biddlZddlmZddlmZmZmZmZddlZ ddl Z ddl m Z m Z ddlmZmZmZddlmZdd lmZmZerddlZej2eZeGd d eZ dd Zd ZGddee Zy)N) dataclass)ListOptionalTupleUnion) ConfigMixinregister_to_config) BaseOutputis_scipy_availablelogging) randn_tensor)KarrasDiffusionSchedulersSchedulerMixincXeZdZUdZej ed<dZeej ed<y)EulerDiscreteSchedulerOutputaq 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__rri/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/diffusers/schedulers/scheduling_euler_discrete.pyrr"s' 37(5<<07rrc $|dk(rd}n|dk(rd}ntd|g}t|D]<}||z }|dz|z }|jtd||||z z |>t j |tj S)a Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [0,1]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar. Choose from `cosine` or `exp` Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs cosinecftj|dzdz tjzdz dzS)NgMb?gT㥛 ?r)mathcospits r alpha_bar_fnz)betas_for_alpha_bar..alpha_bar_fnOs-88QY%/$''9A=>!C Crexpc2tj|dzS)Ng()r#r)r&s rr(z)betas_for_alpha_bar..alpha_bar_fnTs88AI& &rz"Unsupported alpha_transform_type: rdtype) ValueErrorrangeappendminrtensorfloat32)num_diffusion_timestepsmax_betaalpha_transform_typer(betasit1t2s rbetas_for_alpha_barr:6s.x' D  & '=>R=STUU E * +M ( (!e. . S\"- R0@@@(KLM <<U]] 33rc(d|z }tj|d}|j}|dj}|dj}||z}||||z z z}|dz}|dd|ddz }tj|dd|g}d|z }|S)a. Rescales betas to have zero terminal SNR Based on https://huggingface.co/papers/2305.08891 (Algorithm 1) Args: betas (`torch.Tensor`): the betas that the scheduler is being initialized with. Returns: `torch.Tensor`: rescaled betas with zero terminal SNR ?rdimrrN)rcumprodsqrtclonecat)r6alphasalphas_cumprodalphas_bar_sqrtalphas_bar_sqrt_0alphas_bar_sqrt_T alphas_bars rrescale_zero_terminal_snrrJcs5[F]]6q1N$))+O(*002'+113((O(,=@Q,QRRO!!#J ^j"o -F YY 1Q0 1F JE Lrc$deZdZdZeDcgc]}|j c}}ZdZe d>de de de de de e ejee fd e d e d e ed e ed e ede e de e de de de dede f"dZedZedZedZd?de fdZdej2de e ej2fdej2fdZ d@de d e e ej6fd!e ee d"e ee fd#Zd$Zd%ej2dej2fd&Zd%ej2de dej2fd'Z dAd%ej2de d(e d)e dej2f d*Z dBd+Z!d,Z"d-d-e d.d/dd0fd1ej2de e ej2fdej2d2e d3e d4e d5e d6e ejFd7ede e$e%ffd8Z&d9ej2d:ej2d!ej2dej2fd;Z'dej2d:ej2d!ej2dej2fd<Z(d=Z)ycc}}w)CEulerDiscreteScheduleruB Euler 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. 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). interpolation_type(`str`, defaults to `"linear"`, *optional*): The interpolation type to compute intermediate sigmas for the scheduler denoising steps. Should be on of `"linear"` or `"log_linear"`. 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. rescale_betas_zero_snr (`bool`, defaults to `False`): Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and dark samples instead of limiting it to samples with medium brightness. Loosely related to [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506). final_sigmas_type (`str`, defaults to `"zero"`): The final `sigma` value for the noise schedule during the sampling process. If `"sigma_min"`, the final sigma is the same as the last sigma in the training schedule. If `zero`, the final sigma is set to 0. rNnum_train_timesteps beta_startbeta_end beta_schedule trained_betasprediction_typeinterpolation_typeuse_karras_sigmasuse_exponential_sigmasuse_beta_sigmas sigma_min sigma_maxtimestep_spacing timestep_type steps_offsetrescale_betas_zero_snrfinal_sigmas_typecN|jjrts tdt |jj|jj |jj gdkDr td|+tj|tj|_ n|dk(r-tj|||tj|_ nk|dk(r6tj|dz|dz|tjdz|_ n0|d k(rt||_ nt|d |j|rt!