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22 lines
753 B
Python
22 lines
753 B
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 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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