wiMCddlZddlmZddlmZmZmZddlZddlm Z m Z ddl m Z m Z ddlmZdd lmZe jeZeGd d e ZGd d ee ZdS)N) dataclass)OptionalTupleUnion) ConfigMixinregister_to_config) BaseOutputlogging) randn_tensor)SchedulerMixincLeZdZUdZejed<dZeejed<dS)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__rry/root/.openclaw/workspace/chatterbox_venv_py311/lib/python3.11/site-packages/diffusers/schedulers/scheduling_edm_euler.pyrrsD  37(5<077777rrcVeZdZdZgZdZe d8d ed ed ed ede dedefdZ e dZ e dZ e dZd9de fdZdZdZdZdejdeeejfdejfdZd:d e d!eeejffd"Zd;dejfd#Zd;dejfd$Zd:d%Zd&Zd'd'ed(d)dd*fd+ejdeeejfdejd,ed-ed.ed/ed0eejd1edee e!ffd2Z"d3ejd4ejd5ejdejfd6Z#d7Z$dS)<EDMEulerSchedulera 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://arxiv.org/abs/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://arxiv.org/abs/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]. r Mb`?T@?karrasepsilon@ sigma_min sigma_max sigma_datasigma_schedulenum_train_timestepsprediction_typerhoc|dvrtd|dd|_tjdd|}|dkr||} n|dkr||} || |_tj| tj d| j g|_ d |_ d|_ d|_|j d |_ dS) N)r" exponentialz-Wrong value for provided for `sigma_schedule=z`.`rr r"r.deviceFcpu) ValueErrornum_inference_stepsrlinspace_compute_karras_sigmas_compute_exponential_sigmasprecondition_noise timestepscatzerosr0sigmasis_scale_input_called _step_index _begin_indexto) selfr&r'r(r)r*r+r,rampr;s r__init__zEDMEulerScheduler.__init__Us !: : :RnRRRSS S$( ~a$788 X % %0066FF } , ,55d;;F0088iQv})M)M)M NOO %*" knnU++ rc,|jjdzdzdzS)Nrr r!)configr'r@s rinit_noise_sigmaz"EDMEulerScheduler.init_noise_sigmavs %q(1,44rc|jS)zg The index counter for current timestep. It will increase 1 after each scheduler step. )r=rEs r step_indexzEDMEulerScheduler.step_index{s rc|jS)zq The index for the first timestep. It should be set from pipeline with `set_begin_index` method. r>rEs r begin_indexzEDMEulerScheduler.begin_indexs   rrrKc||_dS)z Sets the begin index for the scheduler. This function should be run from pipeline before the inference. Args: begin_index (`int`): The begin index for the scheduler. NrJ)r@rKs rset_begin_indexz!EDMEulerScheduler.set_begin_indexs(rcFd|dz|jjdzzdzz }||z}|S)Nr rr!)rDr()r@samplesigmac_in scaled_samples rprecondition_inputsz%EDMEulerScheduler.precondition_inputss3UAX 6 99cAB rct|tjstj|g}dtj|z}|S)Ng?) isinstancerrtensorlog)r@rPc_noises rr7z$EDMEulerScheduler.precondition_noises@%.. *L%))E5)))rc&|jj}|dz|dz|dzzz }|jjdkr||z|dz|dzzdzz }nC|jjdkr| |z|dz|dzzdzz }ntd|jjd||z||zz}|S)Nrr$r! v_predictionzPrediction type z is not supported.)rDr(r+r2)r@rO model_outputrPr(c_skipc_outdenoiseds rprecondition_outputsz&EDMEulerScheduler.precondition_outputss[+ Q%(Z]":; ; &) 3 3J&%(Z]*Bs)JJEE [ (N : :FZ'5!8j!m+C*KKEE_ 0K___`` `F?U\%99rrOtimestepreturnc|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. NT)rH_init_step_indexr;rSr<)r@rOr`rPs rscale_model_inputz#EDMEulerScheduler.scale_model_inputsP ? "  ! !