+h ISSKJr SSKJrJrJrJr SSKrSSK r SSK J r J r SSK JrJr SSKJr SS KJr \R("\5r\"S S \55r"S S \\ 5rg)) dataclass)ListOptionalTupleUnionN) ConfigMixinregister_to_config) BaseOutputlogging) randn_tensor)SchedulerMixinc8\rSrSr%Sr\R \S'Srg)$CMStochasticIterativeSchedulerOutputa& Output class for the scheduler's `step` function. Args: prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images): Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the denoising loop. prev_sampleN) __name__ __module__ __qualname____firstlineno____doc__torchTensor__annotations____static_attributes__rl/home/james-whalen/.local/lib/python3.13/site-packages/diffusers/schedulers/scheduling_consistency_models.pyrrsrrc\rSrSrSrSr\S)S\S\S\S\S\S \S \ 4S jj5r \ S 5r \ S 5r S*S\4SjjrS\R S\\\R 4S\R 4SjrS\\\R(44SjrS+S\\S\\\R04S\\\4SjjrSrSrSrS,SjrSrS-S \R S\\\R 4S\R S!\\R@S"\ S\\!\"44 S#jjr#S$\R S%\R S\R S\R 4S&jr$S'r%S(r&g).CMStochasticIterativeScheduler,ah Multistep and onestep sampling for consistency models. This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic methods the library implements for all schedulers such as loading and saving. Args: num_train_timesteps (`int`, defaults to 40): The number of diffusion steps to train the model. sigma_min (`float`, defaults to 0.002): Minimum noise magnitude in the sigma schedule. Defaults to 0.002 from the original implementation. sigma_max (`float`, defaults to 80.0): Maximum noise magnitude in the sigma schedule. Defaults to 80.0 from the original implementation. sigma_data (`float`, defaults to 0.5): The standard deviation of the data distribution from the EDM [paper](https://huggingface.co/papers/2206.00364). Defaults to 0.5 from the original implementation. s_noise (`float`, defaults to 1.0): The amount of additional noise to counteract loss of detail during sampling. A reasonable range is [1.000, 1.011]. Defaults to 1.0 from the original implementation. rho (`float`, defaults to 7.0): The parameter for calculating the Karras sigma schedule from the EDM [paper](https://huggingface.co/papers/2206.00364). Defaults to 7.0 from the original implementation. clip_denoised (`bool`, defaults to `True`): Whether to clip the denoised outputs to `(-1, 1)`. timesteps (`List` or `np.ndarray` or `torch.Tensor`, *optional*): An explicit timestep schedule that can be optionally specified. The timesteps are expected to be in increasing order. rnum_train_timesteps sigma_min sigma_max sigma_datas_noiserho clip_denoisedcvX0l[R"SSU5nURU5n UR U 5n SUl[ R"U 5Ul[ R"U 5Ul SUl SUl SUl SUl URRS5Ulg)NrrFcpu)init_noise_sigmanplinspace_convert_to_karras sigma_to_tnum_inference_stepsr from_numpysigmas timestepscustom_timestepsis_scale_input_called _step_index _begin_indexto) selfr#r$r%r&r'r(r)rampr3r4s r__init__'CMStochasticIterativeScheduler.__init__Ls!*{{1a!45((.OOF+ $( &&v. )))4 %%*" kknnU+ rcUR$)zW The index counter for current timestep. It will increase 1 after each scheduler step. )r7r:s r step_index)CMStochasticIterativeScheduler.step_indexhs rcUR$)za The index for the first timestep. It should be set from pipeline with `set_begin_index` method. r8r?s r begin_index*CMStochasticIterativeScheduler.begin_indexos    rrDcXlg)z Sets the begin index for the scheduler. This function should be run from pipeline before the inference. Args: begin_index (`int`): The begin index for the scheduler. NrC)r:rDs rset_begin_index.CMStochasticIterativeScheduler.set_begin_indexws (rsampletimestepreturncURcURU5 URURnXS-URRS--S-- nSUlU$)a& 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_indexr3configr&r6)r:rIrJsigmas rscale_model_input0CMStochasticIterativeScheduler.scale_model_inputs` ?? "  ! !( + DOO,1Ht{{'='=q'@@SHI%)" rr3c[U[R5(d#[R"U[RS9nS[R "US-5-nU$)a" 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:r3r4s rr0)CMStochasticIterativeScheduler.sigma_to_tsF&"**--XXfBJJ7F"&&%"88 rNr1devicer4c 0UcUc [S5eUbUb [S5eUb[S[U55HnX4X4S- :dM[S5e USURR:a#[SURRS35e[ R "U[ RS 9nS UlOXRR:a=[S US URRS URRS35eXl URRUR-n[ R"SU5U-R5SSS2R5R[ R5nSUlURRnUSSS2R5nXvS- - nURU5nURU5n[ R "XRR"//5R[ R$5n[&R("U5R+US9Ul[/U5R1S5(a8[&R("U5R+U[&R$S 9UlO([&R("U5R+US9UlSUlSUlUR,R+S5Ulg)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`: .rTTz`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)r\mpsr+) ValueErrorrangelenrOr#r-rXint64r5r1arangeroundcopyastyper/r0 concatenater$float32rr2r9r3str startswithr4r7r8) r:r1r\r4i step_ratior#r;r3s r set_timesteps,CMStochasticIterativeScheduler.set_timestepss&  &9+<de e  *y/DYZ Z  1c)n-<9U#33$%OPP.|t{{>>> STXT_T_TsTsSttuv"((;I$(D !"[[%D%DD -.A-BC 7789 $ ? ?@ M (; $88D3%FF}rcURRnURRnUS-X- S-US--- nX- U-US-US--S-- nXE4$)a4 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. rrM)rOr$r&)r:rPr$r&rvrws r#get_scalings_for_boundary_conditionBCMStochasticIterativeScheduler.get_scalings_for_boundary_condition sn(KK)) [[++ Q5#4":Z]"JK"j0E1Hz1}4LQT3TT}rcUc URnX!:HR5n[U5S:aSOSnX4R5$)Nrr)r4nonzerorcitem)r:rJschedule_timestepsindicesposs rindex_for_timestep1CMStochasticIterativeScheduler.index_for_timestep&sH  %!% %1::< w !3 LLroriginal_samplesrcURRURURS9nURRS:Xav[ R "U5(a[URRUR[ RS9nURUR[ RS9nO@URRUR5nURUR5nURc!UVs/sHo`RXe5PM nnOHURbUR/URS-nOUR/URS-nXGR5n[UR5[UR5:a?URS5n[UR5[UR5:aM?XU--n U $s snf)N)r\rUr`rTrr_)r3r9r\rUtyperis_floating_pointr4rjrDrr@rflattenrc unsqueeze) r:rrr4r3rt step_indicesrP noisy_sampless r add_noise(CMStochasticIterativeScheduler.add_noises'7'>'>FVF\F\]  " " ' '5 0U5L5LY5W5W!%!2!23C3J3JRWR_R_!2!` ! %5%<%rs_"// A',,   H %  :   Q/^[Q/r