# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Dict, Optional, Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from einops import rearrange
from diffusers.utils import logging
from diffusers.models.attention_processor import Attention
from .modeling_resnet import (
Downsample2D, ResnetBlock2D, CausalResnetBlock3D, Upsample2D,
TemporalDownsample2x, TemporalUpsample2x,
CausalDownsample2x, CausalTemporalDownsample2x,
CausalUpsample2x, CausalTemporalUpsample2x,
)
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def get_input_layer(
in_channels: int,
out_channels: int,
norm_num_groups: int,
layer_type: str,
norm_type: str = 'group',
affine: bool = True,
):
if layer_type == 'conv':
input_layer = nn.Conv3d(
in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1,
)
elif layer_type == 'pixel_shuffle':
input_layer = nn.Sequential(
nn.PixelUnshuffle(2),
nn.Conv2d(in_channels * 4, out_channels, kernel_size=1),
)
else:
raise NotImplementedError(f"Not support input layer {layer_type}")
return input_layer
def get_output_layer(
in_channels: int,
out_channels: int,
norm_num_groups: int,
layer_type: str,
norm_type: str = 'group',
affine: bool = True,
):
if layer_type == 'norm_act_conv':
output_layer = nn.Sequential(
nn.GroupNorm(num_channels=in_channels, num_groups=norm_num_groups, eps=1e-6, affine=affine),
nn.SiLU(),
nn.Conv3d(in_channels, out_channels, 3, stride=1, padding=1),
)
elif layer_type == 'pixel_shuffle':
output_layer = nn.Sequential(
nn.Conv2d(in_channels, out_channels * 4, kernel_size=1),
nn.PixelShuffle(2),
)
else:
raise NotImplementedError(f"Not support output layer {layer_type}")
return output_layer
def get_down_block(
down_block_type: str,
num_layers: int,
in_channels: int,
out_channels: int = None,
temb_channels: int = None,
add_spatial_downsample: bool = None,
add_temporal_downsample: bool = None,
resnet_eps: float = 1e-6,
resnet_act_fn: str = 'silu',
resnet_groups: Optional[int] = None,
downsample_padding: Optional[int] = None,
resnet_time_scale_shift: str = "default",
attention_head_dim: Optional[int] = None,
dropout: float = 0.0,
norm_affline: bool = True,
norm_layer: str = 'layer',
):
if down_block_type == "DownEncoderBlock2D":
return DownEncoderBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
dropout=dropout,
add_spatial_downsample=add_spatial_downsample,
add_temporal_downsample=add_temporal_downsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
downsample_padding=downsample_padding,
resnet_time_scale_shift=resnet_time_scale_shift,
)
elif down_block_type == "DownEncoderBlockCausal3D":
return DownEncoderBlockCausal3D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
dropout=dropout,
add_spatial_downsample=add_spatial_downsample,
add_temporal_downsample=add_temporal_downsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
downsample_padding=downsample_padding,
resnet_time_scale_shift=resnet_time_scale_shift,
)
raise ValueError(f"{down_block_type} does not exist.")
def get_up_block(
up_block_type: str,
num_layers: int,
in_channels: int,
out_channels: int,
prev_output_channel: int = None,
temb_channels: int = None,
add_spatial_upsample: bool = None,
add_temporal_upsample: bool = None,
resnet_eps: float = 1e-6,
resnet_act_fn: str = 'silu',
resolution_idx: Optional[int] = None,
resnet_groups: Optional[int] = None,
resnet_time_scale_shift: str = "default",
attention_head_dim: Optional[int] = None,
dropout: float = 0.0,
interpolate: bool = True,
norm_affline: bool = True,
norm_layer: str = 'layer',
) -> nn.Module:
if up_block_type == "UpDecoderBlock2D":
return UpDecoderBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
resolution_idx=resolution_idx,
dropout=dropout,
add_spatial_upsample=add_spatial_upsample,
add_temporal_upsample=add_temporal_upsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
resnet_time_scale_shift=resnet_time_scale_shift,
temb_channels=temb_channels,
interpolate=interpolate,
)
elif up_block_type == "UpDecoderBlockCausal3D":
return UpDecoderBlockCausal3D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
resolution_idx=resolution_idx,
dropout=dropout,
add_spatial_upsample=add_spatial_upsample,
add_temporal_upsample=add_temporal_upsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
resnet_time_scale_shift=resnet_time_scale_shift,
temb_channels=temb_channels,
interpolate=interpolate,
)
raise ValueError(f"{up_block_type} does not exist.")
class UNetMidBlock2D(nn.Module):
"""
A 2D UNet mid-block [`UNetMidBlock2D`] with multiple residual blocks and optional attention blocks.
