o ۷i> @sddlZddlmZddlmZddlZddlZddlm Z m Z ddl m Z ddl mZdd lmZeGd d d e Z ddedededdejfddZGdddee ZdS)N) dataclass)Literal) ConfigMixinregister_to_config) BaseOutput) randn_tensor)SchedulerMixinc@s&eZdZUdZejed<ejed<dS)RePaintSchedulerOutputal 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. 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_samplepred_original_sampleN)__name__ __module__ __qualname____doc__torchTensor__annotations__rr]/home/ubuntu/vllm_env/lib/python3.10/site-packages/diffusers/schedulers/scheduling_repaint.pyr s  r +?cosinenum_diffusion_timestepsmax_betaalpha_transform_type)rexplaplacereturncCs|dkr dd}n|dkrdd}n|dkrdd}ntd|g}t|D]}||}|d |}|td |||||q(tj|tjd S) a> Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [0,1]. Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up to that part of the diffusion process. Args: num_diffusion_timesteps (`int`): The number of betas to produce. max_beta (`float`, defaults to `0.999`): The maximum beta to use; use values lower than 1 to avoid numerical instability. alpha_transform_type (`str`, defaults to `"cosine"`): The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`. Returns: `torch.Tensor`: The betas used by the scheduler to step the model outputs. rcSs t|ddtjddS)NgMb?gT㥛 ?r)mathcospitrrr alpha_bar_fnIs z)betas_for_alpha_bar..alpha_bar_fnrc SsPdtdd|tddtd|d}t|}t|d|S)Ngr ?rgư>)rcopysignlogfabsrsqrt)r#lmbsnrrrrr$Ns4 rcSst|dS)Ng()rrr"rrrr$Usz"Unsupported alpha_transform_type: r dtype) ValueErrorrangeappendminrtensorfloat32)rrrr$betasit1t2rrrbetas_for_alpha_bar/s     "r8c@seZdZdZdZe       d1d ed ed ed edede j dBde fddZ d2de jdedBde jfddZ   d3dedededee jBfddZdd Z  d4d!e jdede jd"e jd#e jd$e jdBd%e deeBfd&d'Zd2d(d)Zd*e jd+e jd,e jde jfd-d.Zd/d0ZdS)5RePaintSchedulerah `RePaintScheduler` is a scheduler for DDPM inpainting inside a given mask. 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. Args: num_train_timesteps (`int`, defaults to 1000): The number of diffusion steps to train the model. beta_start (`float`, defaults to 0.0001): The starting `beta` value of inference. beta_end (`float`, defaults to 0.02): The final `beta` value. beta_schedule (`str`, defaults to `"linear"`): The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear`, `scaled_linear`, `squaredcos_cap_v2`, or `sigmoid`. eta (`float`): The weight of noise for added noise in diffusion step. If its value is between 0.0 and 1.0 it corresponds to the DDIM scheduler, and if its value is between -0.0 and 1.0 it corresponds to the DDPM scheduler. trained_betas (`np.ndarray`, *optional*): Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`. clip_sample (`bool`, defaults to `True`): Clip the predicted sample between -1 and 1 for numerical stability. r -C6?{Gz?linearNTnum_train_timesteps beta_startbeta_end beta_scheduleeta trained_betas clip_samplec Cs(|dur t||_nR|dkrtj|||tjd|_nB|dkr1tj|d|d|tjdd|_n,|dkr;t||_n"|dkrStdd |}t|||||_n t|d |jd |j|_ tj |j d d |_ t d |_ t d |_d |_d|_ttd |ddd|_||_dS)Nr=r, scaled_linearr%rsquaredcos_cap_v2sigmoidiz is not implemented for ?r)dim)r from_numpyr4linspacer3r8rHNotImplementedError __class__alphascumprodalphas_cumprodr2onefinal_alpha_cumprodinit_noise_sigmanum_inference_stepsnparangecopy timestepsrC) selfr?r@rArBrCrDrEr4rrr__init__s( $    " zRePaintScheduler.__init__sampletimesteprcCs|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_inputsz"RePaintScheduler.scale_model_input rW jump_length jump_n_sampledevicec Cst|jj|}||_g}i}td|||D]}|d||<q|}|dkrR|d}||||ddkrN||d||<t|D] } |d}||qB|dks%t||jj|j}t | ||_ dS)u 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. If used, `timesteps` must be `None`. jump_length (`int`, defaults to 10): The number of steps taken forward in time before going backward in time for a single jump (“j” in RePaint paper). Take a look at Figure 9 and 10 in the paper. jump_n_sample (`int`, defaults to 10): The number of times to make a forward time jump for a given chosen time sample. Take a look at Figure 9 and 10 in the paper. device (`str` or `torch.device`, *optional*): The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. rr N) r1configr?rWr/r0getrXarrayrrMtor[) r\rWrbrcrdr[jumpsjr#_rrr set_timestepss$    zRePaintScheduler.set_timestepscCs\||jj|j}|j|}|dkr|j|n|j}d|}d|}||d||}|S)Nrr )rer?rWrSrU)r\r# prev_timestep alpha_prod_talpha_prod_t_prev beta_prod_tbeta_prod_t_prevvariancerrr _get_variances  zRePaintScheduler._get_variance model_outputoriginal_imagemask generator return_dictcCs.|}||jj|j} |j|} | dkr|j| n|j} d| } || d|| d} |jjr7t| dd} |j}t |j |||j d}|j | |d}d}|dkr]|j dkr]||}d| |dd|}| d| ||}| d|d| d|}||d||}|s|| fSt|| dS) aO 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. timestep (`int`): The current discrete timestep in the diffusion chain. sample (`torch.Tensor`): A current instance of a sample created by the diffusion process. original_image (`torch.Tensor`): The original image to inpaint on. mask (`torch.Tensor`): The mask where a value of 0.0 indicates which part of the original image to inpaint. generator (`torch.Generator`, *optional*): A random number generator. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~schedulers.scheduling_repaint.RePaintSchedulerOutput`] or `tuple`. Returns: [`~schedulers.scheduling_repaint.RePaintSchedulerOutput`] or `tuple`: If return_dict is `True`, [`~schedulers.scheduling_repaint.RePaintSchedulerOutput`] is returned, otherwise a tuple is returned where the first element is the sample tensor. rr r%rLrwrdr-rrJ)r r )rer?rWrSrUrErclamprdrshaper-rCrsr )r\rtr_r^rurvrwrxr#rmrnrorpr rdnoise std_dev_trrpred_sample_directionprev_unknown_partprev_known_partpred_prev_samplerrrsteps.$   zRePaintScheduler.stepcCs|jj|j}t|D]8}|j||}|jjdkr*t|j|j |d}| |j}n t|j||j|j d}d|d||d|}q |S)Nmps)r-rwryr r%) rer?rWr/r4rdtyperr{r-rh)r\r^r_rwnr5betar|rrr undo_step[s  zRePaintScheduler.undo_steporiginal_samplesr|r[cCstd)NzCUse `DDPMScheduler.add_noise()` to train for sampling with RePaint.)rO)r\rr|r[rrr add_noiselszRePaintScheduler.add_noisecCs|jjSN)rer?)r\rrr__len__tszRePaintScheduler.__len__)r:r;r<r=r>NTr)raraN)NT)rrrrorderrintfloatstrrXndarrayboolr]rrr`rdrlrs Generatorr tuplerr IntTensorrrrrrrr9cs * / ] r9)rr)r dataclassesrtypingrnumpyrXrconfiguration_utilsrrutilsrutils.torch_utilsrscheduling_utilsr r rrrr8r9rrrrs.      4