update config

model_index.json

@@ -1,7 +1,6 @@
 {
   "_class_name": "StableDiffusionImage2MVCustomPipeline",
-  "_diffusers_version": "0.27.2",
-  "_name_or_path": "lambdalabs/sd-image-variations-diffusers",
+  "_diffusers_version": "0.27.2", 
   "condition_offset": true,
   "feature_extractor": [
     "transformers",
@@ -21,8 +20,8 @@
     "DDIMScheduler"
   ],
   "unet": [
-    "diffusers",
-    "UNet2DConditionModel"
+    "mv_unet",
+    "UnifieldWrappedUNet"
   ],
   "vae": [
     "diffusers",

unet/config.json

@@ -1,7 +1,6 @@
 {
   "_class_name": "UnifieldWrappedUNet",
   "_diffusers_version": "0.27.2",
-  "_name_or_path": "lambdalabs/sd-image-variations-diffusers",
   "act_fn": "silu",
   "addition_embed_type": null,
   "addition_embed_type_num_heads": 64,
@@ -64,5 +63,11 @@
     "CrossAttnUpBlock2D"
   ],
   "upcast_attention": false,
-  "use_linear_projection": false
+  "use_linear_projection": false,
+
+  "multiview_attn_position": "attn1", 
+  "num_modalities": 1,
+  "latent_size": 64,
+  "multiview_chain_pose": "parralle" 
+
 }

unet/mv_unet.py

@@ -0,0 +1,163 @@
+import torch
+from typing import Optional, Tuple, Union
+from diffusers import UNet2DConditionModel
+from diffusers.models.attention_processor import Attention
+
+
+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", 
+            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__", "multiview_attn_position", "num_modalities", "latent_size", "multiview_chain_pose"]
+            })
+        
+        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,
+            ) 
+        
+        switch_multiview_processor(self, enable_filter=lambda name: name.endswith(f"{multiview_attn_position}.processor"))
+        

unet_state_dict.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:0dff2fdba450af0e10c3a847ba66a530170be2e9b9c9f4c834483515e82738b5
-size 3438460972