wi#IddlmZddlmZmZmZmZddlZddl Z ddl m Z m Z ddl mZmZddlmZdd lmZejeZeGd d eZGd d ee ZdS)) dataclass)ListOptionalTupleUnionN) ConfigMixinregister_to_config) BaseOutputlogging) randn_tensor)SchedulerMixinc(eZdZUdZejed<dS)$CMStochasticIterativeSchedulerOutputa> Output class for the scheduler's `step` function. Args: prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images): Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the denoising loop. prev_sampleN)__name__ __module__ __qualname____doc__torchTensor__annotations__/root/.openclaw/workspace/chatterbox_venv_py311/lib/python3.11/site-packages/diffusers/schedulers/scheduling_consistency_models.pyrrs,rrc6eZdZdZdZe d/d ed ed ed edededefdZ e dZ e dZ d0defdZ dejdeeejfdejfdZdeeejffdZ d1deedeeejfd eeefd!Zd"Zd#Zd$Zd2d%Zd&Z d3d'ejdeeejfdejd(eejd)edee e!ff d*Z"d+ejd,ejd ejdejfd-Z#d.Z$dS)4CMStochasticIterativeSchedulera Multistep and onestep sampling for consistency models. 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 40): The number of diffusion steps to train the model. sigma_min (`float`, defaults to 0.002): Minimum noise magnitude in the sigma schedule. Defaults to 0.002 from the original implementation. sigma_max (`float`, defaults to 80.0): Maximum noise magnitude in the sigma schedule. Defaults to 80.0 from the original implementation. sigma_data (`float`, defaults to 0.5): The standard deviation of the data distribution from the EDM [paper](https://huggingface.co/papers/2206.00364). Defaults to 0.5 from the original implementation. s_noise (`float`, defaults to 1.0): The amount of additional noise to counteract loss of detail during sampling. A reasonable range is [1.000, 1.011]. Defaults to 1.0 from the original implementation. rho (`float`, defaults to 7.0): The parameter for calculating the Karras sigma schedule from the EDM [paper](https://huggingface.co/papers/2206.00364). Defaults to 7.0 from the original implementation. clip_denoised (`bool`, defaults to `True`): Whether to clip the denoised outputs to `(-1, 1)`. timesteps (`List` or `np.ndarray` or `torch.Tensor`, *optional*): An explicit timestep schedule that can be optionally specified. The timesteps are expected to be in increasing order. r(Mb`?T@??@Tnum_train_timesteps sigma_min sigma_max sigma_datas_noiserho clip_denoisedc|||_tjdd|}||} || } d|_t j| |_t j| |_ d|_ d|_ d|_ d|_ |jd|_dS)NrrFcpu)init_noise_sigmanplinspace_convert_to_karras sigma_to_tnum_inference_stepsr from_numpysigmas timestepscustom_timestepsis_scale_input_called _step_index _begin_indexto) selfr%r&r'r(r)r*r+rampr5r6s r__init__z'CMStochasticIterativeScheduler.__init__Ls!*{1a!455((..OOF++ $( &v.. ))44 %%*" knnU++ rc|jS)zg The index counter for current timestep. It will increase 1 after each scheduler step. )r9r<s r step_indexz)CMStochasticIterativeScheduler.step_indexhs rc|jS)zq The index for the first timestep. It should be set from pipeline with `set_begin_index` method. r:r@s r begin_indexz*CMStochasticIterativeScheduler.begin_indexos   rrrDc||_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. NrC)r<rDs rset_begin_indexz.CMStochasticIterativeScheduler.set_begin_indexws(rsampletimestepreturnc|j|||j|j}||dz|jjdzzdzz }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. Nrr"T)rA_init_step_indexr5configr(r8)r<rGrHsigmas rscale_model_inputz0CMStochasticIterativeScheduler.scale_model_inputs] ? "  ! !