def marginal_log_mean_coeff(self, t):
        """
        Compute log(alpha_t) of a given continuous-time label t in [0, T].
        """
        if self.schedule == 'discrete':
            return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device),
                                  self.log_alpha_array.to(t.device)).reshape((-1))
        elif self.schedule == 'linear':
            return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
        elif self.schedule == 'cosine':
            log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.))
            log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0
            return log_alpha_t

        Compute log(alpha_t) of a given continuous-time label t in [0, T].
        
ldm/models/diffusion/dpm_solver/dpm_solver.py