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| import torch | |
| class NoiseScheduleVP: | |
| def __init__(self, beta_min=0.05, beta_max=20): | |
| self.beta_min = beta_min | |
| self.beta_max = beta_max | |
| self.T = 1. | |
| def get_noise(self, t, beta_init, beta_term, cumulative=False): | |
| if cumulative: | |
| noise = beta_init*t + 0.5*(beta_term - beta_init)*(t**2) | |
| else: | |
| noise = beta_init + (beta_term - beta_init)*t | |
| return noise | |
| def marginal_log_mean_coeff(self, t): | |
| return -0.25 * t**2 * (self.beta_max - | |
| self.beta_min) - 0.5 * t * self.beta_min | |
| def marginal_std(self, t): | |
| return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t))) | |
| def marginal_lambda(self, t): | |
| log_mean_coeff = self.marginal_log_mean_coeff(t) | |
| log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff)) | |
| return log_mean_coeff - log_std | |
| def inverse_lambda(self, lamb): | |
| tmp = 2. * (self.beta_max - self.beta_min) * torch.logaddexp( | |
| -2. * lamb, | |
| torch.zeros((1, )).to(lamb)) | |
| Delta = self.beta_min**2 + tmp | |
| return tmp / (torch.sqrt(Delta) + self.beta_min) / (self.beta_max - | |
| self.beta_min) | |
| def get_time_steps(self, t_T, t_0, N): | |
| lambda_T = self.marginal_lambda(torch.tensor(t_T)) | |
| lambda_0 = self.marginal_lambda(torch.tensor(t_0)) | |
| logSNR_steps = torch.linspace(lambda_T, lambda_0, N + 1) | |
| return self.inverse_lambda(logSNR_steps) | |
| def reverse_diffusion(self, estimator, spk, z, mask, mu, n_timesteps, stoc): | |
| print("use dpm-solver reverse") | |
| xt = z * mask | |
| yt = xt - mu | |
| T = 1 | |
| eps = 1e-3 | |
| time = self.get_time_steps(T, eps, n_timesteps) | |
| for i in range(n_timesteps): | |
| s = torch.ones((xt.shape[0], )).to(xt.device) * time[i] | |
| t = torch.ones((xt.shape[0], )).to(xt.device) * time[i + 1] | |
| lambda_s = self.marginal_lambda(s) | |
| lambda_t = self.marginal_lambda(t) | |
| h = lambda_t - lambda_s | |
| log_alpha_s = self.marginal_log_mean_coeff(s) | |
| log_alpha_t = self.marginal_log_mean_coeff(t) | |
| sigma_t = self.marginal_std(t) | |
| phi_1 = torch.expm1(h) | |
| noise_s = estimator(spk, yt + mu, mask, mu, s) | |
| lt = 1 - torch.exp(-self.get_noise(s, self.beta_min, self.beta_max, cumulative=True)) | |
| a = torch.exp(log_alpha_t - log_alpha_s) | |
| b = sigma_t * phi_1 * torch.sqrt(lt) | |
| yt = a * yt + (b * noise_s) | |
| xt = yt + mu | |
| return xt | |
| class MaxLikelihood: | |
| def __init__(self, beta_min=0.05, beta_max=20): | |
| self.beta_min = beta_min | |
| self.beta_max = beta_max | |
| def get_noise(self, t, beta_init, beta_term, cumulative=False): | |
| if cumulative: | |
| noise = beta_init*t + 0.5*(beta_term - beta_init)*(t**2) | |
| else: | |
| noise = beta_init + (beta_term - beta_init)*t | |
| return noise | |
| def get_gamma(self, s, t, beta_init, beta_term): | |
| gamma = beta_init*(t-s) + 0.5*(beta_term-beta_init)*(t**2-s**2) | |
| gamma = torch.exp(-0.5*gamma) | |
| return gamma | |
| def get_mu(self, s, t): | |
| gamma_0_s = self.get_gamma(0, s, self.beta_min, self.beta_max) | |
| gamma_0_t = self.get_gamma(0, t, self.beta_min, self.beta_max) | |
| gamma_s_t = self.get_gamma(s, t, self.beta_min, self.beta_max) | |
| mu = gamma_s_t * ((1-gamma_0_s**2) / (1-gamma_0_t**2)) | |
| return mu | |
