from typing import Optional, Tuple, Union from diffusers.models import AutoencoderKL, UNet2DConditionModel from diffusers.models.embeddings import get_fourier_embeds_from_boundingbox import torch import torch.nn as nn class AbsolutePositionalEmbedding(nn.Module): def __init__(self, dim, max_seq_len): super().__init__() self.emb = nn.Embedding(max_seq_len, dim) self.init_() def init_(self): nn.init.normal_(self.emb.weight, std=0.02) def forward(self, x): n = torch.arange(x.shape[1], device=x.device) return self.emb(n)[None, :, :] class InteractDiffusionInteractionProjection(nn.Module): def __init__(self, in_dim, out_dim, fourier_freqs=8): super().__init__() self.in_dim = in_dim self.out_dim = out_dim self.fourier_embedder_dim = fourier_freqs self.position_dim = fourier_freqs * 2 * 4 # 2: sin/cos, 4: xyxy self.interaction_embedding = AbsolutePositionalEmbedding(dim=out_dim, max_seq_len=30) self.position_embedding = AbsolutePositionalEmbedding(dim=out_dim, max_seq_len=3) if isinstance(out_dim, tuple): out_dim = out_dim[0] self.linears = nn.Sequential( nn.Linear(self.in_dim + self.position_dim, 512), nn.SiLU(), nn.Linear(512, 512), nn.SiLU(), nn.Linear(512, out_dim), ) self.linear_action = nn.Sequential( nn.Linear(self.in_dim + self.position_dim, 512), nn.SiLU(), nn.Linear(512, 512), nn.SiLU(), nn.Linear(512, out_dim), ) self.null_positive_feature = torch.nn.Parameter(torch.zeros([self.in_dim])) self.null_action_feature = torch.nn.Parameter(torch.zeros([self.in_dim])) self.null_position_feature = torch.nn.Parameter(torch.zeros([self.position_dim])) def get_between_box(self, bbox1, bbox2): """ Between Set Operation Operation of Box A between Box B from Prof. Jiang idea """ all_x = torch.cat([bbox1[:, :, 0::2], bbox2[:, :, 0::2]],dim=-1) all_y = torch.cat([bbox1[:, :, 1::2], bbox2[:, :, 1::2]],dim=-1) all_x, _ = all_x.sort() all_y, _ = all_y.sort() return torch.stack([all_x[:,:,1], all_y[:,:,1], all_x[:,:,2], all_y[:,:,2]],2) def forward( self, subject_boxes, object_boxes, masks, subject_positive_embeddings, object_positive_embeddings, action_positive_embeddings ): masks = masks.unsqueeze(-1) # embedding position (it may include padding as placeholder) action_boxes = self.get_between_box(subject_boxes, object_boxes) subject_xyxy_embedding = get_fourier_embeds_from_boundingbox(self.fourier_embedder_dim, subject_boxes) # B*N*4 --> B*N*C object_xyxy_embedding = get_fourier_embeds_from_boundingbox(self.fourier_embedder_dim, object_boxes) # B*N*4 --> B*N*C action_xyxy_embedding = get_fourier_embeds_from_boundingbox(self.fourier_embedder_dim, action_boxes) # B*N*4 --> B*N*C # learnable null embedding positive_null = self.null_positive_feature.view(1, 1, -1) xyxy_null = self.null_position_feature.view(1, 1, -1) action_null = self.null_action_feature.view(1, 1, -1) # replace padding with learnable null embedding subject_positive_embeddings = subject_positive_embeddings * masks + (1 - masks) * positive_null object_positive_embeddings = object_positive_embeddings * masks + (1 - masks) * positive_null subject_xyxy_embedding = subject_xyxy_embedding * masks + (1 - masks) * xyxy_null object_xyxy_embedding = object_xyxy_embedding * masks + (1 - masks) * xyxy_null action_xyxy_embedding = action_xyxy_embedding * masks + (1 - masks) * xyxy_null action_positive_embeddings = action_positive_embeddings * masks + (1 - masks) * action_null # project the input embeddings objs_subject = self.linears(torch.cat([subject_positive_embeddings, subject_xyxy_embedding], dim=-1)) objs_object = self.linears(torch.cat([object_positive_embeddings, object_xyxy_embedding], dim=-1)) objs_action = self.linear_action(torch.cat([action_positive_embeddings, action_xyxy_embedding], dim=-1)) # impose role embedding objs_subject = objs_subject + self.interaction_embedding(objs_subject) objs_object = objs_object + self.interaction_embedding(objs_object) objs_action = objs_action + self.interaction_embedding(objs_action) # impose instance embedding objs_subject = objs_subject + self.position_embedding.emb(torch.tensor(0).to(objs_subject.device)) objs_object = objs_object + self.position_embedding.emb(torch.tensor(1).to(objs_object.device)) objs_action = objs_action + self.position_embedding.emb(torch.tensor(2).to(objs_action.device)) objs = torch.cat([objs_subject, objs_action, objs_object], dim=1) return objs class InteractDiffusionUNet2DConditionModel(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, ): super(InteractDiffusionUNet2DConditionModel, self).__init__( sample_size=sample_size, in_channels=in_channels, out_channels=out_channels, center_input_sample=center_input_sample, flip_sin_to_cos=flip_sin_to_cos, freq_shift=freq_shift, down_block_types=down_block_types, mid_block_type=mid_block_type, up_block_types=up_block_types, only_cross_attention=only_cross_attention, block_out_channels=block_out_channels, layers_per_block=layers_per_block, downsample_padding=downsample_padding, mid_block_scale_factor=mid_block_scale_factor, dropout=dropout, act_fn=act_fn, norm_num_groups=norm_num_groups, norm_eps=norm_eps, cross_attention_dim=cross_attention_dim, transformer_layers_per_block=transformer_layers_per_block, reverse_transformer_layers_per_block=reverse_transformer_layers_per_block, encoder_hid_dim=encoder_hid_dim, encoder_hid_dim_type=encoder_hid_dim_type, attention_head_dim=attention_head_dim, num_attention_heads=num_attention_heads, dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, class_embed_type=class_embed_type, addition_embed_type=addition_embed_type, addition_time_embed_dim=addition_time_embed_dim, num_class_embeds=num_class_embeds, upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift, resnet_skip_time_act=resnet_skip_time_act, resnet_out_scale_factor=resnet_out_scale_factor, time_embedding_type=time_embedding_type, time_embedding_dim=time_embedding_dim, time_embedding_act_fn=time_embedding_act_fn, timestep_post_act=timestep_post_act, time_cond_proj_dim=time_cond_proj_dim, conv_in_kernel=conv_in_kernel, conv_out_kernel=conv_out_kernel, projection_class_embeddings_input_dim=projection_class_embeddings_input_dim, attention_type=attention_type, class_embeddings_concat=class_embeddings_concat, mid_block_only_cross_attention=mid_block_only_cross_attention, cross_attention_norm=cross_attention_norm, addition_embed_type_num_heads=addition_embed_type_num_heads ) # load position_net positive_len = 768 if isinstance(self.config.cross_attention_dim, int): positive_len = self.config.cross_attention_dim elif isinstance(self.config.cross_attention_dim, tuple) or isinstance(self.config.cross_attention_dim, list): positive_len = self.config.cross_attention_dim[0] self.position_net = InteractDiffusionInteractionProjection( in_dim=positive_len, out_dim=self.config.cross_attention_dim )