bi9*ddlmZddlmZmZmZddlZddlmZ ddl m Z m Z ddl mZmZmZmZmZej&jGdd ZeGd d eZGd d ee Zy)) dataclass)OptionalTupleUnionN) ConfigMixinregister_to_config)CommonSchedulerStateFlaxKarrasDiffusionSchedulersFlaxSchedulerMixinFlaxSchedulerOutputbroadcast_to_shape_from_leftc eZdZUeed<ej ed<ej ed<ej ed<dZee ed<e dedej dej dej fdZ y)EulerDiscreteSchedulerStatecommoninit_noise_sigma timestepssigmasNnum_inference_stepsc|||||S)Nrrrr)clsrrrrs n/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/diffusers/schedulers/scheduling_euler_discrete_flax.pycreatez"EulerDiscreteSchedulerState.create)s&3Cyaghh) __name__ __module__ __qualname__r __annotations__jnpndarrayrrint classmethodrrrrrrs  kk!{{ KK)-#-i)i=@[[iUXU`U`ijmjujuiirrceZdZUeed<y) FlaxEulerDiscreteSchedulerOutputstateN)rrr rr!rrrr'r'0s &&rr'cFeZdZUdZeDcgc]}|j c}}Zeje d<e dZ e dddddd d ejfd ed ed ededeej$dededejfdZd%deedefdZdedej$dedej$fdZ d&dedededefdZ d'dedej$dedej$dedeeeff dZded ej$d!ej$d"ej$dej$f d#Zd$Zycc}}w)(FlaxEulerDiscreteSchedulera Euler scheduler (Algorithm 2) from Karras et al. (2022) https://huggingface.co/papers/2206.00364. . Based on the original k-diffusion implementation by Katherine Crowson: https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L51 [`~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. 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` or `scaled_linear`. trained_betas (`jnp.ndarray`, optional): option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. 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) dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`): the `dtype` used for params and computation. dtypecy)NTrselfs r has_statez$FlaxEulerDiscreteScheduler.has_stateVsrig-C6?g{Gz?linearNepsilonlinspacenum_train_timesteps beta_startbeta_end beta_schedule trained_betasprediction_typetimestep_spacingc ||_yNr+) r.r3r4r5r6r7r8r9r+s r__init__z#FlaxEulerDiscreteScheduler.__init__Zs  rrreturnc v|tj|}tjd|jj j ddd}d|jz |jz dz}tj|tjdt||}tj|tjdg|jg}|jjdvr|j}n|jdzdzdz}tj|||| S) Nrr ?r<r2trailingrr)r rr"arangeconfigr3roundalphas_cumprodinterplen concatenatearrayr+r9maxr)r.rrrrs r create_statez'FlaxEulerDiscreteScheduler.create_statehs >)006FJJq$++"A"ABHHJ4R4P v,,,0E0EE#MIszz!S['A6J&#))SE*L!MN ;; ' '+C C%zz|  &  1A 5#= *11- 2  rr(sampletimestepctj|j|k(d\}|d}|j|}||dzdzdzz }|S)a  Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the Euler algorithm. Args: state (`EulerDiscreteSchedulerState`): the `FlaxEulerDiscreteScheduler` state data class instance. sample (`jnp.ndarray`): current instance of sample being created by diffusion process. timestep (`int`): current discrete timestep in the diffusion chain. Returns: `jnp.ndarray`: scaled input sample r sizerrrA)r"whererr)r.r(rOrP step_indexsigmas rscale_model_inputz,FlaxEulerDiscreteScheduler.scale_model_input~sR %//X"=AF ]  Z(E1HqLS01 rrshapec |jjdk(r;tj|jjdz d||j }n|jjdk(rm|jj|z}tj d||zjdddjjt}|dz }n"td|jjd|jjz |jjz d z}tj|tj dt||}tj |tj"d g|j g}|jjd vr|j%}n|j%d zdzd z}|j'|||| S)a Sets the timesteps used for the diffusion chain. Supporting function to be run before inference. Args: state (`EulerDiscreteSchedulerState`): the `FlaxEulerDiscreteScheduler` state data class instance. num_inference_steps (`int`): the number of diffusion steps used when generating samples with a pre-trained model. r2r rr<leadingNr@z=timestep_spacing must be one of ['linspace', 'leading'], got rArBrCr)rrrr)rFr9r"r2r3r+rErGcopyastypefloat ValueErrorrrHrIrJrKrLrMreplace)r.r(rrXr step_ratiorrs r set_timestepsz(FlaxEulerDiscreteScheduler.set_timestepss ;; ' ': 5 T[[%D%Dq%H!M`hlhrhrsI [[ ) )Y 688rsv")) A i i i  '':' 'T/!3[T/r