some stylistic changes for the sampler code

import torch

import tqdm

import k_diffusion.sampling

@torch.no_grad()

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

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

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

    '''If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list'''

    from tqdm.auto import trange

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

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

    If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list

    """

    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, get_sigmas_karras

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

        nonlocal step_id

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

            x = x + d_prime * dt

        step_id += 1

        return x

    steps = sigmas.shape[0] - 1

    if restart_list is None:

        if steps >= 20:

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

            restart_list = {}

    restart_list = {int(torch.argmin(abs(sigmas - key), dim=0)): value for key, value in restart_list.items()}

    step_list = []

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

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

                while restart_times > 0:

                    restart_times -= 1

                    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):

    for old_sigma, new_sigma in tqdm.tqdm(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

            last_sigma = old_sigma

        elif last_sigma < old_sigma:

            x = x + k_diffusion.sampling.torch.randn_like(x) * s_noise * (old_sigma ** 2 - last_sigma ** 2) ** 0.5

        x = heun_step(x, old_sigma, new_sigma)

        last_sigma = new_sigma

    return x