mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
60 lines
2.2 KiB
Python
60 lines
2.2 KiB
Python
from __future__ import annotations
|
|
|
|
from contextlib import contextmanager
|
|
|
|
import torch.nn as nn
|
|
from diffusers.models import UNet2DModel
|
|
|
|
def _conv_forward_asymmetric(self, input, weight, bias):
|
|
"""
|
|
Patch for Conv2d._conv_forward that supports asymmetric padding
|
|
"""
|
|
working = nn.functional.pad(input, self.asymmetric_padding["x"], mode=self.asymmetric_padding_mode["x"])
|
|
working = nn.functional.pad(working, self.asymmetric_padding["y"], mode=self.asymmetric_padding_mode["y"])
|
|
return nn.functional.conv2d(
|
|
working,
|
|
weight,
|
|
bias,
|
|
self.stride,
|
|
nn.modules.utils._pair(0),
|
|
self.dilation,
|
|
self.groups,
|
|
)
|
|
|
|
|
|
@contextmanager
|
|
def set_unet_seamless(model: UNet2DModel, seamless: bool, seamless_axes):
|
|
try:
|
|
to_restore = dict()
|
|
if seamless:
|
|
for m in model.modules():
|
|
if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
|
|
m.asymmetric_padding_mode = {}
|
|
m.asymmetric_padding = {}
|
|
m.asymmetric_padding_mode["x"] = "circular" if ("x" in seamless_axes) else "constant"
|
|
m.asymmetric_padding["x"] = (
|
|
m._reversed_padding_repeated_twice[0],
|
|
m._reversed_padding_repeated_twice[1],
|
|
0,
|
|
0,
|
|
)
|
|
m.asymmetric_padding_mode["y"] = "circular" if ("y" in seamless_axes) else "constant"
|
|
m.asymmetric_padding["y"] = (
|
|
0,
|
|
0,
|
|
m._reversed_padding_repeated_twice[2],
|
|
m._reversed_padding_repeated_twice[3],
|
|
)
|
|
|
|
to_restore.append((m, m._conv_forward))
|
|
m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
|
|
|
|
yield
|
|
|
|
finally:
|
|
for module, orig_conv_forward in to_restore:
|
|
module._conv_forward = orig_conv_forward
|
|
if hasattr(m, "asymmetric_padding_mode"):
|
|
del m.asymmetric_padding_mode
|
|
if hasattr(m, "asymmetric_padding"):
|
|
del m.asymmetric_padding |