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Copy pathunet_2d_blocks.py
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3491 lines (3098 loc) · 132 KB
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# 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 ..utils import is_torch_version, logging
from ..utils.torch_utils import apply_freeu
from .activations import get_activation
from .attention_processor import Attention, AttnAddedKVProcessor, AttnAddedKVProcessor2_0
from .dual_transformer_2d import DualTransformer2DModel
from .normalization import AdaGroupNorm
from .resnet import Downsample2D, FirDownsample2D, FirUpsample2D, KDownsample2D, KUpsample2D, ResnetBlock2D, Upsample2D
from .transformer_2d import Transformer2DModel
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def get_down_block(
down_block_type: str,
num_layers: int,
in_channels: int,
out_channels: int,
temb_channels: int,
add_downsample: bool,
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 == "DownBlock2D":
return DownBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
dropout=dropout,
add_downsample=add_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 == "ResnetDownsampleBlock2D":
return ResnetDownsampleBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
dropout=dropout,
add_downsample=add_downsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
resnet_time_scale_shift=resnet_time_scale_shift,
skip_time_act=resnet_skip_time_act,
output_scale_factor=resnet_out_scale_factor,
)
elif down_block_type == "AttnDownBlock2D":
if add_downsample is False:
downsample_type = None
else:
downsample_type = downsample_type or "conv" # default to 'conv'
return AttnDownBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
dropout=dropout,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
downsample_padding=downsample_padding,
attention_head_dim=attention_head_dim,
resnet_time_scale_shift=resnet_time_scale_shift,
downsample_type=downsample_type,
)
elif down_block_type == "CrossAttnDownBlock2D":
if cross_attention_dim is None:
raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock2D")
return CrossAttnDownBlock2D(
num_layers=num_layers,
transformer_layers_per_block=transformer_layers_per_block,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
dropout=dropout,
add_downsample=add_downsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
downsample_padding=downsample_padding,
cross_attention_dim=cross_attention_dim,
num_attention_heads=num_attention_heads,
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention,
upcast_attention=upcast_attention,
resnet_time_scale_shift=resnet_time_scale_shift,
attention_type=attention_type,
)
elif down_block_type == "SimpleCrossAttnDownBlock2D":
if cross_attention_dim is None:
raise ValueError("cross_attention_dim must be specified for SimpleCrossAttnDownBlock2D")
return SimpleCrossAttnDownBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
dropout=dropout,
add_downsample=add_downsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
cross_attention_dim=cross_attention_dim,
attention_head_dim=attention_head_dim,
resnet_time_scale_shift=resnet_time_scale_shift,
skip_time_act=resnet_skip_time_act,
output_scale_factor=resnet_out_scale_factor,
only_cross_attention=only_cross_attention,
cross_attention_norm=cross_attention_norm,
)
elif down_block_type == "SkipDownBlock2D":
return SkipDownBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
dropout=dropout,
add_downsample=add_downsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
downsample_padding=downsample_padding,
resnet_time_scale_shift=resnet_time_scale_shift,
)
elif down_block_type == "AttnSkipDownBlock2D":
return AttnSkipDownBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
dropout=dropout,
add_downsample=add_downsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
attention_head_dim=attention_head_dim,
resnet_time_scale_shift=resnet_time_scale_shift,
)
elif down_block_type == "DownEncoderBlock2D":
return DownEncoderBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
dropout=dropout,
add_downsample=add_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 == "AttnDownEncoderBlock2D":
return AttnDownEncoderBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
dropout=dropout,
add_downsample=add_downsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
downsample_padding=downsample_padding,
attention_head_dim=attention_head_dim,
resnet_time_scale_shift=resnet_time_scale_shift,
)
elif down_block_type == "KDownBlock2D":
return KDownBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
dropout=dropout,
add_downsample=add_downsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
)
elif down_block_type == "KCrossAttnDownBlock2D":
return KCrossAttnDownBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
dropout=dropout,
add_downsample=add_downsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
cross_attention_dim=cross_attention_dim,
attention_head_dim=attention_head_dim,
add_self_attention=True if not add_downsample else False,
)
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,
temb_channels: int,
add_upsample: bool,
