for i in trange(len(sigmas) - 1, disable=disable):
        denoised = model(x, sigmas[i] * s_in, **extra_args)
        if callback is not None:
            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
        t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
        h = t_next - t

        t_min = min(sigma_fn(t_next), sigma_fn(t))
        t_max = max(sigma_fn(t_next), sigma_fn(t))

        if old_denoised is None or sigmas[i + 1] == 0:
            x = (t_min / t_max) * x - (-h).expm1() * denoised
        else:
            h_last = t - t_fn(sigmas[i - 1])

            h_min = min(h_last, h)
            h_max = max(h_last, h)
            r = h_max / h_min

            h_d = (h_max + h_min) / 2
            denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised
            x = (t_min / t_max) * x - (-h_d).expm1() * denoised_d

        old_denoised = denoised