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# Copyright 2024 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.
# ==============================================================================
#
# Modified from diffusers==0.29.2
#
# ==============================================================================
from typing import Optional, Tuple, Union
import torch
import torch.nn.functional as F
from torch import nn
from einops import rearrange
from diffusers.utils import logging
from diffusers.models.activations import get_activation
from diffusers.models.attention_processor import SpatialNorm
from diffusers.models.attention_processor import Attention
from diffusers.models.normalization import AdaGroupNorm
from diffusers.models.normalization import RMSNorm
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def prepare_causal_attention_mask(n_frame: int, n_hw: int, dtype, device, batch_size: int = None):
seq_len = n_frame * n_hw
mask = torch.full((seq_len, seq_len), float("-inf"), dtype=dtype, device=device)
for i in range(seq_len):
i_frame = i // n_hw
mask[i, : (i_frame + 1) * n_hw] = 0
if batch_size is not None:
mask = mask.unsqueeze(0).expand(batch_size, -1, -1)
return mask
class CausalConv3d(nn.Module):
"""
Implements a causal 3D convolution layer where each position only depends on previous timesteps and current spatial locations.
This maintains temporal causality in video generation tasks.
"""
def __init__(
self,
chan_in,
chan_out,
kernel_size: Union[int, Tuple[int, int, int]],
stride: Union[int, Tuple[int, int, int]] = 1,
dilation: Union[int, Tuple[int, int, int]] = 1,
pad_mode='replicate',
**kwargs
):
super().__init__()
self.pad_mode = pad_mode
padding = (kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size - 1, 0) # W, H, T
self.time_causal_padding = padding
self.conv = nn.Conv3d(chan_in, chan_out, kernel_size, stride=stride, dilation=dilation, **kwargs)
def forward(self, x):
x = F.pad(x, self.time_causal_padding, mode=self.pad_mode)
return self.conv(x)
class UpsampleCausal3D(nn.Module):
"""
A 3D upsampling layer with an optional convolution.
"""
def __init__(
self,
channels: int,
use_conv: bool = False,
use_conv_transpose: bool = False,
out_channels: Optional[int] = None,
name: str = "conv",
kernel_size: Optional[int] = None,
padding=1,
norm_type=None,
eps=None,
elementwise_affine=None,
bias=True,
interpolate=True,
upsample_factor=(2, 2, 2),
):
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
self.interpolate = interpolate
self.upsample_factor = upsample_factor
if norm_type == "ln_norm":
self.norm = nn.LayerNorm(channels, eps, elementwise_affine)
elif norm_type == "rms_norm":
self.norm = RMSNorm(channels, eps, elementwise_affine)
elif norm_type is None:
self.norm = None
else:
raise ValueError(f"unknown norm_type: {norm_type}")
conv = None
if use_conv_transpose:
raise NotImplementedError
elif use_conv:
if kernel_size is None:
kernel_size = 3
conv = CausalConv3d(self.channels, self.out_channels, kernel_size=kernel_size, bias=bias)
if name == "conv":
self.conv = conv
else:
self.Conv2d_0 = conv
def forward(
self,
hidden_states: torch.FloatTensor,
output_size: Optional[int] = None,
scale: float = 1.0,
) -> torch.FloatTensor:
assert hidden_states.shape[1] == self.channels
if self.norm is not None:
raise NotImplementedError
if self.use_conv_transpose:
return self.conv(hidden_states)
# Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16
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()
# if `output_size` is passed we force the interpolation output
# size and do not make use of `scale_factor=2`
if self.interpolate:
B, C, T, H, W = hidden_states.shape
first_h, other_h = hidden_states.split((1, T - 1), dim=2)
if output_size is None:
if T > 1:
other_h = F.interpolate(other_h, scale_factor=self.upsample_factor, mode="nearest")
first_h = first_h.squeeze(2)
first_h = F.interpolate(first_h, scale_factor=self.upsample_factor[1:], mode="nearest")
first_h = first_h.unsqueeze(2)
else:
raise NotImplementedError
if T > 1:
hidden_states = torch.cat((first_h, other_h), dim=2)
else:
hidden_states = first_h
# If the input is bfloat16, we cast back to bfloat16
if dtype == torch.bfloat16:
hidden_states = hidden_states.to(dtype)
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 DownsampleCausal3D(nn.Module):
"""
A 3D downsampling layer with an optional convolution.
