fix progress bar & torchHijack

    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])

    step_list = []

    for i in range(len(sigmas) - 1):

        step_list.append((sigmas[i], sigmas[i + 1]))

        if i + 1 in restart_list:

            restart_steps, restart_times, restart_max = restart_list[i + 1]

            min_idx = i + 1

            max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0))

            if max_idx < min_idx:

                sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1] # remove the zero at the end

                sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1]

                while restart_times > 0:

                    restart_times -= 1

                    x = x + torch.randn_like(x) * s_noise * (sigmas[max_idx] ** 2 - sigmas[min_idx] ** 2) ** 0.5

                    for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:]):

                        x = heun_step(x, old_sigma, new_sigma)

                    step_list.extend([(old_sigma, new_sigma) for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:])])

    last_sigma = None

    for i in trange(len(step_list), disable=disable):

        if last_sigma is None:

            last_sigma = step_list[i][0]

        elif last_sigma < step_list[i][0]:

            x = x + k_diffusion.sampling.torch.randn_like(x) * s_noise * (step_list[i][0] ** 2 - last_sigma ** 2) ** 0.5

        x = heun_step(x, step_list[i][0], step_list[i][1])

        last_sigma = step_list[i][1]

    return x

samplers_data_k_diffusion = [