bin{ddlZddlmZddlmZmZmZmZddlZ 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)DDIMParallelSchedulerOutputaq 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__rrh/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/diffusers/schedulers/scheduling_ddim_parallel.pyrrs' 37(5<<07rrc $|dk(rd}n|dk(rd}ntd|g}t|D]<}||z }|dz|z }|jtd||||z z |>t j |tj S)a Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [0,1]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar. Choose from `cosine` or `exp` Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs cosinecftj|dzdz tjzdz dzS)NgMb?gT㥛 ?r)mathcospits r alpha_bar_fnz)betas_for_alpha_bar..alpha_bar_fnLs-88QY%/$''9A=>!C Crexpc2tj|dzS)Ng()r!r'r$s rr&z)betas_for_alpha_bar..alpha_bar_fnQs88AI& &rz"Unsupported alpha_transform_type: r dtype) ValueErrorrangeappendminrtensorfloat32)num_diffusion_timestepsmax_betaalpha_transform_typer&betasit1t2s rbetas_for_alpha_barr83s.x' D  & '=>R=STUU E * +M ( (!e. . S\"- R0@@@(KLM <<U]] 33rc(d|z }tj|d}|j}|dj}|dj}||z}||||z z z}|dz}|dd|ddz }tj|dd|g}d|z }|S)a. Rescales betas to have zero terminal SNR Based on https://huggingface.co/papers/2305.08891 (Algorithm 1) Args: betas (`torch.Tensor`): the betas that the scheduler is being initialized with. Returns: `torch.Tensor`: rescaled betas with zero terminal SNR ?rdimrr N)rcumprodsqrtclonecat)r4alphasalphas_cumprodalphas_bar_sqrtalphas_bar_sqrt_0alphas_bar_sqrt_T alphas_bars rrescale_zero_terminal_snrrH`s5[F]]6q1N$))+O(*002'+113((O(,=@Q,QRRO!!#J ^j"o -F YY 1Q0 1F JE Lrc leZdZdZeDcgc]}|j c}}ZdZdZe d,de de de de d e eej ee fd ed ed e d e dede de de de defdZd-dej*de e dej*fdZd-dZdZdej*dej*fdZd-de dee ej4ffdZ d.dej*de dej*d e d!ed"e ej*d#edeeeffd$Z d/dej*d%ee dej*d e d!edej*f d&Zd'ej*d(ej*d%ej@dej*fd)Z!dej*d(ej*d%ej@dej*fd*Z"d+Z#ycc}}w)0DDIMParallelSchedulera Denoising diffusion implicit models is a scheduler that extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with non-Markovian guidance. [`~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/2010.02502 Args: num_train_timesteps (`int`): number of diffusion steps used to train the model. beta_start (`float`): the starting `beta` value of inference. beta_end (`float`): the final `beta` value. beta_schedule (`str`): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear`, `scaled_linear`, or `squaredcos_cap_v2`. trained_betas (`np.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. clip_sample (`bool`, default `True`): option to clip predicted sample for numerical stability. clip_sample_range (`float`, default `1.0`): the maximum magnitude for sample clipping. Valid only when `clip_sample=True`. set_alpha_to_one (`bool`, default `True`): each diffusion step uses the value of alphas product at that step and at the previous one. For the final step there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`, otherwise it uses the value of alpha at step 0. steps_offset (`int`, default `0`): An offset added to the inference steps, as required by some model families. prediction_type (`str`, default `epsilon`, optional): prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4 https://imagen.research.google/video/paper.pdf) thresholding (`bool`, default `False`): whether to use the "dynamic thresholding" method (introduced by Imagen, https://huggingface.co/papers/2205.11487). Note that the thresholding method is unsuitable for latent-space diffusion models (such as stable-diffusion). dynamic_thresholding_ratio (`float`, default `0.995`): the ratio for the dynamic thresholding method. Default is `0.995`, the same as Imagen (https://huggingface.co/papers/2205.11487). Valid only when `thresholding=True`. sample_max_value (`float`, default `1.0`): the threshold value for dynamic thresholding. Valid only when `thresholding=True`. timestep_spacing (`str`, default `"leading"`): The way the timesteps should be scaled. Refer to Table 2. of [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information. rescale_betas_zero_snr (`bool`, default `False`): whether to rescale the betas to have zero terminal SNR (proposed by https://huggingface.co/papers/2305.08891). This can enable 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 TNnum_train_timesteps beta_startbeta_end beta_schedule trained_betas clip_sampleset_alpha_to_one steps_offsetprediction_type thresholdingdynamic_thresholding_ratioclip_sample_rangesample_max_valuetimestep_spacingrescale_betas_zero_snrct|+tj|tj|_n|dk(r-tj|||tj|_nk|dk(r6tj|dz|dz|tjdz|_n0|dk(rt ||_nt |d|j|rt|j|_d|jz |_ tj|jd |_ |rtjdn|jd |_ d|_ d|_tjt!j"d |ddd j%j't j(|_y) Nr)linear scaled_linear?rsquaredcos_cap_v2z is not implemented for r:rr;r=)rr/r0r4linspacer8NotImplementedError __class__rHrBr>rCfinal_alpha_cumprodinit_noise_sigmanum_inference_steps from_numpynparangecopyastypeint64 timesteps)selfrKrLrMrNrOrPrQrRrSrTrUrVrWrXrYs r__init__zDDIMParallelScheduler.__init__se(  $m5==IDJ h & H>QY^YfYfgDJ o - C3H[chcpcpquvvDJ 1 1,-@ADJ%7OPTP^P^O_&`a a "24::>DJDJJ& #mmDKKQ? 9I5<<#4dNaNabcNd !$$( ))"))A7J*KDbD*Q*V*V*X*_*_`b`h`h*ijrsampletimestepreturnc|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)rlrnros rscale_model_inputz'DDIMParallelScheduler.scale_model_inputs  rc|&||jj|jzz }|j|}|dk\r|j|n |j}d|z }d|z }||z d||z z z}|S)Nrr )configrKrdrCrb)rlro prev_timestep alpha_prod_talpha_prod_t_prev beta_prod_tbeta_prod_t_prevvariances r _get_variancez#DDIMParallelScheduler._get_variances  $t{{'F'F$JbJb'bbM**84 BOSTBTD// >Z^ZrZr,& 00${2q 1, then we threshold xt0 to the range [-s, s] and then divide by s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing pixels from saturation at each step. We find that dynamic thresholding results in significantly better photorealism as well as better image-text alignment, especially when using very large guidance weights." https://huggingface.co/papers/2205.11487 r r;)r.max)r*shaperr0float64floatreshaperfprodabsquantilertrUclamprW unsqueezeto)rlrnr* batch_sizechannelsremaining_dims abs_sampless r_threshold_samplez'DDIMParallelScheduler._threshold_samples 06 - H~  6 6\\^F Hrww~7N,NOZZ\ NN:t{{'M'MST U KK 1$++66  KKNVaR+a/ HF~F5! rrddevicec ||jjkDr=td|d|jjd|jjd||_|jjdk(rot j d|jjdz |jddd jjt j}nn|jjd k(r|jj|jz}t jd||zjddd jjt j}||jjz }n|jjd k(r|jj|jz }t jt j|jjd| jt j}|dz}n"t|jjd tj|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. z`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.r_rr Nr=leadingtrailingzM is not supported. Please make sure to choose one of 'leading' or 'trailing'.)rtrKr+rdrXrfr_roundrhrirjrgrRrrerrk)rlrdrrk step_ratios r set_timestepsz#DDIMParallelScheduler.set_timesteps@s !