Merge pull request #8589 from vladmandic/unipc

        # sampling

        C, H, W = shape

        size = (batch_size, C, H, W)

        print(f'Data shape for UniPC sampling is {size}')

        # print(f'Data shape for UniPC sampling is {size}')

        device = self.model.betas.device

        if x_T is None:

import torch

import torch.nn.functional as F

import math

from tqdm.auto import trange

class NoiseScheduleVP:

        if method == 'multistep':

            assert steps >= order, "UniPC order must be < sampling steps"

            timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)

            print(f"Running UniPC Sampling with {timesteps.shape[0]} timesteps, order {order}")

            #print(f"Running UniPC Sampling with {timesteps.shape[0]} timesteps, order {order}")

            assert timesteps.shape[0] - 1 == steps

            with torch.no_grad():

                vec_t = timesteps[0].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 range(order, steps + 1):

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

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

                    if lower_order_final:

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