| 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) |
