bit j |tj S)a Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [0,1]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar. Choose from `cosine` or `exp` Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs cosinecftj|dzdz tjzdz dzS)NgMb?gT㥛 ?r)mathcospits r alpha_bar_fnz)betas_for_alpha_bar..alpha_bar_fnLs-88QY%/$''9A=>!C Crexpc2tj|dzS)Ng()r!r'r$s rr&z)betas_for_alpha_bar..alpha_bar_fnQs88AI& &rz"Unsupported alpha_transform_type: r dtype) ValueErrorrangeappendminrtensorfloat32)num_diffusion_timestepsmax_betaalpha_transform_typer&betasit1t2s rbetas_for_alpha_barr83s.x' D  & '=>R=STUU E * +M ( (!e. . S\"- R0@@@(KLM <<U]] 33rceZdZdZeDcgc]}|j c}}ZdZe d0de de de de de e ejee fd e d e ed e ed e ed e ede de de fdZd1dZedZedZedZd2de fdZdej4de e ej4fdej4fdZ d3de e de e ej8fde e de ee fdZd Zd!ej4dej4fd"Zd!ej4de dej4fd#Z d4d!ej4de d$e d%e dej4f d&Z!ed'Z"d(Z# d5d)e ej4ejfde e ej4fde ej4ejfd*ede e$e%ff d+Z&d,ej4d-ej4dej4dej4fd.Z'd/Z(ycc}}w)6HeunDiscreteScheduleru Scheduler with Heun steps for discrete beta schedules. 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. beta_start (`float`, defaults to 0.0001): The starting `beta` value of inference. beta_end (`float`, defaults to 0.02): The final `beta` value. beta_schedule (`str`, defaults to `"linear"`): The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear` or `scaled_linear`. trained_betas (`np.ndarray`, *optional*): Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`. prediction_type (`str`, defaults to `epsilon`, *optional*): Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process), `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen Video](https://imagen.research.google/video/paper.pdf) paper). clip_sample (`bool`, defaults to `True`): Clip the predicted sample for numerical stability. clip_sample_range (`float`, defaults to 1.0): The maximum magnitude for sample clipping. Valid only when `clip_sample=True`. use_karras_sigmas (`bool`, *optional*, defaults to `False`): Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`, the sigmas are determined according to a sequence of noise levels {σi}. use_exponential_sigmas (`bool`, *optional*, defaults to `False`): Whether to use exponential sigmas for step sizes in the noise schedule during the sampling process. use_beta_sigmas (`bool`, *optional*, defaults to `False`): Whether to use beta sigmas for step sizes in the noise schedule during the sampling process. Refer to [Beta Sampling is All You Need](https://huggingface.co/papers/2407.12173) for more information. 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. steps_offset (`int`, defaults to 0): An offset added to the inference steps, as required by some model families. rNnum_train_timesteps beta_startbeta_end beta_schedule trained_betasprediction_typeuse_karras_sigmasuse_exponential_sigmasuse_beta_sigmas clip_sampleclip_sample_rangetimestep_spacing steps_offsetc|jjrts tdt |jj|jj |jj gdkDr td|+tj|tj|_ n|dk(r-tj|||tj|_ n|dk(r6tj|dz|dz|tjdz|_ nJ|d k(rt|d |_ n2|d k(rt|d |_ nt|d |jd|jz |_tj"|j d|_|j'|d|||_d|_d|_|j,j/d|_y)Nz:Make sure to install scipy if you want to use beta sigmas.r znOnly one of `config.use_beta_sigmas`, `config.use_exponential_sigmas`, `config.use_karras_sigmas` can be used.r)linear scaled_linear?rsquaredcos_cap_v2r)r3r'z is not implemented for ?r)dimcpu)configrCr ImportErrorsumrBrAr+rr/r0r4linspacer8NotImplementedError __class__alphascumprodalphas_cumprod set_timesteps _step_index _begin_indexsigmasto)selfr;r<r=r>r?r@rArBrCrDrErFrGs r__init__zHeunDiscreteScheduler.