a JfW@spddlmZmZmZddlZddlZddlmZddl m Z ddZ dd d Z Gd d d Z Gd dde ZdS))OptionalListUnionN)monit)LatentDiffusioncCs<tj|t|tjr8tj|tj|dS)N)nprandomseedtorch manual_seedcuda is_availablemanual_seed_all)r r@/home/music/interactive_symbolic_music_demo/model/sampler_sdf.pyset_seeds     rcCsX|jttd|jdd}|jttd|jdd}|||}||d||}|S)z Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4 rT)dimkeepdim)stdlistrangendim)Z noise_cfgZnoise_pred_textguidance_rescaleZstd_textZstd_cfgZnoise_pred_rescaledrrrrescale_noise_cfgs  rcs0eZdZUdZeed<edfdd ZZS)DiffusionSamplerz/ ## Base class for sampling algorithms modelrcst||_|j|_dS)[ :param model: is the model to predict noise $\epsilon_ ext{cond}(x_t, c)$ N)super__init__rn_steps)selfr __class__rrr!%s zDiffusionSampler.__init__)__name__ __module__ __qualname____doc__r__annotations__r! __classcell__rrr$rrs rc s,eZdZUdZeed<dedfdd Zejeje eje e dd d Z e deje ejeje ee e edddZe d eje e ejdddZe d!ee e ejee e e eje edddZe d"e eje eje e eje eje eje edddZd#ddZZS)$ SDFSamplera ## DDPM Sampler This extends the [`DiffusionSampler` base class](index.html). DDPM samples images by repeatedly removing noise by sampling step by step from $p_ heta(x_{t-1} | x_t)$, egin{align} p_ heta(x_{t-1} | x_t) &= \mathcal{N}ig(x_{t-1}; \mu_ heta(x_t, t), ildeeta_t \mathbf{I} ig) \ \mu_t(x_t, t) &= rac{\sqrt{arlpha_{t-1}}eta_t}{1 - arlpha_t}x_0 + rac{\sqrt{lpha_t}(1 - arlpha_{t-1})}{1-arlpha_t}x_t \ ildeeta_t &= rac{1 - arlpha_{t-1}}{1 - arlpha_t} eta_t \ x_0 &= rac{1}{\sqrt{arlpha_t}} x_t - \Big(\sqrt{ rac{1}{arlpha_t} - 1}\Big)\epsilon_ heta \ \end{align} rFNrc sjt||dur*tjr"dnd|_n||_tjgd|jd|_t|j|_ ||_ tjj j |d|_ |j jj|_||_||_||_d|_d|_t|j j|_|j j}t|jd g|jdd g|_td |jd |jd |j|j|_d |jd |_d |jd |jd |_|jd |_ d |jd |_!Wdn1s\0YdS) rNr cpu) 5ti6iiKiiMidevice)enabledg@gffffff??r?)"r r!r r rr3tensortaulen used_n_steps is_show_imageampautocastr eps_model out_channels out_channelmax_lh debug_modeguidance_scalerno_grad alpha_barbetacat new_tensoralpha_bar_prevsqrt sigma_ddimone_over_sqrt_alpha_bar%sqrt_1m_alpha_bar_over_sqrt_alpha_barsqrt_alpha_barsqrt_1m_alpha_bar) r#rrBrC is_autocastr<r3rDrHr$rrr!Is.     $, zSDFSampler.__init__)xtbackground_cond uncond_scalecCs>|d}|dur.|dur*t||gdn|}|||}|S)a ## Get $\epsilon(x_t, c)$ :param x: is $x_t$ of shape `[batch_size, channels, height, width]` :param t: is $t$ of shape `[batch_size]` :param background_cond: background condition :param autoreg_cond: autoregressive condition :param external_cond: external condition :param c: is the conditional embeddings $c$ of shape `[batch_size, emb_size]` :param uncond_scale: is the unconditional guidance scale $s$. This is used for $\epsilon_ heta(x_t, c) = s\epsilon_ ext{cond}(x_t, c) + (s - 1)\epsilon_ ext{cond}(x_t, c_u)$ :param uncond_cond: is the conditional embedding for empty prompt $c_u$ rNr)sizer rIr)r#rSrTrUrV batch_sizee_trrrget_epss   zSDFSampler.get_epsr5TYes)rSrUrTstep repeat_noise temperaturerVsame_noise_all_measurec! Cstd|j|}|j|}|j| r| r8|jddksDJt|ddddddddf }tj|ddddddddf|gdd}tj|ddddddddf|ddddddddfgdd}|j||||d}|j||||d}||j||}|j dkrt |||j d }n|jddksLJ|ddddddddf}|ddddddddf}|j||||d}|j||||d}||j||}|j dkr4t |||j d }nT| r|jddksJ|j||||d}n&|jddks"J|j||||d}Wdn1sJ0Y|jd}| |dddf|j |}| |dddf|j |}| |dddf|j|}||ddd|df||}| dur| ||| | d }||ddd|df||}|dkr"d}n|r|rntjd|jdd |jd f|jd ddt|jdd d}n"tjdg|jddR|jd }n\|rtj|jd|jdd |jd |jd ddt|jdd d}ntj|j|jd }||}|dkrv|j|d}| |dddf|j|}| |dddfd|j||j|dd}||}|||||} n |||} | ||fS)Np_sampler)axis)rVr)r)reduce_extra_notesrhythm_controlr2r7)printr9r>shaper ones_likerIrZrErrnew_fullrNrOrMrWrandnr3repeatintrPrG)!r#rSrUrTr\r]r^rVr_ X0EditFuncuse_classifier_free_guidanceuse_lshrerfZtau_iZ step_tau_iZnull_lshZnull_background_condZreal_background_condZ e_tau_i_nullZ e_tau_i_realZe_tau_ibsrNrOrM predicted_x0noiseZstep_tau_i_m_1Zsqrt_alpha_bar_prevZsqrt_1m_alpha_bar_prev_m_sigma2direction_to_xtZx_prevrrrr`s   (.J    2 & & @$D ( zSDFSampler.p_sample)x0indexrucCs4|durtj||jd}|j|||j||S)aC ### Sample from $q(x_t|x_0)$ $$q(x_t|x_0) = \mathcal{N} \Big(x_t; \sqrt{arlpha_t} x_0, (1-arlpha_t) \mathbf{I} \Big)$$ :param x0: is $x_0$ of shape `[batch_size, channels, height, width]` :param index: is the time step $t$ index :param noise: is the noise, $\epsilon$ Nr2)r randn_liker3rPrQ)r#rwrxrurrrq_samplePszSDFSampler.q_sampler)rjrUr]r^rVx_lastt_startr_cCs|d}t||r\|dur |n8tj|d|dd|d|jdddt|ddd}n|durh|ntj||jd}ttjt t |j tj d|d}t d |D]J}|j|f|tjd}|j||||||||| | | | | d \}}}|d}q|S) a* ### Sampling Loop :param shape: is the shape of the generated images in the form `[batch_size, channels, height, width]` :param background_cond: background condition :param autoreg_cond: autoregressive condition :param external_cond: external condition :param repeat_noise: specified whether the noise should be same for all samples in the batch :param temperature: is the noise temperature (random noise gets multiplied by this) :param x_last: is $x_T$. If not provided random noise will be used. :param uncond_scale: is the unconditional guidance scale $s$. This is used for $\epsilon_ heta(x_t, c) = s\epsilon_ ext{cond}(x_t, c) + (s - 1)\epsilon_ ext{cond}(x_t, c_u)$ :param t_start: t_start rNrrgrhr2rcdtypeZSample) r]r^rVr_rprqrrrerf)rir rmr3rnrorflipasarrayrrr;int32riteraterllongr`)r#rjrUr]r^rVr{r|r_rprqrrrerfrsrS time_stepsr\tsZpred_x0rYs1rrrsamplees2$H(  zSDFSampler.sample)rSrUr|origmask orig_noiserVr_c  Cs|d} |dur$tj|j|jd}ttjtt |j tj d|d} t d| D]\}}|j| f|tjd}|j||||||| | | d \}}}|dur|dusJ|j||j||d}|||d|}|d}qX|S) a? ### Painting Loop :param x: is $x_{S'}$ of shape `[batch_size, channels, height, width]` :param background_cond: background condition :param autoreg_cond: autoregressive condition :param external_cond: external condition :param t_start: is the sampling step to start from, $S'$ :param orig: is the original image in latent page which we are in paining. If this is not provided, it'll be an image to image transformation. :param mask: is the mask to keep the original image. :param orig_noise: is fixed noise to be added to the original image. :param uncond_scale: is the unconditional guidance scale $s$. This is used for $\epsilon_ heta(x_t, c) = s\epsilon_ ext{cond}(x_t, c) + (s - 1)\epsilon_ ext{cond}(x_t, c_u)$ rNr2r}Paint)rTr\rVr_rprqrr)rur)rWr rmrjr3rrrrrr;rrenumrlrr`rzr9)r#rSrUr|rrrrVr_rprqrrrsrir\r_Zorig_trrrrpaints.! (   zSDFSampler.paintrc CsD||j|j|jg} |jr(tj| tjdS|j| |||||||| d S)Nr})rVr_rprqrrrerf)rArBrCrDr rmfloatr) r#rUrXrVr_rprqrrrerfrjrrrgenerate s zSDFSampler.generate)FFNF) Fr5r5FNFFTr[)N) NFr5r5NrFNFFTr[) NNrNNNr5FNFF) NrNFNFFTr[)r&r'r(r)rr*r!r TensorrrrZrFroboolr`rzrrrrr+rrr$rr,0s B <  VPr,)r)typingrrrnumpyrr Zlabmlrlatent_diffusionrrrrr,rrrrs