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.gitattributes

@@ -33,3 +33,15 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+2D_Stage/material/examples/1.png filter=lfs diff=lfs merge=lfs -text
+2D_Stage/material/examples/2.png filter=lfs diff=lfs merge=lfs -text
+2D_Stage/material/examples/3.png filter=lfs diff=lfs merge=lfs -text
+2D_Stage/material/examples/4.png filter=lfs diff=lfs merge=lfs -text
+2D_Stage/material/examples/5.png filter=lfs diff=lfs merge=lfs -text
+2D_Stage/material/examples/7.png filter=lfs diff=lfs merge=lfs -text
+2D_Stage/material/examples/8.png filter=lfs diff=lfs merge=lfs -text
+3D_Stage/material/examples/1/1.png filter=lfs diff=lfs merge=lfs -text
+3D_Stage/material/examples/1/2.png filter=lfs diff=lfs merge=lfs -text
+3D_Stage/material/examples/1/3.png filter=lfs diff=lfs merge=lfs -text
+3D_Stage/material/examples/1/4.png filter=lfs diff=lfs merge=lfs -text
+final_texture.png filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,14 @@
+# Ignore Python bytecode files
+*.pyc
+*.pyo
+__pycache__/
+
+# Ignore virtual environment directory
+venv/
+
+/3D_stage/outputs/
+input_3D.png
+input.png
+
+# LFS pointer files (large model files downloaded at runtime)
+3D_Stage/load/tets/*.npz

2D_Stage/configs/infer.yaml

@@ -0,0 +1,24 @@
+pretrained_model_path: "sd2-community/stable-diffusion-2-1"
+image_encoder_path: "./models/image_encoder"
+ckpt_dir: "./models/checkpoint"
+
+validation:
+  guidance_scale: 5.0
+  use_inv_latent: False
+  video_length: 4
+
+use_pose_guider: True
+use_noise: False
+use_shifted_noise: False
+unet_condition_type: image
+
+unet_from_pretrained_kwargs:
+  camera_embedding_type: 'e_de_da_sincos'
+  projection_class_embeddings_input_dim: 10  # modify
+  joint_attention: false  # modify
+  num_views: 4
+  sample_size: 96
+  zero_init_conv_in: false
+  zero_init_camera_projection: false  
+  in_channels: 4
+  use_safetensors: true

2D_Stage/material/pose.json

@@ -0,0 +1,38 @@
+[
+    [
+        [
+            0, 0, -1, 0,
+            0, 1, 0, 0,
+            1, 0, 0, 0,
+            1.5, 0, 0, 1
+        ],
+        "pose0.png"
+    ],
+    [
+        [
+            0, 0, 1, 0,
+            0, 1, 0, 0,
+            -1, 0, 0, 0,
+            -1.5, 0, 0, 1
+        ],
+        "pose1.png"
+    ],
+    [
+        [
+            0, 0, 1, 0,
+            0, 1, 0, 0,
+            -1, 0, 0, 0,
+            -1.5, 0, 0, 1
+        ],
+        "pose2.png"
+    ],
+    [
+        [
+            -1, 0, 0, 0,
+            0, 1, 0, 0,
+            0, 0, -1, 0,
+            0, 0, -1.5, 1
+        ],
+        "pose3.png"
+    ]
+]

2D_Stage/tuneavideo/models/PoseGuider.py

@@ -0,0 +1,59 @@
+import os
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+from einops import rearrange
+
+class PoseGuider(nn.Module):
+    def __init__(self, noise_latent_channels=4):
+        super(PoseGuider, self).__init__()
+
+        self.conv_layers = nn.Sequential(
+            nn.Conv2d(in_channels=3, out_channels=16, kernel_size=4, stride=2, padding=1),
+            nn.ReLU(),
+            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=4, stride=2, padding=1),
+            nn.ReLU(),
+            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=4, stride=2, padding=1),
+            nn.ReLU(),
+            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),
+            nn.ReLU()
+        )
+
+        # Final projection layer
+        self.final_proj = nn.Conv2d(in_channels=128, out_channels=noise_latent_channels, kernel_size=1)
+
+        # Initialize layers
+        self._initialize_weights()
+
+    def _initialize_weights(self):
+        # Initialize weights with Gaussian distribution and zero out the final layer
+        for m in self.conv_layers:
+            if isinstance(m, nn.Conv2d):
+                init.normal_(m.weight, mean=0.0, std=0.02)
+                if m.bias is not None:
+                    init.zeros_(m.bias)
+
+        init.zeros_(self.final_proj.weight)
+        if self.final_proj.bias is not None:
+            init.zeros_(self.final_proj.bias)
+
+    def forward(self, pose_image):
+        x = self.conv_layers(pose_image)
+        x = self.final_proj(x)
+
+        return x
+
+    @classmethod
+    def from_pretrained(pretrained_model_path):
+        if not os.path.exists(pretrained_model_path):
+            print(f"There is no model file in {pretrained_model_path}")
+        print(f"loaded PoseGuider's pretrained weights from {pretrained_model_path} ...")
+
+        state_dict = torch.load(pretrained_model_path, map_location="cpu")
+        model = PoseGuider(noise_latent_channels=4)
+        m, u = model.load_state_dict(state_dict, strict=False)
+        print(f"### missing keys: {len(m)}; \n### unexpected keys: {len(u)};")        
+        params = [p.numel() if "temporal" in n else 0 for n, p in model.named_parameters()]
+        print(f"### PoseGuider's Parameters: {sum(params) / 1e6} M")
+        
+        return model

2D_Stage/tuneavideo/models/attention.py

@@ -0,0 +1,344 @@
+# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py
+
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers import ModelMixin
+from diffusers.utils import BaseOutput
+from diffusers.utils.import_utils import is_xformers_available
+from diffusers.models.attention import CrossAttention, FeedForward, AdaLayerNorm
+
+from einops import rearrange, repeat
+
+
+@dataclass
+class Transformer3DModelOutput(BaseOutput):
+    sample: torch.FloatTensor
+
+
+if is_xformers_available():
+    import xformers
+    import xformers.ops
+else:
+    xformers = None
+
+
+class Transformer3DModel(ModelMixin, ConfigMixin):
+    @register_to_config
+    def __init__(
+        self,
+        num_attention_heads: int = 16,
+        attention_head_dim: int = 88,
+        in_channels: Optional[int] = None,
+        num_layers: int = 1,
+        dropout: float = 0.0,
+        norm_num_groups: int = 32,
+        cross_attention_dim: Optional[int] = None,
+        attention_bias: bool = False,
+        activation_fn: str = "geglu",
+        num_embeds_ada_norm: Optional[int] = None,
+        use_linear_projection: bool = False,
+        only_cross_attention: bool = False,
+        upcast_attention: bool = False,
+        use_attn_temp: bool = False,
+    ):
+        super().__init__()
+        self.use_linear_projection = use_linear_projection
+        self.num_attention_heads = num_attention_heads
+        self.attention_head_dim = attention_head_dim
+        inner_dim = num_attention_heads * attention_head_dim
+
+        # Define input layers
+        self.in_channels = in_channels
+
+        self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+        if use_linear_projection:
+            self.proj_in = nn.Linear(in_channels, inner_dim)
+        else:
+            self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
+
+        # Define transformers blocks
+        self.transformer_blocks = nn.ModuleList(
+            [
+                BasicTransformerBlock(
+                    inner_dim,
+                    num_attention_heads,
+                    attention_head_dim,
+                    dropout=dropout,
+                    cross_attention_dim=cross_attention_dim,
+                    activation_fn=activation_fn,
+                    num_embeds_ada_norm=num_embeds_ada_norm,
+                    attention_bias=attention_bias,
+                    only_cross_attention=only_cross_attention,
+                    upcast_attention=upcast_attention,
+                    use_attn_temp = use_attn_temp,
+                )
+                for d in range(num_layers)
+            ]
+        )
+
+        # 4. Define output layers
+        if use_linear_projection:
+            self.proj_out = nn.Linear(in_channels, inner_dim)
+        else:
+            self.proj_out = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
+
+    def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, return_dict: bool = True):
+        # Input
+        assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}."
+        video_length = hidden_states.shape[2]
+        hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w")
+        encoder_hidden_states = repeat(encoder_hidden_states, 'b n c -> (b f) n c', f=video_length)
+
+        batch, channel, height, weight = hidden_states.shape
+        residual = hidden_states
+
+        hidden_states = self.norm(hidden_states)
+        if not self.use_linear_projection:
+            hidden_states = self.proj_in(hidden_states)
+            inner_dim = hidden_states.shape[1]
+            hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
+        else:
+            inner_dim = hidden_states.shape[1]
+            hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
+            hidden_states = self.proj_in(hidden_states)
+
+        # Blocks
+        for block in self.transformer_blocks:
+            hidden_states = block(
+                hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+                timestep=timestep,
+                video_length=video_length
+            )
+
+        # Output
+        if not self.use_linear_projection:
+            hidden_states = (
+                hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
+            )
+            hidden_states = self.proj_out(hidden_states)
+        else:
+            hidden_states = self.proj_out(hidden_states)
+            hidden_states = (
+                hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
+            )
+
+        output = hidden_states + residual
+
+        output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length)
+        if not return_dict:
+            return (output,)
+
+        return Transformer3DModelOutput(sample=output)
+
+
+class BasicTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        dropout=0.0,
+        cross_attention_dim: Optional[int] = None,
+        activation_fn: str = "geglu",
+        num_embeds_ada_norm: Optional[int] = None,
+        attention_bias: bool = False,
+        only_cross_attention: bool = False,
+        upcast_attention: bool = False,
+        use_attn_temp: bool = False
+    ):
+        super().__init__()
+        self.only_cross_attention = only_cross_attention
+        self.use_ada_layer_norm = num_embeds_ada_norm is not None
+        self.use_attn_temp = use_attn_temp
+        # SC-Attn
+        self.attn1 = SparseCausalAttention(
+            query_dim=dim,
+            heads=num_attention_heads,
+            dim_head=attention_head_dim,
+            dropout=dropout,
+            bias=attention_bias,
+            cross_attention_dim=cross_attention_dim if only_cross_attention else None,
+            upcast_attention=upcast_attention,
+        )
+        self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
+
+        # Cross-Attn
+        if cross_attention_dim is not None:
+            self.attn2 = CrossAttention(
+                query_dim=dim,
+                cross_attention_dim=cross_attention_dim,
+                heads=num_attention_heads,
+                dim_head=attention_head_dim,
+                dropout=dropout,
+                bias=attention_bias,
+                upcast_attention=upcast_attention,
+            )
+        else:
+            self.attn2 = None
+
+        if cross_attention_dim is not None:
+            self.norm2 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
+        else:
+            self.norm2 = None
+
+        # Feed-forward
+        self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn)
+        self.norm3 = nn.LayerNorm(dim)
+
+        # Temp-Attn
+        if self.use_attn_temp:
+            self.attn_temp = CrossAttention(
+                query_dim=dim,
+                heads=num_attention_heads,
+                dim_head=attention_head_dim,
+                dropout=dropout,
+                bias=attention_bias,
+                upcast_attention=upcast_attention,
+            )
+            nn.init.zeros_(self.attn_temp.to_out[0].weight.data)
+            self.norm_temp = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
+
+    def set_use_memory_efficient_attention_xformers(self, use_memory_efficient_attention_xformers: bool):
+        if not is_xformers_available():
+            print("Here is how to install it")
+            raise ModuleNotFoundError(
+                "Refer to https://github.com/facebookresearch/xformers for more information on how to install"
+                " xformers",
+                name="xformers",
+            )
+        elif not torch.cuda.is_available():
+            raise ValueError(
+                "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is only"
+                " available for GPU "
+            )
+        else:
+            try:
+                # Make sure we can run the memory efficient attention
+                _ = xformers.ops.memory_efficient_attention(
+                    torch.randn((1, 2, 40), device="cuda"),
+                    torch.randn((1, 2, 40), device="cuda"),
+                    torch.randn((1, 2, 40), device="cuda"),
+                )
+            except Exception as e:
+                raise e
+            self.attn1._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
+            if self.attn2 is not None:
+                self.attn2._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
+            #self.attn_temp._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
+
+    def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, attention_mask=None, video_length=None):
+        # SparseCausal-Attention
+        norm_hidden_states = (
+            self.norm1(hidden_states, timestep) if self.use_ada_layer_norm else self.norm1(hidden_states)
+        )
+
+        if self.only_cross_attention:
+            hidden_states = (
+                self.attn1(norm_hidden_states, encoder_hidden_states, attention_mask=attention_mask) + hidden_states
+            )
+        else:
+            hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask, video_length=video_length) + hidden_states
+
+        if self.attn2 is not None:
+            # Cross-Attention
+            norm_hidden_states = (
+                self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
+            )
+            hidden_states = (
+                self.attn2(
+                    norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask
+                )
+                + hidden_states
+            )
+
+        # Feed-forward
+        hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states
+
+        # Temporal-Attention
+        if self.use_attn_temp:
+            d = hidden_states.shape[1]
+            hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length)
+            norm_hidden_states = (
+                self.norm_temp(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_temp(hidden_states)
+            )
+            hidden_states = self.attn_temp(norm_hidden_states) + hidden_states
+            hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d)
+
+        return hidden_states
+
+
+class SparseCausalAttention(CrossAttention):
+    def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, video_length=None, use_full_attn=True):
+        batch_size, sequence_length, _ = hidden_states.shape
+
+        encoder_hidden_states = encoder_hidden_states
+
+        if self.group_norm is not None:
+            hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = self.to_q(hidden_states)
+        # query = rearrange(query, "(b f) d c -> b (f d) c", f=video_length)
+        dim = query.shape[-1]
+        query = self.reshape_heads_to_batch_dim(query)
+
+        if self.added_kv_proj_dim is not None:
+            raise NotImplementedError
+
+        encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
+        key = self.to_k(encoder_hidden_states)
+        value = self.to_v(encoder_hidden_states)
+
+        former_frame_index = torch.arange(video_length) - 1
+        former_frame_index[0] = 0
+
+        key = rearrange(key, "(b f) d c -> b f d c", f=video_length)
+        if not use_full_attn:
+            key = torch.cat([key[:, [0] * video_length], key[:, former_frame_index]], dim=2)
+        else:
+            # key = torch.cat([key[:, [0] * video_length], key[:, [1] * video_length], key[:, [2] * video_length], key[:, [3] * video_length]], dim=2)
+            key_video_length = [key[:, [i] * video_length] for i in range(video_length)]
+            key = torch.cat(key_video_length, dim=2)
+        key = rearrange(key, "b f d c -> (b f) d c")
+
+        value = rearrange(value, "(b f) d c -> b f d c", f=video_length)
+        if not use_full_attn:
+            value = torch.cat([value[:, [0] * video_length], value[:, former_frame_index]], dim=2)
+        else:
+            # value = torch.cat([value[:, [0] * video_length], value[:, [1] * video_length], value[:, [2] * video_length], value[:, [3] * video_length]], dim=2)
+            value_video_length = [value[:, [i] * video_length] for i in range(video_length)]
+            value = torch.cat(value_video_length, dim=2)
+        value = rearrange(value, "b f d c -> (b f) d c")
+
+        key = self.reshape_heads_to_batch_dim(key)
+        value = self.reshape_heads_to_batch_dim(value)
+
+        if attention_mask is not None:
+            if attention_mask.shape[-1] != query.shape[1]:
+                target_length = query.shape[1]
+                attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
+                attention_mask = attention_mask.repeat_interleave(self.heads, dim=0)
+
+        # attention, what we cannot get enough of
+        if self._use_memory_efficient_attention_xformers:
+            hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask)
+            # Some versions of xformers return output in fp32, cast it back to the dtype of the input
+            hidden_states = hidden_states.to(query.dtype)
+        else:
+            if self._slice_size is None or query.shape[0] // self._slice_size == 1:
+                hidden_states = self._attention(query, key, value, attention_mask)
+            else:
+                hidden_states = self._sliced_attention(query, key, value, sequence_length, dim, attention_mask)
+
+        # linear proj
+        hidden_states = self.to_out[0](hidden_states)
+
+        # dropout
+        hidden_states = self.to_out[1](hidden_states)
+        return hidden_states

