bi IddlmZddlmZmZmZmZddlZddl Z ddl m Z m Z ddl mZmZddlmZdd lmZej(eZeGd d eZGd d ee Zy)) dataclass)ListOptionalTupleUnionN) ConfigMixinregister_to_config) BaseOutputlogging) randn_tensor)SchedulerMixinc0eZdZUdZej ed<y)$CMStochasticIterativeSchedulerOutputa> Output class for the scheduler's `step` function. Args: prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images): Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the denoising loop. prev_sampleN)__name__ __module__ __qualname____doc__torchTensor__annotations__m/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/diffusers/schedulers/scheduling_consistency_models.pyrrsrrcneZdZdZdZe d'dedededededed efd Z e d Z e d Z d(d efdZ dejdeeejfdejfdZdeeej&ffdZ d)deedeeej.fdeeefdZdZdZdZd*dZdZ d+dejdeeejfdejd eej>d!edee e!ff d"Z"d#ejd$ejdejdejfd%Z#d&Z$y),CMStochasticIterativeSchedulera Multistep and onestep sampling for consistency models. This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic methods the library implements for all schedulers such as loading and saving. Args: num_train_timesteps (`int`, defaults to 40): The number of diffusion steps to train the model. sigma_min (`float`, defaults to 0.002): Minimum noise magnitude in the sigma schedule. Defaults to 0.002 from the original implementation. sigma_max (`float`, defaults to 80.0): Maximum noise magnitude in the sigma schedule. Defaults to 80.0 from the original implementation. sigma_data (`float`, defaults to 0.5): The standard deviation of the data distribution from the EDM [paper](https://huggingface.co/papers/2206.00364). Defaults to 0.5 from the original implementation. s_noise (`float`, defaults to 1.0): The amount of additional noise to counteract loss of detail during sampling. A reasonable range is [1.000, 1.011]. Defaults to 1.0 from the original implementation. rho (`float`, defaults to 7.0): The parameter for calculating the Karras sigma schedule from the EDM [paper](https://huggingface.co/papers/2206.00364). Defaults to 7.0 from the original implementation. clip_denoised (`bool`, defaults to `True`): Whether to clip the denoised outputs to `(-1, 1)`. timesteps (`List` or `np.ndarray` or `torch.Tensor`, *optional*): An explicit timestep schedule that can be optionally specified. The timesteps are expected to be in increasing order. rnum_train_timesteps sigma_min sigma_max sigma_datas_noiserho clip_denoisedcr||_tjdd|}|j|} |j | } d|_t j| |_t j| |_ d|_ d|_ d|_ d|_ |jjd|_y)NrrFcpu)init_noise_sigmanplinspace_convert_to_karras sigma_to_tnum_inference_stepsr from_numpysigmas timestepscustom_timestepsis_scale_input_called _step_index _begin_indexto) selfrr r!r"r#r$r%rampr/r0s r__init__z'CMStochasticIterativeScheduler.__init__Ls!*{{1a!45((.OOF+ $( &&v. )))4 %%*" kknnU+ rc|jS)zg The index counter for current timestep. It will increase 1 after each scheduler step. )r3r6s r step_indexz)CMStochasticIterativeScheduler.step_indexhs rc|jS)zq The index for the first timestep. It should be set from pipeline with `set_begin_index` method. r4r:s r begin_indexz*CMStochasticIterativeScheduler.begin_indexos    rr>c||_y)z Sets the begin index for the scheduler. This function should be run from pipeline before the inference. Args: begin_index (`int`): The begin index for the scheduler. Nr=)r6r>s rset_begin_indexz.CMStochasticIterativeScheduler.set_begin_indexws (rsampletimestepreturnc|j|j||j|j}||dz|jjdzzdzz }d|_|S)av Scales the consistency model input by `(sigma**2 + sigma_data**2) ** 0.5`. Args: sample (`torch.Tensor`): The input sample. timestep (`float` or `torch.Tensor`): The current timestep in the diffusion chain. Returns: `torch.Tensor`: A scaled input sample. r?T)r;_init_step_indexr/configr"r2)r6rArBsigmas rscale_model_inputz0CMStochasticIterativeScheduler.scale_model_inputsb ?? "  ! !