scheduler.init_noise_sigma return latents def _denoise_loop( self, timesteps, num_inference_steps, do_classifier_free_guidance, guidance_scale, num_warmup_steps, prompt_embeds, negative_prompt_embeds, latents, cross_attention_kwargs, added_cond_kwargs, extra_step_kwargs, callback, call...
self.trialNumSpinBox.valueChanged.connect(self.updateExamples)# Default states of widgetsself.loadLineEdit.setText(self.defaultBaseDir) self.useNoiseSigmaCheckBox.setCheckState(QtCore.Qt.Checked) self.r = self.gESpinBox.value() self.c = self.gISpinBox.value() self.bumpSigmaLabel.setText("???")...
"sigma_min": 0.0, "sigma_max": 0.0, "rho": 0.0, "eta_noise_seed_delta": 0, "always_discard_next_to_last_sigma": false, "sgm_noise_multiplier": false,
# 需要导入模块: from lasagne import init [as 别名] # 或者: from lasagne.init import Constant [as 别名] def __init__(self, incoming, num_centers, locs=init.Normal(std=1), log_sigma=init.Constant(0.), **kwargs): super(RBFLayer, self).__init__(incoming, **kwargs) self.num_cent...
sigma_d=0.0, step_size=1.0, window_size=0.0, roi=None): '''Compute the eigenvalues of the structure tensor at the given scales for a scalar or multi-channel image or volume. Calls :func:`structureTensor` and :func:`tensorEigenvalues`. ''' st = filters.structureTensor(image, innerScale...
def__init__(self,n_neurons,machine_time_step,timescale_factor,spikes_per_second,ring_buffer_sigma,constraints=None,label=None,a=0.02,c=-65.0,b=0.2,d=2.0,i_offset=0,u_init=-14.0,v_init=-70.0,tau_syn_E=5.0,tau_syn_I=5.0,membrane_noise_sd=2.5):# Instantiate the parent classesAbstrac...
return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) ^^^
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n)# create shogun feature representationfeatures=RealFeatures(data)# use a kernel width of sigma=2, which is 8 in SHOGUN's parametrization# which is k(x,y)=exp(-||x-y||^2 / tau), in constrast to the standard# k(x,y)=exp(-||x-y||^2 / (2*sigma^2)), so tau=2*sigma^2...
aux.setXmippOrigin();for(inti=0; i<6; i++) hz1.push_back(aux);doublesigma2=sigma*sigma;doublek1 =1.0/pow((2.0*PI*sigma),(3.0/2.0));doublek2 =-1.0/(sigma2); FOR_ALL_ELEMENTS_IN_ARRAY1D(hx1[0]) {doublei2=i*i;doubleg = -exp(-i2/(2.0*sigma2)); ...