|j|_ d |jz |_tj$|j"d |_|rd|j&d<d|j&z |j&z dzj)d }t+jd |dz |t,dddj/}tj0|j3tj}d|_|dk(r?|dk(r:tj6|Dcgc]}d|j9zc}|_n||_tj<|tj>d|j@g|_!d|_"||_| |_| |_d|_#d|_$|jBj3d|_!ycc}w)Nz:Make sure to install scipy if you want to use beta sigmas.rznOnly one of `config.use_beta_sigmas`, `config.use_exponential_sigmas`, `config.use_karras_sigmas` can be used.r+linear scaled_linear?rsquaredcos_cap_v2z is not implemented for r<rr=gp>r? continuous v_prediction?deviceFcpu)%configrVr ImportErrorsumrUrTr-rr1r2r6linspacer:NotImplementedError __class__rJrDr@rEflipnpfloatcopy from_numpytonum_inference_stepsrlog timestepsrCzerosrgsigmasis_scale_input_called _step_index _begin_index)selfrMrNrOrPrQrRrSrTrUrVrWrXrYrZr[r\r]ryrwsigmas r__init__zEulerDiscreteScheduler.__init__s* ;; & &/A/CZ[ [  ++T[[-O-OQUQ\Q\QnQno pst tA   $m5==IDJ h & H>QY^YfYfgDJ o - C3H[chcpcpquvvDJ 1 1,-@ADJ%7OPTP^P^O_&`a a !24::>DJDJJ& #mmDKKQ? !'-D   #+++t/B/BBsJPPQRSKK#6#:dk(r&d |jdz |jdz d z}n>|jj>dk(rd}n"td|jj>tj@||ggjtj}t3jB|jEt2j|}|jj dk(rf|jj d k(rMt3jF|dd Dcgc]}d|jzc}jE||_$nGt3jB|jtjjE||_$d|_%d|_&|jEd|_'ycc}wcc}wcc}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. timesteps (`List[int]`, *optional*): Custom timesteps used to support arbitrary timesteps schedule. If `None`, timesteps will be generated based on the `timestep_spacing` attribute. If `timesteps` is passed, `num_inference_steps` and `sigmas` must be `None`, and `timestep_spacing` attribute will be ignored. sigmas (`List[float]`, *optional*): Custom sigmas used to support arbitrary timesteps schedule schedule. If `None`, timesteps and sigmas will be generated based on the relevant scheduler attributes. If `sigmas` is passed, `num_inference_steps` and `timesteps` must be `None`, and the timesteps will be generated based on the custom sigmas schedule. Nz2Only one of `timesteps` or `sigmas` should be set.zIMust pass exactly one of `num_inference_steps` or `timesteps` or `sigmas.zFCan only pass one of `num_inference_steps` or `timesteps` or `sigmas`.z>Cannot set `timesteps` with `config.use_karras_sigmas = True`.zCCannot set `timesteps` with `config.use_exponential_sigmas = True`.z.>rzz.JKNNV\N]DN ii& ["`\&_&_&_"+Xs _-_2_7<_< `ctjtj|d}||ddtjfz }tj|dk\dj dj |jddz }|dz}||}||}||z ||z z } tj | dd} d| z |z| |zz} | j|j} | S)Ng|=r)axisr)rr) rprvmaximumnewaxiscumsumargmaxclipshapereshape) r}r~r log_sigmadistslow_idxhigh_idxlowhighwr's rrz"EulerDiscreteScheduler._sigma_to_tsFF2::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).rWNrXr?rg@r)hasattrrirWrXitemrprl) r}rrurWrXrhoramp min_inv_rho max_inv_rhorys rrz)EulerDiscreteScheduler._convert_to_karrass 4;; , --II 4;; , --II!*!6IIbM'>FVF\F\]  " " ' '5 0U5L5LY5W!%!2!23C3J3JRWR_R_!2!` ! %5%<%>qAFF>L E$("%BF("*,116*/%)%) *'',!'%H, H,H, H,  H,  bjj$u+&= >? H,H, H,$D>H,!)H,"$H,E?H,E?H,H,H, !H,"!%#H,$%H,H,T))  !!(3( eU\\FY@Z_d_k_k4$(+/)-(, C' C'c5<<'(C'DI& C' e% C'J0ELLRWR^R^4TW\a\h\h,dg #1e /3 lplllpu||+,lp lp  lp  lplplpEOO,lplp +U2 3lp\,,||<<   B#5<<# #QVQ]Q]#bgbnbn#J/{?sH,rL)g+?r!) r# dataclassesrtypingrrrrrrprconfiguration_utilsr r utilsr r r utils.torch_utilsrscheduling_utilsrr scipy.statsr get_loggerrrrr:rJrLrrrr s !// A;;,G   H % 8:8 8(!)4Z!Hn /^[n /r