unet/mv_unet.py

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,  
        )