2D_Stage/tuneavideo/models/imageproj.py

@@ -0,0 +1,118 @@
+# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py
+import math
+
+import torch
+import torch.nn as nn
+
+# FFN
+def FeedForward(dim, mult=4):
+    inner_dim = int(dim * mult)
+    return nn.Sequential(
+        nn.LayerNorm(dim),
+        nn.Linear(dim, inner_dim, bias=False),
+        nn.GELU(),
+        nn.Linear(inner_dim, dim, bias=False),
+    )
+    
+def reshape_tensor(x, heads):
+    bs, length, width = x.shape
+    #(bs, length, width) --> (bs, length, n_heads, dim_per_head)
+    x = x.view(bs, length, heads, -1)
+    # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
+    x = x.transpose(1, 2)
+    # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
+    x = x.reshape(bs, heads, length, -1)
+    return x
+
+
+class PerceiverAttention(nn.Module):
+    def __init__(self, *, dim, dim_head=64, heads=8):
+        super().__init__()
+        self.scale = dim_head**-0.5
+        self.dim_head = dim_head
+        self.heads = heads
+        inner_dim = dim_head * heads
+
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+
+        self.to_q = nn.Linear(dim, inner_dim, bias=False)
+        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
+        self.to_out = nn.Linear(inner_dim, dim, bias=False)
+
+
+    def forward(self, x, latents):
+        """
+        Args:
+            x (torch.Tensor): image features
+                shape (b, n1, D)
+            latent (torch.Tensor): latent features
+                shape (b, n2, D)
+        """
+        x = self.norm1(x)
+        latents = self.norm2(latents)
+        
+        b, l, _ = latents.shape
+
+        q = self.to_q(latents)
+        kv_input = torch.cat((x, latents), dim=-2)
+        k, v = self.to_kv(kv_input).chunk(2, dim=-1)
+        
+        q = reshape_tensor(q, self.heads)
+        k = reshape_tensor(k, self.heads)
+        v = reshape_tensor(v, self.heads)
+
+        # attention
+        scale = 1 / math.sqrt(math.sqrt(self.dim_head))
+        weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards
+        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+        out = weight @ v
+        
+        out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
+
+        return self.to_out(out)
+
+class Resampler(nn.Module):
+    def __init__(
+        self,
+        dim=1024,
+        depth=8,
+        dim_head=64,
+        heads=16,
+        num_queries=8,
+        embedding_dim=768,
+        output_dim=1024,
+        ff_mult=4,
+    ):
+        super().__init__()
+        
+        self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
+        
+        self.proj_in = nn.Linear(embedding_dim, dim)
+
+        self.proj_out = nn.Linear(dim, output_dim)
+        self.norm_out = nn.LayerNorm(output_dim)
+        
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(
+                nn.ModuleList(
+                    [
+                        PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
+                        FeedForward(dim=dim, mult=ff_mult),
+                    ]
+                )
+            )
+
+    def forward(self, x):
+        
+        latents = self.latents.repeat(x.size(0), 1, 1)
+        
+        x = self.proj_in(x)
+        
+        for attn, ff in self.layers:
+            latents = attn(x, latents) + latents
+            latents = ff(latents) + latents
+            
+        latents = self.proj_out(latents)
+        return self.norm_out(latents)

2D_Stage/tuneavideo/models/refunet.py

@@ -0,0 +1,125 @@
+import torch
+from einops import rearrange
+from typing import Any, Dict, Optional
+from diffusers.utils.import_utils import is_xformers_available
+from tuneavideo.models.transformer_mv2d import XFormersMVAttnProcessor, MVAttnProcessor
+class ReferenceOnlyAttnProc(torch.nn.Module):
+    def __init__(
+        self,
+        chained_proc,
+        enabled=False,
+        name=None
+    ) -> None:
+        super().__init__()
+        self.enabled = enabled
+        self.chained_proc = chained_proc
+        self.name = name
+
+    def __call__(
+        self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None,
+        mode="w", ref_dict: dict = None, is_cfg_guidance = False,num_views=4,
+            multiview_attention=True,
+            cross_domain_attention=False,
+    ) -> Any:
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        # print(self.enabled)
+        if self.enabled:
+            if mode == 'w':
+                ref_dict[self.name] = encoder_hidden_states
+                res = self.chained_proc(attn, hidden_states, encoder_hidden_states, attention_mask, num_views=1,
+                    multiview_attention=False,
+                    cross_domain_attention=False,)
+            elif mode == 'r':
+                encoder_hidden_states = rearrange(encoder_hidden_states, '(b t) d c-> b (t d) c', t=num_views)
+                if self.name in ref_dict:
+                    encoder_hidden_states = torch.cat([encoder_hidden_states, ref_dict.pop(self.name)], dim=1).unsqueeze(1).repeat(1,num_views,1,1).flatten(0,1)
+                res = self.chained_proc(attn, hidden_states, encoder_hidden_states, attention_mask, num_views=num_views,
+                    multiview_attention=False,
+                    cross_domain_attention=False,)
+            elif mode == 'm':
+                encoder_hidden_states = torch.cat([encoder_hidden_states, ref_dict[self.name]], dim=1)
+            elif mode == 'n':
+                encoder_hidden_states = rearrange(encoder_hidden_states, '(b t) d c-> b (t d) c', t=num_views)
+                encoder_hidden_states = torch.cat([encoder_hidden_states], dim=1).unsqueeze(1).repeat(1,num_views,1,1).flatten(0,1)
+                res = self.chained_proc(attn, hidden_states, encoder_hidden_states, attention_mask, num_views=num_views,
+                    multiview_attention=False,
+                    cross_domain_attention=False,)
+            else:
+                assert False, mode
+        else:
+            res = self.chained_proc(attn, hidden_states, encoder_hidden_states, attention_mask)
+        return res
+        
+class RefOnlyNoisedUNet(torch.nn.Module):
+    def __init__(self, unet, train_sched, val_sched) -> None:
+        super().__init__()
+        self.unet = unet
+        self.train_sched = train_sched
+        self.val_sched = val_sched
+
+        unet_lora_attn_procs = dict()
+        for name, _ in unet.attn_processors.items():
+            if is_xformers_available():
+                default_attn_proc = XFormersMVAttnProcessor()
+            else:
+                default_attn_proc = MVAttnProcessor()
+            unet_lora_attn_procs[name] = ReferenceOnlyAttnProc(
+                default_attn_proc, enabled=name.endswith("attn1.processor"), name=name)
+            
+        self.unet.set_attn_processor(unet_lora_attn_procs)
+
+    def __getattr__(self, name: str):
+        try:
+            return super().__getattr__(name)
+        except AttributeError:
+            return getattr(self.unet, name)
+
+    def forward_cond(self, noisy_cond_lat, timestep, encoder_hidden_states, class_labels, ref_dict, is_cfg_guidance, **kwargs):
+        if is_cfg_guidance:
+            encoder_hidden_states = encoder_hidden_states[1:]
+            class_labels = class_labels[1:]
+        self.unet(
+            noisy_cond_lat, timestep,
+            encoder_hidden_states=encoder_hidden_states,
+            class_labels=class_labels,
+            cross_attention_kwargs=dict(mode="w", ref_dict=ref_dict),
+            **kwargs
+        )
+
+    def forward(
+        self, sample, timestep, encoder_hidden_states, class_labels=None,
+        *args, cross_attention_kwargs,
+        down_block_res_samples=None, mid_block_res_sample=None,
+        **kwargs
+    ):
+        cond_lat = cross_attention_kwargs['cond_lat']
+        is_cfg_guidance = cross_attention_kwargs.get('is_cfg_guidance', False)
+        noise = torch.randn_like(cond_lat)
+        if self.training:
+            noisy_cond_lat = self.train_sched.add_noise(cond_lat, noise, timestep)
+            noisy_cond_lat = self.train_sched.scale_model_input(noisy_cond_lat, timestep)
+        else:
+            noisy_cond_lat = self.val_sched.add_noise(cond_lat, noise, timestep.reshape(-1))
+            noisy_cond_lat = self.val_sched.scale_model_input(noisy_cond_lat, timestep.reshape(-1))
+        ref_dict = {}
+        self.forward_cond(
+            noisy_cond_lat, timestep,
+            encoder_hidden_states, class_labels,
+            ref_dict, is_cfg_guidance, **kwargs
+        )
+        weight_dtype = self.unet.dtype
+        return self.unet(
+            sample, timestep,
+            encoder_hidden_states, *args,
+            class_labels=class_labels,
+            cross_attention_kwargs=dict(mode="r", ref_dict=ref_dict, is_cfg_guidance=is_cfg_guidance),
+            down_block_additional_residuals=[
+                sample.to(dtype=weight_dtype) for sample in down_block_res_samples
+            ] if down_block_res_samples is not None else None,
+            mid_block_additional_residual=(
+                mid_block_res_sample.to(dtype=weight_dtype)
+                if mid_block_res_sample is not None else None
+            ),
+            **kwargs
+        )

2D_Stage/tuneavideo/models/resnet.py

@@ -0,0 +1,210 @@
+# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from einops import rearrange
+
+
+class InflatedConv3d(nn.Conv2d):
+    def forward(self, x):
+        video_length = x.shape[2]
+
+        x = rearrange(x, "b c f h w -> (b f) c h w")
+        x = super().forward(x)
+        x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length)
+
+        return x
+
+
+class Upsample3D(nn.Module):
+    def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.use_conv_transpose = use_conv_transpose
+        self.name = name
+
+        conv = None
+        if use_conv_transpose:
+            raise NotImplementedError
+        elif use_conv:
+            conv = InflatedConv3d(self.channels, self.out_channels, 3, padding=1)
+
+        if name == "conv":
+            self.conv = conv
+        else:
+            self.Conv2d_0 = conv
+
+    def forward(self, hidden_states, output_size=None):
+        assert hidden_states.shape[1] == self.channels
+
+        if self.use_conv_transpose:
+            raise NotImplementedError
+
+        # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16
+        dtype = hidden_states.dtype
+        if dtype == torch.bfloat16:
+            hidden_states = hidden_states.to(torch.float32)
+
+        # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
+        if hidden_states.shape[0] >= 64:
+            hidden_states = hidden_states.contiguous()
+
+        # if `output_size` is passed we force the interpolation output
+        # size and do not make use of `scale_factor=2`
+        if output_size is None:
+            hidden_states = F.interpolate(hidden_states, scale_factor=[1.0, 2.0, 2.0], mode="nearest")
+        else:
+            hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest")
+
+        # If the input is bfloat16, we cast back to bfloat16
+        if dtype == torch.bfloat16:
+            hidden_states = hidden_states.to(dtype)
+
+        if self.use_conv:
+            if self.name == "conv":
+                hidden_states = self.conv(hidden_states)
+            else:
+                hidden_states = self.Conv2d_0(hidden_states)
+
+        return hidden_states
+
+
+class Downsample3D(nn.Module):
+    def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.padding = padding
+        stride = 2
+        self.name = name
+
+        if use_conv:
+            conv = InflatedConv3d(self.channels, self.out_channels, 3, stride=stride, padding=padding)
+        else:
+            raise NotImplementedError
+
+        if name == "conv":
+            self.Conv2d_0 = conv
+            self.conv = conv
+        elif name == "Conv2d_0":
+            self.conv = conv
+        else:
+            self.conv = conv
+
+    def forward(self, hidden_states):
+        assert hidden_states.shape[1] == self.channels
+        if self.use_conv and self.padding == 0:
+            raise NotImplementedError
+
+        assert hidden_states.shape[1] == self.channels
+        hidden_states = self.conv(hidden_states)
+
+        return hidden_states
+
+
+class ResnetBlock3D(nn.Module):
+    def __init__(
+        self,
+        *,
+        in_channels,
+        out_channels=None,
+        conv_shortcut=False,
+        dropout=0.0,
+        temb_channels=512,
+        groups=32,
+        groups_out=None,
+        pre_norm=True,
+        eps=1e-6,
+        non_linearity="swish",
+        time_embedding_norm="default",
+        output_scale_factor=1.0,
+        use_in_shortcut=None,
+    ):
+        super().__init__()
+        self.pre_norm = pre_norm
+        self.pre_norm = True
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+        self.use_conv_shortcut = conv_shortcut
+        self.time_embedding_norm = time_embedding_norm
+        self.output_scale_factor = output_scale_factor
+
+        if groups_out is None:
+            groups_out = groups
+
+        self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
+
+        self.conv1 = InflatedConv3d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+
+        if temb_channels is not None:
+            if self.time_embedding_norm == "default":
+                time_emb_proj_out_channels = out_channels
+            elif self.time_embedding_norm == "scale_shift":
+                time_emb_proj_out_channels = out_channels * 2
+            else:
+                raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ")
+
+            self.time_emb_proj = torch.nn.Linear(temb_channels, time_emb_proj_out_channels)
+        else:
+            self.time_emb_proj = None
+
+        self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)
+        self.dropout = torch.nn.Dropout(dropout)
+        self.conv2 = InflatedConv3d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+
+        if non_linearity == "swish":
+            self.nonlinearity = lambda x: F.silu(x)
+        elif non_linearity == "mish":
+            self.nonlinearity = Mish()
+        elif non_linearity == "silu":
+            self.nonlinearity = nn.SiLU()
+
+        self.use_in_shortcut = self.in_channels != self.out_channels if use_in_shortcut is None else use_in_shortcut
+
+        self.conv_shortcut = None
+        if self.use_in_shortcut:
+            self.conv_shortcut = InflatedConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+    def forward(self, input_tensor, temb):
+        hidden_states = input_tensor
+
+        hidden_states = self.norm1(hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+
+        hidden_states = self.conv1(hidden_states)
+
+        if temb is not None:
+            # temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None, None]
+            temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, :, None, None].permute(0,2,1,3,4)
+
+        if temb is not None and self.time_embedding_norm == "default":
+            hidden_states = hidden_states + temb
+
+        hidden_states = self.norm2(hidden_states)
+
+        if temb is not None and self.time_embedding_norm == "scale_shift":
+            scale, shift = torch.chunk(temb, 2, dim=1)
+            hidden_states = hidden_states * (1 + scale) + shift
+
+        hidden_states = self.nonlinearity(hidden_states)
+
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.conv2(hidden_states)
+
+        if self.conv_shortcut is not None:
+            input_tensor = self.conv_shortcut(input_tensor)
+
+        output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
+
+        return output_tensor
+
+
+class Mish(torch.nn.Module):
+    def forward(self, hidden_states):
+        return hidden_states * torch.tanh(torch.nn.functional.softplus(hidden_states))

