bi,LddlZddlmZddlmZmZmZmZddlZddl m Z m Z ddl m Z mZddlmZdd lmZej&eZeGd d e ZGd d ee Zy)N) dataclass)ListOptionalTupleUnion) ConfigMixinregister_to_config) BaseOutputlogging) randn_tensor)SchedulerMixincXeZdZUdZej ed<dZeej ed<y)EDMEulerSchedulerOutputaq 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_sampleNpred_original_sample) __name__ __module__ __qualname____doc__torchTensor__annotations__rrd/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/diffusers/schedulers/scheduling_edm_euler.pyrrs' 37(5<<07rrceZdZdZgZdZe d4dedededede ded ed efd Z e d Z e d Z e dZd5de fdZdZdZdZdej(deeej(fdej(fdZ d6de deeej.fdeeej(eeffdZd7dej(fdZd7dej(fdZd8dZd Zd!d!ed"d#dd$dfd%ej(deeej(fdej(d&ed'ed(ed)ed*eej>d+e d,eej(dee!e"ffd-Z#d.ej(d/ej(d0ej(dej(fd1Z$d2Z%d3Z&y)9EDMEulerSchedulera5 Implements the Euler scheduler in EDM formulation as presented in Karras et al. 2022 [1]. [1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based Generative Models." https://huggingface.co/papers/2206.00364 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: sigma_min (`float`, *optional*, defaults to 0.002): Minimum noise magnitude in the sigma schedule. This was set to 0.002 in the EDM paper [1]; a reasonable range is [0, 10]. sigma_max (`float`, *optional*, defaults to 80.0): Maximum noise magnitude in the sigma schedule. This was set to 80.0 in the EDM paper [1]; a reasonable range is [0.2, 80.0]. sigma_data (`float`, *optional*, defaults to 0.5): The standard deviation of the data distribution. This is set to 0.5 in the EDM paper [1]. sigma_schedule (`str`, *optional*, defaults to `karras`): Sigma schedule to compute the `sigmas`. By default, we the schedule introduced in the EDM paper (https://huggingface.co/papers/2206.00364). Other acceptable value is "exponential". The exponential schedule was incorporated in this model: https://huggingface.co/stabilityai/cosxl. num_train_timesteps (`int`, defaults to 1000): The number of diffusion steps to train the model. prediction_type (`str`, defaults to `epsilon`, *optional*): Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process), `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen Video](https://imagen.research.google/video/paper.pdf) paper). rho (`float`, *optional*, defaults to 7.0): The rho parameter used for calculating the Karras sigma schedule, which is set to 7.0 in the EDM paper [1]. final_sigmas_type (`str`, defaults to `"zero"`): The final `sigma` value for the noise schedule during the sampling process. If `"sigma_min"`, the final sigma is the same as the last sigma in the training schedule. If `zero`, the final sigma is set to 0. r sigma_min sigma_max sigma_datasigma_schedulenum_train_timestepsprediction_typerhofinal_sigmas_typec b|dvrtd|dd|_tjjj rtj ntj} tj|dz| |z } |dk(r|j| } n|dk(r|j| } | jtj } |j| |_ |jjdk(r| d } n>|jjd k(rd } n"td |jjtj | tj"d | | j$g|_d|_d|_d|_|j&jd|_y)N)karras exponentialz-Wrong value for provided for `sigma_schedule=z`.`rdtyper)r*r zerorC`final_sigmas_type` must be one of 'zero', or 'sigma_min', but got r fill_valuedeviceFcpu) ValueErrornum_inference_stepsrbackendsmps is_availablefloat32float64arange_compute_karras_sigmas_compute_exponential_sigmastoprecondition_noise timestepsconfigr'catfullr3sigmasis_scale_input_called _step_index _begin_index) selfr r!r"r#r$r%r&r' sigmas_dtyperE sigma_lasts r__init__zEDMEulerScheduler.