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Split seamless config into separate file

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Carson Katri 2022-10-17 19:31:20 -04:00
parent e03a3fcf68
commit 99581dbbf7
2 changed files with 32 additions and 24 deletions
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26
ldm/generate.py
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@ -34,6 +34,7 @@ from ldm.invoke.image_util import InitImageResizer
from ldm.invoke.devices import choose_torch_device, choose_precision from ldm.invoke.devices import choose_torch_device, choose_precision
from ldm.invoke.conditioning import get_uc_and_c from ldm.invoke.conditioning import get_uc_and_c
from ldm.invoke.model_cache import ModelCache from ldm.invoke.model_cache import ModelCache
from ldm.invoke.seamless import configure_model_padding
def fix_func(orig): def fix_func(orig):
if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available(): if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
@ -350,31 +351,8 @@ class Generate:
# to the width and height of the image training set # to the width and height of the image training set
width = width or self.width width = width or self.width
height = height or self.height height = height or self.height
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)
for m in model.modules(): configure_model_padding(model, seamless, seamless_axes)
if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
if seamless:
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])
m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
else:
m._conv_forward = nn.Conv2d._conv_forward.__get__(m, nn.Conv2d)
if hasattr(m, 'asymmetric_padding_mode'):
del m.asymmetric_padding_mode
if hasattr(m, 'asymmetric_padding'):
del m.asymmetric_padding
assert cfg_scale > 1.0, 'CFG_Scale (-C) must be >1.0' assert cfg_scale > 1.0, 'CFG_Scale (-C) must be >1.0'
assert threshold >= 0.0, '--threshold must be >=0.0' assert threshold >= 0.0, '--threshold must be >=0.0'

30
ldm/invoke/seamless.py Normal file
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@ -0,0 +1,30 @@
import torch.nn as nn
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)
def configure_model_padding(model, seamless, seamless_axes):
"""
Modifies the 2D convolution layers to use a circular padding mode based on the `seamless` and `seamless_axes` options.
"""
for m in model.modules():
if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
if seamless:
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])
m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
else:
m._conv_forward = nn.Conv2d._conv_forward.__get__(m, nn.Conv2d)
if hasattr(m, 'asymmetric_padding_mode'):
del m.asymmetric_padding_mode
if hasattr(m, 'asymmetric_padding'):
del m.asymmetric_padding