"""
def __init__(
self,
channels: int,
use_conv: bool = False,
out_channels: Optional[int] = None,
padding: int = 1,
name: str = "conv",
kernel_size=3,
norm_type=None,
eps=None,
elementwise_affine=None,
bias=True,
stride=2,
):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.padding = padding
stride = stride
self.name = name
if norm_type == "ln_norm":
self.norm = nn.LayerNorm(channels, eps, elementwise_affine)
elif norm_type == "rms_norm":
self.norm = RMSNorm(channels, eps, elementwise_affine)
elif norm_type is None:
self.norm = None
else:
raise ValueError(f"unknown norm_type: {norm_type}")
if use_conv:
conv = CausalConv3d(
self.channels, self.out_channels, kernel_size=kernel_size, stride=stride, bias=bias
)
else:
raise NotImplementedError
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: torch.FloatTensor, scale: float = 1.0) -> torch.FloatTensor:
assert hidden_states.shape[1] == self.channels
if self.norm is not None:
hidden_states = self.norm(hidden_states.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
assert hidden_states.shape[1] == self.channels
hidden_states = self.conv(hidden_states)
return hidden_states
class ResnetBlockCausal3D(nn.Module):
r"""
A Resnet block.
"""
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",
skip_time_act: bool = False,
# default, scale_shift, ada_group, spatial
time_embedding_norm: str = "default",
kernel: Optional[torch.FloatTensor] = None,
output_scale_factor: float = 1.0,
use_in_shortcut: Optional[bool] = None,
up: bool = False,
down: bool = False,
conv_shortcut_bias: bool = True,
conv_3d_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.up = up
self.down = down
self.output_scale_factor = output_scale_factor
self.time_embedding_norm = time_embedding_norm
self.skip_time_act = skip_time_act
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 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
self.conv1 = CausalConv3d(in_channels, out_channels, kernel_size=3, stride=1)
if temb_channels is not None:
if self.time_embedding_norm == "default":
self.time_emb_proj = linear_cls(temb_channels, out_channels)
elif self.time_embedding_norm == "scale_shift":
self.time_emb_proj = linear_cls(temb_channels, 2 * out_channels)
elif self.time_embedding_norm == "ada_group" or self.time_embedding_norm == "spatial":
self.time_emb_proj = None
else:
raise ValueError(f"Unknown time_embedding_norm : {self.time_embedding_norm} ")
else:
self.time_emb_proj = None
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_3d_out_channels = conv_3d_out_channels or out_channels
self.conv2 = CausalConv3d(out_channels, conv_3d_out_channels, kernel_size=3, stride=1)
self.nonlinearity = get_activation(non_linearity)
self.upsample = self.downsample = None
if self.up:
self.upsample = UpsampleCausal3D(in_channels, use_conv=False)
elif self.down:
self.downsample = DownsampleCausal3D(in_channels, use_conv=False, name="op")
self.use_in_shortcut = self.in_channels != conv_3d_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_3d_out_channels,
kernel_size=1,
stride=1,
bias=conv_shortcut_bias,
)
def forward(
self,
input_tensor: torch.FloatTensor,
temb: torch.FloatTensor,
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)
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, scale=scale)
)
hidden_states = (
self.upsample(hidden_states, scale=scale)
)
elif self.downsample is not None:
input_tensor = (
self.downsample(input_tensor, scale=scale)
)
hidden_states = (
self.downsample(hidden_states, scale=scale)
)
hidden_states = self.conv1(hidden_states)
if self.time_emb_proj is not None:
if not self.skip_time_act:
temb = self.nonlinearity(temb)
temb = (
self.time_emb_proj(temb, scale)[:, :, None, None]
)
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)
if temb is not None and self.time_embedding_norm == "scale_shift":
scale, shift = torch.chunk(temb, 2, dim=1)
hidden_states = hidden_states * (1 + scale) + shift
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
def get_down_block3d(
down_block_type: str,
num_layers: int,
in_channels: int,
out_channels: int,
temb_channels: int,
add_downsample: bool,
downsample_stride: int,
resnet_eps: float,
resnet_act_fn: str,
transformer_layers_per_block: int = 1,
num_attention_heads: Optional[int] = None,
resnet_groups: Optional[int] = None,
cross_attention_dim: Optional[int] = None,
downsample_padding: Optional[int] = None,
dual_cross_attention: bool = False,
use_linear_projection: bool = False,
only_cross_attention: bool = False,
upcast_attention: bool = False,
resnet_time_scale_shift: str = "default",
attention_type: str = "default",
resnet_skip_time_act: bool = False,
resnet_out_scale_factor: float = 1.0,
cross_attention_norm: Optional[str] = None,
attention_head_dim: Optional[int] = None,
downsample_type: Optional[str] = None,
dropout: float = 0.0,
):
# If attn head dim is not defined, we default it to the number of heads
if attention_head_dim is None:
logger.warn(
f"It is recommended to provide `attention_head_dim` when calling `get_down_block`. Defaulting `attention_head_dim` to {num_attention_heads}."
