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import torch
from typing import Optional, Tuple, Union, Any
from diffusers import UNet2DConditionModel
from diffusers.models.attention_processor import Attention
from diffusers.models.unets.unet_2d_condition import UNet2DConditionOutput
def construct_pix2pix_attention(hidden_states_dim, norm_type="none"):
if norm_type == "layernorm":
norm = torch.nn.LayerNorm(hidden_states_dim)
else:
norm = torch.nn.Identity()
attention = Attention(
query_dim=hidden_states_dim,
heads=8,
dim_head=hidden_states_dim // 8,
bias=True,
)
# NOTE: xformers 0.22 does not support batchsize >= 4096
attention.xformers_not_supported = True # hacky solution
return norm, attention
def switch_extra_processor(model, enable_filter=lambda x:True):
def recursive_add_processors(name: str, module: torch.nn.Module):
for sub_name, child in module.named_children():
recursive_add_processors(f"{name}.{sub_name}", child)
if isinstance(module, ExtraAttnProc):
module.enabled = enable_filter(name)
for name, module in model.named_children():
recursive_add_processors(name, module)
def add_extra_processor(model: torch.nn.Module, enable_filter=lambda x:True, **kwargs):
return_dict = torch.nn.ModuleDict()
proj_in_dim = kwargs.get('proj_in_dim', False)
kwargs.pop('proj_in_dim', None)
def recursive_add_processors(name: str, module: torch.nn.Module):
for sub_name, child in module.named_children():
if "ref_unet" not in (sub_name + name):
recursive_add_processors(f"{name}.{sub_name}", child)
if isinstance(module, Attention):
new_processor = ExtraAttnProc(
chained_proc=module.get_processor(),
enabled=enable_filter(f"{name}.processor"),
name=f"{name}.processor",
proj_in_dim=proj_in_dim if proj_in_dim else module.cross_attention_dim,
target_dim=module.cross_attention_dim,
**kwargs
)
module.set_processor(new_processor)
return_dict[f"{name}.processor".replace(".", "__")] = new_processor
for name, module in model.named_children():
recursive_add_processors(name, module)
return return_dict
class ExtraAttnProc(torch.nn.Module):
def __init__(
self,
chained_proc,
enabled=False,
name=None,
mode='extract',
with_proj_in=False,
proj_in_dim=768,
target_dim=None,
pixel_wise_crosspond=False,
norm_type="none", # none or layernorm
crosspond_effect_on="all", # all or first
crosspond_chain_pos="parralle", # before or parralle or after
simple_3d=False,
views=4,
) -> None:
super().__init__()
self.enabled = enabled
self.chained_proc = chained_proc
self.name = name
self.mode = mode
self.with_proj_in=with_proj_in
self.proj_in_dim = proj_in_dim
self.target_dim = target_dim or proj_in_dim
self.hidden_states_dim = self.target_dim
self.pixel_wise_crosspond = pixel_wise_crosspond
self.crosspond_effect_on = crosspond_effect_on
self.crosspond_chain_pos = crosspond_chain_pos
self.views = views
self.simple_3d = simple_3d
if self.with_proj_in and self.enabled:
self.in_linear = torch.nn.Linear(self.proj_in_dim, self.target_dim, bias=False)
if self.target_dim == self.proj_in_dim:
self.in_linear.weight.data = torch.eye(proj_in_dim)
else:
self.in_linear = None
if self.pixel_wise_crosspond and self.enabled:
self.crosspond_norm, self.crosspond_attention = construct_pix2pix_attention(self.hidden_states_dim, norm_type=norm_type)
def do_crosspond_attention(self, hidden_states: torch.FloatTensor, other_states: torch.FloatTensor):
hidden_states = self.crosspond_norm(hidden_states)
batch, L, D = hidden_states.shape
assert hidden_states.shape == other_states.shape, f"got {hidden_states.shape} and {other_states.shape}"
# to -> batch * L, 1, D
hidden_states = hidden_states.reshape(batch * L, 1, D)
other_states = other_states.reshape(batch * L, 1, D)
hidden_states_catted = other_states
hidden_states = self.crosspond_attention(
hidden_states,
encoder_hidden_states=hidden_states_catted,
)
return hidden_states.reshape(batch, L, D)
