bieddlZddlmZddlmZmZmZmZddlZ ddl Z ddl m Z m Z ddlmZddlmZdd lmZmZeGd d eZ dd Zd ZGddee Zy)N) dataclass)ListOptionalTupleUnion) ConfigMixinregister_to_config) BaseOutput) randn_tensor)KarrasDiffusionSchedulersSchedulerMixincXeZdZUdZej ed<dZeej ed<y)DDPMSchedulerOutputaq 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__rr_/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/diffusers/schedulers/scheduling_ddpm.pyrrs' 37(5<<07rrc $|dk(rd}n|dk(rd}ntd|g}t|D]<}||z }|dz|z }|jtd||||z z |>t j |tj S)a Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [0,1]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar. Choose from `cosine` or `exp` Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs cosinecftj|dzdz tjzdz dzS)NgMb?gT㥛 ?r)mathcospits r alpha_bar_fnz)betas_for_alpha_bar..alpha_bar_fnIs-88QY%/$''9A=>!C Crexpc2tj|dzS)Ng()r!r'r$s rr&z)betas_for_alpha_bar..alpha_bar_fnNs88AI& &rz"Unsupported alpha_transform_type: r dtype) ValueErrorrangeappendminrtensorfloat32)num_diffusion_timestepsmax_betaalpha_transform_typer&betasit1t2s rbetas_for_alpha_barr80s.x' D  & '=>R=STUU E * +M ( (!e. . S\"- R0@@@(KLM <<U]] 33rc(d|z }tj|d}|j}|dj}|dj}||z}||||z z z}|dz}|dd|ddz }tj|dd|g}d|z }|S)a. Rescales betas to have zero terminal SNR Based on https://huggingface.co/papers/2305.08891 (Algorithm 1) Args: betas (`torch.Tensor`): the betas that the scheduler is being initialized with. Returns: `torch.Tensor`: rescaled betas with zero terminal SNR ?rdimrr N)rcumprodsqrtclonecat)r4alphasalphas_cumprodalphas_bar_sqrtalphas_bar_sqrt_0alphas_bar_sqrt_T alphas_bars rrescale_zero_terminal_snrrH]s5[F]]6q1N$))+O(*002'+113((O(,=@Q,QRRO!!#J ^j"o -F YY 1Q0 1F JE Lrc eZdZdZeDcgc]}|j c}}ZdZe d'de de de de de e ejee fd e d ed e d ed e de de de de defdZd(dej(de e dej(fdZ d)de e de e ej,fde ee fdZd*dZdej(dej(fdZ d+dej(de dej(dede eeff d Zd!ej(d"ej(dej:dej(fd#Zdej(d"ej(dej:dej(fd$Zd%Z d&Z!ycc}}w), DDPMSchedulera `DDPMScheduler` explores the connections between denoising score matching and Langevin dynamics sampling. 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`, `scaled_linear`, `squaredcos_cap_v2`, or `sigmoid`. trained_betas (`np.ndarray`, *optional*): An array of betas to pass directly to the constructor without using `beta_start` and `beta_end`. variance_type (`str`, defaults to `"fixed_small"`): Clip the variance when adding noise to the denoised sample. Choose from `fixed_small`, `fixed_small_log`, `fixed_large`, `fixed_large_log`, `learned` or `learned_range`. 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`. 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). thresholding (`bool`, defaults to `False`): Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such as Stable Diffusion. dynamic_thresholding_ratio (`float`, defaults to 0.995): The ratio for the dynamic thresholding method. Valid only when `thresholding=True`. sample_max_value (`float`, defaults to 1.0): The threshold value for dynamic thresholding. Valid only when `thresholding=True`. timestep_spacing (`str`, defaults to `"leading"`): 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). r Nnum_train_timesteps beta_startbeta_end beta_schedule trained_betas variance_type clip_sampleprediction_type thresholdingdynamic_thresholding_ratioclip_sample_rangesample_max_valuetimestep_spacing steps_offsetrescale_betas_zero_snrc|+tj|tj|_n|dk(r-tj|||tj|_n|dk(r6tj|dz|dz|tjdz|_np|dk(rt ||_nZ|dk(r;tjdd |}tj |||z z|z|_nt|d |j|rt|j|_d |jz |_ tj|jd |_ tjd |_ d |_d|_d|_tj"t%j&d |dddj)|_||_y)Nr)linear scaled_linear?rsquaredcos_cap_v2sigmoidiz is not implemented for r:rr;Fr=)rr/r0r4linspacer8r_NotImplementedError __class__rHrBr>rConeinit_noise_sigmacustom_timestepsnum_inference_steps from_numpynparangecopy timestepsrP)selfrKrLrMrNrOrPrQrRrSrTrUrVrWrXrYr4s r__init__zDDPMScheduler.__init__s&  $m5==IDJ h & H>QY^YfYfgDJ o - C3H[chcpcpquvvDJ 1 1,-@ADJ i 'NN2q*=>Eu-J1FG*TDJ%7OPTP^P^O_&`a a "24::>DJDJJ& #mmDKKQ?<<$!$!&#' ))"))A7J*KDbD*Q*V*V*XY*rsampletimestepreturnc|S)a Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Args: sample (`torch.Tensor`): The input sample. timestep (`int`, *optional*): The current timestep in the diffusion chain. Returns: `torch.Tensor`: A scaled input sample. r)rmrorps rscale_model_inputzDDPMScheduler.scale_model_inputs  rrgdevicerlc l| | td|tdt|D]}||||dz k\std|d|jjk\r#td|jjdt j |t j}d |_nZ||jjkDr=td |d |jjd |jjd ||_ d|_|jjdk(rot jd|jjdz |jdddjjt j}nn|jjdk(r|jj|jz}t jd||zjdddjjt j}||jj z }n|jjdk(r|jj|jz }t jt j|jjd| jt j}|dz}n"t|jjdt#j$|j'||_y)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. If used, `timesteps` must be `None`. 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 spacing between timesteps. If `None`, then the default timestep spacing strategy of equal spacing between timesteps is used. If `timesteps` is passed, `num_inference_steps` must be `None`. NzACan only pass one of `num_inference_steps` or `custom_timesteps`.r z/`custom_timesteps` must be in descending order.rz=`timesteps` must start before `self.config.train_timesteps`: .r)Tz`num_inference_steps`: z6 cannot be larger than `self.config.train_timesteps`: zG as the unet model trained with this scheduler can only handle maximal z timesteps.Frar=leadingtrailingzY is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.)r+r,lenconfigrKriarrayint64rfrgrWraroundrkastyperjrXrrhtorl)rmrgrtrlr5 step_ratios r set_timestepszDDPMScheduler.set_timestepss*  *y/D`a a  1c)n- XQ<9QU#33$%VWW X|t{{>>> STXT_T_TsTsSttuv"((;I$(D !"T[[%D%DD -.A-BC 7789 $ ? ?@ M (;D $$)D !{{++z9KK4;;#B#BQ#FH[\UWTrT#TVVBHH%  --:![[<<@X@XX  YYq*=>KRRTUYWYUYZ__ahhikiqiqr T[[555 --;![[<?.P;;xU3   KK55M M )H$!/ /yy*Hyyx0Hm +%H/ /yy0Hi '% % o -ii)Gii/G&*a/Dg~TW(< 1, then we threshold xt0 to the range [-s, s] and then divide by s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing pixels from saturation at each step. We find that dynamic thresholding results in significantly better photorealism as well as better image-text alignment, especially when using very large guidance weights." https://huggingface.co/papers/2205.11487 r r;)r.max)r*shaperr0float64floatreshaperiprodabsquantilerzrTrrV unsqueezer)rmror* batch_sizechannelsremaining_dims abs_sampless r_threshold_samplezDDPMScheduler._threshold_samplems 06 - H~  6 6\\^F Hrww~7N,NOZZ\ NN:t{{'M'MST U KK 1$++66  KKNVaR+a/ HF~F5! r model_output return_dictcX|}|j|}|jd|jddzk(r7|jdvr)tj||jdd\}}nd}|j |} |dk\r|j |n |j } d| z } d| z } | | z } d| z }|jjdk(r|| dz|zz | dzz }nj|jjd k(r|}nN|jjd k(r| dz|z| dz|zz }n#td |jjd |jjr|j|}nQ|jjr;|j|jj |jj}| dz|z| z }| dz| z| z }||z||zz}d}|dkDr|j}t!|j|||j" }|jdk(r|j%|||z}nW|jdk(r/|j%||}tj&d|z|z}n|j%||dz|z}||z}|s||fSt)||S)aX Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion process from the learned model outputs (most often the predicted noise). Args: model_output (`torch.Tensor`): The direct output from learned diffusion model. timestep (`float`): The current discrete timestep in the diffusion chain. sample (`torch.Tensor`): A current instance of a sample created by the diffusion process. generator (`torch.Generator`, *optional*): A random number generator. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~schedulers.scheduling_ddpm.DDPMSchedulerOutput`] or `tuple`. Returns: [`~schedulers.scheduling_ddpm.DDPMSchedulerOutput`] or `tuple`: If return_dict is `True`, [`~schedulers.scheduling_ddpm.DDPMSchedulerOutput`] is returned, otherwise a tuple is returned where the first element is the sample tensor. r r)rrr;Nrepsilonr]ro v_predictionzprediction_type given as zM must be one of `epsilon`, `sample` or `v_prediction` for the DDPMScheduler.) generatorrtr*r)rr)rr)rrrPrsplitrCrdrzrRr+rSrrQrrUrtr r*rr'r)rmrrprorrr%rrrr beta_prod_tbeta_prod_t_prevcurrent_alpha_trrpred_original_sample_coeffcurrent_sample_coeffpred_prev_samplerrtvariance_noises rstepzDDPMScheduler.steps< ''*   a FLLOa$7 7D&QU`%`".37:JJ[X68LLOcflOll q5!((F)""ilN`N`N!!%66--aDV-WZhh##6--aDV-W 99S8^4~E ..qEW.X\__cqq+h6 $  #/?Vjkkroriginal_samplesnoisec|jj|j|_|jj|j}|j|j}||dz}|j }t |j t |j kr=|jd}t |j t |j kr=d||z dz}|j }t |j t |j kr=|jd}t |j t |j kr=||z||zz}|SN)rtr)r]r=r rCrrtr*flattenryrr)rmrrrlrCsqrt_alpha_prodsqrt_one_minus_alpha_prod noisy_sampless r add_noisezDDPMScheduler.add_noisesa#1144>qAFF>L E$("%BF* (",1#&"% )',!3+ 3+3+ 3+  3+  bjj$u+&= >? 3+3+3+3+3+%*3+!3+ 3+3+3+ !%!3+3+j  Y^YeYe&.2+/)- H@%c]H@c5<<'(H@DI& H@T&P L elllelel el  el "E) *elN,,||??   45<< QVQ`Q`ejeqeq&/ i ?sE2rJ)g+?r)r! dataclassesrtypingrrrrnumpyrirconfiguration_utilsr r utilsr utils.torch_utilsr scheduling_utilsrrrr8rHrJrrrrsd" !// A,G 8*8 8&!)4Z!HmNKmr