o Gi02@sddlmZddlZddlZddlmZddlmZm Z ddl m Z ddl m Z mZmZmZmZmZe eZejjGdd d ZeGd d d eZGd d d eeZdS)) dataclassN) ConfigMixinregister_to_config)logging)CommonSchedulerStateFlaxKarrasDiffusionSchedulersFlaxSchedulerMixinFlaxSchedulerOutputadd_noise_commonget_velocity_commonc@sTeZdZUeed<ejed<ejed<dZeed<e dedejdejfddZ dS)DDPMSchedulerStatecommoninit_noise_sigma timestepsNnum_inference_stepscCs||||dS)Nrrr)clsrrrrr]/home/ubuntu/.local/lib/python3.10/site-packages/diffusers/schedulers/scheduling_ddpm_flax.pycreate/szDDPMSchedulerState.create) __name__ __module__ __qualname__r__annotations__jnpndarrayrint classmethodrrrrrr&s    rc@seZdZUeed<dS)FlaxDDPMSchedulerOutputstateN)rrrrrrrrrr 9s r c@seZdZUdZddeDZejed<e ddZ e ddd d d d d dej f de dedededejd BdedededejfddZd7ded BdefddZ d7dedejde d Bdejfd d!Z "d8ded#e d$edefd%d&Zd9defd'd(Z d:ded)ejde dejd*ejd Bd+edeeBfd,d-Zded.ejd/ejd0ejdejf d1d2Zdedejd/ejd0ejdejf d3d4Zd5d6Z d S);FlaxDDPMSchedulera Denoising diffusion probabilistic models (DDPMs) explores the connections between denoising score matching and Langevin dynamics sampling. [`~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/2006.11239 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. variance_type (`str`): options to clip the variance used 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`, default `True`): option to clip predicted sample between -1 and 1 for numerical stability. prediction_type (`str`, default `epsilon`): indicates whether the model predicts the noise (epsilon), or the samples. One of `epsilon`, `sample`. `v-prediction` is not supported for this scheduler. dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`): the `dtype` used for params and computation. cCsg|]}|jqSr)name).0errr _szFlaxDDPMScheduler.dtypecCsdSNTrselfrrr has_statecszFlaxDDPMScheduler.has_stateig-C6?g{Gz?linearN fixed_smallTepsilonnum_train_timesteps beta_startbeta_end beta_schedule trained_betas variance_type clip_sampleprediction_typec Cstd| |_dS)NzFlax classes are deprecated and will be removed in Diffusers v1.0.0. We recommend migrating to PyTorch classes or pinning your version of Diffusers.)loggerwarningr') r*r/r0r1r2r3r4r5r6r'rrr__init__gs  zFlaxDDPMScheduler.__init__rreturncCsP|dur t|}tjd|jd}td|jjddd}t j|||dS)N?r'rr) rrrarrayr'arangeconfigr/roundr)r*rrrrrr create_statezs zFlaxDDPMScheduler.create_stater!sampletimestepcCs|S)a Args: state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` state data class instance. sample (`jnp.ndarray`): input sample timestep (`int`, optional): current timestep Returns: `jnp.ndarray`: scaled input sample r)r*r!rCrDrrrscale_model_inputsz#FlaxDDPMScheduler.scale_model_inputrrshapecCs8|jj|}td||ddd}|j||dS)a Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference. Args: state (`DDIMSchedulerState`): the `FlaxDDPMScheduler` state data class instance. num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. rNr=)rr)r@r/rr?rAreplace)r*r!rrF step_ratiorrrr set_timestepss zFlaxDDPMScheduler.set_timestepsc Cs|jj|}t|dk|jj|dtjd|jd}d|d||jj|}|dur1|jj}|dkr>tj |dd}|S|dkrNt tj |dd}|S|d krZ|jj|}|S|d krit |jj|}|S|d kro|S|d kr|}|jj|} |dd } | | d| |}|S)Nrrr;r<r-g#B ;)a_minfixed_small_log fixed_largefixed_large_loglearned learned_ranger) ralphas_cumprodrwherer>r'betasr@r4cliplog) r*r!tpredicted_variancer4 alpha_prod_talpha_prod_t_prevvariancemin_logmax_logfracrrr _get_variances2 *     zFlaxDDPMScheduler._get_variance model_outputkey return_dictcs|dur tjdtjdkr3jd|jddkr3jjdvr3tj|jddd\ndj j }t dkj j dtj dj d}d|} d|} jjd krk|| d |d } n&jjd krt} njjd kr|d || d } n td jjdjjrt| dd} |d j j| } j jd | | } | | | |}fdd}t dk|tjjj d}||}|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 (`DDPMSchedulerState`): the `FlaxDDPMScheduler` state data class instance. model_output (`jnp.ndarray`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`jnp.ndarray`): current instance of sample being created by diffusion process. key (`jax.Array`): a PRNG key. return_dict (`bool`): option for returning tuple rather than FlaxDDPMSchedulerOutput class Returns: [`FlaxDDPMSchedulerOutput`] or `tuple`: [`FlaxDDPMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. Nrrr)rNrO)axisr;r<r.?rC v_predictionzprediction_type given as z? must be one of `epsilon`, `sample` for the FlaxDDPMScheduler.r=csBtjjddd}tjj|jjd}jdd|S)Nr)numr)rFr')rVrb)jaxrandomsplitnormalrFr'r]) split_keynoiser_r^rVr*r!rUrrrandom_variancesz/FlaxDDPMScheduler.step..random_variance) prev_sampler!)rerfr_lenrFr@r4rrgrrPrQr>r'r6 ValueErrorr5rSrRalphaszerosr )r*r!r^rDrCr_r`rWrX beta_prod_tbeta_prod_t_prevpred_original_samplepred_original_sample_coeffcurrent_sample_coeffpred_prev_samplerlrYrrkrsteps@   *   " zFlaxDDPMScheduler.steporiginal_samplesrjrcCt|j|||SN)r r)r*r!ryrjrrrr add_noise+zFlaxDDPMScheduler.add_noisecCrzr{)r r)r*r!rCrjrrrr get_velocity4r}zFlaxDDPMScheduler.get_velocitycCs|jjSr{)r@r/r)rrr__len__=szFlaxDDPMScheduler.__len__r{)r)NNr()!rrr__doc__r _compatiblesrr'rpropertyr+rfloat32rfloatstrrboolr9rrrBrEtuplerIr]reArrayr rxr|r~rrrrrr">s       ' Y   r") dataclassesrflaxre jax.numpynumpyrconfiguration_utilsrrutilsrscheduling_utils_flaxrr r r r r get_loggerrr7structrr r"rrrrs