| def get_nu(self, s, t): | |
| gamma_0_s = self.get_gamma(0, s, self.beta_min, self.beta_max) | |
| gamma_0_t = self.get_gamma(0, t, self.beta_min, self.beta_max) | |
| gamma_s_t = self.get_gamma(s, t, self.beta_min, self.beta_max) | |
| nu = gamma_0_s * ((1-gamma_s_t**2) / (1-gamma_0_t**2)) | |
| return nu | |
| def get_sigma(self, s, t): | |
| gamma_0_s = self.get_gamma(0, s, self.beta_min, self.beta_max) | |
| gamma_0_t = self.get_gamma(0, t, self.beta_min, self.beta_max) | |
| gamma_s_t = self.get_gamma(s, t, self.beta_min, self.beta_max) | |
| sigma = torch.sqrt(((1 - gamma_0_s**2) * (1 - gamma_s_t**2)) / (1 - gamma_0_t**2)) | |
| return sigma | |
| def get_kappa(self, t, h, noise): | |
| nu = self.get_nu(t-h, t) | |
| gamma_0_t = self.get_gamma(0, t, self.beta_min, self.beta_max) | |
| kappa = (nu*(1-gamma_0_t**2)/(gamma_0_t*noise*h) - 1) | |
| return kappa | |
| def get_omega(self, t, h, noise): | |
| mu = self.get_mu(t-h, t) | |
| kappa = self.get_kappa(t, h, noise) | |
| gamma_0_t = self.get_gamma(0, t, self.beta_min, self.beta_max) | |
| omega = (mu-1)/(noise*h) + (1+kappa)/(1-gamma_0_t**2) - 0.5 | |
| return omega | |
| def reverse_diffusion(self, estimator, spk, z, mask, mu, n_timesteps, stoc=False): | |
| print("use MaxLikelihood reverse") | |
| h = 1.0 / n_timesteps | |
| xt = z * mask | |
| for i in range(n_timesteps): | |
| t = (1.0 - i*h) * torch.ones(z.shape[0], dtype=z.dtype, | |
| device=z.device) | |
| time = t.unsqueeze(-1).unsqueeze(-1) | |
| noise_t = self.get_noise(time, self.beta_min, self.beta_max, | |
| cumulative=False) | |
| kappa_t_h = self.get_kappa(t, h, noise_t) | |
| omega_t_h = self.get_omega(t, h, noise_t) | |
| sigma_t_h = self.get_sigma(t-h, t) | |
| es = estimator(spk, xt, mask, mu, t) | |
| dxt = ((0.5+omega_t_h)*(xt - mu) + (1+kappa_t_h) * es) | |
| dxt_stoc = torch.randn(z.shape, dtype=z.dtype, device=z.device, | |
| requires_grad=False) | |
| dxt_stoc = dxt_stoc * sigma_t_h | |
| dxt = dxt * noise_t * h + dxt_stoc | |
| xt = (xt + dxt) * mask | |
| return xt | |
| class GradRaw: | |
| def __init__(self, beta_min=0.05, beta_max=20): | |
| self.beta_min = beta_min | |
| self.beta_max = beta_max | |
| def get_noise(self, t, beta_init, beta_term, cumulative=False): | |
| if cumulative: | |
| noise = beta_init*t + 0.5*(beta_term - beta_init)*(t**2) | |
| else: | |
| noise = beta_init + (beta_term - beta_init)*t | |
| return noise | |
| def reverse_diffusion(self, estimator, spk, z, mask, mu, n_timesteps, stoc=False): | |
| print("use grad-raw reverse") | |
| h = 1.0 / n_timesteps | |
| xt = z * mask | |
| for i in range(n_timesteps): | |
| t = (1.0 - (i + 0.5)*h) * \ | |
| torch.ones(z.shape[0], dtype=z.dtype, device=z.device) | |
| time = t.unsqueeze(-1).unsqueeze(-1) | |
| noise_t = self.get_noise(time, self.beta_min, self.beta_max, | |
| cumulative=False) | |
| if stoc: # adds stochastic term | |
| dxt_det = 0.5 * (mu - xt) - estimator(spk, xt, mask, mu, t) | |
| dxt_det = dxt_det * noise_t * h | |
| dxt_stoc = torch.randn(z.shape, dtype=z.dtype, device=z.device, | |
| requires_grad=False) | |
| dxt_stoc = dxt_stoc * torch.sqrt(noise_t * h) | |
| dxt = dxt_det + dxt_stoc | |
| else: | |
| dxt = 0.5 * (mu - xt - estimator(spk, xt, mask, mu, t)) | |
| dxt = dxt * noise_t * h | |
| xt = (xt - dxt) * mask | |
| return xt | |