Added type hints and improved error message in append_dims function

ldm/models/diffusion/sampling_util.py

@@ -2,21 +2,22 @@ import torch
 import numpy as np
 
 
-def append_dims(x, target_dims):
+def append_dims(x: torch.Tensor, target_dims: int) -> torch.Tensor:
     """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')
+        raise ValueError(f'Input tensor has {x.ndim} dimensions but target_dims is {target_dims}, which is less than the number of dimensions in the input tensor.')
     return x[(...,) + (None,) * dims_to_append]
 
 
-def norm_thresholding(x0, value):
+def norm_thresholding(x0: torch.Tensor, value: float) -> torch.Tensor:
     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):
+def spatial_norm_thresholding(x0: torch.Tensor, value: float) -> torch.Tensor:
     # b c h w
     s = x0.pow(2).mean(1, keepdim=True).sqrt().clamp(min=value)
     return x0 * (value / s)
+