bi%ddlmZddlmZmZmZddlZddlZddlm Z ddlm Z ddl m Z mZddlmZdd lmZej&jGd d ZeGd d eZGddee Zy)) dataclass)OptionalTupleUnionN)random) ConfigMixinregister_to_config) BaseOutput)FlaxSchedulerMixinceZdZUdZeeed<dZeejed<dZ eejed<e dZ y)KarrasVeSchedulerStateNnum_inference_steps timestepsschedulec|SN)clss i/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/diffusers/schedulers/scheduling_karras_ve_flax.pycreatezKarrasVeSchedulerState.create$s u ) __name__ __module__ __qualname__rrint__annotations__rjnpndarrayr classmethodrrrrrrsK*.#-'+Ix $+&*Hhs{{#*rrcXeZdZUdZej ed<ej ed<eed<y)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__rr rrrrrr#r#)s#   !!rr#ceZdZdZedZe ddedededededef d Zd Z d d e d e d e de fdZ d e dejdedej"de ejeff dZ d!d e dejdededejdedeee ffdZ d!d e dejdededejdejdejdedeee ffdZd e fdZy)"FlaxKarrasVeScheduleraP 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://huggingface.co/papers/2206.00364 [2] Song, Yang, et al. "Score-based generative modeling through stochastic differential equations." https://huggingface.co/papers/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://huggingface.co/papers/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]. cy)NTrselfs r has_statezFlaxKarrasVeScheduler.has_state[sr sigma_min sigma_maxs_noises_churns_mins_maxcyrr)r,r.r/r0r1r2r3s r__init__zFlaxKarrasVeScheduler.__init___s rc*tjSr)rrr+s r create_statez"FlaxKarrasVeScheduler.create_stateks%,,..rr&rshapereturnctjd|dddj}|Dcgc]X}|jjdz|jj dz|jjdzz ||dz z zzZ}}|j |tj|tj|Scc}w)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. rNrr )dtype)rrr) rarangecopyconfigr/r.replacearrayfloat32)r,r&rr8rirs r set_timestepsz#FlaxKarrasVeScheduler.set_timestepsnsJJq"56tt<AAC    %%q(;;((!+dkk.C.CQ.FFAQdghQhLijk  }} 3YYxs{{;   sACsamplesigmakeyc|jj|cxkr|jjkr1nn.t|jj|j z d}nd}t j|d}|jjt j||jz}|||zz}||dz|dzz dz|zz}||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)rGr8r?) r?r2r3minr1rrsplitr0normalr8) r,r&rErFrGgammaeps sigma_hat sample_hats radd_noise_to_inputz(FlaxKarrasVeScheduler.add_noise_to_inputs ;;   :):): : ++e.G.GGTEEll3A&kk!!FMMc$NNEEM)  1 uax 7C?#EF 9$$r model_outputrP sigma_prevrQ return_dictcb|||zz}||z |z }|||z |zz} |s| ||fSt| ||S)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. r$r%r&r#) r,r&rSrPrTrQrUpred_original_sampler% sample_prevs rstepzFlaxKarrasVeScheduler.stepsX6 *I ,DD #779D  J$:j#HH U3 3!kjX]^^rrZr%c t|||zz} || z |z } |||z d|zd| zzzz}|s|||fSt|||S)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 rJrWrX) r,r&rSrPrTrQrZr%rUrYderivative_corrs r step_correctz"FlaxKarrasVeScheduler.step_correctsi8 +Z,-FF&)==K J$:sZ?ORUXgRg?g#hh U3 3!kjX]^^rctr)NotImplementedError)r,r&original_samplesnoisers r add_noisezFlaxKarrasVeScheduler.add_noises !##rN)g{Gz?dg&1?Pg?2)r)T)rrrr'propertyr-r floatr5r7rrrrDrr jaxArrayrRboolrr#r[r^rcrrrr)r)<s<                 /WY + BE NS  8%%% % % YY % s{{E! " %B!"_%"_kk"_ "_  "_ KK "_"_ !5( )"_Z!#_%#_kk#_ #_  #_ KK #_[[#_KK#_#_ !5( )#_J$5$rr)) dataclassesrtypingrrrflaxri jax.numpynumpyrrconfiguration_utilsr r utilsr scheduling_utils_flaxr structrr#r)rrrrust ")) A5 "" "$r$. r$r