Spaces:
Sleeping
Sleeping
File size: 1,486 Bytes
ee98197 |
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 |
from typing import Tuple
import torch.nn as nn
import torch.nn.functional as F
from diffusers.models.modeling_utils import ModelMixin
from .motion_module import zero_module
from .resnet import InflatedConv3d
class VKpsGuider(ModelMixin):
def __init__(
self,
conditioning_embedding_channels: int,
conditioning_channels: int = 3,
block_out_channels: Tuple[int] = (16, 32, 64, 128),
):
super().__init__()
self.conv_in = InflatedConv3d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1)
self.blocks = nn.ModuleList([])
for i in range(len(block_out_channels) - 1):
channel_in = block_out_channels[i]
channel_out = block_out_channels[i + 1]
self.blocks.append(InflatedConv3d(channel_in, channel_in, kernel_size=3, padding=1))
self.blocks.append(InflatedConv3d(channel_in, channel_out, kernel_size=3, padding=1, stride=2))
self.conv_out = zero_module(InflatedConv3d(
block_out_channels[-1],
conditioning_embedding_channels,
kernel_size=3,
padding=1,
))
def forward(self, conditioning):
embedding = self.conv_in(conditioning)
embedding = F.silu(embedding)
for block in self.blocks:
embedding = block(embedding)
embedding = F.silu(embedding)
embedding = self.conv_out(embedding)
return embedding
|