o Gi%@szddlmZddlZddlZddlmZmZddlm Z ddl m Z ddl m Z eGd d d e ZGd d d e eZdS) ) dataclassN) ConfigMixinregister_to_config) BaseOutput) randn_tensor)SchedulerMixinc@s8eZdZUdZejed<ejed<dZejdBed<dS)KarrasVeOutputa 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. derivative (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images): Derivative of predicted original image sample (x_0). 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_sample derivativeNpred_original_sample)__name__ __module__ __qualname____doc__torchTensor__annotations__r rrh/home/ubuntu/.local/lib/python3.10/site-packages/diffusers/schedulers/deprecated/scheduling_karras_ve.pyr s   r c@seZdZdZdZe      d-d ed ed ed ed edef ddZd.dej de dej fddZ d.de de ej BfddZ d.dej dedejdBdeej effddZ d/d ej d!ed"ed#ej d$edeeBf d%d&Z d/d ej d!ed"ed#ej d'ej d(ej d$edeeBfd)d*Zd+d,ZdS)0KarrasVeScheduleraI A stochastic scheduler tailored to variance-expanding models. 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. > [!TIP] > For more details on the parameters, see [Appendix E](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`, defaults to 0.02): The minimum noise magnitude. sigma_max (`float`, defaults to 100): The maximum noise magnitude. s_noise (`float`, defaults to 1.007): The amount of additional noise to counteract loss of detail during sampling. A reasonable range is [1.000, 1.011]. s_churn (`float`, defaults to 80): The parameter controlling the overall amount of stochasticity. A reasonable range is [0, 100]. s_min (`float`, defaults to 0.05): The start value of the sigma range to add noise (enable stochasticity). A reasonable range is [0, 10]. s_max (`float`, defaults to 50): The end value of the sigma range to add noise. A reasonable range is [0.2, 80]. r{Gz?d&1?P皙?2 sigma_min sigma_maxs_noises_churns_mins_maxcCs||_d|_d|_d|_dSN)init_noise_sigmanum_inference_steps timestepsschedule)selfrrr r!r"r#rrr__init__Ms  zKarrasVeScheduler.__init__NsampletimestepreturncCs|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)r)r+r,rrrscale_model_input_sz#KarrasVeScheduler.scale_model_inputr&devicecsb_tdjddd}t||_fddjD}tj|tj |d_ dS)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. device (`str` or `torch.device`, *optional*): The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. rNcs<g|]}jjdjjdjjd|dqS)r)configrr).0ir&r)rr }s  "z3KarrasVeScheduler.set_timesteps..)dtyper/) r&nparangecopyr from_numpytor'tensorfloat32r()r)r&r/r'r(rr5r set_timestepsps  zKarrasVeScheduler.set_timestepssigma generatorcCs|jj|kr|jjkrnn t|jj|jd}nd}|jjt|j|d |j }|||}||d|dd|}||fS)u Explicit Langevin-like "churn" step of adding noise to the sample according to a `gamma_i ≥ 0` to reach a higher noise level `sigma_hat = sigma_i + gamma_i*sigma_i`. Args: sample (`torch.Tensor`): The input sample. sigma (`float`): generator (`torch.Generator`, *optional*): A random number generator. g4y?r)rAr?) r2r"r#minr!r&r rshaper<r/)r)r+r@rAgammaeps sigma_hat sample_hatrrradd_noise_to_inputs  z$KarrasVeScheduler.add_noise_to_inputT model_outputrG sigma_prevrH return_dictc CsB|||}|||}||||}|s||fSt|||dS)a Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion process from the learned model outputs (most often the predicted noise). Args: model_output (`torch.Tensor`): The direct output from learned diffusion model. sigma_hat (`float`): sigma_prev (`float`): sample_hat (`torch.Tensor`): return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~schedulers.scheduling_karras_ve.KarrasVESchedulerOutput`] or `tuple`. Returns: [`~schedulers.scheduling_karras_ve.KarrasVESchedulerOutput`] or `tuple`: If return_dict is `True`, [`~schedulers.scheduling_karras_ve.KarrasVESchedulerOutput`] is returned, otherwise a tuple is returned where the first element is the sample tensor. r r r r ) r)rJrGrKrHrLr r sample_prevrrrsteps  zKarrasVeScheduler.steprOr c CsN|||}|||} |||d|d| }|s ||fSt|||dS)a Corrects the predicted sample based on the `model_output` of the network. Args: model_output (`torch.Tensor`): The direct output from learned diffusion model. sigma_hat (`float`): TODO sigma_prev (`float`): TODO sample_hat (`torch.Tensor`): TODO sample_prev (`torch.Tensor`): TODO derivative (`torch.Tensor`): TODO return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~schedulers.scheduling_ddpm.DDPMSchedulerOutput`] or `tuple`. Returns: prev_sample (TODO): updated sample in the diffusion chain. derivative (TODO): TODO rBrMrN) r)rJrGrKrHrOr rLr derivative_corrrrr step_corrects  zKarrasVeScheduler.step_correctcCstr$)NotImplementedError)r)original_samplesnoiser'rrr add_noiseszKarrasVeScheduler.add_noise)rrrrrrr$)T)rrrrorderrfloatr*rrintr.strr/r? GeneratortuplerIboolr rPrRrVrrrrr0s     / 'r) dataclassesrnumpyr8rconfiguration_utilsrrutilsrutils.torch_utilsrscheduling_utilsr r rrrrrs