__init__s" ;; & &/A/CZ[ [  ++T[[-O-OQUQ\Q\QnQno pst tA   $m5==IDJ h & H>QY^YfYfgDJ o - C3H[chcpcpquvvDJ 1 1,-@W_`DJ e #,-@W\]DJ%7OPTP^P^O_&`a aDJJ& #mmDKKQ? .6IJ!2 kknnU+ rc| |j}||k(j}t|dkDrdnd}||jS)Nr r) timestepsnonzerolenitem)r^timestepschedule_timestepsindicesposs rindex_for_timestepz(HeunDiscreteScheduler.index_for_timestepsL  %!% %1::< wj9d|_ycc}wcc}wcc}w)a 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. num_train_timesteps (`int`, *optional*): The number of diffusion steps used when training the model. If `None`, the default `num_train_timesteps` attribute is used. timesteps (`List[int]`, *optional*): Custom timesteps used to support arbitrary spacing between timesteps. If `None`, timesteps will be generated based on the `timestep_spacing` attribute. If `timesteps` is passed, `num_inference_steps` must be `None`, and `timestep_spacing` attribute will be ignored. NzEMust pass exactly one of `num_inference_steps` or `custom_timesteps`.zACan only pass one of `num_inference_steps` or `custom_timesteps`.z=Cannot use `timesteps` with `config.use_karras_sigmas = True`zCCannot set `timesteps` with `config.use_exponential_sigmas = True`.z+BDW_a_i_ijkomokopuuw --:0D4L4LL  YYq*=>KRRTUYWYUYZ__ahhikisist T[[555 --;043K3KK  YY':A {KRRTYY[bbcecmcmn Q  {{3345NOA 3 33t7J7JJsRSVVF^ 9bii3v;&?H ;; ( (,,vSWSkSk,lFSY!Z%$"2"25*"E!Z[I [[ / /11FXk1lFSY!Z%$"2"25*"E!Z[I [[ ( (**VQd*eFSY!Z%$"2"25*"E!Z[I#077 C!!&),,F,;iiVAb\-K-KA-NPVWYWZP[ \] $$Y/ IIy!}im.M.Ma.PQR "V5==I $ kknnU+ /"["["[s6UU0U ctjtj|d}||ddtjfz }tj|dk\dj dj |jddz }|dz}||}||}||z ||z z } tj | dd} d| z |z| |zz} | j|j} | S)Ng|=r)axisr)rlr ) rrmaximumnewaxiscumsumargmaxclipshapereshape) r^rzr log_sigmadistslow_idxhigh_idxlowhighwr%s rrz!HeunDiscreteScheduler._sigma_to_tdsFF2::eU34 Jq"**}55))UaZq188a8@EE*JZJZ[\J]`aJaEbQ;!(#9_t , GGAq! Ug H , IIekk "rrct|jdr|jj}nd}t|jdr|jj}nd}||n|dj }||n|dj }d}t j dd|}|d|z z}|d|z z}||||z zz|z} | S)z6Constructs the noise schedule of Karras et al. (2022). sigma_minN sigma_maxrrg@r )hasattrrPrrrdrrS) r^rr|rrrhoramp min_inv_rho max_inv_rhor\s rrz(HeunDiscreteScheduler._convert_to_karras|s 4;; , --II 4;; , --II!*!6IIbMTUCUZ]B]3]#^+q.1,-$ [[ ( (H 4#/ +DKK,G,G+HHtu  ;; " "#7#=#=... 0M0M$   $ $ #779DJi'B$.D DG DK!#77:EJ..;q@JB[[F$(D DGDKzB.  A$  +{Ymnnroriginal_samplesnoisec|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)Nrmpsr)rr)r\r]r}r*typeris_floating_pointrar0rsrirprflattenrc unsqueeze) r^rrrar\rfr% step_indicesrz noisy_sampless r add_noisezHeunDiscreteScheduler.add_noise@s'7'>'>FVF\F\]  " " ' '5 0U5L5LY5W!%!2!23C3J3JRWR_R_!2!` ! %5%<%>qAFF>L E$(#%BF(,116*/&+#& *., .,., .,  .,  bjj$u+&= >? .,.,$D>.,!).,"$.,d^.,!.,.,.,.,b #33  !!(3( u||+,  8.2+/-1)- Z,%c]Z,c5<<'(Z,&c] Z, DI& Z,z0ELLRWR^R^4TW\a\h\h.dg1! eoELL"**45eou||+,eoellBJJ./ eo  eo *E1 2 eoP,,||<<   B/q?sG-r:)g+?r)r! dataclassesrtypingrrrrnumpyrrconfiguration_utilsr r utilsr r scheduling_utilsrr scipy.statsrrr8r:rrrrsf !// A2G 8*8 8(!)4XC/NKC/r