from typing import Optional, Tuple, Union
import numpy as np
import torch
import torch.nn as nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...modeling_utils import ModelMixin
from ...models.attention import DualTransformer2DModel, Transformer2DModel
from ...models.embeddings import TimestepEmbedding, Timesteps
from ...models.unet_2d_condition import UNet2DConditionOutput
from ...utils import logging
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def get_down_block(
down_block_type,
num_layers,
in_channels,
out_channels,
temb_channels,
add_downsample,
resnet_eps,
resnet_act_fn,
attn_num_head_channels,
resnet_groups=None,
cross_attention_dim=None,
downsample_padding=None,
dual_cross_attention=False,
use_linear_projection=False,
only_cross_attention=False,
):
down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type
if down_block_type == "DownBlockFlat":
return DownBlockFlat(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
add_downsample=add_downsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
downsample_padding=downsample_padding,
)
elif down_block_type == "CrossAttnDownBlockFlat":
if cross_attention_dim is None:
raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlockFlat")
return CrossAttnDownBlockFlat(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
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,
attn_num_head_channels=attn_num_head_channels,
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention,
)
raise ValueError(f"{down_block_type} is not supported.")
def get_up_block(
up_block_type,
num_layers,
in_channels,
out_channels,
prev_output_channel,
temb_channels,
add_upsample,
resnet_eps,
resnet_act_fn,
attn_num_head_channels,
resnet_groups=None,
cross_attention_dim=None,
dual_cross_attention=False,
use_linear_projection=False,
only_cross_attention=False,
):
up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
if up_block_type == "UpBlockFlat":
return UpBlockFlat(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
prev_output_channel=prev_output_channel,
temb_channels=temb_channels,
add_upsample=add_upsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
)
elif up_block_type == "CrossAttnUpBlockFlat":
if cross_attention_dim is None:
raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlockFlat")
return CrossAttnUpBlockFlat(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
prev_output_channel=prev_output_channel,
temb_channels=temb_channels,
add_upsample=add_upsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
cross_attention_dim=cross_attention_dim,
attn_num_head_channels=attn_num_head_channels,
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention,
)
raise ValueError(f"{up_block_type} is not supported.")
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel with UNet2DConditionModel->UNetFlatConditionModel, nn.Conv2d->LinearMultiDim, Block2D->BlockFlat
class UNetFlatConditionModel(ModelMixin, ConfigMixin):
r"""
UNetFlatConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a
timestep and returns sample shaped output.
This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
implements for all the models (such as downloading or saving, etc.)
Parameters:
sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
Height and width of input/output sample.
in_channels (`int`, *optional*, defaults to 4): The number of channels in the input sample.
out_channels (`int`, *optional*, defaults to 4): The number of channels in the output.
center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
Whether to flip the sin to cos in the time embedding.
freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlockFlat", "CrossAttnDownBlockFlat", "CrossAttnDownBlockFlat", "DownBlockFlat")`):
The tuple of downsample blocks to use.
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlockFlat", "CrossAttnUpBlockFlat", "CrossAttnUpBlockFlat", "CrossAttnUpBlockFlat",)`):
The tuple of upsample blocks to use.
block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
The tuple of output channels for each block.
layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
cross_attention_dim (`int`, *optional*, defaults to 1280): The dimension of the cross attention features.
attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
"""
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
sample_size: Optional[int] = None,
in_channels: int = 4,
out_channels: int = 4,
center_input_sample: bool = False,
flip_sin_to_cos: bool = True,
freq_shift: int = 0,
down_block_types: Tuple[str] = (
"CrossAttnDownBlockFlat",
"CrossAttnDownBlockFlat",
"CrossAttnDownBlockFlat",
"DownBlockFlat",
),
up_block_types: Tuple[str] = (
"UpBlockFlat",
"CrossAttnUpBlockFlat",
"CrossAttnUpBlockFlat",
"CrossAttnUpBlockFlat",
),
only_cross_attention: Union[bool, Tuple[bool]] = False,
block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
layers_per_block: int = 2,
downsample_padding: int = 1,
mid_block_scale_factor: float = 1,
act_fn: str = "silu",
norm_num_groups: int = 32,
norm_eps: float = 1e-5,
cross_attention_dim: int = 1280,
attention_head_dim: Union[int, Tuple[int]] = 8,
dual_cross_attention: bool = False,
use_linear_projection: bool = False,
num_class_embeds: Optional[int] = None,
):
super().__init__()
self.sample_size = sample_size
time_embed_dim = block_out_channels[0] * 4
# input
self.conv_in = LinearMultiDim(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))
# time
self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
timestep_input_dim = block_out_channels[0]
self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
# class embedding
if num_class_embeds is not None:
self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
self.down_blocks = nn.ModuleList([])
self.mid_block = None
self.up_blocks = nn.ModuleList([])
if isinstance(only_cross_attention, bool):
only_cross_attention = [only_cross_attention] * len(down_block_types)
if isinstance(attention_head_dim, int):
attention_head_dim = (attention_head_dim,) * len(down_block_types)
# down
output_channel = block_out_channels[0]
for i, down_block_type in enumerate(down_block_types):
input_channel = output_channel
output_channel = block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1
down_block = get_down_block(
down_block_type,
num_layers=layers_per_block,
in_channels=input_channel,
out_channels=output_channel,
temb_channels=time_embed_dim,
add_downsample=not is_final_block,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
cross_attention_dim=cross_attention_dim,
attn_num_head_channels=attention_head_dim[i],
downsample_padding=downsample_padding,
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention[i],
)
self.down_blocks.append(down_block)
# mid
self.mid_block = UNetMidBlockFlatCrossAttn(
in_channels=block_out_channels[-1],
temb_channels=time_embed_dim,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
output_scale_factor=mid_block_scale_factor,
resnet_time_scale_shift="default",
cross_attention_dim=cross_attention_dim,
attn_num_head_channels=attention_head_dim[-1],
resnet_groups=norm_num_groups,
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
)
# count how many layers upsample the images
self.num_upsamplers = 0
# up
reversed_block_out_channels = list(reversed(block_out_channels))
reversed_attention_head_dim = list(reversed(attention_head_dim))
only_cross_attention = list(reversed(only_cross_attention))
output_channel = reversed_block_out_channels[0]
for i, up_block_type in enumerate(up_block_types):
is_final_block = i == len(block_out_channels) - 1
prev_output_channel = output_channel
output_channel = reversed_block_out_channels[i]
input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
# add upsample block for all BUT final layer
if not is_final_block:
add_upsample = True
self.num_upsamplers += 1
else:
add_upsample = False
up_block = get_up_block(
up_block_type,
num_layers=layers_per_block + 1,
in_channels=input_channel,
out_channels=output_channel,
prev_output_channel=prev_output_channel,
temb_channels=time_embed_dim,
add_upsample=add_upsample,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
cross_attention_dim=cross_attention_dim,
attn_num_head_channels=reversed_attention_head_dim[i],
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention[i],
)
self.up_blocks.append(up_block)
prev_output_channel = output_channel
# out
self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)
self.conv_act = nn.SiLU()
self.conv_out = LinearMultiDim(block_out_channels[0], out_channels, kernel_size=3, padding=1)
def set_attention_slice(self, slice_size):
head_dims = self.config.attention_head_dim
head_dims = [head_dims] if isinstance(head_dims, int) else head_dims
if slice_size is not None and any(dim % slice_size != 0 for dim in head_dims):
raise ValueError(
f"Make sure slice_size {slice_size} is a common divisor of "
f"the number of heads used in cross_attention: {head_dims}"
)
if slice_size is not None and slice_size > min(head_dims):
raise ValueError(
f"slice_size {slice_size} has to be smaller or equal to "
f"the lowest number of heads used in cross_attention: min({head_dims}) = {min(head_dims)}"
)
for block in self.down_blocks:
if hasattr(block, "attentions") and block.attentions is not None:
block.set_attention_slice(slice_size)
self.mid_block.set_attention_slice(slice_size)
for block in self.up_blocks:
if hasattr(block, "attentions") and block.attentions is not None:
block.set_attention_slice(slice_size)
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, (CrossAttnDownBlockFlat, DownBlockFlat, CrossAttnUpBlockFlat, UpBlockFlat)):
module.gradient_checkpointing = value
def forward(
self,
sample: torch.FloatTensor,
timestep: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor,
class_labels: Optional[torch.Tensor] = None,
return_dict: bool = True,
) -> Union[UNet2DConditionOutput, Tuple]:
r"""
Args:
sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps
encoder_hidden_states (`torch.FloatTensor`): (batch, channel, height, width) encoder hidden states
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
Returns:
[`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
[`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When
returning a tuple, the first element is the sample tensor.
"""
# By default samples have to be AT least a multiple of the overall upsampling factor.
# The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
# However, the upsampling interpolation output size can be forced to fit any upsampling size
# on the fly if necessary.
default_overall_up_factor = 2**self.num_upsamplers
# upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
forward_upsample_size = False
upsample_size = None
if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
logger.info("Forward upsample size to force interpolation output size.")
forward_upsample_size = True
# 0. center input if necessary
if self.config.center_input_sample:
sample = 2 * sample - 1.0
# 1. time
timesteps = timestep
if not torch.is_tensor(timesteps):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timesteps = timesteps.expand(sample.shape[0])
t_emb = self.time_proj(timesteps)
# timesteps does not contain any weights and will always return f32 tensors
# but time_embedding might actually be running in fp16. so we need to cast here.
# there might be better ways to encapsulate this.
t_emb = t_emb.to(dtype=self.dtype)
emb = self.time_embedding(t_emb)
if self.config.num_class_embeds is not None:
if class_labels is None:
raise ValueError("class_labels should be provided when num_class_embeds > 0")
class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
emb = emb + class_emb
# 2. pre-process
sample = self.conv_in(sample)
# 3. down
down_block_res_samples = (sample,)
for downsample_block in self.down_blocks:
if hasattr(downsample_block, "attentions") and downsample_block.attentions is not None:
sample, res_samples = downsample_block(
hidden_states=sample,
temb=emb,
encoder_hidden_states=encoder_hidden_states,
)
else:
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
down_block_res_samples += res_samples
# 4. mid
sample = self.mid_block(sample, emb, encoder_hidden_states=encoder_hidden_states)
# 5. up
for i, upsample_block in enumerate(self.up_blocks):
is_final_block = i == len(self.up_blocks) - 1
res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
# if we have not reached the final block and need to forward the
# upsample size, we do it here
if not is_final_block and forward_upsample_size:
upsample_size = down_block_res_samples[-1].shape[2:]
if hasattr(upsample_block, "attentions") and upsample_block.attentions is not None:
sample = upsample_block(
hidden_states=sample,
temb=emb,
res_hidden_states_tuple=res_samples,
encoder_hidden_states=encoder_hidden_states,
upsample_size=upsample_size,
)
else:
sample = upsample_block(
hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
)
# 6. post-process
sample = self.conv_norm_out(sample)
sample = self.conv_act(sample)
sample = self.conv_out(sample)
if not return_dict:
return (sample,)
return UNet2DConditionOutput(sample=sample)
class LinearMultiDim(nn.Linear):
def __init__(self, in_features, out_features=None, second_dim=4, *args, **kwargs):
in_features = [in_features, second_dim, 1] if isinstance(in_features, int) else list(in_features)
if out_features is None:
out_features = in_features
out_features = [out_features, second_dim, 1] if isinstance(out_features, int) else list(out_features)
self.in_features_multidim = in_features
self.out_features_multidim = out_features
super().__init__(np.array(in_features).prod(), np.array(out_features).prod())
def forward(self, input_tensor, *args, **kwargs):
shape = input_tensor.shape
n_dim = len(self.in_features_multidim)
input_tensor = input_tensor.reshape(*shape[0:-n_dim], self.in_features)
output_tensor = super().forward(input_tensor)
output_tensor = output_tensor.view(*shape[0:-n_dim], *self.out_features_multidim)
return output_tensor
class ResnetBlockFlat(nn.Module):
def __init__(
self,
*,
in_channels,
out_channels=None,
dropout=0.0,
temb_channels=512,
groups=32,
groups_out=None,
pre_norm=True,
eps=1e-6,
time_embedding_norm="default",
use_in_shortcut=None,
second_dim=4,
**kwargs,
):
super().__init__()
self.pre_norm = pre_norm
self.pre_norm = True
in_channels = [in_channels, second_dim, 1] if isinstance(in_channels, int) else list(in_channels)
self.in_channels_prod = np.array(in_channels).prod()
self.channels_multidim = in_channels
if out_channels is not None:
out_channels = [out_channels, second_dim, 1] if isinstance(out_channels, int) else list(out_channels)
out_channels_prod = np.array(out_channels).prod()
self.out_channels_multidim = out_channels
else:
out_channels_prod = self.in_channels_prod
self.out_channels_multidim = self.channels_multidim
self.time_embedding_norm = time_embedding_norm
if groups_out is None:
groups_out = groups
self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=self.in_channels_prod, eps=eps, affine=True)
self.conv1 = torch.nn.Conv2d(self.in_channels_prod, out_channels_prod, kernel_size=1, padding=0)
if temb_channels is not None:
self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels_prod)
else:
self.time_emb_proj = None
self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels_prod, eps=eps, affine=True)
self.dropout = torch.nn.Dropout(dropout)
self.conv2 = torch.nn.Conv2d(out_channels_prod, out_channels_prod, kernel_size=1, padding=0)
self.nonlinearity = nn.SiLU()
self.use_in_shortcut = (
self.in_channels_prod != out_channels_prod if use_in_shortcut is None else use_in_shortcut
)
self.conv_shortcut = None
if self.use_in_shortcut:
self.conv_shortcut = torch.nn.Conv2d(
self.in_channels_prod, out_channels_prod, kernel_size=1, stride=1, padding=0
)
def forward(self, input_tensor, temb):
shape = input_tensor.shape
n_dim = len(self.channels_multidim)
input_tensor = input_tensor.reshape(*shape[0:-n_dim], self.in_channels_prod, 1, 1)
input_tensor = input_tensor.view(-1, self.in_channels_prod, 1, 1)
hidden_states = input_tensor
hidden_states = self.norm1(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
hidden_states = self.conv1(hidden_states)
if temb is not None:
temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None]
hidden_states = hidden_states + temb
hidden_states = self.norm2(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.conv2(hidden_states)
if self.conv_shortcut is not None:
input_tensor = self.conv_shortcut(input_tensor)
output_tensor = input_tensor + hidden_states
output_tensor = output_tensor.view(*shape[0:-n_dim], -1)
output_tensor = output_tensor.view(*shape[0:-n_dim], *self.out_channels_multidim)
return output_tensor
# Copied from diffusers.models.unet_2d_blocks.DownBlock2D with DownBlock2D->DownBlockFlat, ResnetBlock2D->ResnetBlockFlat, Downsample2D->LinearMultiDim
class DownBlockFlat(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,
output_scale_factor=1.0,
add_downsample=True,
downsample_padding=1,
):
super().__init__()
resnets = []
for i in range(num_layers):
in_channels = in_channels if i == 0 else out_channels
resnets.append(
ResnetBlockFlat(
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,
)
)
self.resnets = nn.ModuleList(resnets)
if add_downsample:
self.downsamplers = nn.ModuleList(
[
LinearMultiDim(
out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
)
]
)
else:
self.downsamplers = None
self.gradient_checkpointing = False
def forward(self, hidden_states, temb=None):
output_states = ()
for resnet in self.resnets:
if self.training and self.gradient_checkpointing:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs)
return custom_forward
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
else:
hidden_states = resnet(hidden_states, temb)
output_states += (hidden_states,)
if self.downsamplers is not None:
for downsampler in self.downsamplers:
hidden_states = downsampler(hidden_states)
output_states += (hidden_states,)
return hidden_states, output_states
# Copied from diffusers.models.unet_2d_blocks.CrossAttnDownBlock2D with CrossAttnDownBlock2D->CrossAttnDownBlockFlat, ResnetBlock2D->ResnetBlockFlat, Downsample2D->LinearMultiDim
class CrossAttnDownBlockFlat(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,
attn_num_head_channels=1,
cross_attention_dim=1280,
attention_type="default",
output_scale_factor=1.0,
downsample_padding=1,
add_downsample=True,
dual_cross_attention=False,
use_linear_projection=False,
only_cross_attention=False,
):
super().__init__()
resnets = []
attentions = []
self.attention_type = attention_type
self.attn_num_head_channels = attn_num_head_channels
for i in range(num_layers):
in_channels = in_channels if i == 0 else out_channels
resnets.append(
ResnetBlockFlat(
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,
)
)
if not dual_cross_attention:
attentions.append(
Transformer2DModel(
attn_num_head_channels,
out_channels // attn_num_head_channels,
in_channels=out_channels,
num_layers=1,
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention,
)
)
else:
attentions.append(
DualTransformer2DModel(
attn_num_head_channels,
out_channels // attn_num_head_channels,
in_channels=out_channels,
num_layers=1,
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
)
)
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
if add_downsample:
self.downsamplers = nn.ModuleList(
[
LinearMultiDim(
out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
)
]
)
else:
self.downsamplers = None
self.gradient_checkpointing = False
def set_attention_slice(self, slice_size):
head_dims = self.attn_num_head_channels
head_dims = [head_dims] if isinstance(head_dims, int) else head_dims
if slice_size is not None and any(dim % slice_size != 0 for dim in head_dims):
raise ValueError(
f"Make sure slice_size {slice_size} is a common divisor of "
f"the number of heads used in cross_attention: {head_dims}"
)
if slice_size is not None and slice_size > min(head_dims):
raise ValueError(
f"slice_size {slice_size} has to be smaller or equal to "
f"the lowest number of heads used in cross_attention: min({head_dims}) = {min(head_dims)}"
)
for attn in self.attentions:
attn._set_attention_slice(slice_size)
def forward(self, hidden_states, temb=None, encoder_hidden_states=None):
output_states = ()
for resnet, attn in zip(self.resnets, self.attentions):
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
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states
)[0]
else:
hidden_states = resnet(hidden_states, temb)
hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample
output_states += (hidden_states,)
if self.downsamplers is not None:
for downsampler in self.downsamplers:
hidden_states = downsampler(hidden_states)
output_states += (hidden_states,)
return hidden_states, output_states
# Copied from diffusers.models.unet_2d_blocks.UpBlock2D with UpBlock2D->UpBlockFlat, ResnetBlock2D->ResnetBlockFlat, Upsample2D->LinearMultiDim
class UpBlockFlat(nn.Module):
def __init__(
self,
in_channels: int,
prev_output_channel: 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,
output_scale_factor=1.0,
add_upsample=True,
):
super().__init__()
resnets = []
for i in range(num_layers):
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
resnet_in_channels = prev_output_channel if i == 0 else out_channels
resnets.append(
ResnetBlockFlat(
in_channels=resnet_in_channels + res_skip_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([LinearMultiDim(out_channels, use_conv=True, out_channels=out_channels)])
else:
self.upsamplers = None
self.gradient_checkpointing = False
def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):
for resnet in self.resnets:
# pop res hidden states
res_hidden_states = res_hidden_states_tuple[-1]
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
if self.training and self.gradient_checkpointing:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs)
return custom_forward
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
else:
hidden_states = resnet(hidden_states, temb)
if self.upsamplers is not None:
for upsampler in self.upsamplers:
hidden_states = upsampler(hidden_states, upsample_size)
return hidden_states
# Copied from diffusers.models.unet_2d_blocks.CrossAttnUpBlock2D with CrossAttnUpBlock2D->CrossAttnUpBlockFlat, ResnetBlock2D->ResnetBlockFlat, Upsample2D->LinearMultiDim
class CrossAttnUpBlockFlat(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
prev_output_channel: 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,
attn_num_head_channels=1,
cross_attention_dim=1280,
attention_type="default",
output_scale_factor=1.0,
add_upsample=True,
dual_cross_attention=False,
use_linear_projection=False,
only_cross_attention=False,
):
super().__init__()
resnets = []
attentions = []
self.attention_type = attention_type
self.attn_num_head_channels = attn_num_head_channels
for i in range(num_layers):
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
resnet_in_channels = prev_output_channel if i == 0 else out_channels
resnets.append(
ResnetBlockFlat(
in_channels=resnet_in_channels + res_skip_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,
)
)
if not dual_cross_attention:
attentions.append(