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 == "UpBlock2D":
return UpBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
prev_output_channel=prev_output_channel,
temb_channels=temb_channels,
resolution_idx=resolution_idx,
dropout=dropout,
add_upsample=add_upsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
resnet_time_scale_shift=resnet_time_scale_shift,
)
elif up_block_type == "ResnetUpsampleBlock2D":
return ResnetUpsampleBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
prev_output_channel=prev_output_channel,
temb_channels=temb_channels,
resolution_idx=resolution_idx,
dropout=dropout,
add_upsample=add_upsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
resnet_time_scale_shift=resnet_time_scale_shift,
skip_time_act=resnet_skip_time_act,
output_scale_factor=resnet_out_scale_factor,
)
elif up_block_type == "CrossAttnUpBlock2D":
if cross_attention_dim is None:
raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock2D")
return CrossAttnUpBlock2D(
num_layers=num_layers,
transformer_layers_per_block=transformer_layers_per_block,
in_channels=in_channels,
out_channels=out_channels,
prev_output_channel=prev_output_channel,
temb_channels=temb_channels,
resolution_idx=resolution_idx,
dropout=dropout,
add_upsample=add_upsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
cross_attention_dim=cross_attention_dim,
num_attention_heads=num_attention_heads,
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention,
upcast_attention=upcast_attention,
resnet_time_scale_shift=resnet_time_scale_shift,
attention_type=attention_type,
)
elif up_block_type == "SimpleCrossAttnUpBlock2D":
if cross_attention_dim is None:
raise ValueError("cross_attention_dim must be specified for SimpleCrossAttnUpBlock2D")
return SimpleCrossAttnUpBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
prev_output_channel=prev_output_channel,
temb_channels=temb_channels,
resolution_idx=resolution_idx,
dropout=dropout,
add_upsample=add_upsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
cross_attention_dim=cross_attention_dim,
attention_head_dim=attention_head_dim,
resnet_time_scale_shift=resnet_time_scale_shift,
skip_time_act=resnet_skip_time_act,
output_scale_factor=resnet_out_scale_factor,
only_cross_attention=only_cross_attention,
cross_attention_norm=cross_attention_norm,
)
elif up_block_type == "AttnUpBlock2D":
if add_upsample is False:
upsample_type = None
else:
upsample_type = upsample_type or "conv" # default to 'conv'
return AttnUpBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
prev_output_channel=prev_output_channel,
temb_channels=temb_channels,
resolution_idx=resolution_idx,
dropout=dropout,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
attention_head_dim=attention_head_dim,
resnet_time_scale_shift=resnet_time_scale_shift,
upsample_type=upsample_type,
)
elif up_block_type == "SkipUpBlock2D":
return SkipUpBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
prev_output_channel=prev_output_channel,
temb_channels=temb_channels,
resolution_idx=resolution_idx,
dropout=dropout,
add_upsample=add_upsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_time_scale_shift=resnet_time_scale_shift,
)
elif up_block_type == "AttnSkipUpBlock2D":
return AttnSkipUpBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
prev_output_channel=prev_output_channel,
temb_channels=temb_channels,
resolution_idx=resolution_idx,
dropout=dropout,
add_upsample=add_upsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
attention_head_dim=attention_head_dim,
resnet_time_scale_shift=resnet_time_scale_shift,
)
elif 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_upsample=add_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,
)
elif up_block_type == "AttnUpDecoderBlock2D":
return AttnUpDecoderBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
resolution_idx=resolution_idx,
dropout=dropout,
add_upsample=add_upsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
attention_head_dim=attention_head_dim,
resnet_time_scale_shift=resnet_time_scale_shift,
temb_channels=temb_channels,
)
elif up_block_type == "KUpBlock2D":
return KUpBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
resolution_idx=resolution_idx,
dropout=dropout,
add_upsample=add_upsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
)
elif up_block_type == "KCrossAttnUpBlock2D":
return KCrossAttnUpBlock2D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
resolution_idx=resolution_idx,
dropout=dropout,
add_upsample=add_upsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
cross_attention_dim=cross_attention_dim,
attention_head_dim=attention_head_dim,
)
raise ValueError(f"{up_block_type} does not exist.")
class AutoencoderTinyBlock(nn.Module):
"""
Tiny Autoencoder block used in [`AutoencoderTiny`]. It is a mini residual module consisting of plain conv + ReLU
blocks.
Args:
in_channels (`int`): The number of input channels.
out_channels (`int`): The number of output channels.
act_fn (`str`):
` The activation function to use. Supported values are `"swish"`, `"mish"`, `"gelu"`, and `"relu"`.
Returns:
`torch.FloatTensor`: A tensor with the same shape as the input tensor, but with the number of channels equal to
`out_channels`.
