o pi%@sddlmZddlmZmZmZddlZddlZddlm Z ddlm Z ddl m Z mZddlmZdd lmZejjGd d d ZeGd d d eZGdddee ZdS)) dataclass)OptionalTupleUnionN)random) ConfigMixinregister_to_config) BaseOutput)FlaxSchedulerMixinc@sNeZdZUdZeeed<dZeej ed<dZ eej ed<e ddZ dS)KarrasVeSchedulerStateNnum_inference_steps timestepsschedulecCs|SN)clsrrl/home/ubuntu/SoloSpeech/.venv/lib/python3.10/site-packages/diffusers/schedulers/scheduling_karras_ve_flax.pycreate$szKarrasVeSchedulerState.create) __name__ __module__ __qualname__rrint__annotations__rjnpndarrayr classmethodrrrrrr s r c@s.eZdZUdZejed<ejed<eed<dS)FlaxKarrasVeOutputa= Output class for the scheduler's step function output. Args: prev_sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images): Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the denoising loop. derivative (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images): Derivative of predicted original image sample (x_0). state (`KarrasVeSchedulerState`): the `FlaxKarrasVeScheduler` state data class. prev_sample derivativestateN)rrr__doc__rrrr rrrrr)s   rc@s(eZdZdZeddZe      d/d ed ed ed ededef ddZddZ d0de de de de fddZ de dejdedejde ejeff ddZ d1de d!ejd"ed#ed$ejd%edeee ffd&d'Z d1de d!ejd"ed#ed$ejd(ejd)ejd%edeee ffd*d+Zde fd,d-Zd.S)2FlaxKarrasVeSchedulera8 Stochastic sampling from Karras et al. [1] tailored to the Variance-Expanding (VE) models [2]. Use Algorithm 2 and the VE column of Table 1 from [1] for reference. [1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based Generative Models." https://arxiv.org/abs/2206.00364 [2] Song, Yang, et al. "Score-based generative modeling through stochastic differential equations." https://arxiv.org/abs/2011.13456 [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. For more details on the parameters, see the original paper's Appendix E.: "Elucidating the Design Space of Diffusion-Based Generative Models." https://arxiv.org/abs/2206.00364. The grid search values used to find the optimal {s_noise, s_churn, s_min, s_max} for a specific model are described in Table 5 of the paper. Args: sigma_min (`float`): minimum noise magnitude sigma_max (`float`): maximum noise magnitude s_noise (`float`): the amount of additional noise to counteract loss of detail during sampling. A reasonable range is [1.000, 1.011]. s_churn (`float`): the parameter controlling the overall amount of stochasticity. A reasonable range is [0, 100]. s_min (`float`): the start value of the sigma range where we add noise (enable stochasticity). A reasonable range is [0, 10]. s_max (`float`): the end value of the sigma range where we add noise. A reasonable range is [0.2, 80]. cCsdS)NTrselfrrr has_state[szFlaxKarrasVeScheduler.has_state{Gz?d&1?P皙?2 sigma_min sigma_maxs_noises_churns_mins_maxcCsdSrr)r%r-r.r/r0r1r2rrr__init___s zFlaxKarrasVeScheduler.__init__cCstSr)r rr$rrr create_stateksz"FlaxKarrasVeScheduler.create_staterr!rshapereturncsJtdddd}fdd|D}|jtj|tjd|dS)a Sets the continuous timesteps used for the diffusion chain. Supporting function to be run before inference. Args: state (`KarrasVeSchedulerState`): the `FlaxKarrasVeScheduler` state data class. num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. rNcs<g|]}jjdjjdjjd|dqS)rr )configr.r-).0irr%rr |s  "z7FlaxKarrasVeScheduler.set_timesteps..)dtype)rrr)rarangecopyreplacearrayfloat32)r%r!rr5rrrr;r set_timestepsns z#FlaxKarrasVeScheduler.set_timestepssamplesigmakeyc Cs|jj|kr|jjkrnn t|jj|jd}nd}tj|dd}|jjtj ||j d}|||}||d|dd|}||fS)u Explicit Langevin-like "churn" step of adding noise to the sample according to a factor gamma_i ≥ 0 to reach a higher noise level sigma_hat = sigma_i + gamma_i*sigma_i. TODO Args: g4y?rr )num)rFr5r?) r8r1r2minr0rrsplitr/normalr5) r%r!rDrErFgammaeps sigma_hat sample_hatrrradd_noise_to_inputs  z(FlaxKarrasVeScheduler.add_noise_to_inputT model_outputrN sigma_prevrO return_dictc CsD|||}|||}||||} |s| ||fSt| ||dS)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: state (`KarrasVeSchedulerState`): the `FlaxKarrasVeScheduler` state data class. model_output (`torch.Tensor` or `np.ndarray`): direct output from learned diffusion model. sigma_hat (`float`): TODO sigma_prev (`float`): TODO sample_hat (`torch.Tensor` or `np.ndarray`): TODO return_dict (`bool`): option for returning tuple rather than FlaxKarrasVeOutput class Returns: [`~schedulers.scheduling_karras_ve_flax.FlaxKarrasVeOutput`] or `tuple`: Updated sample in the diffusion chain and derivative. [`~schedulers.scheduling_karras_ve_flax.FlaxKarrasVeOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. rr r!r) r%r!rQrNrRrOrSpred_original_sampler sample_prevrrrsteps   zFlaxKarrasVeScheduler.steprWr c CsP|||} || |} |||d|d| }|s!|||fSt|||dS)a' Correct the predicted sample based on the output model_output of the network. TODO complete description Args: state (`KarrasVeSchedulerState`): the `FlaxKarrasVeScheduler` state data class. model_output (`torch.Tensor` or `np.ndarray`): direct output from learned diffusion model. sigma_hat (`float`): TODO sigma_prev (`float`): TODO sample_hat (`torch.Tensor` or `np.ndarray`): TODO sample_prev (`torch.Tensor` or `np.ndarray`): TODO derivative (`torch.Tensor` or `np.ndarray`): TODO return_dict (`bool`): option for returning tuple rather than FlaxKarrasVeOutput class Returns: prev_sample (TODO): updated sample in the diffusion chain. derivative (TODO): TODO rHrTrU) r%r!rQrNrRrOrWr rSrVderivative_corrrrr step_corrects   z"FlaxKarrasVeScheduler.step_correctcCstr)NotImplementedError)r%r!original_samplesnoiserrrr add_noiseszFlaxKarrasVeScheduler.add_noiseN)r'r(r)r*r+r,)r)T)rrrr"propertyr&r floatr3r4r rrrCrrjaxArrayrPboolrrrXrZr^rrrrr#<s      !  -  %r#) dataclassesrtypingrrrflaxra jax.numpynumpyrrconfiguration_utilsrr utilsr scheduling_utils_flaxr structr rr#rrrrs