InvokeAI/invokeai/backend/util/mask.py

54 lines
1.6 KiB
Python

import torch
def to_standard_mask_dim(mask: torch.Tensor) -> torch.Tensor:
"""Standardize the dimensions of a mask tensor.
Args:
mask (torch.Tensor): A mask tensor. The shape can be (1, h, w) or (h, w).
Returns:
torch.Tensor: The output mask tensor. The shape is (1, h, w).
"""
# Get the mask height and width.
if mask.ndim == 2:
mask = mask.unsqueeze(0)
elif mask.ndim == 3 and mask.shape[0] == 1:
pass
else:
raise ValueError(f"Unsupported mask shape: {mask.shape}. Expected (1, h, w) or (h, w).")
return mask
def to_standard_float_mask(mask: torch.Tensor, out_dtype: torch.dtype) -> torch.Tensor:
"""Standardize the format of a mask tensor.
Args:
mask (torch.Tensor): A mask tensor. The dtype can be any bool, float, or int type. The shape must be (1, h, w)
or (h, w).
out_dtype (torch.dtype): The dtype of the output mask tensor. Must be a float type.
Returns:
torch.Tensor: The output mask tensor. The dtype is out_dtype. The shape is (1, h, w). All values are either 0.0
or 1.0.
"""
if not out_dtype.is_floating_point:
raise ValueError(f"out_dtype must be a float type, but got {out_dtype}")
mask = to_standard_mask_dim(mask)
mask = mask.to(out_dtype)
# Set masked regions to 1.0.
if mask.dtype == torch.bool:
mask = mask.to(out_dtype)
else:
mask = mask.to(out_dtype)
mask_region = mask > 0.5
mask[mask_region] = 1.0
mask[~mask_region] = 0.0
return mask