restart-sampler with correct steps

def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1.):

    """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)"""

    '''Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}'''

    restart_list = {0.1: [10, 2, 2]}

    from tqdm.auto import trange

    extra_args = {} if extra_args is None else extra_args

    s_in = x.new_ones([x.shape[0]])

    step_id = 0

    from k_diffusion.sampling import to_d, append_zero

    def heun_step(x, old_sigma, new_sigma):

    def heun_step(x, old_sigma, new_sigma, second_order = True):

        nonlocal step_id

        denoised = model(x, old_sigma * s_in, **extra_args)

        d = to_d(x, old_sigma, denoised)

        if callback is not None:

            callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised})

        dt = new_sigma - old_sigma

        if new_sigma == 0:

        if new_sigma == 0 or not second_order:

            # Euler method

            x = x + d * dt

        else:

            x = x + d_prime * dt

        step_id += 1

        return x

    # print(sigmas)

    temp_list = dict()

    for key, value in restart_list.items():

        temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value

    restart_list = temp_list

    def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'):

        ramp = torch.linspace(0, 1, n).to(device)

        min_inv_rho = (sigma_min ** (1 / rho))

            max_inv_rho = max_inv_rho.to(device)

        sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho

        return append_zero(sigmas).to(device)

    steps = sigmas.shape[0] - 1

    if steps >= 20:

        restart_steps = 9

        restart_times = 2 if steps >= 36 else 1

        sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2], sigmas[0], device=sigmas.device)

        restart_list = {0.1: [restart_steps + 1, restart_times, 2]}

    else:

        restart_list = dict()

    temp_list = dict()

    for key, value in restart_list.items():

        temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value

    restart_list = temp_list

    for i in trange(len(sigmas) - 1, disable=disable):

        x = heun_step(x, sigmas[i], sigmas[i+1])

        if i + 1 in restart_list: