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import torch
from typing import Optional, Tuple, Union
from diffusers import UNet2DConditionModel
from diffusers.models.attention_processor import Attention
from diffusers.models.unets.unet_2d_condition import UNet2DConditionOutput
def switch_multiview_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, Attention):
processor = module.get_processor()
if isinstance(processor, multiviewAttnProc):
processor.enabled = enable_filter(f"{name}.processor")
for name, module in model.named_children():
recursive_add_processors(name, module)
def add_multiview_processor(model: torch.nn.Module, enable_filter=lambda x:True, **kwargs):
return_dict = torch.nn.ModuleDict()
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 = multiviewAttnProc(
chained_proc=module.get_processor(),
enabled=enable_filter(f"{name}.processor"),
name=f"{name}.processor",
hidden_states_dim=module.inner_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 multiviewAttnProc(torch.nn.Module):
def __init__(
self,
chained_proc,
enabled=False,
name=None,
hidden_states_dim=None,
chain_pos="parralle", # before or parralle or after
num_modalities=1,
views=4,
base_img_size=64,
) -> None:
super().__init__()
self.enabled = enabled
self.chained_proc = chained_proc
self.name = name
self.hidden_states_dim = hidden_states_dim
self.num_modalities = num_modalities
self.views = views
self.base_img_size = base_img_size
self.chain_pos = chain_pos
self.diff_joint_attn = True
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
**kwargs
) -> torch.Tensor:
if not self.enabled:
return self.chained_proc(attn, hidden_states, encoder_hidden_states, attention_mask, **kwargs)
B, L, C = hidden_states.shape
mv = self.views
hidden_states = hidden_states.reshape(B // mv, mv, L, C).reshape(-1, mv * L, C)
hidden_states = self.chained_proc(attn, hidden_states, encoder_hidden_states, attention_mask, **kwargs)
return hidden_states.reshape(B // mv, mv, L, C).reshape(-1, L, C)
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,
multiview_attn_position: str = "attn1",
n_views: int = 4,
num_modalities: int = 1,
latent_size: int = 64,
multiview_chain_pose: str = "parralle",
**kwargs
):
super().__init__(**{
k: v for k, v in locals().items() if k not in
["self", "kwargs", "__class__", "n_views", "num_modalities", "latent_size", "multiview_chain_pose", "multiview_attn_position"]
})
self.n_views = n_views
add_multiview_processor(
model = self,
enable_filter = lambda name: name.endswith(f"{multiview_attn_position}.processor"),
num_modalities = num_modalities,
base_img_size = latent_size,
chain_pos = multiview_chain_pose,
views=n_views
)
switch_multiview_processor(self, enable_filter=lambda name: name.endswith(f"{multiview_attn_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]:
sample = torch.cat([sample, condition_latens], dim=1)
return self.forward(
sample, timestep, encoder_hidden_states, class_labels=class_labels,
) |