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from functools import partial
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from diffusers.models.activations import get_activation
from diffusers.models.attention_processor import SpatialNorm
from diffusers.models.lora import LoRACompatibleConv, LoRACompatibleLinear
from diffusers.models.normalization import AdaGroupNorm
from timm.models.layers import drop_path, to_2tuple, trunc_normal_
from .modeling_causal_conv import CausalConv3d, CausalGroupNorm
class CausalResnetBlock3D(nn.Module):
r"""
A Resnet block.
Parameters:
in_channels (`int`): The number of channels in the input.
out_channels (`int`, *optional*, default to be `None`):
The number of output channels for the first conv2d layer. If None, same as `in_channels`.
dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.
temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.
groups (`int`, *optional*, default to `32`): The number of groups to use for the first normalization layer.
groups_out (`int`, *optional*, default to None):
The number of groups to use for the second normalization layer. if set to None, same as `groups`.
eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization.
non_linearity (`str`, *optional*, default to `"swish"`): the activation function to use.
time_embedding_norm (`str`, *optional*, default to `"default"` ): Time scale shift config.
By default, apply timestep embedding conditioning with a simple shift mechanism. Choose "scale_shift" or
"ada_group" for a stronger conditioning with scale and shift.
kernel (`torch.FloatTensor`, optional, default to None): FIR filter, see
[`~models.resnet.FirUpsample2D`] and [`~models.resnet.FirDownsample2D`].
output_scale_factor (`float`, *optional*, default to be `1.0`): the scale factor to use for the output.
use_in_shortcut (`bool`, *optional*, default to `True`):
If `True`, add a 1x1 nn.conv2d layer for skip-connection.
up (`bool`, *optional*, default to `False`): If `True`, add an upsample layer.
down (`bool`, *optional*, default to `False`): If `True`, add a downsample layer.
conv_shortcut_bias (`bool`, *optional*, default to `True`): If `True`, adds a learnable bias to the
`conv_shortcut` output.
conv_2d_out_channels (`int`, *optional*, default to `None`): the number of channels in the output.
If None, same as `out_channels`.
"""
def __init__(
self,
*,
in_channels: int,
out_channels: Optional[int] = None,
conv_shortcut: bool = False,
dropout: float = 0.0,
temb_channels: int = 512,
groups: int = 32,
groups_out: Optional[int] = None,
pre_norm: bool = True,
eps: float = 1e-6,
non_linearity: str = "swish",
time_embedding_norm: str = "default", # default, scale_shift, ada_group, spatial
output_scale_factor: float = 1.0,
use_in_shortcut: Optional[bool] = None,
conv_shortcut_bias: bool = True,
conv_2d_out_channels: Optional[int] = None,
):
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.output_scale_factor = output_scale_factor
self.time_embedding_norm = time_embedding_norm
linear_cls = nn.Linear
if groups_out is None:
groups_out = groups
if self.time_embedding_norm == "ada_group":
self.norm1 = AdaGroupNorm(temb_channels, in_channels, groups, eps=eps)
elif self.time_embedding_norm == "spatial":
self.norm1 = SpatialNorm(in_channels, temb_channels)
else:
self.norm1 = CausalGroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
self.conv1 = CausalConv3d(in_channels, out_channels, kernel_size=3, stride=1)
if self.time_embedding_norm == "ada_group":
self.norm2 = AdaGroupNorm(temb_channels, out_channels, groups_out, eps=eps)
elif self.time_embedding_norm == "spatial":
self.norm2 = SpatialNorm(out_channels, temb_channels)
else:
self.norm2 = CausalGroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)
self.dropout = torch.nn.Dropout(dropout)
conv_2d_out_channels = conv_2d_out_channels or out_channels
self.conv2 = CausalConv3d(out_channels, conv_2d_out_channels, kernel_size=3, stride=1)
self.nonlinearity = get_activation(non_linearity)
self.upsample = self.downsample = None
self.use_in_shortcut = self.in_channels != conv_2d_out_channels if use_in_shortcut is None else use_in_shortcut
self.conv_shortcut = None
if self.use_in_shortcut:
self.conv_shortcut = CausalConv3d(
in_channels,
conv_2d_out_channels,
kernel_size=1,
stride=1,
bias=conv_shortcut_bias,
)
def forward(
self,
input_tensor: torch.FloatTensor,
temb: torch.FloatTensor = None,
is_init_image=True,
temporal_chunk=False,
) -> torch.FloatTensor:
hidden_states = input_tensor
if self.time_embedding_norm == "ada_group" or self.time_embedding_norm == "spatial":
hidden_states = self.norm1(hidden_states, temb)
else:
hidden_states = self.norm1(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
hidden_states = self.conv1(hidden_states, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
if temb is not None and self.time_embedding_norm == "default":
hidden_states = hidden_states + temb
if self.time_embedding_norm == "ada_group" or self.time_embedding_norm == "spatial":
hidden_states = self.norm2(hidden_states, temb)
else:
hidden_states = self.norm2(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.conv2(hidden_states, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
if self.conv_shortcut is not None:
input_tensor = self.conv_shortcut(input_tensor, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
return output_tensor
class ResnetBlock2D(nn.Module):
r"""
A Resnet block.
