bi4_ddlZddlZddlmZddlmZmZmZmZddl Z ddl Z ddl Z ddl mZddlmZmZddlmZdd lmZmZeGd d eZ dd ZGd deeZy)N) dataclass)ListOptionalTupleUnion) integrate) ConfigMixinregister_to_config) BaseOutput)KarrasDiffusionSchedulersSchedulerMixincXeZdZUdZej ed<dZeej ed<y)LMSDiscreteSchedulerOutputaq Output class for the scheduler's `step` function output. 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. pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images): The predicted denoised sample `(x_{0})` based on the model output from the current timestep. `pred_original_sample` can be used to preview progress or for guidance. prev_sampleNpred_original_sample) __name__ __module__ __qualname____doc__torchTensor__annotations__rrg/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/diffusers/schedulers/scheduling_lms_discrete.pyrrs' 37(5<<07rrc $|dk(rd}n|dk(rd}ntd|g}t|D]<}||z }|dz|z }|jtd||||z z |>t j |tj S)a Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [0,1]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar. Choose from `cosine` or `exp` Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs cosinecftj|dzdz tjzdz dzS)NgMb?gT㥛 ?r )mathcospits r alpha_bar_fnz)betas_for_alpha_bar..alpha_bar_fnJs-88QY%/$''9A=>!C Crexpc2tj|dzS)Ng()r!r'r$s rr&z)betas_for_alpha_bar..alpha_bar_fnOs88AI& &rz"Unsupported alpha_transform_type: r dtype) ValueErrorrangeappendminrtensorfloat32)num_diffusion_timestepsmax_betaalpha_transform_typer&betasit1t2s rbetas_for_alpha_barr81s.x' D  & '=>R=STUU E * +M ( (!e. . S\"- R0@@@(KLM <<U]] 33rcleZdZdZeDcgc]}|j c}}ZdZe d/de de de de de e ejee fd e ed e ed e ed e d e de fdZedZedZedZd0de fdZdej2de e ej2fdej2fdZdZd1de de e ej8ffdZd1dZdZdZ d ej2dej2fd!Z!d ej2de dej2fd"Z" d2d ej2de d#e d$e dej2f d%Z# d3d&ej2de e ej2fdej2d'e d(ede e$e%ff d)Z&d*ej2d+ej2d,ej2dej2fd-Z'd.Z(ycc}}w)4LMSDiscreteScheduleru A linear multistep scheduler 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`. 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. 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). 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. r Nnum_train_timesteps beta_startbeta_end beta_schedule trained_betasuse_karras_sigmasuse_exponential_sigmasuse_beta_sigmasprediction_typetimestep_spacing steps_offsetc t|jj|jj|jjgdkDr t d|+t j|t j|_ n|dk(r-t j|||t j|_ nk|dk(r6t j|dz|dz|t jdz|_ n0|dk(rt||_ nt|d |jd |jz |_t j|jd |_t#j$d|j z |j z dz} t#j&| ddd dggj)t"j} t j*| |_d|_||_|j1|dg|_d|_d|_d|_|j,j;d|_y)Nr znOnly one of `config.use_beta_sigmas`, `config.use_exponential_sigmas`, `config.use_karras_sigmas` can be used.r)linear scaled_linear?r squaredcos_cap_v2z is not implemented for ?r)dimFcpu)sumconfigrBrAr@r+rr/r0r4linspacer8NotImplementedError __class__alphascumprodalphas_cumprodnparray concatenateastype from_numpysigmasnum_inference_steps set_timesteps derivativesis_scale_input_called _step_index _begin_indexto) selfr;r<r=r>r?r@rArBrCrDrEr]s r__init__zLMSDiscreteScheduler.