2D_Stage/tuneavideo/models/transformer_mv2d.py

@@ -0,0 +1,1010 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from dataclasses import dataclass
+from typing import Any, Dict, Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models.embeddings import ImagePositionalEmbeddings
+from diffusers.utils import BaseOutput, deprecate
+try: 
+    from diffusers.utils import maybe_allow_in_graph
+except:
+    from diffusers.utils.torch_utils import maybe_allow_in_graph
+from diffusers.models.attention import FeedForward, AdaLayerNorm, AdaLayerNormZero, Attention
+from diffusers.models.embeddings import PatchEmbed
+from diffusers.models.lora import LoRACompatibleConv, LoRACompatibleLinear
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.utils.import_utils import is_xformers_available
+
+from einops import rearrange
+import pdb
+import random
+
+
+if is_xformers_available():
+    import xformers
+    import xformers.ops
+else:
+    xformers = None
+
+
+@dataclass
+class TransformerMV2DModelOutput(BaseOutput):
+    """
+    The output of [`Transformer2DModel`].
+
+    Args:
+        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
+            The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability
+            distributions for the unnoised latent pixels.
+    """
+
+    sample: torch.FloatTensor
+
+
+class TransformerMV2DModel(ModelMixin, ConfigMixin):
+    """
+    A 2D Transformer model for image-like data.
+
+    Parameters:
+        num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
+        attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
+        in_channels (`int`, *optional*):
+            The number of channels in the input and output (specify if the input is **continuous**).
+        num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
+        sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).
+            This is fixed during training since it is used to learn a number of position embeddings.
+        num_vector_embeds (`int`, *optional*):
+            The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**).
+            Includes the class for the masked latent pixel.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward.
+        num_embeds_ada_norm ( `int`, *optional*):
+            The number of diffusion steps used during training. Pass if at least one of the norm_layers is
+            `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are
+            added to the hidden states.
+
+            During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`.
+        attention_bias (`bool`, *optional*):
+            Configure if the `TransformerBlocks` attention should contain a bias parameter.
+    """
+
+    @register_to_config
+    def __init__(
+        self,
+        num_attention_heads: int = 16,
+        attention_head_dim: int = 88,
+        in_channels: Optional[int] = None,
+        out_channels: Optional[int] = None,
+        num_layers: int = 1,
+        dropout: float = 0.0,
+        norm_num_groups: int = 32,
+        cross_attention_dim: Optional[int] = None,
+        attention_bias: bool = False,
+        sample_size: Optional[int] = None,
+        num_vector_embeds: Optional[int] = None,
+        patch_size: Optional[int] = None,
+        activation_fn: str = "geglu",
+        num_embeds_ada_norm: Optional[int] = None,
+        use_linear_projection: bool = False,
+        only_cross_attention: bool = False,
+        upcast_attention: bool = False,
+        norm_type: str = "layer_norm",
+        norm_elementwise_affine: bool = True,
+        num_views: int = 1,
+        joint_attention: bool=False,
+        joint_attention_twice: bool=False,
+        multiview_attention: bool=True,
+        cross_domain_attention: bool=False
+    ):
+        super().__init__()
+        self.use_linear_projection = use_linear_projection
+        self.num_attention_heads = num_attention_heads
+        self.attention_head_dim = attention_head_dim
+        inner_dim = num_attention_heads * attention_head_dim
+
+        # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
+        # Define whether input is continuous or discrete depending on configuration
+        self.is_input_continuous = (in_channels is not None) and (patch_size is None)
+        self.is_input_vectorized = num_vector_embeds is not None
+        self.is_input_patches = in_channels is not None and patch_size is not None
+
+        if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
+            deprecation_message = (
+                f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"
+                " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config."
+                " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect"
+                " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it"
+                " would be very nice if you could open a Pull request for the `transformer/config.json` file"
+            )
+            deprecate("norm_type!=num_embeds_ada_norm", "1.0.0", deprecation_message, standard_warn=False)
+            norm_type = "ada_norm"
+
+        if self.is_input_continuous and self.is_input_vectorized:
+            raise ValueError(
+                f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"
+                " sure that either `in_channels` or `num_vector_embeds` is None."
+            )
+        elif self.is_input_vectorized and self.is_input_patches:
+            raise ValueError(
+                f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"
+                " sure that either `num_vector_embeds` or `num_patches` is None."
+            )
+        elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
+            raise ValueError(
+                f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"
+                f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None."
+            )
+
+        # 2. Define input layers
+        if self.is_input_continuous:
+            self.in_channels = in_channels
+
+            self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+            if use_linear_projection:
+                self.proj_in = LoRACompatibleLinear(in_channels, inner_dim)
+            else:
+                self.proj_in = LoRACompatibleConv(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
+        elif self.is_input_vectorized:
+            assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
+            assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"
+
+            self.height = sample_size
+            self.width = sample_size
+            self.num_vector_embeds = num_vector_embeds
+            self.num_latent_pixels = self.height * self.width
+
+            self.latent_image_embedding = ImagePositionalEmbeddings(
+                num_embed=num_vector_embeds, embed_dim=inner_dim, height=self.height, width=self.width
+            )
+        elif self.is_input_patches:
+            assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"
+
+            self.height = sample_size
+            self.width = sample_size
+
+            self.patch_size = patch_size
+            self.pos_embed = PatchEmbed(
+                height=sample_size,
+                width=sample_size,
+                patch_size=patch_size,
+                in_channels=in_channels,
+                embed_dim=inner_dim,
+            )
+
+        # 3. Define transformers blocks
+        self.transformer_blocks = nn.ModuleList(
+            [
+                BasicMVTransformerBlock(
+                    inner_dim,
+                    num_attention_heads,
+                    attention_head_dim,
+                    dropout=dropout,
+                    cross_attention_dim=cross_attention_dim,
+                    activation_fn=activation_fn,
+                    num_embeds_ada_norm=num_embeds_ada_norm,
+                    attention_bias=attention_bias,
+                    only_cross_attention=only_cross_attention,
+                    upcast_attention=upcast_attention,
+                    norm_type=norm_type,
+                    norm_elementwise_affine=norm_elementwise_affine,
+                    num_views=num_views,
+                    joint_attention=joint_attention,
+                    joint_attention_twice=joint_attention_twice,
+                    multiview_attention=multiview_attention,
+                    cross_domain_attention=cross_domain_attention
+                )
+                for d in range(num_layers)
+            ]
+        )
+
+        # 4. Define output layers
+        self.out_channels = in_channels if out_channels is None else out_channels
+        if self.is_input_continuous:
+            # TODO: should use out_channels for continuous projections
+            if use_linear_projection:
+                self.proj_out = LoRACompatibleLinear(inner_dim, in_channels)
+            else:
+                self.proj_out = LoRACompatibleConv(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
+        elif self.is_input_vectorized:
+            self.norm_out = nn.LayerNorm(inner_dim)
+            self.out = nn.Linear(inner_dim, self.num_vector_embeds - 1)
+        elif self.is_input_patches:
+            self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6)
+            self.proj_out_1 = nn.Linear(inner_dim, 2 * inner_dim)
+            self.proj_out_2 = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        timestep: Optional[torch.LongTensor] = None,
+        class_labels: Optional[torch.LongTensor] = None,
+        cross_attention_kwargs: Dict[str, Any] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        return_dict: bool = True,
+    ):
+        """
+        The [`Transformer2DModel`] forward method.
+
+        Args:
+            hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous):
+                Input `hidden_states`.
+            encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
+                Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
+                self-attention.
+            timestep ( `torch.LongTensor`, *optional*):
+                Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
+            class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):
+                Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in
+                `AdaLayerZeroNorm`.
+            encoder_attention_mask ( `torch.Tensor`, *optional*):
+                Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:
+
+                    * Mask `(batch, sequence_length)` True = keep, False = discard.
+                    * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.
+
+                If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format
+                above. This bias will be added to the cross-attention scores.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
+                tuple.
+
+        Returns:
+            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
+            `tuple` where the first element is the sample tensor.
+        """
+        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
+        #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
+        #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
+        # expects mask of shape:
+        #   [batch, key_tokens]
+        # adds singleton query_tokens dimension:
+        #   [batch,                    1, key_tokens]
+        # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
+        #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
+        #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
+        if attention_mask is not None and attention_mask.ndim == 2:
+            # assume that mask is expressed as:
+            #   (1 = keep,      0 = discard)
+            # convert mask into a bias that can be added to attention scores:
+            #       (keep = +0,     discard = -10000.0)
+            attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
+            attention_mask = attention_mask.unsqueeze(1)
+
+        # convert encoder_attention_mask to a bias the same way we do for attention_mask
+        if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
+            encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0
+            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
+
+        # 1. Input
+        if self.is_input_continuous:
+            batch, _, height, width = hidden_states.shape
+            residual = hidden_states
+
+            hidden_states = self.norm(hidden_states)
+            if not self.use_linear_projection:
+                hidden_states = self.proj_in(hidden_states)
+                inner_dim = hidden_states.shape[1]
+                hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
+            else:
+                inner_dim = hidden_states.shape[1]
+                hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
+                hidden_states = self.proj_in(hidden_states)
+        elif self.is_input_vectorized:
+            hidden_states = self.latent_image_embedding(hidden_states)
+        elif self.is_input_patches:
+            hidden_states = self.pos_embed(hidden_states)
+
+        # 2. Blocks
+        for block in self.transformer_blocks:
+            hidden_states = block(
+                hidden_states,
+                attention_mask=attention_mask,
+                encoder_hidden_states=encoder_hidden_states,
+                encoder_attention_mask=encoder_attention_mask,
+                timestep=timestep,
+                cross_attention_kwargs=cross_attention_kwargs,
+                class_labels=class_labels,
+            )
+
+        # 3. Output
+        if self.is_input_continuous:
+            if not self.use_linear_projection:
+                hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
+                hidden_states = self.proj_out(hidden_states)
+            else:
+                hidden_states = self.proj_out(hidden_states)
+                hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
+
+            output = hidden_states + residual
+        elif self.is_input_vectorized:
+            hidden_states = self.norm_out(hidden_states)
+            logits = self.out(hidden_states)
+            # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
+            logits = logits.permute(0, 2, 1)
+
+            # log(p(x_0))
+            output = F.log_softmax(logits.double(), dim=1).float()
+        elif self.is_input_patches:
+            # TODO: cleanup!
+            conditioning = self.transformer_blocks[0].norm1.emb(
+                timestep, class_labels, hidden_dtype=hidden_states.dtype
+            )
+            shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1)
+            hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None]
+            hidden_states = self.proj_out_2(hidden_states)
+
+            # unpatchify
+            height = width = int(hidden_states.shape[1] ** 0.5)
+            hidden_states = hidden_states.reshape(
+                shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)
+            )
+            hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
+            output = hidden_states.reshape(
+                shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)
+            )
+
+        if not return_dict:
+            return (output,)
+
+        return TransformerMV2DModelOutput(sample=output)
+
+
+@maybe_allow_in_graph
+class BasicMVTransformerBlock(nn.Module):
+    r"""
+    A basic Transformer block.
+
+    Parameters:
+        dim (`int`): The number of channels in the input and output.
+        num_attention_heads (`int`): The number of heads to use for multi-head attention.
+        attention_head_dim (`int`): The number of channels in each head.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
+        only_cross_attention (`bool`, *optional*):
+            Whether to use only cross-attention layers. In this case two cross attention layers are used.
+        double_self_attention (`bool`, *optional*):
+            Whether to use two self-attention layers. In this case no cross attention layers are used.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
+        num_embeds_ada_norm (:
+            obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
+        attention_bias (:
+            obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        dropout=0.0,
+        cross_attention_dim: Optional[int] = None,
+        activation_fn: str = "geglu",
+        num_embeds_ada_norm: Optional[int] = None,
+        attention_bias: bool = False,
+        only_cross_attention: bool = False,
+        double_self_attention: bool = False,
+        upcast_attention: bool = False,
+        norm_elementwise_affine: bool = True,
+        norm_type: str = "layer_norm",
+        final_dropout: bool = False,
+        num_views: int = 1,
+        joint_attention: bool = False,
+        joint_attention_twice: bool = False,
+        multiview_attention: bool = True,
+        cross_domain_attention: bool = False
+    ):
+        super().__init__()
+        self.only_cross_attention = only_cross_attention
