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import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
class Pseudo3DConv(nn.Module):
def __init__(
self,
dim,
dim_out,
kernel_size,
**kwargs
):
super().__init__()
self.spatial_conv = nn.Conv2d(dim, dim_out, kernel_size, **kwargs)
self.temporal_conv = nn.Conv1d(dim_out, dim_out, kernel_size, padding=kernel_size // 2)
self.temporal_conv = nn.Conv1d(dim_out, dim_out, 3, padding=1)
nn.init.dirac_(self.temporal_conv.weight.data) # initialized to be identity
nn.init.zeros_(self.temporal_conv.bias.data)
def forward(
self,
x,
convolve_across_time = True
):
b, c, *_, h, w = x.shape
is_video = x.ndim == 5
convolve_across_time &= is_video
if is_video:
x = rearrange(x, 'b c f h w -> (b f) c h w')
#with torch.no_grad():
# x = self.spatial_conv(x)
x = self.spatial_conv(x)
if is_video:
x = rearrange(x, '(b f) c h w -> b c f h w', b = b)
b, c, *_, h, w = x.shape
if not convolve_across_time:
return x
if is_video:
x = rearrange(x, 'b c f h w -> (b h w) c f')
x = self.temporal_conv(x)
x = rearrange(x, '(b h w) c f -> b c f h w', h = h, w = w)
return x
class Upsample2D(nn.Module):
"""
An upsampling layer with an optional convolution.
Parameters:
channels: channels in the inputs and outputs.
use_conv: a bool determining if a convolution is applied.
use_conv_transpose:
out_channels:
"""
def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.use_conv_transpose = use_conv_transpose
self.name = name
conv = None
if use_conv_transpose:
conv = nn.ConvTranspose2d(channels, self.out_channels, 4, 2, 1)
elif use_conv:
conv = Pseudo3DConv(self.channels, self.out_channels, 3, padding=1)
# TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
if name == "conv":
self.conv = conv
else:
self.Conv2d_0 = conv
def forward(self, hidden_states, output_size=None):
assert hidden_states.shape[1] == self.channels
if self.use_conv_transpose:
return self.conv(hidden_states)
# Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16
# TODO(Suraj): Remove this cast once the issue is fixed in PyTorch
# https://github.com/pytorch/pytorch/issues/86679
dtype = hidden_states.dtype
if dtype == torch.bfloat16:
hidden_states = hidden_states.to(torch.float32)
# upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
if hidden_states.shape[0] >= 64:
hidden_states = hidden_states.contiguous()
b, c, *_, h, w = hidden_states.shape
is_video = hidden_states.ndim == 5
if is_video:
hidden_states = rearrange(hidden_states, 'b c f h w -> (b f) c h w')
# if `output_size` is passed we force the interpolation output
# size and do not make use of `scale_factor=2`
if output_size is None:
hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode="nearest")
else:
hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest")
if is_video:
hidden_states = rearrange(hidden_states, '(b f) c h w -> b c f h w', b = b)
# If the input is bfloat16, we cast back to bfloat16
if dtype == torch.bfloat16:
hidden_states = hidden_states.to(dtype)
# TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
if self.use_conv:
if self.name == "conv":
hidden_states = self.conv(hidden_states)
else:
hidden_states = self.Conv2d_0(hidden_states)
return hidden_states
class Downsample2D(nn.Module):
"""
A downsampling layer with an optional convolution.
Parameters:
channels: channels in the inputs and outputs.
use_conv: a bool determining if a convolution is applied.
out_channels:
padding:
"""
def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.padding = padding
stride = 2
self.name = name
if use_conv:
conv = Pseudo3DConv(self.channels, self.out_channels, 3, stride=stride, padding=padding)
else:
assert self.channels == self.out_channels
conv = nn.AvgPool2d(kernel_size=stride, stride=stride)
# TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
if name == "conv":
self.Conv2d_0 = conv
self.conv = conv
elif name == "Conv2d_0":
self.conv = conv
else:
self.conv = conv
def forward(self, hidden_states):
assert hidden_states.shape[1] == self.channels
if self.use_conv and self.padding == 0:
pad = (0, 1, 0, 1)
hidden_states = F.pad(hidden_states, pad, mode="constant", value=0)
assert hidden_states.shape[1] == self.channels
if self.use_conv:
hidden_states = self.conv(hidden_states)
else:
b, c, *_, h, w = hidden_states.shape
is_video = hidden_states.ndim == 5
if is_video:
hidden_states = rearrange(hidden_states, 'b c f h w -> (b f) c h w')
hidden_states = self.conv(hidden_states)
if is_video:
hidden_states = rearrange(hidden_states, '(b f) c h w -> b c f h w', b = b)
return hidden_states
class ResnetBlockPseudo3D(nn.Module):
def __init__(
self,
*,
in_channels,
out_channels=None,
conv_shortcut=False,
dropout=0.0,
temb_channels=512,
groups=32,
groups_out=None,
pre_norm=True,
eps=1e-6,
time_embedding_norm="default",
kernel=None,
output_scale_factor=1.0,
use_in_shortcut=None,
up=False,
down=False,
):
super().__init__()
self.pre_norm = pre_norm
self.pre_norm = True
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.use_conv_shortcut = conv_shortcut
self.time_embedding_norm = time_embedding_norm
self.up = up
self.down = down
self.output_scale_factor = output_scale_factor
print('OUTPUT_SCALE_FACTOR:', output_scale_factor)
if groups_out is None:
groups_out = groups
self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
self.conv1 = Pseudo3DConv(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
if temb_channels is not None:
self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels)
else:
self.time_emb_proj = None
self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)
self.dropout = torch.nn.Dropout(dropout)
self.conv2 = Pseudo3DConv(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
self.nonlinearity = nn.SiLU()
self.upsample = self.downsample = None
if self.up:
self.upsample = Upsample2D(in_channels, use_conv=False)
elif self.down:
self.downsample = Downsample2D(in_channels, use_conv=False, padding=1, name="op")
self.use_in_shortcut = self.in_channels != self.out_channels if use_in_shortcut is None else use_in_shortcut
self.conv_shortcut = None
if self.use_in_shortcut:
self.conv_shortcut = Pseudo3DConv(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
def forward(self, input_tensor, temb):
hidden_states = input_tensor
hidden_states = self.norm1(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
if self.upsample is not None:
# upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
if hidden_states.shape[0] >= 64:
input_tensor = input_tensor.contiguous()
hidden_states = hidden_states.contiguous()
input_tensor = self.upsample(input_tensor)
hidden_states = self.upsample(hidden_states)
elif self.downsample is not None:
input_tensor = self.downsample(input_tensor)
hidden_states = self.downsample(hidden_states)
hidden_states = self.conv1(hidden_states)
if temb is not None:
b, c, *_, h, w = hidden_states.shape
is_video = hidden_states.ndim == 5
if is_video:
b, c, f, h, w = hidden_states.shape
hidden_states = rearrange(hidden_states, 'b c f h w -> (b f) c h w')
temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None]
hidden_states = hidden_states + temb.repeat_interleave(f, 0)
hidden_states = rearrange(hidden_states, '(b f) c h w -> b c f h w', b=b)
else:
temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None]
hidden_states = hidden_states + temb
hidden_states = self.norm2(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.conv2(hidden_states)
if self.conv_shortcut is not None:
input_tensor = self.conv_shortcut(input_tensor)
output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
return output_tensor
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