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add pbar to unipc
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@ -71,7 +71,7 @@ class UniPCSampler(object):
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# sampling
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C, H, W = shape
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size = (batch_size, C, H, W)
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print(f'Data shape for UniPC sampling is {size}')
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# print(f'Data shape for UniPC sampling is {size}')
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device = self.model.betas.device
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if x_T is None:
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@ -1,6 +1,7 @@
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import torch
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import torch.nn.functional as F
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import math
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from tqdm.auto import trange
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class NoiseScheduleVP:
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@ -750,7 +751,7 @@ class UniPC:
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if method == 'multistep':
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assert steps >= order, "UniPC order must be < sampling steps"
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timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
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print(f"Running UniPC Sampling with {timesteps.shape[0]} timesteps, order {order}")
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#print(f"Running UniPC Sampling with {timesteps.shape[0]} timesteps, order {order}")
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assert timesteps.shape[0] - 1 == steps
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with torch.no_grad():
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vec_t = timesteps[0].expand((x.shape[0]))
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@ -766,7 +767,7 @@ class UniPC:
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self.after_update(x, model_x)
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model_prev_list.append(model_x)
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t_prev_list.append(vec_t)
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for step in range(order, steps + 1):
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for step in trange(order, steps + 1):
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vec_t = timesteps[step].expand(x.shape[0])
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if lower_order_final:
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step_order = min(order, steps + 1 - step)
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