+
+        self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
+        self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm"
+
+        if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
+            raise ValueError(
+                f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
+                f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
+            )
+
+        # Define 3 blocks. Each block has its own normalization layer.
+        # 1. Self-Attn
+        if self.use_ada_layer_norm:
+            self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)
+        elif self.use_ada_layer_norm_zero:
+            self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm)
+        else:
+            self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
+
+        self.multiview_attention = multiview_attention
+        self.cross_domain_attention = cross_domain_attention
+        # import pdb;pdb.set_trace()
+        self.attn1 = CustomAttention(
+            query_dim=dim,
+            heads=num_attention_heads,
+            dim_head=attention_head_dim,
+            dropout=dropout,
+            bias=attention_bias,
+            cross_attention_dim=cross_attention_dim if only_cross_attention else None,
+            upcast_attention=upcast_attention,
+            processor=MVAttnProcessor()
+        )
+
+        # 2. Cross-Attn
+        if cross_attention_dim is not None or double_self_attention:
+            # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
+            # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
+            # the second cross attention block.
+            self.norm2 = (
+                AdaLayerNorm(dim, num_embeds_ada_norm)
+                if self.use_ada_layer_norm
+                else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
+            )
+            self.attn2 = Attention(
+                query_dim=dim,
+                cross_attention_dim=cross_attention_dim if not double_self_attention else None,
+                heads=num_attention_heads,
+                dim_head=attention_head_dim,
+                dropout=dropout,
+                bias=attention_bias,
+                upcast_attention=upcast_attention,
+            )  # is self-attn if encoder_hidden_states is none
+        else:
+            self.norm2 = None
+            self.attn2 = None
+
+        # 3. Feed-forward
+        self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
+        self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout)
+
+        # let chunk size default to None
+        self._chunk_size = None
+        self._chunk_dim = 0
+
+        self.num_views = num_views
+
+        self.joint_attention = joint_attention
+
+        if self.joint_attention:
+            # Joint task -Attn
+            self.attn_joint = CustomJointAttention(
+                query_dim=dim,
+                heads=num_attention_heads,
+                dim_head=attention_head_dim,
+                dropout=dropout,
+                bias=attention_bias,
+                cross_attention_dim=cross_attention_dim if only_cross_attention else None,
+                upcast_attention=upcast_attention,
+                processor=JointAttnProcessor()
+            )
+            nn.init.zeros_(self.attn_joint.to_out[0].weight.data)
+            self.norm_joint = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
+
+
+        self.joint_attention_twice = joint_attention_twice
+
+        if self.joint_attention_twice:
+            print("joint twice")
+            # Joint task -Attn
+            self.attn_joint_twice = CustomJointAttention(
+                query_dim=dim,
+                heads=num_attention_heads,
+                dim_head=attention_head_dim,
+                dropout=dropout,
+                bias=attention_bias,
+                cross_attention_dim=cross_attention_dim if only_cross_attention else None,
+                upcast_attention=upcast_attention,
+                processor=JointAttnProcessor()
+            )
+            nn.init.zeros_(self.attn_joint_twice.to_out[0].weight.data)
+            self.norm_joint_twice = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
+
+    def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int):
+        # Sets chunk feed-forward
+        self._chunk_size = chunk_size
+        self._chunk_dim = dim
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        timestep: Optional[torch.LongTensor] = None,
+        cross_attention_kwargs: Dict[str, Any] = None,
+        class_labels: Optional[torch.LongTensor] = None,
+    ):
+        assert attention_mask is None # not supported yet
+        # Notice that normalization is always applied before the real computation in the following blocks.
+        # 1. Self-Attention
+        if self.use_ada_layer_norm:
+            norm_hidden_states = self.norm1(hidden_states, timestep)
+        elif self.use_ada_layer_norm_zero:
+            norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
+                hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
+            )
+        else:
+            norm_hidden_states = self.norm1(hidden_states)
+
+        cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
+        attn_output = self.attn1(
+            norm_hidden_states,
+            encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+            attention_mask=attention_mask,
+            num_views=self.num_views,
+            multiview_attention=self.multiview_attention,
+            cross_domain_attention=self.cross_domain_attention,
+            **cross_attention_kwargs,
+        )
+
+
+        if self.use_ada_layer_norm_zero:
+            attn_output = gate_msa.unsqueeze(1) * attn_output
+        hidden_states = attn_output + hidden_states
+
+        # joint attention twice
+        if self.joint_attention_twice:
+            norm_hidden_states = (
+                self.norm_joint_twice(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_joint_twice(hidden_states)
+            )
+            hidden_states = self.attn_joint_twice(norm_hidden_states) + hidden_states
+
+        # 2. Cross-Attention
+        if self.attn2 is not None:
+            norm_hidden_states = (
+                self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
+            )
+            attn_output = self.attn2(
+                norm_hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+                attention_mask=encoder_attention_mask,
+                **cross_attention_kwargs,
+            )
+            hidden_states = attn_output + hidden_states
+
+        # 3. Feed-forward
+        norm_hidden_states = self.norm3(hidden_states)
+
+        if self.use_ada_layer_norm_zero:
+            norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+
+        if self._chunk_size is not None:
+            # "feed_forward_chunk_size" can be used to save memory
+            if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
+                raise ValueError(
+                    f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
+                )
+
+            num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
+            ff_output = torch.cat(
+                [self.ff(hid_slice) for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)],
+                dim=self._chunk_dim,
+            )
+        else:
+            ff_output = self.ff(norm_hidden_states)
+
+        if self.use_ada_layer_norm_zero:
+            ff_output = gate_mlp.unsqueeze(1) * ff_output
+
+        hidden_states = ff_output + hidden_states
+
+        if self.joint_attention:
+            norm_hidden_states = (
+                self.norm_joint(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_joint(hidden_states)
+            )
+            hidden_states = self.attn_joint(norm_hidden_states) + hidden_states
+
+        return hidden_states
+    
+
+class CustomAttention(Attention):
+    def set_use_memory_efficient_attention_xformers(
+        self, use_memory_efficient_attention_xformers: bool, *args, **kwargs
+    ):
+        processor = XFormersMVAttnProcessor()
+        self.set_processor(processor)
+        # print("using xformers attention processor")
+
+
+class CustomJointAttention(Attention):
+    def set_use_memory_efficient_attention_xformers(
+        self, use_memory_efficient_attention_xformers: bool, *args, **kwargs
+    ):
+        processor = XFormersJointAttnProcessor()
+        self.set_processor(processor)
+        # print("using xformers attention processor")
+
+class MVAttnProcessor:
+    r"""
+    Default processor for performing attention-related computations.
+    """
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+        num_views=1,
+        multiview_attention=True
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        # print('query', query.shape, 'key', key.shape, 'value', value.shape)
+        #([bx4, 1024, 320]) key torch.Size([bx4, 1024, 320]) value torch.Size([bx4, 1024, 320])
+        # pdb.set_trace()
+        # multi-view self-attention
+        if multiview_attention:
+            if num_views <= 6:
+                # after use xformer; possible to train with 6 views
+                # key = rearrange(key, "(b t) d c -> b (t d) c", t=num_views).repeat_interleave(num_views, dim=0)
+                # value = rearrange(value, "(b t) d c -> b (t d) c", t=num_views).repeat_interleave(num_views, dim=0)
+                key = rearrange(key, '(b t) d c-> b (t d) c', t=num_views).unsqueeze(1).repeat(1,num_views,1,1).flatten(0,1)
+                value = rearrange(value, '(b t) d c-> b (t d) c', t=num_views).unsqueeze(1).repeat(1,num_views,1,1).flatten(0,1)
+
+            else:# apply sparse attention
+                pass
+                # print("use sparse attention")  
+                # # seems that the sparse random sampling cause problems
+                # # don't use random sampling, just fix the indexes
+                # onekey = rearrange(key, "(b t) d c -> b t d c", t=num_views) 
+                # onevalue = rearrange(value, "(b t) d c -> b t d c", t=num_views)
+                # allkeys = []
+                # allvalues = []
+                # all_indexes = {
+                #     0 : [0, 2, 3, 4],
+                #     1: [0, 1, 3, 5],
+                #     2: [0, 2, 3, 4],
+                #     3: [0, 2, 3, 4],
+                #     4: [0, 2, 3, 4],
+                #     5: [0, 1, 3, 5]
+                # }
+                # for jj in range(num_views):
+                #     # valid_index = [x for x in range(0, num_views) if x!= jj]
+                #     # indexes = random.sample(valid_index, 3) + [jj] + [0]
+                #     indexes = all_indexes[jj]
+
+                #     indexes = torch.tensor(indexes).long().to(key.device)
+                #     allkeys.append(onekey[:, indexes])
+                #     allvalues.append(onevalue[:, indexes])
+                # keys = torch.stack(allkeys, dim=1) # checked, should be dim=1
+                # values = torch.stack(allvalues, dim=1) 
+                # key = rearrange(keys, 'b t f d c -> (b t) (f d) c')
+                # value = rearrange(values, 'b t f d c -> (b t) (f d) c')
+
+
+        query = attn.head_to_batch_dim(query).contiguous()
+        key = attn.head_to_batch_dim(key).contiguous()
+        value = attn.head_to_batch_dim(value).contiguous()
+        
+        attention_probs = attn.get_attention_scores(query, key, attention_mask)
+        hidden_states = torch.bmm(attention_probs, value)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+        
+        return hidden_states
+
+
+class XFormersMVAttnProcessor:
+    r"""
+    Default processor for performing attention-related computations.
+    """
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+        num_views=1.,
+        multiview_attention=True,
+        cross_domain_attention=False,
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+
+        # from yuancheng; here attention_mask is None
+        if attention_mask is not None:
+            # expand our mask's singleton query_tokens dimension:
+            #   [batch*heads,            1, key_tokens] ->
+            #   [batch*heads, query_tokens, key_tokens]
+            # so that it can be added as a bias onto the attention scores that xformers computes:
+            #   [batch*heads, query_tokens, key_tokens]
+            # we do this explicitly because xformers doesn't broadcast the singleton dimension for us.
+            _, query_tokens, _ = hidden_states.shape
+            attention_mask = attention_mask.expand(-1, query_tokens, -1)
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key_raw = attn.to_k(encoder_hidden_states)
+        value_raw = attn.to_v(encoder_hidden_states)
+
+        # print('query', query.shape, 'key', key.shape, 'value', value.shape)
+        #([bx4, 1024, 320]) key torch.Size([bx4, 1024, 320]) value torch.Size([bx4, 1024, 320])
+        # pdb.set_trace()
+        # multi-view self-attention
+        if multiview_attention:
+            key = rearrange(key_raw, "(b t) d c -> b (t d) c", t=num_views).repeat_interleave(num_views, dim=0)
+            value = rearrange(value_raw, "(b t) d c -> b (t d) c", t=num_views).repeat_interleave(num_views, dim=0)
+
+            if cross_domain_attention:
+                # memory efficient, cross domain attention
+                key_0, key_1 = torch.chunk(key_raw, dim=0, chunks=2)  # keys shape (b t) d c
+                value_0, value_1 = torch.chunk(value_raw, dim=0, chunks=2)
+                key_cross = torch.concat([key_1, key_0], dim=0)
+                value_cross = torch.concat([value_1, value_0], dim=0) #  shape (b t) d c
+                key = torch.cat([key, key_cross], dim=1)
+                value = torch.cat([value, value_cross], dim=1)  #  shape (b t) (t+1 d) c
+        else:
+            # print("don't use multiview attention.")
+            key = key_raw
+            value = value_raw
+
+        query = attn.head_to_batch_dim(query)
+        key = attn.head_to_batch_dim(key)
+        value = attn.head_to_batch_dim(value)
+
+        hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+        
+        return hidden_states
+
+
+
+class XFormersJointAttnProcessor:
+    r"""
+    Default processor for performing attention-related computations.
+    """
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+        num_tasks=2
+    ):
+        
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+
+        # from yuancheng; here attention_mask is None
+        if attention_mask is not None:
+            # expand our mask's singleton query_tokens dimension:
+            #   [batch*heads,            1, key_tokens] ->
+            #   [batch*heads, query_tokens, key_tokens]
+            # so that it can be added as a bias onto the attention scores that xformers computes:
+            #   [batch*heads, query_tokens, key_tokens]
+            # we do this explicitly because xformers doesn't broadcast the singleton dimension for us.
+            _, query_tokens, _ = hidden_states.shape
+            attention_mask = attention_mask.expand(-1, query_tokens, -1)
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        assert num_tasks == 2  # only support two tasks now
+
+        key_0, key_1 = torch.chunk(key, dim=0, chunks=2)  # keys shape (b t) d c
+        value_0, value_1 = torch.chunk(value, dim=0, chunks=2)
+        key = torch.cat([key_0, key_1], dim=1)  # (b t) 2d c
+        value = torch.cat([value_0, value_1], dim=1)  # (b t) 2d c
+        key = torch.cat([key]*2, dim=0)   # ( 2 b t) 2d c
+        value = torch.cat([value]*2, dim=0)  # (2 b t) 2d c
+
+        
+        query = attn.head_to_batch_dim(query).contiguous()
+        key = attn.head_to_batch_dim(key).contiguous()
+        value = attn.head_to_batch_dim(value).contiguous()
+
+        hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+        
+        return hidden_states
+
+
+class JointAttnProcessor:
+    r"""
+    Default processor for performing attention-related computations.
+    """
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+        num_tasks=2
+    ):
+        
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        assert num_tasks == 2  # only support two tasks now
+
+        key_0, key_1 = torch.chunk(key, dim=0, chunks=2)  # keys shape (b t) d c
+        value_0, value_1 = torch.chunk(value, dim=0, chunks=2)
+        key = torch.cat([key_0, key_1], dim=1)  # (b t) 2d c
+        value = torch.cat([value_0, value_1], dim=1)  # (b t) 2d c
+        key = torch.cat([key]*2, dim=0)   # ( 2 b t) 2d c
+        value = torch.cat([value]*2, dim=0)  # (2 b t) 2d c
+
+        
+        query = attn.head_to_batch_dim(query).contiguous()
+        key = attn.head_to_batch_dim(key).contiguous()
+        value = attn.head_to_batch_dim(value).contiguous()
+
+        attention_probs = attn.get_attention_scores(query, key, attention_mask)
+        hidden_states = torch.bmm(attention_probs, value)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+        
+        return hidden_states