)
attention_head_dim = num_attention_heads
down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type
if down_block_type == "DownEncoderBlockCausal3D":
return DownEncoderBlockCausal3D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
dropout=dropout,
add_downsample=add_downsample,
downsample_stride=downsample_stride,
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_block3d(
up_block_type: str,
num_layers: int,
in_channels: int,
out_channels: int,
prev_output_channel: int,
temb_channels: int,
add_upsample: bool,
upsample_scale_factor: Tuple,
resnet_eps: float,
resnet_act_fn: str,
resolution_idx: Optional[int] = None,
transformer_layers_per_block: int = 1,
num_attention_heads: Optional[int] = None,
resnet_groups: Optional[int] = None,
cross_attention_dim: Optional[int] = None,
dual_cross_attention: bool = False,
use_linear_projection: bool = False,
only_cross_attention: bool = False,
upcast_attention: bool = False,
resnet_time_scale_shift: str = "default",
attention_type: str = "default",
resnet_skip_time_act: bool = False,
resnet_out_scale_factor: float = 1.0,
cross_attention_norm: Optional[str] = None,
attention_head_dim: Optional[int] = None,
upsample_type: Optional[str] = None,
dropout: float = 0.0,
) -> nn.Module:
# If attn head dim is not defined, we default it to the number of heads
if attention_head_dim is None:
logger.warn(
f"It is recommended to provide `attention_head_dim` when calling `get_up_block`. Defaulting `attention_head_dim` to {num_attention_heads}."
)
attention_head_dim = num_attention_heads
up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
if 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_upsample=add_upsample,
upsample_scale_factor=upsample_scale_factor,
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,
)
raise ValueError(f"{up_block_type} does not exist.")
class UNetMidBlockCausal3D(nn.Module):
"""
A 3D UNet mid-block [`UNetMidBlockCausal3D`] with multiple residual blocks and optional attention blocks.
"""
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 = [
ResnetBlockCausal3D(
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:
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(
ResnetBlockCausal3D(
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)
for attn, resnet in zip(self.attentions, self.resnets[1:]):
if attn is not None:
B, C, T, H, W = hidden_states.shape
hidden_states = rearrange(hidden_states, "b c f h w -> b (f h w) c")
attention_mask = prepare_causal_attention_mask(
T, H * W, hidden_states.dtype, hidden_states.device, batch_size=B
)
hidden_states = attn(hidden_states, temb=temb, attention_mask=attention_mask)
hidden_states = rearrange(hidden_states, "b (f h w) c -> b c f h w", f=T, h=H, w=W)
hidden_states = resnet(hidden_states, temb)
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_downsample: bool = True,
downsample_stride: int = 2,
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(
ResnetBlockCausal3D(
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_downsample:
self.downsamplers = nn.ModuleList(
[
DownsampleCausal3D(
out_channels,
use_conv=True,
out_channels=out_channels,
padding=downsample_padding,
name="op",
stride=downsample_stride,
)
]
)
else:
self.downsamplers = None
def forward(self, hidden_states: torch.FloatTensor, scale: float = 1.0) -> torch.FloatTensor:
for resnet in self.resnets:
hidden_states = resnet(hidden_states, temb=None, scale=scale)
if self.downsamplers is not None:
for downsampler in self.downsamplers:
hidden_states = downsampler(hidden_states, scale)
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_upsample: bool = True,
upsample_scale_factor=(2, 2, 2),
temb_channels: Optional[int] = None,
):
super().__init__()
resnets = []
for i in range(num_layers):
input_channels = in_channels if i == 0 else out_channels
resnets.append(
ResnetBlockCausal3D(
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_upsample:
self.upsamplers = nn.ModuleList(
[
UpsampleCausal3D(
out_channels,
use_conv=True,
out_channels=out_channels,
upsample_factor=upsample_scale_factor,
)
]
)
else:
self.upsamplers = None
self.resolution_idx = resolution_idx
def forward(
self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, scale: float = 1.0
) -> 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)
return hidden_states
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