( + + + DO,E1Ht{'=q'@@SHI%)" rr5ct|tjs tj|tj}dtj|dzz}|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)r<r5r6s rr2z)CMStochasticIterativeScheduler.sigma_to_tsL&"*-- 8XfBJ777F"&%"8"88 rNr3devicer6c  ||td||td|tdt|D]&}||||dz krtd'|d|jjkrtd|jjdt j|t j }d |_n||jjkr-td |d |jjd |jjd||_ |jj|j z}t j d||z ddd t j}d|_|jj}|ddd }||dz z }||}||}t j||jjgg t j}t'j|||_t/|dr9t'j||t&j |_n-t'j|||_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`.rz(`timesteps` must be in descending order.rz=`timesteps` must start before `self.config.train_timesteps`: .rPTz`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)rWmpsr-) ValueErrorrangelenrLr%r/rTint64r7r3arangeroundcopyastyper1r2 concatenater&float32rr4r;r5str startswithr6r9r:) r<r3rWr6i step_ratior%r=r5s r set_timestepsz,CMStochasticIterativeScheduler.set_timestepss &  &9+<dee e  *y/DYZZ Z  1c)nn-- Q QQ<9QU#333$%OPPP4|t{>>> ; 7;;; "(;;;I$(D ! !"T[%DDD M.AMM 7MM $ ?MMM (;D $8D3%FFu}rc|jj}|jj}|dz||z dz|dzzz }||z |z|dz|dzzdzz }||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")rLr&r()r<rMr&r(rorps r#get_scalings_for_boundary_conditionzBCMStochasticIterativeScheduler.get_scalings_for_boundary_condition sj(K) [+ Q59#4":Z]"JK"j0E1Hz1}4LQT3TTu}rc||j}||k}t|dkrdnd}||S)Nrr)r6nonzeror^item)r<rHschedule_timestepsindicesposs rindex_for_timestepz1CMStochasticIterativeScheduler.index_for_timestep'sW  %!% %1::<< w<#;;t~'<==#66x@@D   #0D   r model_output generator return_dictct|ttjtjfrt d|jd|jst d|j j }|j j }|j |||j|j }|j dz|j jkr|j|j dz} n |jd} ||\} } | |z| |zz} |j jr| dd} t)|jdkr#t-|j|j|j|} ntj|} | |j jz}| ||}| ||d z|d zz d zzz}|xjdz c_|s|fSt;| 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.NrrZ)rQrWr~)minmaxrr")r)rRintr IntTensor LongTensorr\ __class__r8loggerwarningrLr&r'rArKr5r%rsr+clampr^r6r shaperQrW zeros_liker)r9r)r<r}rHrGr~rr&r'rM sigma_nextrorpdenoisednoisez sigma_hatrs rstepz#CMStochasticIterativeScheduler.step>s@ heou7G H I I GGGG )  NNE    K) K) ? "  ! !( + + + DO, ?Q !@ @ @T_q%89JJRJ@@GG <'&6/9 ; $ -~~b!,,H t~   " " ",*<\EXdmEE$\22E DK' '$$ $BB ilY\&Ac%I!II  A "> !3 LLLLroriginal_samplesrc`j|j|j}|jjdkrft j|rRj|jt j||jt j}n9j|j||j}j fd|D}n4j j g|j dz}nj g|j dz}|| }t|j t|j kr?|d}t|j t|j k?|||zz}|S)N)rWrQr[rPc<g|]}|Sr)rz).0trwr<s r z.s*^^^qD33A7IJJ^^^rrrZ)r5r;rWrQtyperis_floating_pointr6rerDrArflattenr^ unsqueeze) r<rrr6r5 step_indicesrM noisy_samplesrws ` @r add_noisez(CMStochasticIterativeScheduler.add_noises'7'>FVF\]]  " '5 0 0U5LY5W5W 0!%!2!23C3JRWR_!2!`!` ! %5%propertyrArDrFrrrrNr/rSr2rrfrWrrjr1rqrsrzrK Generatorrrrrrrrrrr,s: E$& ",, ,, ,  ,  ,,,,,,6  X !!X!((3(((( eU\FY@Z_d_k2ubj'8!9*.2+/)- H,H,%c]H,c5<'(H,DI& H,H,H,H,V   8 # # # #11104 [M[Ml[Mu|+,[M [M EO, [M  [M 3U: ;[M[M[M[M|,|<   B/////rr) dataclassesrtypingrrrrnumpyr/rconfiguration_utilsr r utilsr r utils.torch_utilsr scheduling_utilsr get_loggerrrrrrrrrs0"!!!!!//////////// AAAAAAAA'''''''',,,,,,,,,,,,  H % %      :    R/R/R/R/R/^[R/R/R/R/R/r