mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
72 lines
2.6 KiB
Python
72 lines
2.6 KiB
Python
from __future__ import annotations
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from contextlib import contextmanager
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from typing import Callable, Dict, List, Optional, Tuple
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import torch
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import torch.nn as nn
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from diffusers import UNet2DConditionModel
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from diffusers.models.lora import LoRACompatibleConv
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from invokeai.backend.stable_diffusion.extensions.base import ExtensionBase
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class SeamlessExt(ExtensionBase):
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def __init__(
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self,
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seamless_axes: List[str],
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):
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super().__init__()
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self._seamless_axes = seamless_axes
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@contextmanager
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def patch_unet(self, unet: UNet2DConditionModel, cached_weights: Optional[Dict[str, torch.Tensor]] = None):
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with self.static_patch_model(
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model=unet,
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seamless_axes=self._seamless_axes,
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):
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yield
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@staticmethod
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@contextmanager
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def static_patch_model(
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model: torch.nn.Module,
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seamless_axes: List[str],
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):
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if not seamless_axes:
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yield
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return
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x_mode = "circular" if "x" in seamless_axes else "constant"
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y_mode = "circular" if "y" in seamless_axes else "constant"
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# override conv_forward
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# https://github.com/huggingface/diffusers/issues/556#issuecomment-1993287019
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def _conv_forward_asymmetric(
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self, input: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] = None
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):
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self.paddingX = (self._reversed_padding_repeated_twice[0], self._reversed_padding_repeated_twice[1], 0, 0)
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self.paddingY = (0, 0, self._reversed_padding_repeated_twice[2], self._reversed_padding_repeated_twice[3])
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working = torch.nn.functional.pad(input, self.paddingX, mode=x_mode)
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working = torch.nn.functional.pad(working, self.paddingY, mode=y_mode)
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return torch.nn.functional.conv2d(
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working, weight, bias, self.stride, torch.nn.modules.utils._pair(0), self.dilation, self.groups
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)
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original_layers: List[Tuple[nn.Conv2d, Callable]] = []
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try:
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for layer in model.modules():
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if not isinstance(layer, torch.nn.Conv2d):
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continue
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if isinstance(layer, LoRACompatibleConv) and layer.lora_layer is None:
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layer.lora_layer = lambda *x: 0
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original_layers.append((layer, layer._conv_forward))
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layer._conv_forward = _conv_forward_asymmetric.__get__(layer, torch.nn.Conv2d)
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yield
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finally:
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for layer, orig_conv_forward in original_layers:
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layer._conv_forward = orig_conv_forward
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