from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.utils.checkpoint
from diffusers import ModelMixin
from diffusers.configuration_utils import (ConfigMixin,
register_to_config)
from diffusers.utils import BaseOutput, logging
from .embeddings import TimestepEmbedding, Timesteps
from .unet_blocks import (DownBlock2D,
UNetMidMCABlock2D,
UpBlock2D,
get_down_block,
get_up_block)
logger = logging.get_logger(__name__)
@dataclass
class UNetOutput(BaseOutput):
sample: torch.FloatTensor
class UNet(ModelMixin, ConfigMixin):
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
sample_size: Optional[int] = None,
in_channels: int = 4,
out_channels: int = 4,
flip_sin_to_cos: bool = True,
freq_shift: int = 0,
down_block_types: Tuple[str] = None,
up_block_types: Tuple[str] = None,
block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
layers_per_block: int = 1,
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: int = 8,
channel_attn: bool = False,
content_encoder_downsample_size: int = 4,
content_start_channel: int = 16,
reduction: int = 32,
):
super().__init__()
self.content_encoder_downsample_size = content_encoder_downsample_size
self.sample_size = sample_size
time_embed_dim = block_out_channels[0] * 4
# input
self.conv_in = nn.Conv2d(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)
self.down_blocks = nn.ModuleList([])
self.mid_block = None
self.up_blocks = nn.ModuleList([])
# 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
if i != 0:
content_channel = content_start_channel * (2 ** (i-1))
else:
content_channel = 0
print("Load the down block ", down_block_type)
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,
downsample_padding=downsample_padding,
content_channel=content_channel,
reduction=reduction,
channel_attn=channel_attn,
)
self.down_blocks.append(down_block)
# mid
self.mid_block = UNetMidMCABlock2D(
in_channels=block_out_channels[-1],
temb_channels=time_embed_dim,
channel_attn=channel_attn,
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,
resnet_groups=norm_num_groups,
content_channel=content_start_channel*(2**(content_encoder_downsample_size - 1)),
reduction=reduction,
)
# count how many layers upsample the images
self.num_upsamplers = 0
# up
reversed_block_out_channels = list(reversed(block_out_channels))
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
content_channel = content_start_channel * (2 ** (content_encoder_downsample_size - i - 1))
print("Load the up block ", up_block_type)
up_block = get_up_block(
up_block_type,
num_layers=layers_per_block + 1, # larger 1 than the down block
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=attention_head_dim,
upblock_index=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 = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1)
def set_attention_slice(self, slice_size):
if slice_size is not None and self.config.attention_head_dim % slice_size != 0:
raise ValueError(
f"Make sure slice_size {slice_size} is a divisor of "
f"the number of heads used in cross_attention {self.config.attention_head_dim}"
)
if slice_size is not None and slice_size > self.config.attention_head_dim:
raise ValueError(
f"Chunk_size {slice_size} has to be smaller or equal to "
f"the number of heads used in cross_attention {self.config.attention_head_dim}"
)
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, (DownBlock2D, UpBlock2D)):
module.gradient_checkpointing = value
def forward(
self,
sample: torch.FloatTensor,
timestep: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor,
content_encoder_downsample_size: int = 4,
return_dict: bool = False,
) -> Union[UNetOutput, Tuple]:
# 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
# 1. time
timesteps = timestep # only one time
if not torch.is_tensor(timesteps):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device)
elif torch.is_tensor(timesteps) and 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) # projection
# 2. pre-process
sample = self.conv_in(sample)
# 3. down
down_block_res_samples = (sample,)
for index, downsample_block in enumerate(self.down_blocks):
if (hasattr(downsample_block, "attentions") and downsample_block.attentions is not None) or hasattr(downsample_block, "content_attentions"):
sample, res_samples = downsample_block(
hidden_states=sample,
temb=emb,
encoder_hidden_states=encoder_hidden_states,
index=index,
)
else:
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
down_block_res_samples += res_samples
# 4. mid
if self.mid_block is not None:
sample = self.mid_block(
sample,
emb,
index=content_encoder_downsample_size,
encoder_hidden_states=encoder_hidden_states
)
# 5. up
offset_out_sum = 0
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) or hasattr(upsample_block, "content_attentions"):
sample, offset_out = upsample_block(
hidden_states=sample,
temb=emb,
res_hidden_states_tuple=res_samples,
style_structure_features=encoder_hidden_states[3],
encoder_hidden_states=encoder_hidden_states[2],
)
offset_out_sum += offset_out
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, offset_out_sum)
return UNetOutput(sample=sample)