biEddlZddlmZddlmZmZmZmZddlZ ddl Z ddl m Z m Z ddlmZddlmZmZeGddeZ d d Zd ZGd d ee Zy)N) dataclass)ListOptionalTupleUnion) ConfigMixinregister_to_config)SchedulerMixin) BaseOutput deprecatecXeZdZUdZej ed<dZeej ed<y)DDIMSchedulerOutputaq 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_ddim_inverse.pyrrs' 37(5<<07rrc $|dk(rd}n|dk(rd}ntd|g}t|D]<}||z }|dz|z }|jtd||||z z |>t j |tj S)a Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [0,1]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): the number of betas to produce. max_beta (`float`): the maximum beta to use; use values lower than 1 to prevent singularities. alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar. Choose from `cosine` or `exp` Returns: betas (`np.ndarray`): the betas used by the scheduler to step the model outputs cosinecftj|dzdz tjzdz dzS)NgMb?gT㥛 ?)mathcospits r alpha_bar_fnz)betas_for_alpha_bar..alpha_bar_fnJs-88QY%/$''9A=>!C Crexpc2tj|dzS)Ng()rr%r"s rr$z)betas_for_alpha_bar..alpha_bar_fnOs88AI& &rz"Unsupported alpha_transform_type: dtype) ValueErrorrangeappendminrtensorfloat32)num_diffusion_timestepsmax_betaalpha_transform_typer$betasit1t2s rbetas_for_alpha_barr71s.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)r3alphasalphas_cumprodalphas_bar_sqrtalphas_bar_sqrt_0alphas_bar_sqrt_T alphas_bars rrescale_zero_terminal_snrrG^s5[F]]6q1N$))+O(*002'+113((O(,=@Q,QRRO!!#J ^j"o -F YY 1Q0 1F JE LrcreZdZdZdZdgZdgZe ddede de d e d e e e jee fd ed ed ede de de defdZddej&de edej&fdZddede e ej*ffdZ d dej&dedej&dede eeff dZdZy)!DDIMInverseSchedulera `DDIMInverseScheduler` is the reverse scheduler of [`DDIMScheduler`]. 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`, or `squaredcos_cap_v2`. trained_betas (`np.ndarray`, *optional*): Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`. 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`. set_alpha_to_one (`bool`, defaults to `True`): Each diffusion step uses the alphas product value 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 0, otherwise it uses the alpha value at step `num_train_timesteps - 1`. steps_offset (`int`, defaults to 0): An offset added to the inference steps, as required by some model families. 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 `"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. 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'kwargsset_alpha_to_zeroNnum_train_timesteps beta_startbeta_end beta_schedule trained_betas clip_sampleset_alpha_to_one steps_offsetprediction_typeclip_sample_rangetimestep_spacingrescale_betas_zero_snrc  | jddd}tdd|d| d}|+tj|tj|_n|dk(r-tj |||tj|_nk|dk(r6tj |d z|d z|tjd z|_n0|d k(rt||_nt|d |j| rt|j |_d |j z |_ tj|jd|_ |rtjd n|jd|_d |_d|_tj"t%j&d|j)j+t$j,|_y)NrKzVThe `set_alpha_to_zero` argument is deprecated. Please use `set_alpha_to_one` instead.z1.0.0F) standard_warnr(linear scaled_linear?rsquaredcos_cap_v2z is not implemented for r9rr:)getr rr.r/r3linspacer7NotImplementedError __class__rGrAr=rBinitial_alpha_cumprodinit_noise_sigmanum_inference_steps from_numpynparangecopyastypeint64 timesteps)selfrLrMrNrOrPrQrRrSrTrUrVrWrJdeprecation_messages r__init__zDDIMInverseScheduler.__init__s" ::)4 0 <h  )74GW\ ]%&9:   $m5==IDJ h & H>QY^YfYfgDJ o - C3H[chcpcpquvvDJ 1 1,-@ADJ%7OPTP^P^O_&`a a "24::>DJDJJ& #mmDKKQ?;KU\\#%6PTPcPcdePf"!$$( ))"))A7J*K*P*P*R*Y*YZ\ZbZb*cdrsampletimestepreturnc|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)rlrorps rscale_model_inputz&DDIMInverseScheduler.scale_model_inputs  rrddevicec ||jjkDr=td|d|jjd|jjd||_|jjdk(r|jj|jz}t j d||zjjjt j}||jjz }n|jjdk(r|jj|jz }t jt j |jjd| ddd jt j}|d z}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.leadingrtrailingNr<r'zM is not supported. Please make sure to choose one of 'leading' or 'trailing'.)configrLr*rdrVrfrgroundrhrirjrSrretork)rlrdrt step_ratiorks r set_timestepsz"DDIMInverseScheduler.set_timestepss !@!@ @)*=)>?KK3345 KK;;,&  ;; & &) 3$*[S-AL-P$PT`ehTi#i 'L [[ ( (H 4#/ "\c%:=Q%QQU`ehUiiL [[ ( (N :$0#$5#?;PSCSWcBc#c (#-=cAQU[@[+DKK,G,G+HI""  ;; " "#7#=#=... 0M0M$ "#%6!6C @< O(C03GGJ__ !56 6"{Qeffrc.|jjSN)rxrL)rls r__len__zDDIMInverseScheduler.__len__us{{...r) ig-C6?g{Gz?rZNTTrrr9rvFr)T)rrrrorderignore_for_config_deprecated_kwargsr intfloatstrrrrfndarrayrboolrnrrrsrtr|rrrrrrrrIrIs'R E! -.$("%BF !%(#& )',6e 6e6e 6e  6e  bjj$u+&= >? 6e6e6e6e6e!6e6e!%6e6er  Y^YeYe"$@$@eCDU>V$@V! RgllRgRg Rg  Rg "E) * Rgh/rrI)g+?r)r dataclassesrtypingrrrrnumpyrfrdiffusers.configuration_utilsrr %diffusers.schedulers.scheduling_utilsr diffusers.utilsr r rr7rGrIrrrrsa" !// I@1 8*8 8(!)4Z!Ht/>;t/r