3jF@SrSSKJr SSKrSSKJrJr SSKJrJ r SSK J r SS K J r \ R"\5r\"S S \55r"S S \ \5rg)aM LTXEulerAncestralRFScheduler This scheduler implements a K-diffusion style Euler-Ancestral sampler specialized for flow / CONST parameterization, closely mirroring ComfyUI's `sample_euler_ancestral_RF` implementation used for LTX-Video. Reference implementation (ComfyUI): comfy.k_diffusion.sampling.sample_euler_ancestral_RF ) dataclassN) ConfigMixinregister_to_config) BaseOutputlogging) randn_tensor)SchedulerMixinc8\rSrSr%Sr\R \S'Srg)"LTXEulerAncestralRFSchedulerOutput&z Output class for the scheduler's `step` function output. Args: prev_sample (`torch.FloatTensor`): Updated sample for the next step in the denoising process. prev_sampleN) __name__ __module__ __qualname____firstlineno____doc__torch FloatTensor__annotations____static_attributes__rp/home/wildlama/miniconda3/lib/python3.13/site-packages/diffusers/schedulers/scheduling_ltx_euler_ancestral_rf.pyr r &s"""rr c\rSrSrSrS/rSr\S"S\S\ S\ 4Sjj5r \ S \4S j5r \ S \4S j5r S#S \4S jjrS$S\ \R -S\R S-S \4SjjrS\ \R -4SjrS%S\S-S\\R(-S-S\\ \R -S-S\\ \R -S-S\ S-4 SjjrS\R S\R S \R 4SjrS&S\R0S\ \R -S\R0S\R2S-S\S \\\R0-4 SjjrS \4S jrS!rg)'LTXEulerAncestralRFScheduler3a Euler-Ancestral scheduler for LTX-Video (RF / CONST parametrization). This scheduler is intended for models where the network is trained with a CONST-like parameterization (as in LTXV / FLUX). It approximates ComfyUI's `sample_euler_ancestral_RF` sampler and is useful when reproducing ComfyUI workflows inside diffusers. The scheduler can either: - reuse the [`FlowMatchEulerDiscreteScheduler`] sigma / timestep logic when only `num_inference_steps` is provided (default diffusers-style usage), or - follow an explicit ComfyUI-style sigma schedule when `sigmas` (or `timesteps`) are passed to [`set_timesteps`]. Args: num_train_timesteps (`int`, defaults to 1000): Included for config compatibility; not used to build the schedule. eta (`float`, defaults to 1.0): Stochasticity parameter. `eta=0.0` yields deterministic DDIM-like sampling; `eta=1.0` matches ComfyUI's default RF behavior. s_noise (`float`, defaults to 1.0): Global scaling factor for the stochastic noise term. FlowMatchEulerDiscreteSchedulerr num_train_timestepsetas_noisecJSUlSUlSUlSUlSUlgN)num_inference_stepssigmas timesteps _step_index _begin_index)selfr r!r"s r__init__%LTXEulerAncestralRFScheduler.__init__Ns*)- +/ .2 $!%rreturncUR$r$)r(r*s r step_index'LTXEulerAncestralRFScheduler.step_index\srcUR$)z The index for the first timestep. It can be set from a pipeline with `set_begin_index` to support image-to-image like workflows that start denoising part-way through the schedule. r)r/s r begin_index(LTXEulerAncestralRFScheduler.begin_index`s    rr4cXlg)zs Included for API compatibility; not strictly needed here but kept to allow pipelines that call `set_begin_index`. Nr3)r*r4s rset_begin_index,LTXEulerAncestralRFScheduler.set_begin_indexhs (rNtimestepschedule_timestepscfUc$URc [S5eURn[U[R5(aUR UR 5nX!:HR5n[U5S:aSOSn[U5S:Xa [S5eX4R5$)an Map a (continuous) `timestep` value to an index into `self.timesteps`. This follows the convention used in other discrete schedulers: if the same timestep value appears multiple times in the schedule (which can happen when starting in the middle of the schedule), the *second* occurrence is used for the first `step` call so that no sigma is accidentally skipped. 