add vae

unet/config.json

@@ -1,5 +1,5 @@
 {
-  "_class_name": "UNet2DConditionModel",
+  "_class_name": "InteractDiffusionUNet2DConditionModel",
   "_diffusers_version": "0.27.0.dev0",
   "act_fn": "silu",
   "addition_embed_type": null,

unet/interactdiffusion_unet_2d_condition.py

@@ -0,0 +1,230 @@
+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
+        )

vae/config.json

@@ -0,0 +1,33 @@
+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.27.0.dev0",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "force_upcast": true,
+  "in_channels": 3,
+  "latent_channels": 4,
+  "latents_mean": null,
+  "latents_std": null,
+  "layers_per_block": 2,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 512,
+  "scaling_factor": 0.18215,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ]
+}

vae/diffusion_pytorch_model.fp16.safetensors

@@ -0,0 +1,3 @@
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+oid sha256:4fbcf0ebe55a0984f5a5e00d8c4521d52359af7229bb4d81890039d2aa16dd7c
+size 167335342

vae/diffusion_pytorch_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b4d2b5932bb4151e54e694fd31ccf51fca908223c9485bd56cd0e1d83ad94c49
+size 334643268