84 lines
2.1 KiB
Python
84 lines
2.1 KiB
Python
from typing import Tuple, Union
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import torch
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from .dual_conv3d import DualConv3d
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from .causal_conv3d import CausalConv3d
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import comfy.ops
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ops = comfy.ops.disable_weight_init
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def make_conv_nd(
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dims: Union[int, Tuple[int, int]],
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in_channels: int,
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out_channels: int,
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kernel_size: int,
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stride=1,
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padding=0,
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dilation=1,
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groups=1,
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bias=True,
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causal=False,
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):
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if dims == 2:
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return ops.Conv2d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups,
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bias=bias,
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)
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elif dims == 3:
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if causal:
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return CausalConv3d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups,
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bias=bias,
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)
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return ops.Conv3d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups,
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bias=bias,
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)
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elif dims == (2, 1):
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return DualConv3d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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bias=bias,
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)
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else:
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raise ValueError(f"unsupported dimensions: {dims}")
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def make_linear_nd(
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dims: int,
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in_channels: int,
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out_channels: int,
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bias=True,
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):
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if dims == 2:
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return ops.Conv2d(
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in_channels=in_channels, out_channels=out_channels, kernel_size=1, bias=bias
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)
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elif dims == 3 or dims == (2, 1):
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return ops.Conv3d(
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in_channels=in_channels, out_channels=out_channels, kernel_size=1, bias=bias
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)
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else:
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raise ValueError(f"unsupported dimensions: {dims}")
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