(self, x_start, t)
| 227 | self.register_buffer('spec_max', torch.FloatTensor(spec_max)[None, None, :hparams['keep_bins']]) |
| 228 | |
| 229 | def q_mean_variance(self, x_start, t): |
| 230 | mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start |
| 231 | variance = extract(1. - self.alphas_cumprod, t, x_start.shape) |
| 232 | log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape) |
| 233 | return mean, variance, log_variance |
| 234 | |
| 235 | def predict_start_from_noise(self, x_t, t, noise): |
| 236 | return ( |