Parameters:
in_channels (`int`): The number of channels in the input.
out_channels (`int`, *optional*, default to be `None`):
The number of output channels for the first conv2d layer. If None, same as `in_channels`.
dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.
temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.
groups (`int`, *optional*, default to `32`): The number of groups to use for the first normalization layer.
groups_out (`int`, *optional*, default to None):
The number of groups to use for the second normalization layer. if set to None, same as `groups`.
eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization.
non_linearity (`str`, *optional*, default to `"swish"`): the activation function to use.
time_embedding_norm (`str`, *optional*, default to `"default"` ): Time scale shift config.
By default, apply timestep embedding conditioning with a simple shift mechanism. Choose "scale_shift" or
"ada_group" for a stronger conditioning with scale and shift.
kernel (`torch.FloatTensor`, optional, default to None): FIR filter, see
[`~models.resnet.FirUpsample2D`] and [`~models.resnet.FirDownsample2D`].
output_scale_factor (`float`, *optional*, default to be `1.0`): the scale factor to use for the output.
use_in_shortcut (`bool`, *optional*, default to `True`):
If `True`, add a 1x1 nn.conv2d layer for skip-connection.
up (`bool`, *optional*, default to `False`): If `True`, add an upsample layer.
down (`bool`, *optional*, default to `False`): If `True`, add a downsample layer.
conv_shortcut_bias (`bool`, *optional*, default to `True`): If `True`, adds a learnable bias to the
`conv_shortcut` output.
conv_2d_out_channels (`int`, *optional*, default to `None`): the number of channels in the output.
If None, same as `out_channels`.
"""
def __init__(
self,
*,
in_channels: int,
out_channels: Optional[int] = None,
conv_shortcut: bool = False,
dropout: float = 0.0,
temb_channels: int = 512,
groups: int = 32,
groups_out: Optional[int] = None,
pre_norm: bool = True,
eps: float = 1e-6,
non_linearity: str = "swish",
time_embedding_norm: str = "default", # default, scale_shift, ada_group, spatial
output_scale_factor: float = 1.0,
use_in_shortcut: Optional[bool] = None,
conv_shortcut_bias: bool = True,
conv_2d_out_channels: Optional[int] = None,
):
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.output_scale_factor = output_scale_factor
self.time_embedding_norm = time_embedding_norm
linear_cls = nn.Linear
conv_cls = nn.Conv3d
if groups_out is None:
groups_out = groups
if self.time_embedding_norm == "ada_group":
self.norm1 = AdaGroupNorm(temb_channels, in_channels, groups, eps=eps)
elif self.time_embedding_norm == "spatial":
self.norm1 = SpatialNorm(in_channels, temb_channels)
else:
self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
self.conv1 = conv_cls(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
if self.time_embedding_norm == "ada_group":
self.norm2 = AdaGroupNorm(temb_channels, out_channels, groups_out, eps=eps)
elif self.time_embedding_norm == "spatial":
self.norm2 = SpatialNorm(out_channels, temb_channels)
else:
self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)
self.dropout = torch.nn.Dropout(dropout)
conv_2d_out_channels = conv_2d_out_channels or out_channels
self.conv2 = conv_cls(out_channels, conv_2d_out_channels, kernel_size=3, stride=1, padding=1)
self.nonlinearity = get_activation(non_linearity)
self.upsample = self.downsample = None
self.use_in_shortcut = self.in_channels != conv_2d_out_channels if use_in_shortcut is None else use_in_shortcut