"""
def __init__(self, in_channels: int, out_channels: int, act_fn: str):
super().__init__()
act_fn = get_activation(act_fn)
self.conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
act_fn,
nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
act_fn,
nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
)
self.skip = (
nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
if in_channels != out_channels
else nn.Identity()
)
self.fuse = nn.ReLU()
def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:
return self.fuse(self.conv(x) + self.skip(x))
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:
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)
for attn, resnet in zip(self.attentions, self.resnets[1:]):
if attn is not None:
hidden_states = attn(hidden_states, temb=temb)
hidden_states = resnet(hidden_states, temb)
return hidden_states
class UNetMidBlock2DCrossAttn(nn.Module):
def __init__(
self,
in_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
transformer_layers_per_block: Union[int, Tuple[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,
num_attention_heads: int = 1,
output_scale_factor: float = 1.0,
cross_attention_dim: int = 1280,
dual_cross_attention: bool = False,
use_linear_projection: bool = False,
upcast_attention: bool = False,
attention_type: str = "default",
):
super().__init__()
self.has_cross_attention = True
self.num_attention_heads = num_attention_heads
resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
# support for variable transformer layers per block
if isinstance(transformer_layers_per_block, int):
transformer_layers_per_block = [transformer_layers_per_block] * num_layers
# 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 = []
for i in range(num_layers):
if not dual_cross_attention:
attentions.append(
Transformer2DModel(
num_attention_heads,
in_channels // num_attention_heads,
in_channels=in_channels,
num_layers=transformer_layers_per_block[i],
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
use_linear_projection=use_linear_projection,
upcast_attention=upcast_attention,
attention_type=attention_type,
)
)
else:
attentions.append(
DualTransformer2DModel(
num_attention_heads,
in_channels // num_attention_heads,
in_channels=in_channels,
num_layers=1,
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
)
)
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)
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.FloatTensor,
temb: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
) -> torch.FloatTensor:
lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0
hidden_states = self.resnets[0](hidden_states, temb, scale=lora_scale)
for attn, resnet in zip(self.attentions, self.resnets[1:]):
if self.training and self.gradient_checkpointing:
def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
if return_dict is not None:
return module(*inputs, return_dict=return_dict)
else:
return module(*inputs)
return custom_forward
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
hidden_states = attn(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
cross_attention_kwargs=cross_attention_kwargs,
attention_mask=attention_mask,
encoder_attention_mask=encoder_attention_mask,
return_dict=False,
)[0]
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(resnet),
hidden_states,
temb,
**ckpt_kwargs,
)
else:
hidden_states = attn(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
cross_attention_kwargs=cross_attention_kwargs,
attention_mask=attention_mask,
encoder_attention_mask=encoder_attention_mask,
return_dict=False,
)[0]
hidden_states = resnet(hidden_states, temb, scale=lora_scale)
return hidden_states
class UNetMidBlock2DSimpleCrossAttn(nn.Module):
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",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
attention_head_dim: int = 1,
output_scale_factor: float = 1.0,
cross_attention_dim: int = 1280,
skip_time_act: bool = False,
only_cross_attention: bool = False,
cross_attention_norm: Optional[str] = None,
):
super().__init__()
self.has_cross_attention = True
self.attention_head_dim = attention_head_dim
resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
self.num_heads = in_channels // self.attention_head_dim
# 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,
skip_time_act=skip_time_act,
)
]
attentions = []
for _ in range(num_layers):
processor = (
AttnAddedKVProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnAddedKVProcessor()
)
attentions.append(
Attention(
query_dim=in_channels,
cross_attention_dim=in_channels,
heads=self.num_heads,
dim_head=self.attention_head_dim,
added_kv_proj_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
bias=True,
upcast_softmax=True,
only_cross_attention=only_cross_attention,
cross_attention_norm=cross_attention_norm,
processor=processor,
)
)
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,
skip_time_act=skip_time_act,
)
)
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
def forward(
self,
hidden_states: torch.FloatTensor,
temb: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
) -> torch.FloatTensor:
cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
lora_scale = cross_attention_kwargs.get("scale", 1.0)
if attention_mask is None:
# if encoder_hidden_states is defined: we are doing cross-attn, so we should use cross-attn mask.
mask = None if encoder_hidden_states is None else encoder_attention_mask
else:
# when attention_mask is defined: we don't even check for encoder_attention_mask.
# this is to maintain compatibility with UnCLIP, which uses 'attention_mask' param for cross-attn masks.
# TODO: UnCLIP should express cross-attn mask via encoder_attention_mask param instead of via attention_mask.
# then we can simplify this whole if/else block to:
# mask = attention_mask if encoder_hidden_states is None else encoder_attention_mask
mask = attention_mask
hidden_states = self.resnets[0](hidden_states, temb, scale=lora_scale)
for attn, resnet in zip(self.attentions, self.resnets[1:]):
# attn
hidden_states = attn(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=mask,
**cross_attention_kwargs,
)
# resnet
hidden_states = resnet(hidden_states, temb, scale=lora_scale)
return hidden_states
class AttnDownBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_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,
attention_head_dim: int = 1,
output_scale_factor: float = 1.0,
downsample_padding: int = 1,
downsample_type: str = "conv",
):
super().__init__()
resnets = []
attentions = []
self.downsample_type = downsample_type
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`: {out_channels}."
)
attention_head_dim = out_channels
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=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.append(
Attention(
out_channels,
heads=out_channels // attention_head_dim,
dim_head=attention_head_dim,
rescale_output_factor=output_scale_factor,
eps=resnet_eps,
norm_num_groups=resnet_groups,
residual_connection=True,
bias=True,
upcast_softmax=True,
_from_deprecated_attn_block=True,
)
)
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
if downsample_type == "conv":
self.downsamplers = nn.ModuleList(
[
Downsample2D(
out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
)
]
)
elif downsample_type == "resnet":
self.downsamplers = nn.ModuleList(
[
ResnetBlock2D(
in_channels=out_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,
down=True,
)
]
)
else:
self.downsamplers = None
def forward(
self,
hidden_states: torch.FloatTensor,
temb: Optional[torch.FloatTensor] = None,
upsample_size: Optional[int] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
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