mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
84 lines
2.9 KiB
Python
84 lines
2.9 KiB
Python
from __future__ import annotations
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from contextlib import contextmanager
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from typing import List, Union
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import torch.nn as nn
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from diffusers.models import AutoencoderKL, UNet2DConditionModel
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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(
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working,
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weight,
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bias,
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self.stride,
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nn.modules.utils._pair(0),
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self.dilation,
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self.groups,
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)
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@contextmanager
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def set_seamless(model: Union[UNet2DConditionModel, AutoencoderKL], seamless_axes: List[str]):
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try:
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to_restore = []
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skipped_layers = 0
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skip_second_resnet = True
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skip_conv2 = True
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for m_name, m in model.named_modules():
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if not isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
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continue
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if isinstance(model, UNet2DConditionModel) and m_name.startswith("down_blocks.") and ".resnets." in m_name:
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# down_blocks.1.resnets.1.conv1
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_, block_num, _, resnet_num, submodule_name = m_name.split(".")
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block_num = int(block_num)
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resnet_num = int(resnet_num)
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# if block_num >= seamless_down_blocks:
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if block_num >= len(model.down_blocks) - skipped_layers:
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continue
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if resnet_num > 0 and skip_second_resnet:
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continue
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if submodule_name == "conv2" and skip_conv2:
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continue
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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"] = (
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m._reversed_padding_repeated_twice[0],
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m._reversed_padding_repeated_twice[1],
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0,
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0,
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)
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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"] = (
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0,
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0,
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m._reversed_padding_repeated_twice[2],
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m._reversed_padding_repeated_twice[3],
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)
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to_restore.append((m, m._conv_forward))
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m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
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yield
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finally:
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for module, orig_conv_forward in to_restore:
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module._conv_forward = orig_conv_forward
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if hasattr(module, "asymmetric_padding_mode"):
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del module.asymmetric_padding_mode
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if hasattr(module, "asymmetric_padding"):
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del module.asymmetric_padding
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