2D_Stage/tuneavideo/models/unet.py

@@ -0,0 +1,497 @@
+# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py
+
+from dataclasses import dataclass
+from typing import List, Optional, Tuple, Union
+
+import os
+import json
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers import ModelMixin
+from diffusers.utils import BaseOutput, logging
+from diffusers.models.embeddings import TimestepEmbedding, Timesteps
+from .unet_blocks import (
+    CrossAttnDownBlock3D,
+    CrossAttnUpBlock3D,
+    DownBlock3D,
+    UNetMidBlock3DCrossAttn,
+    UpBlock3D,
+    get_down_block,
+    get_up_block,
+)
+from .resnet import InflatedConv3d
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@dataclass
+class UNet3DConditionOutput(BaseOutput):
+    sample: torch.FloatTensor
+
+
+class UNet3DConditionModel(ModelMixin, ConfigMixin):
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    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] = (
+            "CrossAttnDownBlock3D",
+            "CrossAttnDownBlock3D",
+            "CrossAttnDownBlock3D",
+            "DownBlock3D",
+        ),
+        mid_block_type: str = "UNetMidBlock3DCrossAttn",
+        up_block_types: Tuple[str] = (
+            "UpBlock3D",
+            "CrossAttnUpBlock3D",
+            "CrossAttnUpBlock3D",
+            "CrossAttnUpBlock3D"
+        ),
+        only_cross_attention: Union[bool, Tuple[bool]] = False,
+        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
+        layers_per_block: int = 2,
+        downsample_padding: int = 1,
+        mid_block_scale_factor: float = 1,
+        act_fn: str = "silu",
+        norm_num_groups: int = 32,
+        norm_eps: float = 1e-5,
+        cross_attention_dim: int = 1280,
+        attention_head_dim: Union[int, Tuple[int]] = 8,
+        dual_cross_attention: bool = False,
+        use_linear_projection: bool = False,
+        class_embed_type: Optional[str] = None,
+        num_class_embeds: Optional[int] = None,
+        upcast_attention: bool = False,
+        resnet_time_scale_shift: str = "default",
+        use_attn_temp: bool = False,
+        camera_input_dim: int = 12,
+        camera_hidden_dim: int = 320,
+        camera_output_dim: int = 1280,
+    ):
+        super().__init__()
+
+        self.sample_size = sample_size
+        time_embed_dim = block_out_channels[0] * 4
+
+        # input
+        self.conv_in = InflatedConv3d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))
+
+        # time
+        self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
+        timestep_input_dim = block_out_channels[0]
+
+        self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
+
+        # class embedding
+        if class_embed_type is None and num_class_embeds is not None:
+            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
+        elif class_embed_type == "timestep":
+            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
+        elif class_embed_type == "identity":
+            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
+        else:
+            self.class_embedding = None
+
+        # camera metrix
+        # def init_linear(l, stddev):
+        #     nn.init.normal_(l.weight, std=stddev)
+        #     if l.bias is not None:
+        #         nn.init.constant_(l.bias, 0.0)
+        # self.camera_embedding_1 = nn.Linear(camera_input_dim, camera_hidden_dim)
+        # self.camera_embedding_2 = nn.Linear(camera_hidden_dim, camera_output_dim)
+        # init_linear(self.camera_embedding_1, 0.25)
+        # init_linear(self.camera_embedding_2, 0.25)
+
+        self.camera_embedding = nn.Sequential(
+            nn.Linear(camera_input_dim, time_embed_dim),
+            nn.SiLU(),
+            nn.Linear(time_embed_dim, time_embed_dim),
+        )
+
+        self.down_blocks = nn.ModuleList([])
+        self.mid_block = None
+        self.up_blocks = nn.ModuleList([])
+
+        if isinstance(only_cross_attention, bool):
+            only_cross_attention = [only_cross_attention] * len(down_block_types)
+
+        if isinstance(attention_head_dim, int):
+            attention_head_dim = (attention_head_dim,) * len(down_block_types)
+
+        # down
+        output_channel = block_out_channels[0]
+        for i, down_block_type in enumerate(down_block_types):
+            input_channel = output_channel
+            output_channel = block_out_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+
+            down_block = get_down_block(
+                down_block_type,
+                num_layers=layers_per_block,
+                in_channels=input_channel,
+                out_channels=output_channel,
+                temb_channels=time_embed_dim,
+                add_downsample=not is_final_block,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=cross_attention_dim,
+                attn_num_head_channels=attention_head_dim[i],
+                downsample_padding=downsample_padding,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                upcast_attention=upcast_attention,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                use_attn_temp=use_attn_temp
+            )
+            self.down_blocks.append(down_block)
+
+        # mid
+        if mid_block_type == "UNetMidBlock3DCrossAttn":
+            self.mid_block = UNetMidBlock3DCrossAttn(
+                in_channels=block_out_channels[-1],
+                temb_channels=time_embed_dim,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                cross_attention_dim=cross_attention_dim,
+                attn_num_head_channels=attention_head_dim[-1],
+                resnet_groups=norm_num_groups,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                upcast_attention=upcast_attention,
+            )
+        else:
+            raise ValueError(f"unknown mid_block_type : {mid_block_type}")
+
+        # count how many layers upsample the videos
+        self.num_upsamplers = 0
+
+        # up
+        reversed_block_out_channels = list(reversed(block_out_channels))
+        reversed_attention_head_dim = list(reversed(attention_head_dim))
+        only_cross_attention = list(reversed(only_cross_attention))
+        output_channel = reversed_block_out_channels[0]
+        for i, up_block_type in enumerate(up_block_types):
+            is_final_block = i == len(block_out_channels) - 1
+
+            prev_output_channel = output_channel
+            output_channel = reversed_block_out_channels[i]
+            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
+
+            # add upsample block for all BUT final layer
+            if not is_final_block:
+                add_upsample = True
+                self.num_upsamplers += 1
+            else:
+                add_upsample = False
+
+            up_block = get_up_block(
+                up_block_type,
+                num_layers=layers_per_block + 1,
+                in_channels=input_channel,
+                out_channels=output_channel,
+                prev_output_channel=prev_output_channel,
+                temb_channels=time_embed_dim,
+                add_upsample=add_upsample,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=cross_attention_dim,
+                attn_num_head_channels=reversed_attention_head_dim[i],
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                upcast_attention=upcast_attention,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                use_attn_temp=use_attn_temp,
+            )
+            self.up_blocks.append(up_block)
+            prev_output_channel = output_channel
+
+        # out
+        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)
+        self.conv_act = nn.SiLU()
+        self.conv_out = InflatedConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1)
+
+    def set_attention_slice(self, slice_size):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+
+        Args:
+            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is
+                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
+                must be a multiple of `slice_size`.
+        """
+        sliceable_head_dims = []
+
+        def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):
+            if hasattr(module, "set_attention_slice"):
+                sliceable_head_dims.append(module.sliceable_head_dim)
+
+            for child in module.children():
+                fn_recursive_retrieve_slicable_dims(child)
+
+        # retrieve number of attention layers
+        for module in self.children():
+            fn_recursive_retrieve_slicable_dims(module)
+
+        num_slicable_layers = len(sliceable_head_dims)
+
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = [dim // 2 for dim in sliceable_head_dims]
+        elif slice_size == "max":
+            # make smallest slice possible
+            slice_size = num_slicable_layers * [1]
+
+        slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
+
+        if len(slice_size) != len(sliceable_head_dims):
+            raise ValueError(
+                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
+                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
+            )
+
+        for i in range(len(slice_size)):
+            size = slice_size[i]
+            dim = sliceable_head_dims[i]
+            if size is not None and size > dim:
+                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
+
+        # Recursively walk through all the children.
+        # Any children which exposes the set_attention_slice method
+        # gets the message
+        def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
+            if hasattr(module, "set_attention_slice"):
+                module.set_attention_slice(slice_size.pop())
+
+            for child in module.children():
+                fn_recursive_set_attention_slice(child, slice_size)
+
+        reversed_slice_size = list(reversed(slice_size))
+        for module in self.children():
+            fn_recursive_set_attention_slice(module, reversed_slice_size)
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):
+            module.gradient_checkpointing = value
+
+    def forward(
+        self,
+        sample: torch.FloatTensor,
+        timestep: Union[torch.Tensor, float, int],
+        encoder_hidden_states: torch.Tensor,
+        camera_matrixs: Optional[torch.Tensor] = None,
+        class_labels: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        return_dict: bool = True,
+    ) -> Union[UNet3DConditionOutput, Tuple]:
+        r"""
+        Args:
+            sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
+            timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps
+            encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
+            [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When
+            returning a tuple, the first element is the sample tensor.
+        """
+        # By default samples have to be AT least a multiple of the overall upsampling factor.
+        # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
+        # However, the upsampling interpolation output size can be forced to fit any upsampling size
+        # on the fly if necessary.
+        default_overall_up_factor = 2**self.num_upsamplers
+
+        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
+        forward_upsample_size = False
+        upsample_size = None
+
+        if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
+            logger.info("Forward upsample size to force interpolation output size.")
+            forward_upsample_size = True
+
+        # prepare attention_mask
+        if attention_mask is not None:
+            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
+            attention_mask = attention_mask.unsqueeze(1)
+
+        # center input if necessary
+        if self.config.center_input_sample:
+            sample = 2 * sample - 1.0
+        # time
+        timesteps = timestep
+        if not torch.is_tensor(timesteps):
+            # This would be a good case for the `match` statement (Python 3.10+)
+            is_mps = sample.device.type == "mps"
+            if isinstance(timestep, float):
+                dtype = torch.float32 if is_mps else torch.float64
+            else:
+                dtype = torch.int32 if is_mps else torch.int64
+            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
+        elif len(timesteps.shape) == 0:
+            timesteps = timesteps[None].to(sample.device)
+
+        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+        timesteps = timesteps.expand(sample.shape[0])
+
+        t_emb = self.time_proj(timesteps)
+
+        # timesteps does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=self.dtype)
+        emb = self.time_embedding(t_emb) #torch.Size([32, 1280])
+        emb = torch.unsqueeze(emb, 1)
+        if camera_matrixs is not None:
+            # came emb
+            cam_emb = self.camera_embedding(camera_matrixs)
+            # cam_emb = self.camera_embedding_2(cam_emb)
+            emb = emb.repeat(1,cam_emb.shape[1],1) #torch.Size([32, 4, 1280])
+            emb = emb + cam_emb
+        
+        # import pdb;pdb.set_trace()
+        if self.class_embedding is not None:
+            # if class_labels is None:
+            #     raise ValueError("class_labels should be provided when num_class_embeds > 0")
+            if class_labels is not None:
+                
+                if self.config.class_embed_type == "timestep":
+                    class_labels = self.time_proj(class_labels)
+
+                class_emb = self.class_embedding(class_labels)
+                emb = emb + class_emb
+
+        # pre-process
+        sample = self.conv_in(sample)
+
+        # down
+        down_block_res_samples = (sample,)
+        for downsample_block in self.down_blocks:
+            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
+                sample, res_samples = downsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=attention_mask,
+                )
+            else:
+                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
+
+            down_block_res_samples += res_samples
+
+        # mid
+        sample = self.mid_block(
+            sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask
+        )
+
+        # up
+        for i, upsample_block in enumerate(self.up_blocks):
+            is_final_block = i == len(self.up_blocks) - 1
+
+            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
+            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
+
+            # if we have not reached the final block and need to forward the
+            # upsample size, we do it here
+            if not is_final_block and forward_upsample_size:
+                upsample_size = down_block_res_samples[-1].shape[2:]
+
+            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
+                sample = upsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    res_hidden_states_tuple=res_samples,
+                    encoder_hidden_states=encoder_hidden_states,
+                    upsample_size=upsample_size,
+                    attention_mask=attention_mask,
+                )
+            else:
+                sample = upsample_block(
+                    hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
+                )
+        # post-process
+        sample = self.conv_norm_out(sample)
+        sample = self.conv_act(sample)
+        sample = self.conv_out(sample)
+
+        if not return_dict:
+            return (sample,)
+
+        return UNet3DConditionOutput(sample=sample)
+
+    @classmethod
+    def from_pretrained_2d(cls, pretrained_model_path, subfolder=None):
+        if subfolder is not None:
+            pretrained_model_path = os.path.join(pretrained_model_path, subfolder)
+
+        config_file = os.path.join(pretrained_model_path, 'config.json')
+        if not os.path.isfile(config_file):
+            raise RuntimeError(f"{config_file} does not exist")
+        with open(config_file, "r") as f:
+            config = json.load(f)
+        config["_class_name"] = cls.__name__
+        config["down_block_types"] = [
+            "CrossAttnDownBlock3D",
+            "CrossAttnDownBlock3D",
