direct output from the learned diffusion model. timestep (`float`): The current timestep in the diffusion chain. sample (`torch.Tensor`): A current instance of a sample created by the diffusion process. generator (`torch.Generator`, *optional*): A random number generator. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~schedulers.scheduling_consistency_models.CMStochasticIterativeSchedulerOutput`] or `tuple`. Returns: [`~schedulers.scheduling_consistency_models.CMStochasticIterativeSchedulerOutput`] or `tuple`: If return_dict is `True`, [`~schedulers.scheduling_consistency_models.CMStochasticIterativeSchedulerOutput`] is returned, otherwise a tuple is returned where the first element is the sample tensor. zLPassing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to `z\.step()` is not supported. Make sure to pass one of the `scheduler.timesteps` as a timestep.zThe `scale_model_input` function should be called before `step` to ensure correct denoising. See `StableDiffusionPipeline` for a usage example.Nr rQ)rIrOrp)minmaxrr)r)rJintr IntTensor LongTensorrS __class__r3loggerwarningrEr"r#r<rDr0r!rgr'clamprUr1r shaperIrO zeros_liker%r4r)r7rorBrArprqr"r#rF sigma_nextrdredenoisednoisez sigma_hatrrrrstep>sD         z#CMStochasticIterativeScheduler.steporiginal_samplesrcs jj|j|jd}|jjdkr)t|r)jj|jtjd|j|jtjd}n j|j||j}j durFfdd|D}nj durUj g|j d}n j g|j d}|| }t |j t |j kr}|d}t |j t |j ksn|||}|S)N)rOrIrRrHcsg|]}|qSr)rm).0trjr7rr sz.rrQ)r0r6rOrItyperis_floating_pointr1r\r?r<r{flattenrU unsqueeze)r7rrr1r0 step_indicesrF noisy_samplesrrr add_noises"      z(CMStochasticIterativeScheduler.add_noisecCs|jjSrn)rEr!r;rrr__len__sz&CMStochasticIterativeScheduler.__len__)rrrrrr T)r)NNNrn)NT)%rrrrorderr rtfloatboolr9propertyr<r?r@rrrrGr*rKr-rr]rOrrar,rfrgrmrD Generatorrrrrrrrrrr,s   $    K      ^ !r) dataclassesrtypingrrrrnumpyr*rconfiguration_utilsrr utilsr r utils.torch_utilsr scheduling_utilsr get_loggerrrxrrrrrrs