Args:
in_channels (`int`): The number of input channels.
temb_channels (`int`): The number of temporal embedding channels.
dropout (`float`, *optional*, defaults to 0.0): The dropout rate.
num_layers (`int`, *optional*, defaults to 1): The number of residual blocks.
resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks.
resnet_time_scale_shift (`str`, *optional*, defaults to `default`):
The type of normalization to apply to the time embeddings. This can help to improve the performance of the
model on tasks with long-range temporal dependencies.
resnet_act_fn (`str`, *optional*, defaults to `swish`): The activation function for the resnet blocks.
resnet_groups (`int`, *optional*, defaults to 32):
The number of groups to use in the group normalization layers of the resnet blocks.
attn_groups (`Optional[int]`, *optional*, defaults to None): The number of groups for the attention blocks.
resnet_pre_norm (`bool`, *optional*, defaults to `True`):
Whether to use pre-normalization for the resnet blocks.
add_attention (`bool`, *optional*, defaults to `True`): Whether to add attention blocks.
attention_head_dim (`int`, *optional*, defaults to 1):
Dimension of a single attention head. The number of attention heads is determined based on this value and
the number of input channels.
output_scale_factor (`float`, *optional*, defaults to 1.0): The output scale factor.
Returns:
`torch.FloatTensor`: The output of the last residual block, which is a tensor of shape `(batch_size,
in_channels, height, width)`.
"""
def __init__(
self,
in_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default", # default, spatial
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
attn_groups: Optional[int] = None,
resnet_pre_norm: bool = True,
add_attention: bool = True,
attention_head_dim: int = 1,
output_scale_factor: float = 1.0,
):
super().__init__()
resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
self.add_attention = add_attention
if attn_groups is None:
attn_groups = resnet_groups if resnet_time_scale_shift == "default" else None
# there is always at least one resnet
resnets = [
ResnetBlock2D(
in_channels=in_channels,
out_channels=in_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
]
attentions = []
if attention_head_dim is None:
logger.warn(
f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {in_channels}."
)
attention_head_dim = in_channels
for _ in range(num_layers):
if self.add_attention:
# Spatial attention
attentions.append(
Attention(
in_channels,
heads=in_channels // attention_head_dim,
dim_head=attention_head_dim,
rescale_output_factor=output_scale_factor,
eps=resnet_eps,
norm_num_groups=attn_groups,
spatial_norm_dim=temb_channels if resnet_time_scale_shift == "spatial" else None,
residual_connection=True,
bias=True,
upcast_softmax=True,
_from_deprecated_attn_block=True,
)
)
else:
attentions.append(None)
resnets.append(
ResnetBlock2D(
in_channels=in_channels,
out_channels=in_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
hidden_states = self.resnets[0](hidden_states, temb)
t = hidden_states.shape[2]
for attn, resnet in zip(self.attentions, self.resnets[1:]):
if attn is not None:
hidden_states = rearrange(hidden_states, 'b c t h w -> b t c h w')
hidden_states = rearrange(hidden_states, 'b t c h w -> (b t) c h w')
hidden_states = attn(hidden_states, temb=temb)
hidden_states = rearrange(hidden_states, '(b t) c h w -> b t c h w', t=t)
hidden_states = rearrange(hidden_states, 'b t c h w -> b c t h w')
hidden_states = resnet(hidden_states, temb)
return hidden_states
class CausalUNetMidBlock2D(nn.Module):
"""
A 2D UNet mid-block [`UNetMidBlock2D`] with multiple residual blocks and optional attention blocks.
