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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 | from typing import Optional
import torch
import torch.nn as nn
from distvae.models.upsampling import PatchUpsample2D
from distvae.modules.adapters.layers.conv_adapters import Conv2dAdapter
from diffusers.models.upsampling import Upsample2D
class Upsample2DAdapter(nn.Module):
def __init__(
self,
upsample2d: Upsample2D,
*,
conv_block_size = 0,
):
super().__init__()
assert upsample2d.norm is None, "upsample2dBlock2DAdapter dose not support normalization"
if upsample2d.name == "conv":
assert not isinstance(upsample2d.conv, nn.ConvTranspose2d), "upsample2dBlock2DAdapter dose not support transpose conv"
else:
assert not isinstance(upsample2d.Conv2d_0, nn.ConvTranspose2d), "upsample2dBlock2DAdapter dose not support transpose conv"
self.upsample2d = PatchUpsample2D(
channels=upsample2d.channels,
use_conv=upsample2d.use_conv,
use_conv_transpose=upsample2d.use_conv_transpose,
out_channels=upsample2d.out_channels,
name=upsample2d.name,
kernel_size=None,
padding=1,
interpolate=upsample2d.interpolate
)
if upsample2d.name == "conv":
self.upsample2d.conv = Conv2dAdapter(upsample2d.conv, block_size=conv_block_size)
else:
self.upsample2d.Conv2d_0 = Conv2dAdapter(upsample2d.Conv2d_0, block_size=conv_block_size)
def forward(
self, hidden_states: torch.FloatTensor, output_size: Optional[int] = None, *args, **kwargs
):
return self.upsample2d(hidden_states, output_size, *args, **kwargs) |