+hz/SSKJr SSKJrJrJr SSKrSSKrSSK J r J r SSK J r Jr SSKJr SS KJr \R&"\5r\"S S \ 55r"S S \\ 5rg)) dataclass)OptionalTupleUnionN) ConfigMixinregister_to_config) BaseOutputlogging) randn_tensor)SchedulerMixinc8\rSrSr%Sr\R \S'Srg)$FlowMatchHeunDiscreteSchedulerOutputa2 Output class for the scheduler's `step` function output. Args: prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the denoising loop. prev_sampleN) __name__ __module__ __qualname____firstlineno____doc__torch FloatTensor__annotations____static_attributes__rr/home/james-whalen/.local/lib/python3.13/site-packages/diffusers/schedulers/scheduling_flow_match_heun_discrete.pyrrs"""rrc\rSrSrSr/rSr\S&S\S\ 4Sjj5r \ S5r \ S 5r S'S \4S jjrS(S \R S\\ \R 4S\\R S\R 4SjjrSrS(S\S\\\R,44SjjrS(SjrSr\ S5rSS\ "S5SS S4S\R S\\ \R 4S \R S\ S\ S\ S \ S!\\R6S"\S\\\44S#jjrS$r S%r!g ))FlowMatchHeunDiscreteScheduler,a Heun 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 1000): The number of diffusion steps to train the model. timestep_spacing (`str`, defaults to `"linspace"`): The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information. shift (`float`, defaults to 1.0): The shift value for the timestep schedule. r?num_train_timestepsshiftc[R"SX[RS9SSS2R5n[R "U5R [RS9nX1- nX$-SUS- U--- nXA-UlSUlSUl UR S5Ul URSR5Ul URSR5Ul g)Nr )dtypecpur)nplinspacefloat32copyr from_numpyto timesteps _step_index _begin_indexsigmasitem sigma_min sigma_max)selfr#r$r/r2s r__init__'FlowMatchHeunDiscreteScheduler.__init__@s KK#6SUS]S]^_cac_cdiik $$Y/222G 01 V';#;<5 ii& R--/Q,,.rcUR$)zW The index counter for current timestep. It will increase 1 after each scheduler step. )r0r6s r step_index)FlowMatchHeunDiscreteScheduler.step_indexUs rcUR$)za The index for the first timestep. It should be set from pipeline with `set_begin_index` method. r1r:s r begin_index*FlowMatchHeunDiscreteScheduler.begin_index\s    rr?cXlg)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_index.FlowMatchHeunDiscreteScheduler.set_begin_indexds (rNsampletimestepnoisereturncURcURU5 URURnXC-SU- U--nU$)z Forward process in flow-matching Args: sample (`torch.FloatTensor`): The input sample. timestep (`int`, *optional*): The current timestep in the diffusion chain. Returns: `torch.FloatTensor`: A scaled input sample. r")r;_init_step_indexr2)r6rDrErFsigmas r scale_noise*FlowMatchHeunDiscreteScheduler.scale_noisensH& ?? "  ! !( + DOO,#+!77 rc2XRR-$Nconfigr#)r6rJs r _sigma_to_t*FlowMatchHeunDiscreteScheduler._sigma_to_ts{{6666rnum_inference_stepsdevicecjXl[R"URUR5URUR 5U5nX0R R- nUR RU-SUR RS- U--- n[R"U5R[RUS9nX@R R-n[R"USSUSSRS5/5nURUS9Ul[R"U[R "SUR"S9/5n[R"USSUSSRS5USS/5UlSUlSUlSUlSUlg)au Sets the discrete 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 )r&rTNr)rTr')rSr)r*rQr5r4rPr#r$rr-r.r+catrepeat_interleaver/zerosrTr2prev_derivativedtr0r1)r6rSrTr/r2s r set_timesteps,FlowMatchHeunDiscreteScheduler.set_timestepssq$7 KK   T^^ ,d.>.>t~~.NPc [[<<<""V+qDKK4E4E4IV3S/ST!!&),,5==,P[[<<< IIy!}im.M.Ma.PQR "V4FEKK&--$HIJiiVAb\-K-KA-NPVWYWZP[ \]  $ rcUc URnX!:HR5n[U5S:aSOSnX4R5$)Nr r)r/nonzerolenr3)r6rEschedule_timestepsindicesposs rindex_for_timestep1FlowMatchHeunDiscreteScheduler.index_for_timestepsH  %!% %1::< w[U[R5(d[U[R5(a [ S5eUR cURU5 UR[R5nUR(a6URUR n URUR S-n O5URUR S- n URUR n XZs=::aU::a(O O%[U[UR5S- - S5OSn XS--n U S:aA[URUR UR"US9nX-nX?U S-U S-- S ---nUR(a(X1U -- nUU- U - nX- nUUlUUlX0lONX1U -- nUU- U - nS UR$U--nUR&nUR(nSUlSUlSUlUUU--nURUR 5nU=R*S- slU (dU4$[-US 9$) 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.FloatTensor`): The direct output from learned diffusion model. timestep (`float`): The current discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): A current instance of a sample created by the diffusion process. s_churn (`float`): s_tmin (`float`): s_tmax (`float`): s_noise (`float`, defaults to 1.0): Scaling factor for noise added to the sample. generator (`torch.Generator`, *optional*): A random number generator. return_dict (`bool`): Whether or not to return a [`~schedulers.scheduling_flow_match_heun_discrete.FlowMatchHeunDiscreteSchedulerOutput`] tuple. Returns: [`~schedulers.scheduling_flow_match_heun_discrete.FlowMatchHeunDiscreteSchedulerOutput`] or `tuple`: If return_dict is `True`, [`~schedulers.scheduling_flow_match_heun_discrete.FlowMatchHeunDiscreteSchedulerOutput`] is returned, otherwise a tuple is returned where the first element is the sample tensor. zPassing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to `FlowMatchHeunDiscreteScheduler.step()` is not supported. Make sure to pass one of the `scheduler.timesteps` as a timestep.Nr g4y?rlr)r&rTrsrg?)r)rfintr IntTensor LongTensor ValueErrorr;rIr.r+rjr2minr_r shaper&rTrYrZrDr0r)r6rnrErDrorprqrrrsrtrJ sigma_nextgamma sigma_hatrFepsdenoised derivativerZrs rstep#FlowMatchHeunDiscreteScheduler.stepsOT x % %(EOO44(E$4$455G  ?? "  ! !( +5==)  $ $KK0ET__q%89JKK! 34ET__5JEKE^X^E^Gs4;;/!34jAdgQY' 19 "",*<*<\EXEXdmE/CY\E1H%<$DDDF  $ $u 44H 8+y8J'B$.D DG Kz 99H 8+z9J 4 4z ABJB[[F$(D DGDKzB. !nn\%7%78  A> !3 LLrc.URR$rNrOr:s r__len__&FlowMatchHeunDiscreteScheduler.__len__@s{{...r) r1r0rZrSrYrDr5r4r2r/)ir")rrN)"rrrrr _compatiblesorderr rvfloatr7propertyr;r?rBrrrrrKrQstrrTr[rcrIrj Generatorboolrrrrrrrrr r ,s L E$(/ ///(  !!(3(.2 !!u0001))*     67 ! !eCDU>V !D #1e /3 vM''vMu0001vM!! vM  vM  vMvMvMEOO,vMvM 3U: ;vMp/rr ) dataclassesrtypingrrrnumpyr)rconfiguration_utilsrr utilsr r utils.torch_utilsr scheduling_utilsr get_loggerrloggerrr rrrrs_")) A',,   H %  #: #  #U/^[U/r