def __call__(
self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None,
ref_dict: dict = None, mode=None, **kwargs
) -> Any:
if not self.enabled:
return self.chained_proc(attn, hidden_states, encoder_hidden_states, attention_mask, **kwargs)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
assert ref_dict is not None
if (mode or self.mode) == 'extract':
ref_dict[self.name] = hidden_states
hidden_states1 = self.chained_proc(attn, hidden_states, encoder_hidden_states, attention_mask, **kwargs)
if self.pixel_wise_crosspond and self.crosspond_chain_pos == "after":
ref_dict[self.name] = hidden_states1
return hidden_states1
elif (mode or self.mode) == 'inject':
ref_state = ref_dict.pop(self.name)
if self.with_proj_in:
ref_state = self.in_linear(ref_state)
B, L, D = ref_state.shape
if hidden_states.shape[0] == B:
modalities = 1
views = 1
else:
modalities = hidden_states.shape[0] // B // self.views
views = self.views
if self.pixel_wise_crosspond:
if self.crosspond_effect_on == "all":
ref_state = ref_state[:, None].expand(-1, modalities * views, -1, -1).reshape(-1, *ref_state.shape[-2:])
if self.crosspond_chain_pos == "before":
hidden_states = hidden_states + self.do_crosspond_attention(hidden_states, ref_state)
hidden_states1 = self.chained_proc(attn, hidden_states, encoder_hidden_states, attention_mask, **kwargs)
if self.crosspond_chain_pos == "parralle":
hidden_states1 = hidden_states1 + self.do_crosspond_attention(hidden_states, ref_state)
if self.crosspond_chain_pos == "after":
hidden_states1 = hidden_states1 + self.do_crosspond_attention(hidden_states1, ref_state)
return hidden_states1
else:
assert self.crosspond_effect_on == "first"
# hidden_states [B * modalities * views, L, D]
# ref_state [B, L, D]
ref_state = ref_state[:, None].expand(-1, modalities, -1, -1).reshape(-1, ref_state.shape[-2], ref_state.shape[-1]) # [B * modalities, L, D]
def do_paritial_crosspond(hidden_states, ref_state):
first_view_hidden_states = hidden_states.view(-1, views, hidden_states.shape[1], hidden_states.shape[2])[:, 0] # [B * modalities, L, D]
hidden_states2 = self.do_crosspond_attention(first_view_hidden_states, ref_state) # [B * modalities, L, D]
hidden_states2_padded = torch.zeros_like(hidden_states).reshape(-1, views, hidden_states.shape[1], hidden_states.shape[2])
hidden_states2_padded[:, 0] = hidden_states2
hidden_states2_padded = hidden_states2_padded.reshape(-1, hidden_states.shape[1], hidden_states.shape[2])
return hidden_states2_padded
if self.crosspond_chain_pos == "before":
hidden_states = hidden_states + do_paritial_crosspond(hidden_states, ref_state)
hidden_states1 = self.chained_proc(attn, hidden_states, encoder_hidden_states, attention_mask, **kwargs) # [B * modalities * views, L, D]
if self.crosspond_chain_pos == "parralle":
hidden_states1 = hidden_states1 + do_paritial_crosspond(hidden_states, ref_state)
if self.crosspond_chain_pos == "after":
hidden_states1 = hidden_states1 + do_paritial_crosspond(hidden_states1, ref_state)
return hidden_states1
elif self.simple_3d:
B, L, C = encoder_hidden_states.shape
mv = self.views
encoder_hidden_states = encoder_hidden_states.reshape(B // mv, mv, L, C)
ref_state = ref_state[:, None]
encoder_hidden_states = torch.cat([encoder_hidden_states, ref_state], dim=1)
encoder_hidden_states = encoder_hidden_states.reshape(B // mv, 1, (mv+1) * L, C)
encoder_hidden_states = encoder_hidden_states.repeat(1, mv, 1, 1).reshape(-1, (mv+1) * L, C)
return self.chained_proc(attn, hidden_states, encoder_hidden_states, attention_mask, **kwargs)
else:
ref_state = ref_state[:, None].expand(-1, modalities * views, -1, -1).reshape(-1, ref_state.shape[-2], ref_state.shape[-1])
encoder_hidden_states = torch.cat([encoder_hidden_states, ref_state], dim=1)
return self.chained_proc(attn, hidden_states, encoder_hidden_states, attention_mask, **kwargs)
else:
raise NotImplementedError("mode or self.mode is required to be 'extract' or 'inject'")
class UnifieldWrappedUNet(UNet2DConditionModel):
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] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
),
mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
only_cross_attention: Union[bool, Tuple[bool]] = False,
block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
layers_per_block: Union[int, Tuple[int]] = 2,
downsample_padding: int = 1,
mid_block_scale_factor: float = 1,
dropout: float = 0.0,
act_fn: str = "silu",
norm_num_groups: Optional[int] = 32,
norm_eps: float = 1e-5,
cross_attention_dim: Union[int, Tuple[int]] = 1280,
transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None,
encoder_hid_dim: Optional[int] = None,
encoder_hid_dim_type: Optional[str] = None,
attention_head_dim: Union[int, Tuple[int]] = 8,
num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
dual_cross_attention: bool = False,
use_linear_projection: bool = False,
class_embed_type: Optional[str] = None,
addition_embed_type: Optional[str] = None,
addition_time_embed_dim: Optional[int] = None,
num_class_embeds: Optional[int] = None,
upcast_attention: bool = False,
resnet_time_scale_shift: str = "default",
resnet_skip_time_act: bool = False,
resnet_out_scale_factor: float = 1.0,
time_embedding_type: str = "positional",
time_embedding_dim: Optional[int] = None,
time_embedding_act_fn: Optional[str] = None,
timestep_post_act: Optional[str] = None,
time_cond_proj_dim: Optional[int] = None,
conv_in_kernel: int = 3,
conv_out_kernel: int = 3,
projection_class_embeddings_input_dim: Optional[int] = None,
attention_type: str = "default",
class_embeddings_concat: bool = False,
mid_block_only_cross_attention: Optional[bool] = None,
cross_attention_norm: Optional[str] = None,
addition_embed_type_num_heads: int = 64,
init_self_attn_ref: bool = False,
self_attn_ref_other_model_name: str = 'lambdalabs/sd-image-variations-diffusers',
self_attn_ref_position: str = "attn1",
self_attn_ref_pixel_wise_crosspond: bool = False,
self_attn_ref_effect_on: str = "all",
self_attn_ref_chain_pos: str = "parralle",
use_simple3d_attn: bool = False,
**kwargs
):
super().__init__(**{
k: v for k, v in locals().items() if k not in
["self", "kwargs", "__class__",
"init_self_attn_ref", "self_attn_ref_other_model_name", "self_attn_ref_position", "self_attn_ref_pixel_wise_crosspond",
"self_attn_ref_effect_on", "self_attn_ref_chain_pos", "use_simple3d_attn"
]
})
self.ref_unet: UNet2DConditionModel = UNet2DConditionModel.from_pretrained(
self_attn_ref_other_model_name, subfolder="unet", torch_dtype=self.dtype
)
add_extra_processor(
model=self.ref_unet,
enable_filter=lambda name: name.endswith(f"{self_attn_ref_position}.processor"),
mode='extract',
with_proj_in=False,
pixel_wise_crosspond=False,
)
add_extra_processor(
model=self,
enable_filter=lambda name: name.endswith(f"{self_attn_ref_position}.processor"),
mode='inject',
with_proj_in=False,
pixel_wise_crosspond=self_attn_ref_pixel_wise_crosspond,
crosspond_effect_on=self_attn_ref_effect_on,
crosspond_chain_pos=self_attn_ref_chain_pos,
simple_3d=use_simple3d_attn,
)
switch_extra_processor(self, enable_filter=lambda name: name.endswith(f"{self_attn_ref_position}.processor"))
def __call__(
self,
sample: torch.Tensor,
timestep: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor,
condition_latens: torch.Tensor = None,
class_labels: Optional[torch.Tensor] = None,
) -> Union[UNet2DConditionOutput, Tuple]:
ref_dict = {}
self.ref_unet(condition_latens, timestep, encoder_hidden_states, cross_attention_kwargs=dict(ref_dict=ref_dict))
return self.forward(
sample, timestep, encoder_hidden_states,
class_labels=class_labels,
cross_attention_kwargs=dict(ref_dict=ref_dict, mode='inject'),
)
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