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c0bba28 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | import torch
import torch.nn as nn
from distvae.models.resnet import PatchResnetBlock2D
from distvae.modules.adapters.layers.conv_adapters import Conv2dAdapter
from distvae.modules.adapters.layers.norm_adapters import GroupNormAdapter
from diffusers.models.resnet import ResnetBlock2D
class ResnetBlock2DAdapter(nn.Module):
def __init__(
self,
resnet: ResnetBlock2D,
*,
conv_block_size = 0,
):
super().__init__()
assert resnet.time_emb_proj is None, "temb_channels is not supported in ResnetBlock2DAdapter currently"
assert resnet.up is False, "up sample is not supported in ResnetBlock2DAdapter currently"
assert resnet.down is False, "ResnetBlock2DAdapter dose not support down sample is not supported in currently"
self.resnet = PatchResnetBlock2D(
in_channels=resnet.in_channels,
out_channels=resnet.out_channels,
conv_shortcut=resnet.use_conv_shortcut,
dropout=0,
temb_channels=None,
groups=1,
groups_out=None,
pre_norm=resnet.pre_norm,
skip_time_act=resnet.skip_time_act,
time_embedding_norm=resnet.time_embedding_norm,
output_scale_factor=resnet.output_scale_factor,
use_in_shortcut=resnet.use_in_shortcut,
up=resnet.up,
down=resnet.down,
)
self.resnet.use_in_shortcut = resnet.use_in_shortcut
self.resnet.conv1 = Conv2dAdapter(resnet.conv1, block_size=conv_block_size)
self.resnet.norm1 = GroupNormAdapter(resnet.norm1)
self.resnet.conv2 = Conv2dAdapter(resnet.conv2, block_size=conv_block_size)
self.resnet.norm2 = GroupNormAdapter(resnet.norm2)
self.resnet.dropout = resnet.dropout
self.resnet.nonlinearity = resnet.nonlinearity
self.resnet.conv_shortcut = Conv2dAdapter(resnet.conv_shortcut, block_size=conv_block_size) if resnet.conv_shortcut is not None else None
def forward(self, x, temb: torch.FloatTensor = None, *args, **kwargs):
return self.resnet(x, temb, *args, **kwargs) |