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