8Timesteps have not been set. Call `set_timesteps` first.r rzaPassed `timestep` is not in `self.timesteps`. Make sure to use values from `scheduler.timesteps`.) r' ValueError isinstancerTensortodevicenonzerolenitem)r*r9r:indicesposs rindex_for_timestep/LTXEulerAncestralRFScheduler.index_for_timestepos  %~~% ![\\!%  h - -{{#5#<#<=H%1::< wrr?r@rArGr(r))r*r9s r_init_step_index-LTXEulerAncestralRFScheduler._init_step_indexsp >> !WX X    #(ELL11#;;t~~'<'<=#66x@D #00D rr%rAr&r'muc UcUcUc [S5eSSKJn URUR5nUR UUSUSS9 UR UlURRUS9UlURRUS9Ul SUl SUl gUcUn[U[5(a$[R"U[R S9n OU[U[R"5(aUR[R S9n O[%S['U5S 35eU R(S:wa"[S [+U R,5S 35eU S R/5R15S :a'[2R5S U S R155 UbU RU5n Xl[7[9URSS55n X-Ul Ub5U[;U5S- :wa#[2R5SU[;U5S- 5 [;U5S- UlSUl SUl g)a Set the sigma / timestep schedule for sampling. When `sigmas` or `timesteps` are provided explicitly, they are used as the RF sigma schedule (ComfyUI-style) and are expected to include the terminal 0.0. When both are `None`, the scheduler reuses the [`FlowMatchEulerDiscreteScheduler`] logic to generate sigmas from `num_inference_steps` and the stored config (including any resolution-dependent shifting, Karras/beta schedules, etc.). Args: num_inference_steps (`int`, *optional*): Number of denoising steps. If provided together with explicit `sigmas`/`timesteps`, they are expected to be consistent and are otherwise ignored with a warning. device (`str` or `torch.device`, *optional*): Device to move the internal tensors to. sigmas (`list[float]` or `torch.Tensor`, *optional*): Explicit sigma schedule, e.g. `[1.0, 0.99, ..., 0.0]`. timesteps (`list[float]` or `torch.Tensor`, *optional*): Optional alias for `sigmas`. If `sigmas` is None and `timesteps` is provided, timesteps are treated as sigmas. mu (`float`, *optional*): Optional shift parameter used when delegating to [`FlowMatchEulerDiscreteScheduler.set_timesteps`] and `config.use_dynamic_shifting` is `True`. NzzLTXEulerAncestralRFScheduler.set_timesteps requires either explicit `sigmas`/`timesteps` or a `num_inference_steps` value.r )r)r%rAr&rLr')rA)dtypez-`sigmas` must be a list or torch.Tensor, got .z(`sigmas` must be a 1D tensor, got shape gư>zThe last sigma in the schedule is not zero (%.6f). For best compatibility with ComfyUI's RF sampler, the terminal sigma should be 0.0.r z{Provided `num_inference_steps=%d` does not match `len(sigmas)-1=%d`. Overriding `num_inference_steps` with `len(sigmas)-1`.)r=$scheduling_flow_match_euler_discreter from_configconfig set_timestepsr%r&r@r'r(r)r>listrtensorfloat32r? TypeErrortypendimtupleshapeabsrDloggerwarningfloatgetattrrC) r*r%rAr&r'rLkwargsrbase_scheduler sigmas_tensor num_trains rrU*LTXEulerAncestralRFScheduler.set_timestepss:B >i/"* 8 ^<HHUN  ( ($7 ) (6'I'ID $)//22&2ADK+55888GDN#D  $D   >F fd # #!LLu}}EM  - -"IIEMMI:MKDQWL>YZ[\ \    "GmNaNaHbGccdef f   " ' ' )D 0 NN!b!&&(    ),,V4M$ '$++/DdKL &2  */Bc&kTUo/U NNI#F a  $'v;?  rsigmasamplecURUR:a:UR"/URQSP76nURUR:aM:U$)z> Helper to broadcast a scalar sigma to the shape of `sample`. r )r[viewr])r*rhris r_sigma_broadcast-LTXEulerAncestralRFScheduler._sigma_broadcast sEjj6;;&JJ/ /Q/Ejj6;;& r model_output generator return_dictcZ[U[[R[R45(a [ S5eUR b URc [ S5eURcURU5 URnU[UR 5S- :aUnGO7UR[R5nUR[R5n UR Un UR US-n URU RS5U5n URU RS5U5n XU -- nU R5R!5S:aUnGON[#UR$R&5n[#UR$R(5nSX- S- U--nU U-nSU - nSU- nURURS5U5nURURS5U5nURURS5U5nUU - nUU-SU- U--nUS:axUS:arU S-US-US--US-S-- - R+SS 9R-5n[/UR0XHR2UR4S 9nUUS-- U-UU-U--nURUR45n[7URS-[UR 5S- 5UlU(dU4$[9US 9$) a Perform a single Euler-Ancestral RF update step. Args: model_output (`torch.FloatTensor`): Raw model output at the current step. Interpreted under the CONST parametrization as `v_t`, with denoised state reconstructed as `x0 = x_t - sigma_t * v_t`. timestep (`float` or `torch.Tensor`): The current sigma value (must match one entry in `self.timesteps`). sample (`torch.FloatTensor`): Current latent sample `x_t`. generator (`torch.Generator`, *optional*): Optional generator for reproducible noise. return_dict (`bool`): If `True`, return a `LTXEulerAncestralRFSchedulerOutput`; otherwise return a tuple where the first element is the updated sample. zPassing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to `LTXEulerAncestralRFScheduler.step()` is not supported. Make sure to pass one of the `scheduler.timesteps` values as `timestep`.zGScheduler has not been initialized. Call `set_timesteps` before `step`.r g:0yE>?grg-q=)min)rorArN)r)r>intr IntTensor LongTensorr=r&r'r(rJrCr@rXrlrkr^rDrarTr!r"clampsqrtr r]rArNrsr )r*rnr9rirorpirsample_fmodel_output_frh sigma_nextsigma_b sigma_next_bdenoisedxr!r"downstep_ratio sigma_down alpha_ip1 alpha_down sigma_down_b alpha_ip1_b alpha_down_b sigma_ratio renoise_coeffnoises rstep!LTXEulerAncestralRFScheduler.steps4 heoou7G7G H I IN  ;; $.."8fg g    #  ! !( +    DKK 1$ $ Kyy/H)__U]];NKKNEQU+J++EJJqM8DG001CXNL N"::H~~$$&-DKKOO, 3 34"% (:S(@C'G!G'.8 *,  :-  $44Z__Q5GR "33INN14ExP #44Z__Q5GR *W4 (*cK.?8-KK93%q<?[!^+K|]^afOf+gg3" ) )OO[c[i[iE% u(<=BU]EZ]dEddA$$v||,Kt//!3S5E5IJ> !1kJJrcB[[URSS55$)Nr rQ)rtrbrTr/s r__len__$LTXEulerAncestralRFScheduler.__len__~s74;;(=tDEEr)r)r(r%r&r')rQrrrr)rr$)NNNNN)NT) rrrrr _compatiblesorderrrtrar+propertyr0r4r7rr?rGrJstrrArVrUrlr Generatorboolr r\rrrrrrrr3s.66L E$( &  & & & & C  !S!!(3(Y]# ,#BG,,QUBU# #B 1)= 1 +/,0487; m! 4Zm!ell"T)m!U ell*T1 m! ;-4 m! DL m!^ellELLU\\-1 gK''gK%,,&gK!! gK ??T) gK  gK ,eE4E4E.F FgKRFFrr)r dataclassesrrconfiguration_utilsrrutilsrrutils.torch_utilsr scheduling_utilsr get_loggerrr_r rrrrrs]" A',,   H %  # #  #NF>;NFr