self.conv_shortcut = None
if self.use_in_shortcut:
self.conv_shortcut = conv_cls(
in_channels,
conv_2d_out_channels,
kernel_size=1,
stride=1,
padding=0,
bias=conv_shortcut_bias,
)
def forward(
self,
input_tensor: torch.FloatTensor,
temb: torch.FloatTensor = None,
scale: float = 1.0,
) -> torch.FloatTensor:
hidden_states = input_tensor
if self.time_embedding_norm == "ada_group" or self.time_embedding_norm == "spatial":
hidden_states = self.norm1(hidden_states, temb)
else:
hidden_states = self.norm1(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
hidden_states = self.conv1(hidden_states)
if temb is not None and self.time_embedding_norm == "default":
hidden_states = hidden_states + temb
if self.time_embedding_norm == "ada_group" or self.time_embedding_norm == "spatial":
hidden_states = self.norm2(hidden_states, temb)
else:
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
class CausalDownsample2x(nn.Module):
"""A 2D downsampling layer with an optional convolution.
Parameters:
channels (`int`):
number of channels in the inputs and outputs.
use_conv (`bool`, default `False`):
option to use a convolution.
out_channels (`int`, optional):
number of output channels. Defaults to `channels`.
padding (`int`, default `1`):
padding for the convolution.
name (`str`, default `conv`):
name of the downsampling 2D layer.
"""
def __init__(
self,
channels: int,
use_conv: bool = True,
out_channels: Optional[int] = None,
name: str = "conv",
kernel_size=3,
bias=True,
):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
stride = (1, 2, 2)
self.name = name
if use_conv:
conv = CausalConv3d(
self.channels, self.out_channels, kernel_size=kernel_size, stride=stride, bias=bias
)
else:
assert self.channels == self.out_channels
conv = nn.AvgPool3d(kernel_size=stride, stride=stride)
self.conv = conv
def forward(self, hidden_states: torch.FloatTensor, is_init_image=True, temporal_chunk=False) -> torch.FloatTensor:
assert hidden_states.shape[1] == self.channels
hidden_states = self.conv(hidden_states, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
return hidden_states
class Downsample2D(nn.Module):
"""A 2D downsampling layer with an optional convolution.
Parameters:
channels (`int`):
number of channels in the inputs and outputs.
use_conv (`bool`, default `False`):
option to use a convolution.
out_channels (`int`, optional):
number of output channels. Defaults to `channels`.
padding (`int`, default `1`):
padding for the convolution.
name (`str`, default `conv`):
name of the downsampling 2D layer.
"""
def __init__(
self,
channels: int,
use_conv: bool = True,
out_channels: Optional[int] = None,
padding: int = 0,
name: str = "conv",
kernel_size=3,
bias=True,
):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.padding = padding
stride = (1, 2, 2)
self.name = name
conv_cls = nn.Conv3d
if use_conv:
conv = conv_cls(
self.channels, self.out_channels, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias
)
else:
assert self.channels == self.out_channels
conv = nn.AvgPool2d(kernel_size=stride, stride=stride)
self.conv = conv
def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
assert hidden_states.shape[1] == self.channels
if self.use_conv and self.padding == 0:
pad = (0, 1, 0, 1, 1, 1)
hidden_states = F.pad(hidden_states, pad, mode="constant", value=0)
assert hidden_states.shape[1] == self.channels
hidden_states = self.conv(hidden_states)
return hidden_states
class TemporalDownsample2x(nn.Module):
"""A Temporal downsampling layer with an optional convolution.