Args:
in_channels (`int`): The number of input channels.
temb_channels (`int`): The number of temporal embedding channels.
dropout (`float`, *optional*, defaults to 0.0): The dropout rate.
num_layers (`int`, *optional*, defaults to 1): The number of residual blocks.
resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks.
resnet_time_scale_shift (`str`, *optional*, defaults to `default`):
The type of normalization to apply to the time embeddings. This can help to improve the performance of the
model on tasks with long-range temporal dependencies.
resnet_act_fn (`str`, *optional*, defaults to `swish`): The activation function for the resnet blocks.
resnet_groups (`int`, *optional*, defaults to 32):
The number of groups to use in the group normalization layers of the resnet blocks.
attn_groups (`Optional[int]`, *optional*, defaults to None): The number of groups for the attention blocks.
resnet_pre_norm (`bool`, *optional*, defaults to `True`):
Whether to use pre-normalization for the resnet blocks.
add_attention (`bool`, *optional*, defaults to `True`): Whether to add attention blocks.
attention_head_dim (`int`, *optional*, defaults to 1):
Dimension of a single attention head. The number of attention heads is determined based on this value and
the number of input channels.
output_scale_factor (`float`, *optional*, defaults to 1.0): The output scale factor.
Returns:
`torch.FloatTensor`: The output of the last residual block, which is a tensor of shape `(batch_size,
in_channels, height, width)`.
"""
def __init__(
self,
in_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default", # default, spatial
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
attn_groups: Optional[int] = None,
resnet_pre_norm: bool = True,
add_attention: bool = True,
attention_head_dim: int = 1,
output_scale_factor: float = 1.0,
):
super().__init__()
resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
self.add_attention = add_attention
if attn_groups is None:
attn_groups = resnet_groups if resnet_time_scale_shift == "default" else None
# there is always at least one resnet
resnets = [
CausalResnetBlock3D(
in_channels=in_channels,
out_channels=in_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
]
attentions = []
if attention_head_dim is None:
logger.warn(
f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {in_channels}."
)
attention_head_dim = in_channels
for _ in range(num_layers):
if self.add_attention:
# Spatial attention
attentions.append(
Attention(
in_channels,
heads=in_channels // attention_head_dim,
dim_head=attention_head_dim,
rescale_output_factor=output_scale_factor,
eps=resnet_eps,
norm_num_groups=attn_groups,
spatial_norm_dim=temb_channels if resnet_time_scale_shift == "spatial" else None,
residual_connection=True,
bias=True,
upcast_softmax=True,
_from_deprecated_attn_block=True,
)
)
else:
attentions.append(None)
resnets.append(
CausalResnetBlock3D(
in_channels=in_channels,
out_channels=in_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None,
is_init_image=True, temporal_chunk=False) -> torch.FloatTensor:
hidden_states = self.resnets[0](hidden_states, temb, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
t = hidden_states.shape[2]
for attn, resnet in zip(self.attentions, self.resnets[1:]):
if attn is not None:
hidden_states = rearrange(hidden_states, 'b c t h w -> b t c h w')
hidden_states = rearrange(hidden_states, 'b t c h w -> (b t) c h w')
hidden_states = attn(hidden_states, temb=temb)
hidden_states = rearrange(hidden_states, '(b t) c h w -> b t c h w', t=t)
hidden_states = rearrange(hidden_states, 'b t c h w -> b c t h w')
hidden_states = resnet(hidden_states, temb, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
return hidden_states
class DownEncoderBlockCausal3D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
output_scale_factor: float = 1.0,
add_spatial_downsample: bool = True,
add_temporal_downsample: bool = False,
downsample_padding: int = 1,
):
super().__init__()
resnets = []
for i in range(num_layers):
in_channels = in_channels if i == 0 else out_channels
resnets.append(
CausalResnetBlock3D(
in_channels=in_channels,
out_channels=out_channels,
temb_channels=None,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
self.resnets = nn.ModuleList(resnets)
if add_spatial_downsample:
self.downsamplers = nn.ModuleList(
[
CausalDownsample2x(
out_channels, use_conv=True, out_channels=out_channels,
)
]
)
else:
self.downsamplers = None
if add_temporal_downsample:
self.temporal_downsamplers = nn.ModuleList(
[
CausalTemporalDownsample2x(
out_channels, use_conv=True, out_channels=out_channels,
)
]
)
else:
self.temporal_downsamplers = None
def forward(self, hidden_states: torch.FloatTensor, is_init_image=True, temporal_chunk=False) -> torch.FloatTensor:
for resnet in self.resnets:
hidden_states = resnet(hidden_states, temb=None, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
if self.downsamplers is not None:
for downsampler in self.downsamplers:
