| | 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 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): |
| | |
| | s = x0.pow(2).mean(1, keepdim=True).sqrt().clamp(min=value) |
| | return x0 * (value / s) |