@!@ @)*=)>?KK3345 KK;;>BDWX2!  [[ ) )Y 688DZ^ZrZr,&  ;; & &) 3$*[S-AL-P$PT`ehTi#i 'L [[ ( (H 4#/ "\c%:=Q%QQU`ehUiiL [[ ( (N :$0#$5#?;PSCSWcBc#c (#-=cAQU[@[+DKK,G,G+HI""  ;; # ##'#9#9:N#O [[ $ $#7#=#=... 0M0M$ %%h >(s++ #"\c%:=Q%QQU`ehUiiL"#%6!6A!E3 OR^ ^(C03GGJ__ 7)i.C 6 %!- &&)LDWDW_k_q_q"!>1H%0K$  +{Ymnnrrkc|j td|dk(sJ|}||jj|jzz }|jdgdg|j dz z}|jdgdg|j dz z}|j j|j|_|jj|j|_ |j |}|j tj|d} tjd| |dk<d|z } |jjdk(r|| d z|zz |d zz } |} n|jjd k(r|} ||d z| zz | d zz } n_|jjd k(r#|d z|z| d z|zz } |d z|z| d z|zz} n#td |jjd |jjr|j| } nQ|jj r;| j#|jj$ |jj$} |j'||j|jj| j(} || d zz}|r||d z| zz | d zz } d| z |dzz d z| z}| d z| z|z}|S)a Batched version of the `step` function, to be able to reverse the SDE for multiple samples/timesteps at once. Also, does not add any noise to the predicted sample, which is necessary for parallel sampling where the noise is pre-sampled by the pipeline. Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion process from the learned model outputs (most often the predicted noise). Args: model_output (`torch.Tensor`): direct output from learned diffusion model. timesteps (`List[int]`): current discrete timesteps in the diffusion chain. This is now a list of integers. sample (`torch.Tensor`): current instance of sample being created by diffusion process. eta (`float`): weight of noise for added noise in diffusion step. use_clipped_model_output (`bool`): if `True`, compute "corrected" `model_output` from the clipped predicted original sample. Necessary because predicted original sample is clipped to [-1, 1] when `self.config.clip_sample` is `True`. If no clipping has happened, "corrected" `model_output` would coincide with the one provided as input and `use_clipped_model_output` will have not effect. Returns: `torch.Tensor`: sample tensor at previous timestep. rr=r rr}r:rr]rnrrrr)rdr+rtrKviewndimrCrrrbrr~r/rSrTrrPrrVrr)rlrrkrnrrr%rrvrwrxrrrzrrrs rbatch_step_no_noisez)DDIMParallelScheduler.batch_step_no_noises(@  # # +s czz T[[448P8PPP AFF2 8! 1 1A 56 8RBA3,*;*;a*?#@B#1144\5H5HI#'#;#;#>#>|?R?R#S **1-  // 6q0IJ(- S(9&1*%,&  ;; & &) 3$*[S-AL-P$PT`ehTi#i 'L [[ ( (H 4#/ "\c%:=Q%QQU`ehUiiL [[ ( (N :$0#$5#?;PSCSWcBc#c (#-=cAQU[@[+DKK,G,G+HI""  ;; # ##'#9#9:N#O [[ $ $#7#=#=... 0M0M$ T4++Av699,:M:MNSSUfUlUlm(s++ #"\c%:=Q%QQU`ehUiiL"#%6!6A!E3 OR^ ^(C03GGJ__ roriginal_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)rr)r]r=r rCrrr*flattenlenrr)rlrrrkrCsqrt_alpha_prodsqrt_one_minus_alpha_prod noisy_sampless r add_noisezDDIMParallelScheduler.add_noiseWsa#1144>qAFF>L E$("%BF !%(",1#&"% )',!1k 1k1k 1k  1k  bjj$u+&= >? 1k1k1k1k1k1k%*1k!1k 1k1k !%!1k1kh  Y^YeYe"   D+@+@eCDU>V+@d).15 {oll{o{o {o  {o #' {o!.{o{o *E1 2{oD). jllj9j j  j #' j jZ,,||??   65<< QVQ`Q`ejeqeq&/W?sF0rJ)g+?r)r! dataclassesrtypingrrrrnumpyrfrconfiguration_utilsr r utilsr utils.torch_utilsr scheduling_utilsrrrr8rHrJrrrrsd$ !// A,G 8*8 8(!)4Z!HB/NKB/r