( + + + DO,))&%88%)" rNr3r0c||_tjdd|j}|jjdkr||}n%|jjdkr||}|tj|}| ||_ tj |tj d|j g|_d|_d|_|jd|_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. rr r"r.)dtyper0r/Nr1)r3rr4rDr)r5r6r?float32r7r8r9r:r0r;r=r>)r@r3r0rAr;s r set_timestepszEDMEulerScheduler.set_timestepss$7 ~aD$<== ; % 1 10066FF [ '= 8 855d;;Fv>>0088iQv})M)M)M NOO  knnU++ rc|p |jj}|p |jj}|jj}|d|z z}|d|z z}||||z zz|z}|S)z6Constructs the noise schedule of Karras et al. (2022).r )rDr&r'r,)r@rAr&r'r, min_inv_rho max_inv_rhor;s rr5z(EDMEulerScheduler._compute_karras_sigmassc6!6 6!6 koAG, AG,  k(A BBsJ rc|p |jj}|p |jj}tjt j|t j|t| d}|S)zImplementation closely follows k-diffusion. https://github.com/crowsonkb/k-diffusion/blob/6ab5146d4a5ef63901326489f31f1d8e7dd36b48/k_diffusion/sampling.py#L26 r) rDr&r'rr4mathrWlenexpflip)r@rAr&r'r;s rr6z-EDMEulerScheduler._compute_exponential_sigmasso 6!6 6!6  3 3TXi5H5H#d))TTXXZZ__`abb rc||j}||k}t|dkrdnd}||S)Nr r)r8nonzerornitem)r@r`schedule_timestepsindicesposs rindex_for_timestepz$EDMEulerScheduler.index_for_timestepsW  %!% %1::<< w<)r@r`s rrcz"EDMEulerScheduler._init_step_indexsb   #(EL11 >#;;t~'<==#66x@@D   #0D   rinfg?Tr[s_churns_tmins_tmaxs_noise generator return_dictc Xt|ttjtjfrt d|jstd|j | || tj }|j |j } || cxkr|kr+nn(t|t|j dz z dnd} t!|j|j|j|} | |z} | | dzz}| dkr|| |d z| d zz d zzz}||||}||z |z }|j |j dz|z }|||zz}| |j}|xjdz c_| s|fSt-|| S) 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. timestep (`float`): The current discrete timestep in the diffusion chain. sample (`torch.Tensor`): A current instance of a sample created by the diffusion process. s_churn (`float`): s_tmin (`float`): s_tmax (`float`): s_noise (`float`, defaults to 1.0): Scaling factor for noise added to the sample. generator (`torch.Generator`, *optional*): A random number generator. return_dict (`bool`): Whether or not to return a [`~schedulers.scheduling_euler_discrete.EDMEulerSchedulerOutput`] or tuple. Returns: [`~schedulers.scheduling_euler_discrete.EDMEulerSchedulerOutput`] or `tuple`: If return_dict is `True`, [`~schedulers.scheduling_euler_discrete.EDMEulerSchedulerOutput`] is returned, otherwise a tuple is returned where the first element is the sample tensor. zPassing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to `EDMEulerScheduler.step()` is not supported. Make sure to pass one of the `scheduler.timesteps` as a timestep.zThe `scale_model_input` function should be called before `step` to ensure correct denoising. See `StableDiffusionPipeline` for a usage example.Nr g4y?rz)rfr0rrrr!)rr)rUintr IntTensor LongTensorr2r<loggerwarningrHrcr?rgr;minrnr shaperfr0r_r=r)r@r[r`rOr|r}r~rrrrPgammanoiseeps sigma_hatr derivativedtrs rstepzEDMEulerScheduler.stepsN heou7G H I I G )  NNE    ? "  ! !( + + +5=)) DO,EKuE^E^E^E^X^E^E^E^E^E^Gs4;//!34jAAAdg  l&8AT`i   goUQY' 199cY\E1H%<$DDDF $88yYY33y@ [1, - 9zB. "nn\%788  A "> !&;Uijjjjroriginal_samplesrr8c`j|j|j}|jjdkrft j|rRj|jt j||jt j}n9j|j||j}j fd|D}n4j j g|j dz}nj g|j dz}|| }t|j t|j kr?|d}t|j t|j k?|||zz}|S)N)r0rfmps)rfc<g|]}|Sr)rw).0trtr@s r z/EDMEulerScheduler.add_noise..s*^^^qD33A7IJJ^^^rr)r;r?r0rftyperis_floating_pointr8rgrKrHrflattenrn unsqueeze) r@rrr8r; step_indicesrP noisy_samplesrts ` @r add_noisezEDMEulerScheduler.add_noiseps'7'>FVF\]]  " '5 0 0U5LY5W5W 0!%!2!23C3JRWR_!2!`!` ! %5%V,,,,6  el    SXS_ # # # #111e /3 ]k]kl]ku|+,]k ]k  ]k  ]k]k]kEO,]k]k &- .]k]k]k]k@,|<   B/////rr)rm dataclassesrtypingrrrrconfiguration_utilsrr utilsr r utils.torch_utilsr scheduling_utilsr get_loggerrrrrrrrrs' !!!!!!)))))))))) AAAAAAAA'''''''',,,,,,,,,,,,  H % % 88888j88 8"a/a/a/a/a/ a/a/a/a/a/r