__init__s  ++T[[-O-OQUQ\Q\QnQno pst tA   $m5==IDJ h & H>QY^YfYfgDJ o - C3H[chcpcpquvvDJ 1 1,-@ADJ%7OPTP^P^O_&`a aDJJ& #mmDKKQ?A 3 33t7J7JJsRS"u 56==bjjI&&v. $( !2 .5%*" kknnU+ rc|jjdvr|jjS|jjdzdzdzS)N)rRtrailingr r rI)rQrDr]maxres rinit_noise_sigmaz%LMSDiscreteScheduler.init_noise_sigmasH ;; ' '+C C;;??$ $ !Q&*s22rc|jS)zg The index counter for current timestep. It will increase 1 after each scheduler step. )rbrjs r step_indexzLMSDiscreteScheduler.step_indexs rc|jS)zq The index for the first timestep. It should be set from pipeline with `set_begin_index` method. rcrjs r begin_indexz LMSDiscreteScheduler.begin_indexs    rrpc||_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. Nro)rerps rset_begin_indexz$LMSDiscreteScheduler.set_begin_indexs (rsampletimestepreturnc|j|j||j|j}||dzdzdzz }d|_|S)a Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.Tensor`): The input sample. timestep (`float` or `torch.Tensor`): The current timestep in the diffusion chain. Returns: `torch.Tensor`: A scaled input sample. r r rIT)rm_init_step_indexr]ra)rersrtsigmas rscale_model_inputz&LMSDiscreteScheduler.scale_model_inputsR ?? "  ! !( + DOO,E1HqLS01%)" rcfd}tj|jjdzdd}|S)z Compute the linear multistep coefficient. Args: order (): t (): current_order (): cd}tD]F}|k(r ||j|z z jz j|z z z z}H|S)NrK)r,r])tauprodk current_orderorderrer%s rlms_derivativez@LMSDiscreteScheduler.get_lms_coefficient..lms_derivativesvD5\ k A%t{{1q511dkk!mBS6TW[WbWbcdghchWi6ijj kKrr -C6?)epsrelr)rquadr])rerr%rrintegrated_coeffs```` rget_lms_coefficientz(LMSDiscreteScheduler.get_lms_coefficientsD %>>.$++a.$++VWZ[V[J\eijklmrr^devicec ||_|jjdk(rTtjd|jj dz |tj dddj}nw|jjdk(r|jj |jz}tjd||zjdddjjtj }||jjz }n|jjdk(r|jj |jz }tj|jj d| jjjtj }|dz}n"t|jjd tjd|jz |jz d z}tj|}tj |tjdt#||}|jj$rF|j'| }tj|Dcgc]}|j)||c}}n|jj*rG|j-|| }tj|Dcgc]}|j)||c}}n\|jj.rF|j1|| }tj|Dcgc]}|j)||c}}tj2|d ggjtj }t5j6|j9||_t5j6|j9|t4j |_d|_d|_ |j:j9d|_g|_!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. rRrr r)NrMleadingrhzY is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.rI) in_sigmas)rr^rN)rrr*rO)"r^rQrDrXrRr;r0copyarangeroundr[rEr+rYrWloginterplenr@_convert_to_karras _sigma_to_trA_convert_to_exponentialrB_convert_to_betarZrr\rdr] timestepsrbrcr`)rer^rr step_ratior] log_sigmasrxs rr_z"LMSDiscreteScheduler.set_timestepssh$7  ;; ' ': 5 At{{'F'F'JL_gigqgqr"df [[ ) )Y 688DFSY!Z%$"2"25*"E!Z[I [[ / /11FXk1lFSY!Z%$"2"25*"E!Z[I [[ ( (**VQd*eFSY!Z%$"2"25*"E!Z[I#077 C&&v.111@ )))477vU]]7[ kknnU+ !"["["[s"Q ?QQc| |j}||k(j}t|dkDrdnd}||jS)Nr r)rnonzeroritem)rertschedule_timestepsindicesposs rindex_for_timestepz'LMSDiscreteScheduler.index_for_timestepBsL  %!% %1::< wz,LMSDiscreteScheduler.step..s# #45*EJ # s)rr)rawarningswarnrmrwr]rQrCr+r`r-rpopr.r,rrPzipreversedrbr) rerrtrsrrrxrr curr_order lms_coeffsrs rstepzLMSDiscreteScheduler.steps<)) MME  ?? "  ! !( + DOO, ;; & &) 3#)EL,@#@ [[ ( (N :#/E6UAX\c'>FVF\F\]  " " ' '5 0U5L5LY5W!%!2!23C3J3JRWR_R_!2!` ! %5%<%>qAFF>L E$("%BF,116*/( */, /,/, /,  /,  bjj$u+&= >? /,$D>/,!)/,"$/,/,/,/,/,b33  !!(3( eU\\FY@Z_d_k_k0 ,88eCDU>V8v #10 ELL U\\ TW\a\h\h.dgrsd !// AG 88 8(!)4XK/>;K/r