+            "CrossAttnDownBlock3D",
+            "DownBlock3D"
+        ]
+        config["up_block_types"] = [
+            "UpBlock3D",
+            "CrossAttnUpBlock3D",
+            "CrossAttnUpBlock3D",
+            "CrossAttnUpBlock3D"
+        ]
+        
+        from diffusers.utils import WEIGHTS_NAME, SAFETENSORS_WEIGHTS_NAME
+        # model = cls.from_config(config)
+        # model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)
+        # if not os.path.isfile(model_file):
+        #     raise RuntimeError(f"{model_file} does not exist")
+        # state_dict = torch.load(model_file, map_location="cpu")
+
+        import safetensors
+        model = cls.from_config(config)
+        model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)
+        if not os.path.isfile(model_file):
+            model_file = os.path.join(pretrained_model_path, SAFETENSORS_WEIGHTS_NAME)
+            if not os.path.isfile(model_file):
+                raise RuntimeError(f"{model_file} does not exist")
+            else:
+               state_dict = safetensors.torch.load_file(model_file, device="cpu") 
+        else:
+            state_dict = torch.load(model_file, map_location="cpu")
+            
+        for k, v in model.state_dict().items():
+            if '_temp.' in k or 'camera_embedding' in k or 'class_embedding' in k:
+                state_dict.update({k: v})
+        for k in list(state_dict.keys()):
+            if 'camera_embedding_' in k:
+                v = state_dict.pop(k)
+        model.load_state_dict(state_dict)
+
+        return model

2D_Stage/tuneavideo/models/unet_blocks.py

@@ -0,0 +1,596 @@
+# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py
+
+import torch
+from torch import nn
+
+# from .attention import Transformer3DModel
+from .resnet import Downsample3D, ResnetBlock3D, Upsample3D
+
+
+def get_down_block(
+    down_block_type,
+    num_layers,
+    in_channels,
+    out_channels,
+    temb_channels,
+    add_downsample,
+    resnet_eps,
+    resnet_act_fn,
+    attn_num_head_channels,
+    resnet_groups=None,
+    cross_attention_dim=None,
+    downsample_padding=None,
+    dual_cross_attention=False,
+    use_linear_projection=False,
+    only_cross_attention=False,
+    upcast_attention=False,
+    resnet_time_scale_shift="default",
+    use_attn_temp=False,
+):
+    down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type
+    if down_block_type == "DownBlock3D":
+        return DownBlock3D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            add_downsample=add_downsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            downsample_padding=downsample_padding,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+        )
+    elif down_block_type == "CrossAttnDownBlock3D":
+        if cross_attention_dim is None:
+            raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock3D")
+        return CrossAttnDownBlock3D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            add_downsample=add_downsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            downsample_padding=downsample_padding,
+            cross_attention_dim=cross_attention_dim,
+            attn_num_head_channels=attn_num_head_channels,
+            dual_cross_attention=dual_cross_attention,
+            use_linear_projection=use_linear_projection,
+            only_cross_attention=only_cross_attention,
+            upcast_attention=upcast_attention,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+            use_attn_temp=use_attn_temp,
+        )
+    raise ValueError(f"{down_block_type} does not exist.")
+
+
+def get_up_block(
+    up_block_type,
+    num_layers,
+    in_channels,
+    out_channels,
+    prev_output_channel,
+    temb_channels,
+    add_upsample,
+    resnet_eps,
+    resnet_act_fn,
+    attn_num_head_channels,
+    resnet_groups=None,
+    cross_attention_dim=None,
+    dual_cross_attention=False,
+    use_linear_projection=False,
+    only_cross_attention=False,
+    upcast_attention=False,
+    resnet_time_scale_shift="default",
+    use_attn_temp=False,
+):
+    up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
+    if up_block_type == "UpBlock3D":
+        return UpBlock3D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            prev_output_channel=prev_output_channel,
+            temb_channels=temb_channels,
+            add_upsample=add_upsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+        )
+    elif up_block_type == "CrossAttnUpBlock3D":
+        if cross_attention_dim is None:
+            raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock3D")
+        return CrossAttnUpBlock3D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            prev_output_channel=prev_output_channel,
+            temb_channels=temb_channels,
+            add_upsample=add_upsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            cross_attention_dim=cross_attention_dim,
+            attn_num_head_channels=attn_num_head_channels,
+            dual_cross_attention=dual_cross_attention,
+            use_linear_projection=use_linear_projection,
+            only_cross_attention=only_cross_attention,
+            upcast_attention=upcast_attention,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+            use_attn_temp=use_attn_temp,
+        )
+    raise ValueError(f"{up_block_type} does not exist.")
+
+
+class UNetMidBlock3DCrossAttn(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        attn_num_head_channels=1,
+        output_scale_factor=1.0,
+        cross_attention_dim=1280,
+        dual_cross_attention=False,
+        use_linear_projection=False,
+        upcast_attention=False,
+    ):
+        super().__init__()
+
+        self.has_cross_attention = True
+        self.attn_num_head_channels = attn_num_head_channels
+        resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
+
+        # there is always at least one resnet
+        resnets = [
+            ResnetBlock3D(
+                in_channels=in_channels,
+                out_channels=in_channels,
+                temb_channels=temb_channels,
+                eps=resnet_eps,
+                groups=resnet_groups,
+                dropout=dropout,
+                time_embedding_norm=resnet_time_scale_shift,
+                non_linearity=resnet_act_fn,
+                output_scale_factor=output_scale_factor,
+                pre_norm=resnet_pre_norm,
+            )
+        ]
+        attentions = []
+
+        for _ in range(num_layers):
+            if dual_cross_attention:
+                raise NotImplementedError
+            attentions.append(
+                Transformer3DModel(
+                    attn_num_head_channels,
+                    in_channels // attn_num_head_channels,
+                    in_channels=in_channels,
+                    num_layers=1,
+                    cross_attention_dim=cross_attention_dim,
+                    norm_num_groups=resnet_groups,
+                    use_linear_projection=use_linear_projection,
+                    upcast_attention=upcast_attention,
+                )
+            )
+            resnets.append(
+                ResnetBlock3D(
+                    in_channels=in_channels,
+                    out_channels=in_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+    def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None):
+        hidden_states = self.resnets[0](hidden_states, temb)
+        for attn, resnet in zip(self.attentions, self.resnets[1:]):
+            hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample
+            hidden_states = resnet(hidden_states, temb)
+
+        return hidden_states
+
+
+class CrossAttnDownBlock3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        attn_num_head_channels=1,
+        cross_attention_dim=1280,
+        output_scale_factor=1.0,
+        downsample_padding=1,
+        add_downsample=True,
+        dual_cross_attention=False,
+        use_linear_projection=False,
+        only_cross_attention=False,
+        upcast_attention=False,
+        use_attn_temp=False,
+    ):
+        super().__init__()
+        resnets = []
+        attentions = []
+
+        self.has_cross_attention = True
+        self.attn_num_head_channels = attn_num_head_channels
+
+        for i in range(num_layers):
+            in_channels = in_channels if i == 0 else out_channels
+            resnets.append(
+                ResnetBlock3D(
+                    in_channels=in_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+            if dual_cross_attention:
+                raise NotImplementedError
+            attentions.append(
+                Transformer3DModel(
+                    attn_num_head_channels,
+                    out_channels // attn_num_head_channels,
+                    in_channels=out_channels,
+                    num_layers=1,
+                    cross_attention_dim=cross_attention_dim,
+                    norm_num_groups=resnet_groups,
+                    use_linear_projection=use_linear_projection,
+                    only_cross_attention=only_cross_attention,
+                    upcast_attention=upcast_attention,
+                    use_attn_temp=use_attn_temp,
+                )
+            )
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+        if add_downsample:
+            self.downsamplers = nn.ModuleList(
+                [
+                    Downsample3D(
+                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
+                    )
+                ]
+            )
+        else:
+            self.downsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None):
+        output_states = ()
+
+        for resnet, attn in zip(self.resnets, self.attentions):
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(attn, return_dict=False),
+                    hidden_states,
+                    encoder_hidden_states,
+                )[0]
+            else:
+                hidden_states = resnet(hidden_states, temb)
+                hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample
+
+            output_states += (hidden_states,)
+
+        if self.downsamplers is not None:
+            for downsampler in self.downsamplers:
+                hidden_states = downsampler(hidden_states)
+
+            output_states += (hidden_states,)
+
+        return hidden_states, output_states
+
+
+class DownBlock3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        output_scale_factor=1.0,
+        add_downsample=True,
+        downsample_padding=1,
+    ):
+        super().__init__()
+        resnets = []
+
+        for i in range(num_layers):
+            in_channels = in_channels if i == 0 else out_channels
+            resnets.append(
+                ResnetBlock3D(
+                    in_channels=in_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+
+        self.resnets = nn.ModuleList(resnets)
+
+        if add_downsample:
+            self.downsamplers = nn.ModuleList(
+                [
+                    Downsample3D(
+                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
+                    )
+                ]
+            )
+        else:
+            self.downsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(self, hidden_states, temb=None):
+        output_states = ()
+
+        for resnet in self.resnets:
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs)
+
+                    return custom_forward
+
+                hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
+            else:
+                hidden_states = resnet(hidden_states, temb)
+
+            output_states += (hidden_states,)
+
+        if self.downsamplers is not None:
+            for downsampler in self.downsamplers:
+                hidden_states = downsampler(hidden_states)
+
+            output_states += (hidden_states,)
+
+        return hidden_states, output_states
+
+
+class CrossAttnUpBlock3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        prev_output_channel: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        attn_num_head_channels=1,
+        cross_attention_dim=1280,
+        output_scale_factor=1.0,
+        add_upsample=True,
+        dual_cross_attention=False,
+        use_linear_projection=False,
+        only_cross_attention=False,
+        upcast_attention=False,
+        use_attn_temp=False,
+    ):
+        super().__init__()
+        resnets = []
+        attentions = []
+
+        self.has_cross_attention = True
+        self.attn_num_head_channels = attn_num_head_channels
+
+        for i in range(num_layers):
+            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
+            resnet_in_channels = prev_output_channel if i == 0 else out_channels
+
+            resnets.append(
+                ResnetBlock3D(
+                    in_channels=resnet_in_channels + res_skip_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+            if dual_cross_attention:
+                raise NotImplementedError
+            attentions.append(
+                Transformer3DModel(
+                    attn_num_head_channels,
+                    out_channels // attn_num_head_channels,
+                    in_channels=out_channels,
+                    num_layers=1,
+                    cross_attention_dim=cross_attention_dim,
+                    norm_num_groups=resnet_groups,
+                    use_linear_projection=use_linear_projection,
+                    only_cross_attention=only_cross_attention,
+                    upcast_attention=upcast_attention,
+                    use_attn_temp=use_attn_temp,
+                )
+            )
+
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+        if add_upsample:
+            self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)])
+        else:
+            self.upsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states,
+        res_hidden_states_tuple,
+        temb=None,
+        encoder_hidden_states=None,
+        upsample_size=None,
+        attention_mask=None,
+    ):
+        for resnet, attn in zip(self.resnets, self.attentions):
+            # pop res hidden states
+            res_hidden_states = res_hidden_states_tuple[-1]
+            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(attn, return_dict=False),
+                    hidden_states,
+                    encoder_hidden_states,
+                )[0]
+            else:
+                hidden_states = resnet(hidden_states, temb)
+                hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample
+
+        if self.upsamplers is not None:
+            for upsampler in self.upsamplers:
+                hidden_states = upsampler(hidden_states, upsample_size)
+
+        return hidden_states
+
+
+class UpBlock3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        prev_output_channel: int,
+        out_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        output_scale_factor=1.0,
+        add_upsample=True,
+    ):
+        super().__init__()
+        resnets = []
+
+        for i in range(num_layers):
+            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
+            resnet_in_channels = prev_output_channel if i == 0 else out_channels
+
+            resnets.append(
+                ResnetBlock3D(
+                    in_channels=resnet_in_channels + res_skip_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+
+        self.resnets = nn.ModuleList(resnets)
+
+        if add_upsample:
+            self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)])
+        else:
+            self.upsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):
+        for resnet in self.resnets:
+            # pop res hidden states
+            res_hidden_states = res_hidden_states_tuple[-1]
+            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs)
+
+                    return custom_forward
+
+                hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
+            else:
+                hidden_states = resnet(hidden_states, temb)
+
+        if self.upsamplers is not None:
+            for upsampler in self.upsamplers:
+                hidden_states = upsampler(hidden_states, upsample_size)
+
+        return hidden_states