hidden_states = downsampler(hidden_states, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
if self.temporal_downsamplers is not None:
for temporal_downsampler in self.temporal_downsamplers:
hidden_states = temporal_downsampler(hidden_states, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
return hidden_states
class DownEncoderBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
output_scale_factor: float = 1.0,
add_spatial_downsample: bool = True,
add_temporal_downsample: bool = False,
downsample_padding: int = 1,
):
super().__init__()
resnets = []
for i in range(num_layers):
in_channels = in_channels if i == 0 else out_channels
resnets.append(
ResnetBlock2D(
in_channels=in_channels,
out_channels=out_channels,
temb_channels=None,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
self.resnets = nn.ModuleList(resnets)
if add_spatial_downsample:
self.downsamplers = nn.ModuleList(
[
Downsample2D(
out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
)
]
)
else:
self.downsamplers = None
if add_temporal_downsample:
self.temporal_downsamplers = nn.ModuleList(
[
TemporalDownsample2x(
out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding,
)
]
)
else:
self.temporal_downsamplers = None
def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
for resnet in self.resnets:
hidden_states = resnet(hidden_states, temb=None)
if self.downsamplers is not None:
for downsampler in self.downsamplers:
hidden_states = downsampler(hidden_states)
if self.temporal_downsamplers is not None:
for temporal_downsampler in self.temporal_downsamplers:
hidden_states = temporal_downsampler(hidden_states)
return hidden_states
class UpDecoderBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
resolution_idx: Optional[int] = None,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default", # default, spatial
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
output_scale_factor: float = 1.0,
add_spatial_upsample: bool = True,
add_temporal_upsample: bool = False,
temb_channels: Optional[int] = None,
interpolate: bool = True,
):
super().__init__()
resnets = []
for i in range(num_layers):
input_channels = in_channels if i == 0 else out_channels
resnets.append(
ResnetBlock2D(
in_channels=input_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
self.resnets = nn.ModuleList(resnets)
if add_spatial_upsample:
self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels, interpolate=interpolate)])
else:
self.upsamplers = None
if add_temporal_upsample:
self.temporal_upsamplers = nn.ModuleList([TemporalUpsample2x(out_channels, use_conv=True, out_channels=out_channels, interpolate=interpolate)])
else:
self.temporal_upsamplers = None
self.resolution_idx = resolution_idx
def forward(
self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, scale: float = 1.0, is_image: bool = False,
) -> torch.FloatTensor:
for resnet in self.resnets:
hidden_states = resnet(hidden_states, temb=temb, scale=scale)
if self.upsamplers is not None:
for upsampler in self.upsamplers:
hidden_states = upsampler(hidden_states)
if self.temporal_upsamplers is not None:
for temporal_upsampler in self.temporal_upsamplers:
hidden_states = temporal_upsampler(hidden_states, is_image=is_image)
return hidden_states
class UpDecoderBlockCausal3D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
resolution_idx: Optional[int] = None,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default", # default, spatial
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
output_scale_factor: float = 1.0,
add_spatial_upsample: bool = True,
add_temporal_upsample: bool = False,
temb_channels: Optional[int] = None,
interpolate: bool = True,
):
super().__init__()
resnets = []
for i in range(num_layers):
input_channels = in_channels if i == 0 else out_channels
resnets.append(
CausalResnetBlock3D(
in_channels=input_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
self.resnets = nn.ModuleList(resnets)
if add_spatial_upsample:
self.upsamplers = nn.ModuleList([CausalUpsample2x(out_channels, use_conv=True, out_channels=out_channels, interpolate=interpolate)])
else:
self.upsamplers = None
if add_temporal_upsample:
self.temporal_upsamplers = nn.ModuleList([CausalTemporalUpsample2x(out_channels, use_conv=True, out_channels=out_channels, interpolate=interpolate)])
else:
self.temporal_upsamplers = None
self.resolution_idx = resolution_idx
def forward(
self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None,
is_init_image=True, temporal_chunk=False,
) -> torch.FloatTensor:
for resnet in self.resnets:
hidden_states = resnet(hidden_states, temb=temb, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
if self.upsamplers is not None:
for upsampler in self.upsamplers:
hidden_states = upsampler(hidden_states, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
if self.temporal_upsamplers is not None:
for temporal_upsampler in self.temporal_upsamplers:
hidden_states = temporal_upsampler(hidden_states, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
return hidden_states