Transformer2DModel(
attn_num_head_channels,
out_channels // attn_num_head_channels,
in_channels=out_channels,
num_layers=1,
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention,
)
)
else:
attentions.append(
DualTransformer2DModel(
attn_num_head_channels,
out_channels // attn_num_head_channels,
in_channels=out_channels,
num_layers=1,
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
)
)
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
if add_upsample:
self.upsamplers = nn.ModuleList([LinearMultiDim(out_channels, use_conv=True, out_channels=out_channels)])
else:
self.upsamplers = None
self.gradient_checkpointing = False
def set_attention_slice(self, slice_size):
head_dims = self.attn_num_head_channels
head_dims = [head_dims] if isinstance(head_dims, int) else head_dims
if slice_size is not None and any(dim % slice_size != 0 for dim in head_dims):
raise ValueError(
f"Make sure slice_size {slice_size} is a common divisor of "
f"the number of heads used in cross_attention: {head_dims}"
)
if slice_size is not None and slice_size > min(head_dims):
raise ValueError(
f"slice_size {slice_size} has to be smaller or equal to "
f"the lowest number of heads used in cross_attention: min({head_dims}) = {min(head_dims)}"
)
for attn in self.attentions:
attn._set_attention_slice(slice_size)
self.gradient_checkpointing = False
def forward(
self,
hidden_states,
res_hidden_states_tuple,
temb=None,
encoder_hidden_states=None,
upsample_size=None,
):
for resnet, attn in zip(self.resnets, self.attentions):
# pop res hidden states
res_hidden_states = res_hidden_states_tuple[-1]
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=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
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states
)[0]
else:
hidden_states = resnet(hidden_states, temb)
hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample
if self.upsamplers is not None:
for upsampler in self.upsamplers:
hidden_states = upsampler(hidden_states, upsample_size)
return hidden_states
# Copied from diffusers.models.unet_2d_blocks.UNetMidBlock2DCrossAttn with UNetMidBlock2DCrossAttn->UNetMidBlockFlatCrossAttn, ResnetBlock2D->ResnetBlockFlat
class UNetMidBlockFlatCrossAttn(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,
attn_num_head_channels=1,
attention_type="default",
output_scale_factor=1.0,
cross_attention_dim=1280,
dual_cross_attention=False,
use_linear_projection=False,
):
super().__init__()
self.attention_type = attention_type
self.attn_num_head_channels = attn_num_head_channels
resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
# there is always at least one resnet
resnets = [
ResnetBlockFlat(
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 _ in range(num_layers):
if not dual_cross_attention:
attentions.append(
Transformer2DModel(
attn_num_head_channels,
in_channels // attn_num_head_channels,
in_channels=in_channels,
num_layers=1,
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
use_linear_projection=use_linear_projection,
)
)
else:
attentions.append(
DualTransformer2DModel(
attn_num_head_channels,
in_channels // attn_num_head_channels,
in_channels=in_channels,
num_layers=1,
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
)
)
resnets.append(
ResnetBlockFlat(
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 set_attention_slice(self, slice_size):
head_dims = self.attn_num_head_channels
head_dims = [head_dims] if isinstance(head_dims, int) else head_dims
if slice_size is not None and any(dim % slice_size != 0 for dim in head_dims):
raise ValueError(
f"Make sure slice_size {slice_size} is a common divisor of "
f"the number of heads used in cross_attention: {head_dims}"
)
if slice_size is not None and slice_size > min(head_dims):
raise ValueError(
f"slice_size {slice_size} has to be smaller or equal to "
f"the lowest number of heads used in cross_attention: min({head_dims}) = {min(head_dims)}"
)
for attn in self.attentions:
attn._set_attention_slice(slice_size)
def forward(self, hidden_states, temb=None, encoder_hidden_states=None):
hidden_states = self.resnets[0](hidden_states, temb)
for attn, resnet in zip(self.attentions, self.resnets[1:]):
hidden_states = attn(hidden_states, encoder_hidden_states).sample
hidden_states = resnet(hidden_states, temb)
return hidden_states