Parameters:
channels (`int`):
number of channels in the inputs and outputs.
use_conv (`bool`, default `False`):
option to use a convolution.
out_channels (`int`, optional):
number of output channels. Defaults to `channels`.
padding (`int`, default `1`):
padding for the convolution.
name (`str`, default `conv`):
name of the downsampling 2D layer.
"""
def __init__(
self,
channels: int,
use_conv: bool = False,
out_channels: Optional[int] = None,
padding: int = 0,
kernel_size=3,
bias=True,
):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.padding = padding
stride = (2, 1, 1)
conv_cls = nn.Conv3d
if use_conv:
conv = conv_cls(
self.channels, self.out_channels, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias
)
else:
raise NotImplementedError("Not implemented for temporal downsample without")
self.conv = conv
def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
assert hidden_states.shape[1] == self.channels
if self.use_conv and self.padding == 0:
if hidden_states.shape[2] == 1:
# image
pad = (1, 1, 1, 1, 1, 1)
else:
# video
pad = (1, 1, 1, 1, 0, 1)
hidden_states = F.pad(hidden_states, pad, mode="constant", value=0)
hidden_states = self.conv(hidden_states)
return hidden_states
class CausalTemporalDownsample2x(nn.Module):
"""A Temporal downsampling layer with an optional convolution.
Parameters:
channels (`int`):
number of channels in the inputs and outputs.
use_conv (`bool`, default `False`):
option to use a convolution.
out_channels (`int`, optional):
number of output channels. Defaults to `channels`.
padding (`int`, default `1`):
padding for the convolution.
name (`str`, default `conv`):
name of the downsampling 2D layer.
"""
def __init__(
self,
channels: int,
use_conv: bool = False,
out_channels: Optional[int] = None,
kernel_size=3,
bias=True,
):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
stride = (2, 1, 1)
conv_cls = nn.Conv3d
if use_conv:
conv = CausalConv3d(
self.channels, self.out_channels, kernel_size=kernel_size, stride=stride, bias=bias
)
else:
raise NotImplementedError("Not implemented for temporal downsample without")
self.conv = conv
def forward(self, hidden_states: torch.FloatTensor, is_init_image=True, temporal_chunk=False) -> torch.FloatTensor:
assert hidden_states.shape[1] == self.channels
hidden_states = self.conv(hidden_states, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
return hidden_states
class Upsample2D(nn.Module):
"""A 2D upsampling layer with an optional convolution.
Parameters:
channels (`int`):
number of channels in the inputs and outputs.
use_conv (`bool`, default `False`):
option to use a convolution.
out_channels (`int`, optional):
number of output channels. Defaults to `channels`.
name (`str`, default `conv`):
name of the upsampling 2D layer.
"""
def __init__(
self,
channels: int,
use_conv: bool = False,
out_channels: Optional[int] = None,
name: str = "conv",
kernel_size: Optional[int] = None,
padding=1,
bias=True,
interpolate=False,
):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.name = name
self.interpolate = interpolate
conv_cls = nn.Conv3d
conv = None
if interpolate:
raise NotImplementedError("Not implemented for spatial upsample with interpolate")
else:
if kernel_size is None:
kernel_size = 3
conv = conv_cls(self.channels, self.out_channels * 4, kernel_size=kernel_size, padding=padding, bias=bias)
self.conv = conv
self.conv.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, (nn.Linear, nn.Conv2d, nn.Conv3d)):
trunc_normal_(m.weight, std=.02)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def forward(
self,
hidden_states: torch.FloatTensor,
) -> torch.FloatTensor:
assert hidden_states.shape[1] == self.channels
hidden_states = self.conv(hidden_states)
hidden_states = rearrange(hidden_states, 'b (c p1 p2) t h w -> b c t (h p1) (w p2)', p1=2, p2=2)
return hidden_states
class CausalUpsample2x(nn.Module):
"""A 2D upsampling layer with an optional convolution.
