Method q_sample

usr/diff/shallow_diffusion_tts.py:286–291

(self, x_start, t, noise=None)

        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise

    def q_sample(self, x_start, t, noise=None):
        noise = default(noise, lambda: torch.randn_like(x_start))
        return (
                extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
                extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
        )

    def p_losses(self, x_start, t, cond, noise=None, nonpadding=None):
        noise = default(noise, lambda: torch.randn_like(x_start))