__init__Xso !: :Mn=NcRS S$( (-(:(:(G(G(Iu}}u}} 1A5\JM`` X %008F } ,55f=F5==)008 ;; ( (K 7J [[ * *f 4JUVZVaVaVsVsUtu iiDZX^XeXe)f gh %*" kknnU+ rc@|jjdzdzdzS)Nrr?)rBr!rIs rinit_noise_sigmaz"EDMEulerScheduler.init_noise_sigmas# %%q(1,44rc|jS)zg The index counter for current timestep. It will increase 1 after each scheduler step. )rGrOs r step_indexzEDMEulerScheduler.step_indexs rc|jS)zq The index for the first timestep. It should be set from pipeline with `set_begin_index` method. rHrOs r begin_indexzEDMEulerScheduler.begin_indexs    rrUc||_y)z Sets the begin index for the scheduler. This function should be run from pipeline before the inference. Args: begin_index (`int`): The begin index for the scheduler. NrT)rIrUs rset_begin_indexz!EDMEulerScheduler.set_begin_indexs (rc2|j|}||z}|SN)_get_conditioning_c_in)rIsamplesigmac_in scaled_samples rprecondition_inputsz%EDMEulerScheduler.precondition_inputss!**51 rct|tjstj|g}dtj|z}|S)Ng?) isinstancerrtensorlog)rIr\c_noises rr@z$EDMEulerScheduler.precondition_noises8%.LL%)E5))rcj|jj}|dz|dz|dzzz }|jjdk(r||z|dz|dzzdzz }nR|jjdk(r| |z|dz|dzzdzz }n#td|jjd||z||zz}|S)NrepsilonrN v_predictionzPrediction type z is not supported.)rBr"r%r5)rIr[ model_outputr\r"c_skipc_outdenoiseds rprecondition_outputsz&EDMEulerScheduler.precondition_outputss[[++ Q%(Z]":; ;; & &) 3J&%(Z]*Bs)JJE [[ ( (N :FZ'5!8j!m+C*KKE/ 0K0K/LL^_` `F?U\%99rr[timestepreturnc|j|j||j|j}|j||}d|_|S)a Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the Euler algorithm. Args: sample (`torch.Tensor`): The input sample. timestep (`int`, *optional*): The current timestep in the diffusion chain. Returns: `torch.Tensor`: A scaled input sample. T)rR_init_step_indexrEr_rF)rIr[rmr\s rscale_model_inputz#EDMEulerScheduler.scale_model_inputsM ?? "  ! !( + DOO,))&%8%)" rNr6r3rEc||_tjjj rtj ntj }|$tjdd|j|}n9t|trtj||}n|j|}|jjdk(r|j|}n*|jjdk(r|j|}|jtj |}|j!||_|jj$dk(r|d }n>|jj$d k(rd}n"t'd |jj$tj(|tj*d ||j, g|_d|_d|_|j.jd|_y)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. sigmas (`Union[torch.Tensor, List[float]]`, *optional*): Custom sigmas to use for the denoising process. If not defined, the default behavior when `num_inference_steps` is passed will be used. Nrrr+r)r*)r,r3r r-r.r/r0r1r4)r6rr7r8r9r:r;linspacerafloatrbr?rBr#r=r>r@rAr'r5rCrDr3rErGrH)rIr6r3rErJrKs r set_timestepszEDMEulerScheduler.set_timestepss$$7 (-(:(:(G(G(Iu}}u}} >^^Aq$*B*B,WF  &\\& =FYY|,F ;; % % 1008F [[ ' '= 855f=Fv>008 ;; ( (K 7J [[ * *f 4JUVZVaVaVsVsUtu iiDZX^XeXe)f gh  kknnU+ rc|xs|jj}|xs|jj}|jj}|d|z z}|d|z z}||||z zz|z}|S)z6Constructs the noise schedule of Karras et al. (2022).r)rBr r!r&)rIrampr r!r& min_inv_rho max_inv_rhorEs rr=z(EDMEulerScheduler._compute_karras_sigmas sq6!6!6 6!6!6 kkooAG, AG,  k(A BBsJ rc4|xs|jj}|xs|jj}tjt j |t j |t|jjd}|S)zImplementation closely follows k-diffusion. https://github.com/crowsonkb/k-diffusion/blob/6ab5146d4a5ef63901326489f31f1d8e7dd36b48/k_diffusion/sampling.py#L26 r) rBr r!rrsmathrclenexpflip)rIrwr r!rEs rr>z-EDMEulerScheduler._compute_exponential_sigmassn 6!6!6 6!6!6  3TXXi5H#d)TXXZ__`ab rc| |j}||k(j}t|dkDrdnd}||jS)Nrr)rAnonzeror|item)rIrmschedule_timestepsindicesposs rindex_for_timestepz$EDMEulerScheduler.index_for_timestepsL  %!% %1::< w'>FVF\F\]  " " ' '5 0U5L5LY5W!%!2!23C3J3JRWR_R_!2!` ! %5%<%rrp GeneratorboolrrrrrZrrrrrr1s!FL E!&#'(!'*,*,*, *,  *, ! *,*,*,*,*,X55  !!(3(   eU\\FY@Z_d_k_k4$(+/=A /, /,c5<<'(/,u||T%[89: /,d ell SXS_S_ #1e /3 7;akllaku||+,ak ak  ak  akakakEOO,akak'u||4ak &- .akH,,||<<   B/rr)r{ dataclassesrtypingrrrrrconfiguration_utilsr r utilsr r utils.torch_utilsr scheduling_utilsr get_loggerrrrrrrrrsa !// A',,   H % 8j8 8"O/ O/r