mirror of
https://github.com/invoke-ai/InvokeAI
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30 lines
1.7 KiB
Python
30 lines
1.7 KiB
Python
import torch.nn as nn
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def _conv_forward_asymmetric(self, input, weight, bias):
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"""
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Patch for Conv2d._conv_forward that supports asymmetric padding
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"""
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working = nn.functional.pad(input, self.asymmetric_padding['x'], mode=self.asymmetric_padding_mode['x'])
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working = nn.functional.pad(working, self.asymmetric_padding['y'], mode=self.asymmetric_padding_mode['y'])
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return nn.functional.conv2d(working, weight, bias, self.stride, nn.modules.utils._pair(0), self.dilation, self.groups)
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def configure_model_padding(model, seamless, seamless_axes):
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"""
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Modifies the 2D convolution layers to use a circular padding mode based on the `seamless` and `seamless_axes` options.
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"""
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for m in model.modules():
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if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
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if seamless:
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m.asymmetric_padding_mode = {}
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m.asymmetric_padding = {}
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m.asymmetric_padding_mode['x'] = 'circular' if ('x' in seamless_axes) else 'constant'
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m.asymmetric_padding['x'] = (m._reversed_padding_repeated_twice[0], m._reversed_padding_repeated_twice[1], 0, 0)
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m.asymmetric_padding_mode['y'] = 'circular' if ('y' in seamless_axes) else 'constant'
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m.asymmetric_padding['y'] = (0, 0, m._reversed_padding_repeated_twice[2], m._reversed_padding_repeated_twice[3])
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m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
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else:
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m._conv_forward = nn.Conv2d._conv_forward.__get__(m, nn.Conv2d)
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if hasattr(m, 'asymmetric_padding_mode'):
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del m.asymmetric_padding_mode
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if hasattr(m, 'asymmetric_padding'):
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del m.asymmetric_padding |