( + DOO,E1Ht{{'='=q'@@SHI%)" rr/ct|tjs%tj|tj}dtj |dzz}|S)ab Gets scaled timesteps from the Karras sigmas for input to the consistency model. Args: sigmas (`float` or `np.ndarray`): A single Karras sigma or an array of Karras sigmas. Returns: `float` or `np.ndarray`: A scaled input timestep or scaled input timestep array. dtypeg@o@ggE6) isinstancer)ndarrayarrayfloat64log)r6r/r0s rr,z)CMStochasticIterativeScheduler.sigma_to_tsC&"**-XXfBJJ7F"&&%"88 rNr-devicer0c *| | td| | td|tdt|D]}||||dz k\std|d|jjk\r#td|jjdt j |t j }d |_n||jjkDr=td |d |jjd |jjd||_ |jj|jz}t jd||zjdddjjt j}d|_|jj}|dddj}||dz z }|j|}|j|}t j ||jj"ggjt j$}t'j(|j+||_t/|j1dr:t'j(|j+|t&j$ |_n*t'j(|j+||_d|_d|_|j,j+d|_y)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`: .rKTz`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)rRmpsr') ValueErrorrangelenrGrr)rOint64r1r-arangeroundcopyastyper+r, concatenater float32rr.r5r/str startswithr0r3r4) r6r-rRr0i step_ratiorr7r/s r set_timestepsz,CMStochasticIterativeScheduler.set_timestepss&  &9+<de e  *y/DYZ Z  1c)n- QQ<9QU#33$%OPP Q|t{{>>> STXT_T_TsTsSttuv"((;I$(D !"T[[%D%DD -.A-BC 7789 $ ? ?@ M (;D $88D3%FFu}rc|jj}|jj}|dz||z dz|dzzz }||z |z|dz|dzzdzz }||fS)a Gets the scalings used in the consistency model parameterization (from Appendix C of the [paper](https://huggingface.co/papers/2303.01469)) to enforce boundary condition. `epsilon` in the equations for `c_skip` and `c_out` is set to `sigma_min`. Args: sigma (`torch.Tensor`): The current sigma in the Karras sigma schedule. Returns: `tuple`: A two-element tuple where `c_skip` (which weights the current sample) is the first element and `c_out` (which weights the consistency model output) is the second element. rrE)rGr r")r6rHr r"rjrks r#get_scalings_for_boundary_conditionzBCMStochasticIterativeScheduler.get_scalings_for_boundary_condition st(KK)) [[++ Q59#4":Z]"JK"j0E1Hz1}4LQT3TTu}rc| |j}||k(j}t|dkDrdnd}||jS)Nrr)r0nonzerorYitem)r6rBschedule_timestepsindicesposs rindex_for_timestepz1CMStochasticIterativeScheduler.index_for_timestep&sL  %!% %1::< wrMrrr5r0rRrur3r4)r6rBs rrFz/CMStochasticIterativeScheduler._init_step_index5sU    #(ELL1#;;t~~'<'<=#66x@D #00D r model_output generator return_dictcLt|ttjtjfrt d|j d|jstjd|jj}|jj}|j|j||j|j}|jdz|jj kr|j|jdz} n|jd} |j#|\} } | |z| |zz} |jj$r| j'dd} t)|j*dkDr.t-|j.|j0|j2|} ntj4|} | |jj6z}| j'||}| ||dz|dzz d zzz}|xj8dz 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.rrU)rLrRry)minmaxrrE)r)rMintr IntTensor LongTensorrW __class__r2loggerwarningrGr r!r;rFr/rrnr%clamprYr0r shaperLrR zeros_liker#r3r)r6rxrBrAryrzr r!rH sigma_nextrjrkdenoisednoisez sigma_hatrs rstepz#CMStochasticIterativeScheduler.step=s@ heoou7G7G H I()GG )) NNE  KK)) KK)) ?? "  ! !( + DOO, ??Q !@!@ @T__q%89JRJ@@G <'&6/9 ;; $ $~~b!,H t~~  " "",*<*<\EXEXdmE$$\2E DKK'' '$$ $B ilY\&Ac%I!II  A> !3 LLroriginal_samplesrc|jj|j|j}|jjdk(rvt j |ra|jj|jt j}|j|jt j}n@|jj|j}|j|j}|j |Dcgc]}|j||}}nG|j|jg|jdz}n|jg|jdz}||j}t|jt|jkr=|jd}t|jt|jkr=|||zz} | Scc}w)N)rRrLrVrKrrU)r/r5rRrLtyperis_floating_pointr0r`r>rur;rflattenrY unsqueeze) r6rrr0r/rrt step_indicesrH noisy_sampless r add_noisez(CMStochasticIterativeScheduler.add_noises'7'>'>FVF\F\]  " " ' '5 0U5L5LY5W!%!2!23C3J3JRWR_R_!2!` ! %5%<%r@rrrrIr)rNr,rrarRrrer+rlrnrurF Generatorrrrrrrrrrr,s": E$& ", ,, ,  ,  ,,,,6  !!(3( eU\\FY@Z_d_k_k2ubjj'8!9*.2+/)- G,%c]G,c5<<'(G,DI& G,T 8 #104 [Mll[Mu||+,[M [M EOO, [M  [M 3U: ;[M|,,||<<   B/rr) dataclassesrtypingrrrrnumpyr)rconfiguration_utilsr r utilsr r utils.torch_utilsr scheduling_utilsr get_loggerrrrrrrrrsa"// A',,   H %  :   Q/^[Q/r