bi"ddlZddlmZddlmZmZmZmZddlZddl m Z m Z ddl m Z ddlmZdd lmZeGd d e Z dd ZGd dee Zy)N) dataclass)ListOptionalTupleUnion) ConfigMixinregister_to_config) BaseOutput) randn_tensor)SchedulerMixinc0eZdZUdZej ed<y)DDPMWuerstchenSchedulerOutputaA 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. prev_sampleN)__name__ __module__ __qualname____doc__torchTensor__annotations__j/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/diffusers/schedulers/scheduling_ddpm_wuerstchen.pyrrsrrc $|dk(rd}n|dk(rd}ntd|g}t|D]<}||z }|dz|z }|jtd||||z z |>t j |tj S)a Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [0,1]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar. Choose from `cosine` or `exp` Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs cosinecftj|dzdz tjzdz dzS)NMb?gT㥛 ?r)mathcospits r alpha_bar_fnz)betas_for_alpha_bar..alpha_bar_fnEs-88QY%/$''9A=>!C Crexpc2tj|dzS)Ng()r r&r#s rr%z)betas_for_alpha_bar..alpha_bar_fnJs88AI& &rz"Unsupported alpha_transform_type: r dtype) ValueErrorrangeappendminrtensorfloat32)num_diffusion_timestepsmax_betaalpha_transform_typer%betasit1t2s rbetas_for_alpha_barr7,s.x' D  & '=>R=STUU E * +M ( (!e. . S\"- R0@@@(KLM <<U]] 33rc eZdZdZe ddedefdZdZddejde e d ejfd Z dd e d e e e d eeej ffdZ ddejde dejded eeeff dZdejdejd ejd ejfdZdZdZy)DDPMWuerstchenSchedulera Denoising diffusion probabilistic models (DDPMs) explores the connections between denoising score matching and Langevin dynamics sampling. [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. For more details, see the original paper: https://huggingface.co/papers/2006.11239 Args: scaler (`float`): .... s (`float`): .... scalersc||_tj|g|_tj|jd|jzz tj zdzdz|_d|_y)Nr ?r?)r:rr.r;r!r"_init_alpha_cumprodinit_noise_sigma)selfr:r;s r__init__z DDPMWuerstchenScheduler.__init__is^  qc"#(99TVVq466z-BUXX-MPS-S#TXY#Y !$rc|jdkDrdd|z |jzz }n|jdkr||jz}tj||jj |zd|jj |zz tj zdzdz|j j |z }|jddS)Nr r=rg-C6?gH.?)r:rr!r;tor"r?clamp)rAr$device alpha_cumprods r_alpha_cumprodz&DDPMWuerstchenScheduler._alpha_cumprodvs ;;?QUt{{**A [[1_4;;A 6" "q46699V+<'< = H3 N )),,V45 ""6622rNsampletimestepreturnc|S)aP Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.Tensor`): input sample timestep (`int`, optional): current timestep Returns: `torch.Tensor`: scaled input sample r)rArIrJs rscale_model_inputz)DDPMWuerstchenScheduler.scale_model_inputs  rnum_inference_steps timestepsrFc|tjdd|dz|}t|tjs$tj|j |}||_y)a Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference. Args: num_inference_steps (`Dict[float, int]`): the number of diffusion steps used when generating samples with a pre-trained model. If passed, then `timesteps` must be `None`. device (`str` or `torch.device`, optional): the device to which the timesteps are moved to. {2 / 3: 20, 0.0: 10} Nr>gr rF)rlinspace isinstancerrDrO)rArNrOrFs r set_timestepsz%DDPMWuerstchenScheduler.set_timestepssS  sC1Dq1HQWXI)U\\2 Y/226:I"r model_output return_dictc |j}|j}|}|j|} |j||j|j dg|j ddD cgc]} dc} } |j| |j| j dg|j ddD cgc]} dc} } | | z } d| z j|d| z |zd| z jz z z}t|j ||j|j}d| z d| z zd| z z j|z}||| dk7jj| j dg|j ddD cgc]} dc} zz}|s|j|fSt|j|Scc} wcc} wcc} w)a~ Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion process from the learned model outputs (most often the predicted noise). Args: model_output (`torch.Tensor`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`torch.Tensor`): current instance of sample being created by diffusion process. generator: random number generator. return_dict (`bool`): option for returning tuple rather than DDPMWuerstchenSchedulerOutput class Returns: [`DDPMWuerstchenSchedulerOutput`] or `tuple`: [`DDPMWuerstchenSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. rr Nr>) generatorrFr))r) r)rFprevious_timesteprHviewsizeshapesqrtr floatrDr)rArUrJrIrXrVr)rFr$prev_t_rGalpha_cumprod_prevalphamu std_noisestdpreds rstepzDDPMWuerstchenScheduler.steps2""$$ ''*;++Av6;;AFF1IfTZT`T`abacTdHeqHef ET00@EEfkkRSnucicocopqprcsWt^_XYWtu 22Ek   !Vq5yL.HAP]L]KcKcKe.e%e f Y|GZGZbnbtbtu E c$6673;NOUUWZccC46Q;--/44V[[^dRXR^R^_`_aRbFcQqFcdddGGEN$ $,HHIfWtGds* G 7 G  Goriginal_samplesnoisecP|j}|j}|j||j|j dg|j ddDcgc]}dc}}|j |zd|z j |zz}|j|Scc}w)NrQrr r()rFr)rHrZr[r\r]rD) rArhrirOrFr)r`rG noisy_sampless r add_noisez!DDPMWuerstchenScheduler.add_noises "(( &&J++If+EJJ NN1  ,<,B,B12,F Gq G &**,/??1}CTBZBZB\_dBdd e,,!Hs B# c.|jjSN)confignum_train_timesteps)rAs r__len__zDDPMWuerstchenScheduler.__len__s{{...rc|j|dz jjj}|j|dzdj |j d}|S)Nrr )rOabsargminitemexpandr\)rArJindexr_s rrYz)DDPMWuerstchenScheduler.previous_timesteps_(1+-224;;=BBD *4077q8IJ r)r>rrn)NNN)NT)rrrrr r^rBrHrrrintrMrrstrrFrTboolrrrgrlrqrYrrrr9r9XsB  $ $  $ $3   Y^YeYe  $()-+/ # #DI&#c5<<'( #6 ,Ill,I,I ,I  ,I ,e3 4,I\ -,, -|| -<< -  -/rr9)g+?r)r dataclassesrtypingrrrrrconfiguration_utilsr r utilsr utils.torch_utilsr scheduling_utilsrrr7r9rrrrs[$ !// A,,  J   !)4XNnkNr