2D_Stage/tuneavideo/models/unet_mv2d_blocks.py

@@ -0,0 +1,926 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Any, Dict, Optional, Tuple
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from diffusers.utils import is_torch_version, logging
+# from diffusers.models.attention import AdaGroupNorm
+from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor, AttnAddedKVProcessor2_0
+from diffusers.models.dual_transformer_2d import DualTransformer2DModel
+from diffusers.models.resnet import Downsample2D, FirDownsample2D, FirUpsample2D, KDownsample2D, KUpsample2D, ResnetBlock2D, Upsample2D
+from tuneavideo.models.transformer_mv2d import TransformerMV2DModel
+
+from diffusers.models.unet_2d_blocks import DownBlock2D, ResnetDownsampleBlock2D, AttnDownBlock2D, CrossAttnDownBlock2D, SimpleCrossAttnDownBlock2D, SkipDownBlock2D, AttnSkipDownBlock2D, DownEncoderBlock2D, AttnDownEncoderBlock2D, KDownBlock2D, KCrossAttnDownBlock2D
+from diffusers.models.unet_2d_blocks import UpBlock2D, ResnetUpsampleBlock2D, CrossAttnUpBlock2D, SimpleCrossAttnUpBlock2D, AttnUpBlock2D, SkipUpBlock2D, AttnSkipUpBlock2D, UpDecoderBlock2D, AttnUpDecoderBlock2D, KUpBlock2D, KCrossAttnUpBlock2D
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def get_down_block(
+    down_block_type,
+    num_layers,
+    in_channels,
+    out_channels,
+    temb_channels,
+    add_downsample,
+    resnet_eps,
+    resnet_act_fn,
+    transformer_layers_per_block=1,
+    num_attention_heads=None,
+    resnet_groups=None,
+    cross_attention_dim=None,
+    downsample_padding=None,
+    dual_cross_attention=False,
+    use_linear_projection=False,
+    only_cross_attention=False,
+    upcast_attention=False,
+    resnet_time_scale_shift="default",
+    resnet_skip_time_act=False,
+    resnet_out_scale_factor=1.0,
+    cross_attention_norm=None,
+    attention_head_dim=None,
+    downsample_type=None,
+    num_views=1,
+    joint_attention: bool = False,
+    joint_attention_twice: bool = False,
+    multiview_attention: bool = True,
+    cross_domain_attention: bool=False
+):
+    # If attn head dim is not defined, we default it to the number of heads
+    if attention_head_dim is None:
+        logger.warn(
+            f"It is recommended to provide `attention_head_dim` when calling `get_down_block`. Defaulting `attention_head_dim` to {num_attention_heads}."
+        )
+        attention_head_dim = num_attention_heads
+
+    down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type
+    if down_block_type == "DownBlock2D":
+        return DownBlock2D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            add_downsample=add_downsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            downsample_padding=downsample_padding,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+        )
+    elif down_block_type == "ResnetDownsampleBlock2D":
+        return ResnetDownsampleBlock2D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            add_downsample=add_downsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+            skip_time_act=resnet_skip_time_act,
+            output_scale_factor=resnet_out_scale_factor,
+        )
+    elif down_block_type == "AttnDownBlock2D":
+        if add_downsample is False:
+            downsample_type = None
+        else:
+            downsample_type = downsample_type or "conv"  # default to 'conv'
+        return AttnDownBlock2D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            downsample_padding=downsample_padding,
+            attention_head_dim=attention_head_dim,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+            downsample_type=downsample_type,
+        )
+    elif down_block_type == "CrossAttnDownBlock2D":
+        if cross_attention_dim is None:
+            raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock2D")
+        return CrossAttnDownBlock2D(
+            num_layers=num_layers,
+            transformer_layers_per_block=transformer_layers_per_block,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            add_downsample=add_downsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            downsample_padding=downsample_padding,
+            cross_attention_dim=cross_attention_dim,
+            num_attention_heads=num_attention_heads,
+            dual_cross_attention=dual_cross_attention,
+            use_linear_projection=use_linear_projection,
+            only_cross_attention=only_cross_attention,
+            upcast_attention=upcast_attention,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+        )
+    # custom MV2D attention block
+    elif down_block_type == "CrossAttnDownBlockMV2D":
+        if cross_attention_dim is None:
+            raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlockMV2D")
+        return CrossAttnDownBlockMV2D(
+            num_layers=num_layers,
+            transformer_layers_per_block=transformer_layers_per_block,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            add_downsample=add_downsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            downsample_padding=downsample_padding,
+            cross_attention_dim=cross_attention_dim,
+            num_attention_heads=num_attention_heads,
+            dual_cross_attention=dual_cross_attention,
+            use_linear_projection=use_linear_projection,
+            only_cross_attention=only_cross_attention,
+            upcast_attention=upcast_attention,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+            num_views=num_views,
+            joint_attention=joint_attention,
+            joint_attention_twice=joint_attention_twice,
+            multiview_attention=multiview_attention,
+            cross_domain_attention=cross_domain_attention
+        )
+    elif down_block_type == "SimpleCrossAttnDownBlock2D":
+        if cross_attention_dim is None:
+            raise ValueError("cross_attention_dim must be specified for SimpleCrossAttnDownBlock2D")
+        return SimpleCrossAttnDownBlock2D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            add_downsample=add_downsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            cross_attention_dim=cross_attention_dim,
+            attention_head_dim=attention_head_dim,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+            skip_time_act=resnet_skip_time_act,
+            output_scale_factor=resnet_out_scale_factor,
+            only_cross_attention=only_cross_attention,
+            cross_attention_norm=cross_attention_norm,
+        )
+    elif down_block_type == "SkipDownBlock2D":
+        return SkipDownBlock2D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            add_downsample=add_downsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            downsample_padding=downsample_padding,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+        )
+    elif down_block_type == "AttnSkipDownBlock2D":
+        return AttnSkipDownBlock2D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            add_downsample=add_downsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            attention_head_dim=attention_head_dim,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+        )
+    elif down_block_type == "DownEncoderBlock2D":
+        return DownEncoderBlock2D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            add_downsample=add_downsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            downsample_padding=downsample_padding,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+        )
+    elif down_block_type == "AttnDownEncoderBlock2D":
+        return AttnDownEncoderBlock2D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            add_downsample=add_downsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            downsample_padding=downsample_padding,
+            attention_head_dim=attention_head_dim,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+        )
+    elif down_block_type == "KDownBlock2D":
+        return KDownBlock2D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            add_downsample=add_downsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+        )
+    elif down_block_type == "KCrossAttnDownBlock2D":
+        return KCrossAttnDownBlock2D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            add_downsample=add_downsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            cross_attention_dim=cross_attention_dim,
+            attention_head_dim=attention_head_dim,
+            add_self_attention=True if not add_downsample else False,
+        )
+    raise ValueError(f"{down_block_type} does not exist.")
+
+
+def get_up_block(
+    up_block_type,
+    num_layers,
+    in_channels,
+    out_channels,
+    prev_output_channel,
+    temb_channels,
+    add_upsample,
+    resnet_eps,
+    resnet_act_fn,
+    transformer_layers_per_block=1,
+    num_attention_heads=None,
+    resnet_groups=None,
+    cross_attention_dim=None,
+    dual_cross_attention=False,
+    use_linear_projection=False,
+    only_cross_attention=False,
+    upcast_attention=False,
+    resnet_time_scale_shift="default",
+    resnet_skip_time_act=False,
+    resnet_out_scale_factor=1.0,
+    cross_attention_norm=None,
+    attention_head_dim=None,
+    upsample_type=None,
+    num_views=1,
+    joint_attention: bool = False,
+    joint_attention_twice: bool = False,
+    multiview_attention: bool = True,
+    cross_domain_attention: bool=False
+):
+    # If attn head dim is not defined, we default it to the number of heads
+    if attention_head_dim is None:
+        logger.warn(
+            f"It is recommended to provide `attention_head_dim` when calling `get_up_block`. Defaulting `attention_head_dim` to {num_attention_heads}."
+        )
+        attention_head_dim = num_attention_heads
+
+    up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
+    if up_block_type == "UpBlock2D":
+        return UpBlock2D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            prev_output_channel=prev_output_channel,
+            temb_channels=temb_channels,
+            add_upsample=add_upsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+        )
+    elif up_block_type == "ResnetUpsampleBlock2D":
+        return ResnetUpsampleBlock2D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            prev_output_channel=prev_output_channel,
+            temb_channels=temb_channels,
+            add_upsample=add_upsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+            skip_time_act=resnet_skip_time_act,
+            output_scale_factor=resnet_out_scale_factor,
+        )
+    elif up_block_type == "CrossAttnUpBlock2D":
+        if cross_attention_dim is None:
+            raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock2D")
+        return CrossAttnUpBlock2D(
+            num_layers=num_layers,
+            transformer_layers_per_block=transformer_layers_per_block,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            prev_output_channel=prev_output_channel,
+            temb_channels=temb_channels,
+            add_upsample=add_upsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            cross_attention_dim=cross_attention_dim,
+            num_attention_heads=num_attention_heads,
+            dual_cross_attention=dual_cross_attention,
+            use_linear_projection=use_linear_projection,
+            only_cross_attention=only_cross_attention,
+            upcast_attention=upcast_attention,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+        )
+    # custom MV2D attention block
+    elif up_block_type == "CrossAttnUpBlockMV2D":
+        if cross_attention_dim is None:
+            raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlockMV2D")
+        return CrossAttnUpBlockMV2D(
+            num_layers=num_layers,
+            transformer_layers_per_block=transformer_layers_per_block,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            prev_output_channel=prev_output_channel,
+            temb_channels=temb_channels,
+            add_upsample=add_upsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            cross_attention_dim=cross_attention_dim,
+            num_attention_heads=num_attention_heads,
+            dual_cross_attention=dual_cross_attention,
+            use_linear_projection=use_linear_projection,
+            only_cross_attention=only_cross_attention,
+            upcast_attention=upcast_attention,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+            num_views=num_views,
+            joint_attention=joint_attention,
+            joint_attention_twice=joint_attention_twice,
+            multiview_attention=multiview_attention,
+            cross_domain_attention=cross_domain_attention
+        )    
+    elif up_block_type == "SimpleCrossAttnUpBlock2D":
+        if cross_attention_dim is None:
+            raise ValueError("cross_attention_dim must be specified for SimpleCrossAttnUpBlock2D")
+        return SimpleCrossAttnUpBlock2D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            prev_output_channel=prev_output_channel,
+            temb_channels=temb_channels,
+            add_upsample=add_upsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            cross_attention_dim=cross_attention_dim,
+            attention_head_dim=attention_head_dim,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+            skip_time_act=resnet_skip_time_act,
+            output_scale_factor=resnet_out_scale_factor,
+            only_cross_attention=only_cross_attention,
+            cross_attention_norm=cross_attention_norm,
+        )
+    elif up_block_type == "AttnUpBlock2D":
+        if add_upsample is False:
+            upsample_type = None
+        else:
+            upsample_type = upsample_type or "conv"  # default to 'conv'
+
+        return AttnUpBlock2D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            prev_output_channel=prev_output_channel,
+            temb_channels=temb_channels,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            attention_head_dim=attention_head_dim,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+            upsample_type=upsample_type,
+        )
+    elif up_block_type == "SkipUpBlock2D":
+        return SkipUpBlock2D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            prev_output_channel=prev_output_channel,
+            temb_channels=temb_channels,
+            add_upsample=add_upsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+        )
+    elif up_block_type == "AttnSkipUpBlock2D":
+        return AttnSkipUpBlock2D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            prev_output_channel=prev_output_channel,
+            temb_channels=temb_channels,
+            add_upsample=add_upsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            attention_head_dim=attention_head_dim,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+        )
+    elif up_block_type == "UpDecoderBlock2D":
+        return UpDecoderBlock2D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            add_upsample=add_upsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+            temb_channels=temb_channels,
+        )
+    elif up_block_type == "AttnUpDecoderBlock2D":
+        return AttnUpDecoderBlock2D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            add_upsample=add_upsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            attention_head_dim=attention_head_dim,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+            temb_channels=temb_channels,
+        )
+    elif up_block_type == "KUpBlock2D":
+        return KUpBlock2D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            add_upsample=add_upsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+        )
+    elif up_block_type == "KCrossAttnUpBlock2D":
+        return KCrossAttnUpBlock2D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            add_upsample=add_upsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            cross_attention_dim=cross_attention_dim,
+            attention_head_dim=attention_head_dim,
+        )
+
+    raise ValueError(f"{up_block_type} does not exist.")
+
+
+class UNetMidBlockMV2DCrossAttn(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        transformer_layers_per_block: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        num_attention_heads=1,
+        output_scale_factor=1.0,
+        cross_attention_dim=1280,
+        dual_cross_attention=False,
+        use_linear_projection=False,
+        upcast_attention=False,
+        num_views: int = 1,
+        joint_attention: bool = False,
+        joint_attention_twice: bool = False,
+        multiview_attention: bool = True,
+        cross_domain_attention: bool=False
+    ):
+        super().__init__()
+
+        self.has_cross_attention = True
+        self.num_attention_heads = num_attention_heads
+        resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
+
+        # there is always at least one resnet
+        resnets = [
+            ResnetBlock2D(
+                in_channels=in_channels,
+                out_channels=in_channels,
+                temb_channels=temb_channels,
+                eps=resnet_eps,
+                groups=resnet_groups,
+                dropout=dropout,
+                time_embedding_norm=resnet_time_scale_shift,
+                non_linearity=resnet_act_fn,
+                output_scale_factor=output_scale_factor,
+                pre_norm=resnet_pre_norm,
+            )
+        ]
+        attentions = []
+
+        for _ in range(num_layers):
+            if not dual_cross_attention:
+                attentions.append(
+                    TransformerMV2DModel(
+                        num_attention_heads,
+                        in_channels // num_attention_heads,
+                        in_channels=in_channels,
+                        num_layers=transformer_layers_per_block,
+                        cross_attention_dim=cross_attention_dim,
+                        norm_num_groups=resnet_groups,
+                        use_linear_projection=use_linear_projection,
+                        upcast_attention=upcast_attention,
+                        num_views=num_views,
+                        joint_attention=joint_attention,
+                        joint_attention_twice=joint_attention_twice,
+                        multiview_attention=multiview_attention,
+                        cross_domain_attention=cross_domain_attention
+                    )
+                )
+            else:
+                raise NotImplementedError
+            resnets.append(
+                ResnetBlock2D(
+                    in_channels=in_channels,
+                    out_channels=in_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        temb: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+    ) -> torch.FloatTensor:
+        hidden_states = self.resnets[0](hidden_states, temb)
+        for attn, resnet in zip(self.attentions, self.resnets[1:]):
+            hidden_states = attn(
+                hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+                cross_attention_kwargs=cross_attention_kwargs,
+                attention_mask=attention_mask,
+                encoder_attention_mask=encoder_attention_mask,
+                return_dict=False,
+            )[0]
+            hidden_states = resnet(hidden_states, temb)
+
+        return hidden_states
+
+
+class CrossAttnUpBlockMV2D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        prev_output_channel: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        transformer_layers_per_block: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        num_attention_heads=1,
+        cross_attention_dim=1280,
+        output_scale_factor=1.0,
+        add_upsample=True,
+        dual_cross_attention=False,
+        use_linear_projection=False,
+        only_cross_attention=False,
+        upcast_attention=False,
+        num_views: int = 1,
+        joint_attention: bool = False,
+        joint_attention_twice: bool = False,
+        multiview_attention: bool = True,
+        cross_domain_attention: bool=False
+    ):
+        super().__init__()
+        resnets = []
+        attentions = []
+
+        self.has_cross_attention = True
+        self.num_attention_heads = num_attention_heads
+
+        for i in range(num_layers):
+            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
+            resnet_in_channels = prev_output_channel if i == 0 else out_channels
+
+            resnets.append(
+                ResnetBlock2D(
+                    in_channels=resnet_in_channels + res_skip_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+            if not dual_cross_attention:
+                attentions.append(
+                    TransformerMV2DModel(
+                        num_attention_heads,
+                        out_channels // num_attention_heads,
+                        in_channels=out_channels,
+                        num_layers=transformer_layers_per_block,
+                        cross_attention_dim=cross_attention_dim,
+                        norm_num_groups=resnet_groups,
+                        use_linear_projection=use_linear_projection,
+                        only_cross_attention=only_cross_attention,
+                        upcast_attention=upcast_attention,
+                        num_views=num_views,
+                        joint_attention=joint_attention,
+                        joint_attention_twice=joint_attention_twice,
+                        multiview_attention=multiview_attention,
+                        cross_domain_attention=cross_domain_attention
+                    )
+                )
+            else:
+                raise NotImplementedError
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+        if add_upsample:
+            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
+        else:
+            self.upsamplers = None
+        if num_views == 4:
+            self.gradient_checkpointing = False
+        else:
+            self.gradient_checkpointing = False
+        
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
+        temb: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        upsample_size: Optional[int] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+    ):
+        for resnet, attn in zip(self.resnets, self.attentions):
+            # pop res hidden states
+            res_hidden_states = res_hidden_states_tuple[-1]
+            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(resnet),
+                    hidden_states,
+                    temb,
+                    **ckpt_kwargs,
+                )
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(attn, return_dict=False),
+                    hidden_states,
+                    encoder_hidden_states,
+                    None,  # timestep
+                    None,  # class_labels
+                    cross_attention_kwargs,
+                    attention_mask,
+                    encoder_attention_mask,
+                    **ckpt_kwargs,
+                )[0]
+                # hidden_states = attn(
+                #     hidden_states,
+                #     encoder_hidden_states=encoder_hidden_states,
+                #     cross_attention_kwargs=cross_attention_kwargs,
+                #     attention_mask=attention_mask,
+                #     encoder_attention_mask=encoder_attention_mask,
+                #     return_dict=False,
+                # )[0]
+            else:
+                hidden_states = resnet(hidden_states, temb)
+                hidden_states = attn(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    attention_mask=attention_mask,
+                    encoder_attention_mask=encoder_attention_mask,
+                    return_dict=False,
+                )[0]
+
+        if self.upsamplers is not None:
+            for upsampler in self.upsamplers:
+                hidden_states = upsampler(hidden_states, upsample_size)
+
+        return hidden_states
+
+
+class CrossAttnDownBlockMV2D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        transformer_layers_per_block: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        num_attention_heads=1,
+        cross_attention_dim=1280,
+        output_scale_factor=1.0,
+        downsample_padding=1,
+        add_downsample=True,
+        dual_cross_attention=False,
+        use_linear_projection=False,
+        only_cross_attention=False,
+        upcast_attention=False,
+        num_views: int = 1,
+        joint_attention: bool = False,
+        joint_attention_twice: bool = False,
+        multiview_attention: bool = True,
+        cross_domain_attention: bool=False
+    ):
+        super().__init__()
+        resnets = []
+        attentions = []
+
+        self.has_cross_attention = True
+        self.num_attention_heads = num_attention_heads
+
+        for i in range(num_layers):
+            in_channels = in_channels if i == 0 else out_channels
+            resnets.append(
+                ResnetBlock2D(
+                    in_channels=in_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+            if not dual_cross_attention:
+                attentions.append(
+                    TransformerMV2DModel(
+                        num_attention_heads,
+                        out_channels // num_attention_heads,
+                        in_channels=out_channels,
+                        num_layers=transformer_layers_per_block,
+                        cross_attention_dim=cross_attention_dim,
+                        norm_num_groups=resnet_groups,
+                        use_linear_projection=use_linear_projection,
+                        only_cross_attention=only_cross_attention,
+                        upcast_attention=upcast_attention,
+                        num_views=num_views,
+                        joint_attention=joint_attention,
+                        joint_attention_twice=joint_attention_twice,
+                        multiview_attention=multiview_attention,
+                        cross_domain_attention=cross_domain_attention
+                    )
+                )
+            else:
+                raise NotImplementedError
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+        if add_downsample:
+            self.downsamplers = nn.ModuleList(
+                [
+                    Downsample2D(
+                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
+                    )
+                ]
+            )
+        else:
+            self.downsamplers = None
+        if num_views == 4:
+            self.gradient_checkpointing = False
+        else:
+            self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        temb: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        additional_residuals=None,
+    ):
+        output_states = ()
+
+        blocks = list(zip(self.resnets, self.attentions))
+
+        for i, (resnet, attn) in enumerate(blocks):
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(resnet),
+                    hidden_states,
+                    temb,
+                    **ckpt_kwargs,
+                )
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(attn, return_dict=False),
+                    hidden_states,
+                    encoder_hidden_states,
+                    None,  # timestep
+                    None,  # class_labels
+                    cross_attention_kwargs,
+                    attention_mask,
+                    encoder_attention_mask,
+                    **ckpt_kwargs,
+                )[0]
+            else:
+                # import ipdb
+                # ipdb.set_trace()
+                hidden_states = resnet(hidden_states, temb)
+                hidden_states = attn(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    attention_mask=attention_mask,
+                    encoder_attention_mask=encoder_attention_mask,
+                    return_dict=False,
+                )[0]
+
+            # apply additional residuals to the output of the last pair of resnet and attention blocks
+            if i == len(blocks) - 1 and additional_residuals is not None:
+                hidden_states = hidden_states + additional_residuals
+
+            output_states = output_states + (hidden_states,)
+
+        if self.downsamplers is not None:
+            for downsampler in self.downsamplers:
+                hidden_states = downsampler(hidden_states)
+
+            output_states = output_states + (hidden_states,)
+
+        return hidden_states, output_states
+

