50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
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import torch
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import numpy as np
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def append_dims(x, target_dims):
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"""Appends dimensions to the end of a tensor until it has target_dims dimensions.
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From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py"""
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dims_to_append = target_dims - x.ndim
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if dims_to_append < 0:
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raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
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return x[(...,) + (None,) * dims_to_append]
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def renorm_thresholding(x0, value):
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# renorm
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pred_max = x0.max()
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pred_min = x0.min()
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pred_x0 = (x0 - pred_min) / (pred_max - pred_min) # 0 ... 1
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pred_x0 = 2 * pred_x0 - 1. # -1 ... 1
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s = torch.quantile(
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rearrange(pred_x0, 'b ... -> b (...)').abs(),
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value,
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dim=-1
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)
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s.clamp_(min=1.0)
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s = s.view(-1, *((1,) * (pred_x0.ndim - 1)))
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# clip by threshold
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# pred_x0 = pred_x0.clamp(-s, s) / s # needs newer pytorch # TODO bring back to pure-gpu with min/max
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# temporary hack: numpy on cpu
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pred_x0 = np.clip(pred_x0.cpu().numpy(), -s.cpu().numpy(), s.cpu().numpy()) / s.cpu().numpy()
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pred_x0 = torch.tensor(pred_x0).to(self.model.device)
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# re.renorm
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pred_x0 = (pred_x0 + 1.) / 2. # 0 ... 1
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pred_x0 = (pred_max - pred_min) * pred_x0 + pred_min # orig range
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return pred_x0
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def norm_thresholding(x0, value):
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s = append_dims(x0.pow(2).flatten(1).mean(1).sqrt().clamp(min=value), x0.ndim)
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return x0 * (value / s)
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def spatial_norm_thresholding(x0, value):
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# b c h w
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s = x0.pow(2).mean(1, keepdim=True).sqrt().clamp(min=value)
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return x0 * (value / s)
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