UniPC progress bar adjustment

import torch

import torch.nn.functional as F

import math

from tqdm.auto import trange

import tqdm

class NoiseScheduleVP:

                vec_t = timesteps[0].expand((x.shape[0]))

                model_prev_list = [self.model_fn(x, vec_t)]

                t_prev_list = [vec_t]

                with tqdm.tqdm(total=steps) as pbar:

                    # Init the first `order` values by lower order multistep DPM-Solver.

                    for init_order in range(1, order):

                        vec_t = timesteps[init_order].expand(x.shape[0])

                            self.after_update(x, model_x)

                        model_prev_list.append(model_x)

                        t_prev_list.append(vec_t)

                for step in trange(order, steps + 1):

                        pbar.update()

                    for step in range(order, steps + 1):

                        vec_t = timesteps[step].expand(x.shape[0])

                        if lower_order_final:

                            step_order = min(order, steps + 1 - step)

                            if model_x is None:

                                model_x = self.model_fn(x, vec_t)

                            model_prev_list[-1] = model_x

                        pbar.update()

        else:

            raise NotImplementedError()

        if denoise_to_zero: