code styling

@torch.no_grad()

def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list = {0.1: [10, 2, 2]}):

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]}'''

    from tqdm.auto import trange, tqdm

    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):

        nonlocal step_id

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

    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)

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

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

        if i + 1 in restart_list:

            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], sigmas[max_idx], device=sigmas.device)[:-1] # remove the zero at the end

                for times in range(restart_times):

                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)