Parameters:
channels (`int`):
number of channels in the inputs and outputs.
use_conv (`bool`, default `False`):
option to use a convolution.
out_channels (`int`, optional):
number of output channels. Defaults to `channels`.
name (`str`, default `conv`):
name of the upsampling 2D layer.
"""
def __init__(
self,
channels: int,
use_conv: bool = False,
out_channels: Optional[int] = None,
name: str = "conv",
kernel_size: Optional[int] = 3,
bias=True,
interpolate=False,
):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.name = name
self.interpolate = interpolate
conv = None
if interpolate:
raise NotImplementedError("Not implemented for spatial upsample with interpolate")
else:
conv = CausalConv3d(self.channels, self.out_channels * 4, kernel_size=kernel_size, stride=1, bias=bias)
self.conv = conv
def forward(
self,
hidden_states: torch.FloatTensor,
is_init_image=True, temporal_chunk=False,
) -> torch.FloatTensor:
assert hidden_states.shape[1] == self.channels
hidden_states = self.conv(hidden_states, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
hidden_states = rearrange(hidden_states, 'b (c p1 p2) t h w -> b c t (h p1) (w p2)', p1=2, p2=2)
return hidden_states
class TemporalUpsample2x(nn.Module):
"""A 2D upsampling layer with an optional convolution.
Parameters:
channels (`int`):
number of channels in the inputs and outputs.
use_conv (`bool`, default `False`):
option to use a convolution.
out_channels (`int`, optional):
number of output channels. Defaults to `channels`.
name (`str`, default `conv`):
name of the upsampling 2D layer.
"""
def __init__(
self,
channels: int,
use_conv: bool = True,
out_channels: Optional[int] = None,
kernel_size: Optional[int] = None,
padding=1,
bias=True,
interpolate=False,
):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.interpolate = interpolate
conv_cls = nn.Conv3d
conv = None
if interpolate:
raise NotImplementedError("Not implemented for spatial upsample with interpolate")
else:
# depth to space operator
if kernel_size is None:
kernel_size = 3
conv = conv_cls(self.channels, self.out_channels * 2, kernel_size=kernel_size, padding=padding, bias=bias)
self.conv = conv
def forward(
self,
hidden_states: torch.FloatTensor,
is_image: bool = False,
) -> torch.FloatTensor:
assert hidden_states.shape[1] == self.channels
t = hidden_states.shape[2]
hidden_states = self.conv(hidden_states)
hidden_states = rearrange(hidden_states, 'b (c p) t h w -> b c (p t) h w', p=2)
if t == 1 and is_image:
hidden_states = hidden_states[:, :, 1:]
return hidden_states
class CausalTemporalUpsample2x(nn.Module):
"""A 2D upsampling layer with an optional convolution.
Parameters:
channels (`int`):
number of channels in the inputs and outputs.
use_conv (`bool`, default `False`):
option to use a convolution.
out_channels (`int`, optional):
number of output channels. Defaults to `channels`.
name (`str`, default `conv`):
name of the upsampling 2D layer.
"""
def __init__(
self,
channels: int,
use_conv: bool = True,
out_channels: Optional[int] = None,
kernel_size: Optional[int] = 3,
bias=True,
interpolate=False,
):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.interpolate = interpolate
conv = None
if interpolate:
raise NotImplementedError("Not implemented for spatial upsample with interpolate")
else:
# depth to space operator
conv = CausalConv3d(self.channels, self.out_channels * 2, kernel_size=kernel_size, stride=1, bias=bias)
self.conv = conv
def forward(
self,
hidden_states: torch.FloatTensor,
is_init_image=True, temporal_chunk=False,
) -> torch.FloatTensor:
assert hidden_states.shape[1] == self.channels
t = hidden_states.shape[2]
hidden_states = self.conv(hidden_states, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
hidden_states = rearrange(hidden_states, 'b (c p) t h w -> b c (t p) h w', p=2)
if is_init_image:
hidden_states = hidden_states[:, :, 1:]
return hidden_states |