2D_Stage/tuneavideo/models/unet_mv2d_condition.py

@@ -0,0 +1,1509 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Union
+import os
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+from einops import rearrange
+
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders import UNet2DConditionLoadersMixin
+from diffusers.utils import BaseOutput, logging
+from diffusers.models.activations import get_activation
+from diffusers.models.attention_processor import AttentionProcessor, AttnProcessor
+from diffusers.models.embeddings import (
+    GaussianFourierProjection,
+    ImageHintTimeEmbedding,
+    ImageProjection,
+    ImageTimeEmbedding,
+    TextImageProjection,
+    TextImageTimeEmbedding,
+    TextTimeEmbedding,
+    TimestepEmbedding,
+    Timesteps,
+)
+from diffusers.models.modeling_utils import ModelMixin, load_state_dict, _load_state_dict_into_model
+from diffusers.models.unet_2d_blocks import (
+    CrossAttnDownBlock2D,
+    CrossAttnUpBlock2D,
+    DownBlock2D,
+    UNetMidBlock2DCrossAttn,
+    UNetMidBlock2DSimpleCrossAttn,
+    UpBlock2D,
+)
+from diffusers.utils import (
+    CONFIG_NAME,
+    DIFFUSERS_CACHE,
+    FLAX_WEIGHTS_NAME,
+    HF_HUB_OFFLINE,
+    SAFETENSORS_WEIGHTS_NAME,
+    WEIGHTS_NAME,
+    _add_variant,
+    _get_model_file,
+    deprecate,
+    is_accelerate_available,
+    is_torch_version,
+    logging,
+)
+from diffusers import __version__
+from tuneavideo.models.unet_mv2d_blocks import (
+    CrossAttnDownBlockMV2D,
+    CrossAttnUpBlockMV2D,
+    UNetMidBlockMV2DCrossAttn,
+    get_down_block,
+    get_up_block,
+)
+from diffusers.models.attention_processor import Attention, AttnProcessor
+from diffusers.utils.import_utils import is_xformers_available
+from tuneavideo.models.transformer_mv2d import XFormersMVAttnProcessor, MVAttnProcessor
+from tuneavideo.models.refunet import ReferenceOnlyAttnProc
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@dataclass
+class UNetMV2DConditionOutput(BaseOutput):
+    """
+    The output of [`UNet2DConditionModel`].
+
+    Args:
+        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+            The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
+    """
+
+    sample: torch.FloatTensor = None
+
+class UNetMV2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
+    r"""
+    A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
+    shaped output.
+
+    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
+    for all models (such as downloading or saving).
+
+    Parameters:
+        sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
+            Height and width of input/output sample.
+        in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.
+        out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
+        center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
+        flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
+            Whether to flip the sin to cos in the time embedding.
+        freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
+        down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
+            The tuple of downsample blocks to use.
+        mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`):
+            Block type for middle of UNet, it can be either `UNetMidBlock2DCrossAttn` or
+            `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped.
+        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`):
+            The tuple of upsample blocks to use.
+        only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):
+            Whether to include self-attention in the basic transformer blocks, see
+            [`~models.attention.BasicTransformerBlock`].
+        block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
+            The tuple of output channels for each block.
+        layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
+        downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
+        mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
+        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
+        norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
+            If `None`, normalization and activation layers is skipped in post-processing.
+        norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
+        cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
+            The dimension of the cross attention features.
+        transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1):
+            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
+            [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
+            [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
+        encoder_hid_dim (`int`, *optional*, defaults to None):
+            If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
+            dimension to `cross_attention_dim`.
+        encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
+            If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
+            embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
+        attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
+        num_attention_heads (`int`, *optional*):
+            The number of attention heads. If not defined, defaults to `attention_head_dim`
+        resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
+            for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
+        class_embed_type (`str`, *optional*, defaults to `None`):
+            The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
+            `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
+        addition_embed_type (`str`, *optional*, defaults to `None`):
+            Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
+            "text". "text" will use the `TextTimeEmbedding` layer.
+        addition_time_embed_dim: (`int`, *optional*, defaults to `None`):
+            Dimension for the timestep embeddings.
+        num_class_embeds (`int`, *optional*, defaults to `None`):
+            Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
+            class conditioning with `class_embed_type` equal to `None`.
+        time_embedding_type (`str`, *optional*, defaults to `positional`):
+            The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
+        time_embedding_dim (`int`, *optional*, defaults to `None`):
+            An optional override for the dimension of the projected time embedding.
+        time_embedding_act_fn (`str`, *optional*, defaults to `None`):
+            Optional activation function to use only once on the time embeddings before they are passed to the rest of
+            the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.
+        timestep_post_act (`str`, *optional*, defaults to `None`):
+            The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
+        time_cond_proj_dim (`int`, *optional*, defaults to `None`):
+            The dimension of `cond_proj` layer in the timestep embedding.
+        conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer.
+        conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer.
+        projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when
+            `class_embed_type="projection"`. Required when `class_embed_type="projection"`.
+        class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time
+            embeddings with the class embeddings.
+        mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):
+            Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If
+            `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the
+            `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`
+            otherwise.
+    """
+
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    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] = (
+            "CrossAttnDownBlockMV2D",
+            "CrossAttnDownBlockMV2D",
+            "CrossAttnDownBlockMV2D",
+            "DownBlock2D",
+        ),
+        mid_block_type: Optional[str] = "UNetMidBlockMV2DCrossAttn",
+        up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlockMV2D", "CrossAttnUpBlockMV2D", "CrossAttnUpBlockMV2D"),
+        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,
+        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]] = 1,
+        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: int = 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,
+        class_embeddings_concat: bool = False,
+        mid_block_only_cross_attention: Optional[bool] = None,
+        cross_attention_norm: Optional[str] = None,
+        addition_embed_type_num_heads=64,
+        num_views: int = 1,
+        joint_attention: bool = False,
+        joint_attention_twice: bool = False,
+        multiview_attention: bool = True,
+        cross_domain_attention: bool = False,
+        camera_input_dim: int = 12,
+        camera_hidden_dim: int = 320,
+        camera_output_dim: int = 1280,
+
+    ):
+        super().__init__()
+
+        self.sample_size = sample_size
+
+        if num_attention_heads is not None:
+            raise ValueError(
+                "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."
+            )
+
+        # If `num_attention_heads` is not defined (which is the case for most models)
+        # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
+        # The reason for this behavior is to correct for incorrectly named variables that were introduced
+        # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
+        # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
+        # which is why we correct for the naming here.
+        num_attention_heads = num_attention_heads or attention_head_dim
+
+        # Check inputs
+        if len(down_block_types) != len(up_block_types):
+            raise ValueError(
+                f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
+            )
+
+        if len(block_out_channels) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}."
+            )
+
+        if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
+            )
+
+        # input
+        conv_in_padding = (conv_in_kernel - 1) // 2
+        self.conv_in = nn.Conv2d(
+            in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
+        )
+
+        # time
+        if time_embedding_type == "fourier":
+            time_embed_dim = time_embedding_dim or block_out_channels[0] * 2
+            if time_embed_dim % 2 != 0:
+                raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.")
+            self.time_proj = GaussianFourierProjection(
+                time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
+            )
+            timestep_input_dim = time_embed_dim
+        elif time_embedding_type == "positional":
+            time_embed_dim = time_embedding_dim or block_out_channels[0] * 4
+
+            self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
+            timestep_input_dim = block_out_channels[0]
+        else:
+            raise ValueError(
+                f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
+            )
+
+        self.time_embedding = TimestepEmbedding(
+            timestep_input_dim,
+            time_embed_dim,
+            act_fn=act_fn,
+            post_act_fn=timestep_post_act,
+            cond_proj_dim=time_cond_proj_dim,
+        )
+
+        if encoder_hid_dim_type is None and encoder_hid_dim is not None:
+            encoder_hid_dim_type = "text_proj"
+            self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
+            logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")
+
+        if encoder_hid_dim is None and encoder_hid_dim_type is not None:
+            raise ValueError(
+                f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
+            )
+
+        if encoder_hid_dim_type == "text_proj":
+            self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
+        elif encoder_hid_dim_type == "text_image_proj":
+            # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
+            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
+            # case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)`
+            self.encoder_hid_proj = TextImageProjection(
+                text_embed_dim=encoder_hid_dim,
+                image_embed_dim=cross_attention_dim,
+                cross_attention_dim=cross_attention_dim,
+            )
+        elif encoder_hid_dim_type == "image_proj":
+            # Kandinsky 2.2
+            self.encoder_hid_proj = ImageProjection(
+                image_embed_dim=encoder_hid_dim,
+                cross_attention_dim=cross_attention_dim,
+            )
+        elif encoder_hid_dim_type is not None:
+            raise ValueError(
+                f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
+            )
+        else:
+            self.encoder_hid_proj = None
+
+        # class embedding
+        if class_embed_type is None and num_class_embeds is not None:
+            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
+        elif class_embed_type == "timestep":
+            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)
+        elif class_embed_type == "identity":
+            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
+        elif class_embed_type == "projection":
+            if projection_class_embeddings_input_dim is None:
+                raise ValueError(
+                    "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
+                )
+            # The projection `class_embed_type` is the same as the timestep `class_embed_type` except
+            # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
+            # 2. it projects from an arbitrary input dimension.
+            #
+            # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
+            # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
+            # As a result, `TimestepEmbedding` can be passed arbitrary vectors.
+            self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
+        elif class_embed_type == "simple_projection":
+            if projection_class_embeddings_input_dim is None:
+                raise ValueError(
+                    "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
+                )
+            self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)
+        else:
+            self.class_embedding = None
+
+        if addition_embed_type == "text":
+            if encoder_hid_dim is not None:
+                text_time_embedding_from_dim = encoder_hid_dim
+            else:
+                text_time_embedding_from_dim = cross_attention_dim
+
+            self.add_embedding = TextTimeEmbedding(
+                text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
+            )
+        elif addition_embed_type == "text_image":
+            # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
+            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
+            # case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`
+            self.add_embedding = TextImageTimeEmbedding(
+                text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
+            )
+        elif addition_embed_type == "text_time":
+            self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
+            self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
+        elif addition_embed_type == "image":
+            # Kandinsky 2.2
+            self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
+        elif addition_embed_type == "image_hint":
+            # Kandinsky 2.2 ControlNet
+            self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
+        elif addition_embed_type is not None:
+            raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
+
+        if time_embedding_act_fn is None:
+            self.time_embed_act = None
+        else:
+            self.time_embed_act = get_activation(time_embedding_act_fn)
+
+        self.camera_embedding = nn.Sequential(
+            nn.Linear(camera_input_dim, time_embed_dim),
+            nn.SiLU(),
+            nn.Linear(time_embed_dim, time_embed_dim),
+        )
+
+        self.down_blocks = nn.ModuleList([])
+        self.up_blocks = nn.ModuleList([])
+
+        if isinstance(only_cross_attention, bool):
+            if mid_block_only_cross_attention is None:
+                mid_block_only_cross_attention = only_cross_attention
+
+            only_cross_attention = [only_cross_attention] * len(down_block_types)
+
+        if mid_block_only_cross_attention is None:
+            mid_block_only_cross_attention = False
+
+        if isinstance(num_attention_heads, int):
+            num_attention_heads = (num_attention_heads,) * len(down_block_types)
+
+        if isinstance(attention_head_dim, int):
+            attention_head_dim = (attention_head_dim,) * len(down_block_types)
+
+        if isinstance(cross_attention_dim, int):
+            cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
+
+        if isinstance(layers_per_block, int):
+            layers_per_block = [layers_per_block] * len(down_block_types)
+
+        if isinstance(transformer_layers_per_block, int):
+            transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
+
+        if class_embeddings_concat:
+            # The time embeddings are concatenated with the class embeddings. The dimension of the
+            # time embeddings passed to the down, middle, and up blocks is twice the dimension of the
+            # regular time embeddings
+            blocks_time_embed_dim = time_embed_dim * 2
+        else:
+            blocks_time_embed_dim = time_embed_dim
+
+        # down
+        output_channel = block_out_channels[0]
+        for i, down_block_type in enumerate(down_block_types):
+            input_channel = output_channel
+            output_channel = block_out_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+
+            down_block = get_down_block(
+                down_block_type,
+                num_layers=layers_per_block[i],
+                transformer_layers_per_block=transformer_layers_per_block[i],
+                in_channels=input_channel,
+                out_channels=output_channel,
+                temb_channels=blocks_time_embed_dim,
+                add_downsample=not is_final_block,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=cross_attention_dim[i],
+                num_attention_heads=num_attention_heads[i],
+                downsample_padding=downsample_padding,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                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,
+                cross_attention_norm=cross_attention_norm,
+                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
+                num_views=num_views,
+                joint_attention=joint_attention,
+                joint_attention_twice=joint_attention_twice,
+                multiview_attention=multiview_attention,
+                cross_domain_attention=cross_domain_attention
+            )
+            self.down_blocks.append(down_block)
+
+        # mid
+        if mid_block_type == "UNetMidBlock2DCrossAttn":
+            self.mid_block = UNetMidBlock2DCrossAttn(
+                transformer_layers_per_block=transformer_layers_per_block[-1],
+                in_channels=block_out_channels[-1],
+                temb_channels=blocks_time_embed_dim,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                cross_attention_dim=cross_attention_dim[-1],
+                num_attention_heads=num_attention_heads[-1],
+                resnet_groups=norm_num_groups,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                upcast_attention=upcast_attention,
+            )
+        # custom MV2D attention block  
+        elif mid_block_type == "UNetMidBlockMV2DCrossAttn":
+            self.mid_block = UNetMidBlockMV2DCrossAttn(
+                transformer_layers_per_block=transformer_layers_per_block[-1],
+                in_channels=block_out_channels[-1],
+                temb_channels=blocks_time_embed_dim,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                cross_attention_dim=cross_attention_dim[-1],
+                num_attention_heads=num_attention_heads[-1],
+                resnet_groups=norm_num_groups,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                upcast_attention=upcast_attention,
+                num_views=num_views,
+                joint_attention=joint_attention,
+                joint_attention_twice=joint_attention_twice,
+                multiview_attention=multiview_attention,
+                cross_domain_attention=cross_domain_attention
+            )
+        elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn":
+            self.mid_block = UNetMidBlock2DSimpleCrossAttn(
+                in_channels=block_out_channels[-1],
+                temb_channels=blocks_time_embed_dim,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                cross_attention_dim=cross_attention_dim[-1],
+                attention_head_dim=attention_head_dim[-1],
+                resnet_groups=norm_num_groups,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                skip_time_act=resnet_skip_time_act,
+                only_cross_attention=mid_block_only_cross_attention,
+                cross_attention_norm=cross_attention_norm,
+            )
+        elif mid_block_type is None:
+            self.mid_block = None
+        else:
+            raise ValueError(f"unknown mid_block_type : {mid_block_type}")
+
+        # count how many layers upsample the images
+        self.num_upsamplers = 0
+
+        # up
+        reversed_block_out_channels = list(reversed(block_out_channels))
+        reversed_num_attention_heads = list(reversed(num_attention_heads))
+        reversed_layers_per_block = list(reversed(layers_per_block))
+        reversed_cross_attention_dim = list(reversed(cross_attention_dim))
+        reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block))
+        only_cross_attention = list(reversed(only_cross_attention))
+
+        output_channel = reversed_block_out_channels[0]
+        for i, up_block_type in enumerate(up_block_types):
+            is_final_block = i == len(block_out_channels) - 1
+
+            prev_output_channel = output_channel
+            output_channel = reversed_block_out_channels[i]
+            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
+
+            # add upsample block for all BUT final layer
+            if not is_final_block:
+                add_upsample = True
+                self.num_upsamplers += 1
+            else:
+                add_upsample = False
+
+            up_block = get_up_block(
+                up_block_type,
+                num_layers=reversed_layers_per_block[i] + 1,
+                transformer_layers_per_block=reversed_transformer_layers_per_block[i],
+                in_channels=input_channel,
+                out_channels=output_channel,
+                prev_output_channel=prev_output_channel,
+                temb_channels=blocks_time_embed_dim,
+                add_upsample=add_upsample,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=reversed_cross_attention_dim[i],
+                num_attention_heads=reversed_num_attention_heads[i],
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                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,
+                cross_attention_norm=cross_attention_norm,
+                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
+                num_views=num_views,
+                joint_attention=joint_attention,
+                joint_attention_twice=joint_attention_twice,
+                multiview_attention=multiview_attention,
+                cross_domain_attention=cross_domain_attention
+            )
+            self.up_blocks.append(up_block)
+            prev_output_channel = output_channel
+
+        # out
+        if norm_num_groups is not None:
+            self.conv_norm_out = nn.GroupNorm(
+                num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
+            )
+
+            self.conv_act = get_activation(act_fn)
+
+        else:
+            self.conv_norm_out = None
+            self.conv_act = None
+
+        conv_out_padding = (conv_out_kernel - 1) // 2
+        self.conv_out = nn.Conv2d(
+            block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding
+        )
+
+    @property
+    def attn_processors(self) -> Dict[str, AttentionProcessor]:
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        # set recursively
+        processors = {}
+
+        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
+            if hasattr(module, "set_processor"):
+                processors[f"{name}.processor"] = module.processor
+
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
+        r"""
+        Sets the attention processor to use to compute attention.
+
+        Parameters:
+            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                for **all** `Attention` layers.
+
+                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+                processor. This is strongly recommended when setting trainable attention processors.
+
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"))
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
+
+    def set_default_attn_processor(self):
+        """
+        Disables custom attention processors and sets the default attention implementation.
+        """
+        self.set_attn_processor(AttnProcessor())
+
+    def set_attention_slice(self, slice_size):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module splits the input tensor in slices to compute attention in
+        several steps. This is useful for saving some memory in exchange for a small decrease in speed.
+
+        Args:
+            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
+                When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
+                `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
+                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
+                must be a multiple of `slice_size`.
+        """
+        sliceable_head_dims = []
+
+        def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
+            if hasattr(module, "set_attention_slice"):
+                sliceable_head_dims.append(module.sliceable_head_dim)
+
+            for child in module.children():
+                fn_recursive_retrieve_sliceable_dims(child)
+
+        # retrieve number of attention layers
+        for module in self.children():
+            fn_recursive_retrieve_sliceable_dims(module)
+
+        num_sliceable_layers = len(sliceable_head_dims)
+
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = [dim // 2 for dim in sliceable_head_dims]
+        elif slice_size == "max":
+            # make smallest slice possible
+            slice_size = num_sliceable_layers * [1]
+
+        slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
+
+        if len(slice_size) != len(sliceable_head_dims):
+            raise ValueError(
+                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
+                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
+            )
+
+        for i in range(len(slice_size)):
+            size = slice_size[i]
+            dim = sliceable_head_dims[i]
+            if size is not None and size > dim:
+                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
+
+        # Recursively walk through all the children.
+        # Any children which exposes the set_attention_slice method
+        # gets the message
+        def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
+            if hasattr(module, "set_attention_slice"):
+                module.set_attention_slice(slice_size.pop())
+
+            for child in module.children():
+                fn_recursive_set_attention_slice(child, slice_size)
+
+        reversed_slice_size = list(reversed(slice_size))
+        for module in self.children():
+            fn_recursive_set_attention_slice(module, reversed_slice_size)
+
+    # def _set_gradient_checkpointing(self, module, value=False):
+    #     if isinstance(module, (CrossAttnDownBlock2D, CrossAttnDownBlockMV2D, DownBlock2D, CrossAttnUpBlock2D, CrossAttnUpBlockMV2D, UpBlock2D)):
+    #         module.gradient_checkpointing = value
+
+    def forward(
+        self,
+        sample: torch.FloatTensor,
+        timestep: Union[torch.Tensor, float, int],
+        encoder_hidden_states: torch.Tensor,
+        camera_matrixs: Optional[torch.Tensor] = None,
+        class_labels: Optional[torch.Tensor] = None,
+        timestep_cond: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
+        down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
+        mid_block_additional_residual: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        return_dict: bool = True,
+    ) -> Union[UNetMV2DConditionOutput, Tuple]:
+        r"""
+        The [`UNet2DConditionModel`] forward method.
+
+        Args:
+            sample (`torch.FloatTensor`):
+                The noisy input tensor with the following shape `(batch, channel, height, width)`.
+            timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
+            encoder_hidden_states (`torch.FloatTensor`):
+                The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
+            encoder_attention_mask (`torch.Tensor`):
+                A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
+                `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
+                which adds large negative values to the attention scores corresponding to "discard" tokens.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
+                tuple.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].
+            added_cond_kwargs: (`dict`, *optional*):
+                A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that
+                are passed along to the UNet blocks.
+
+        Returns:
+            [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
+                If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise
+                a `tuple` is returned where the first element is the sample tensor.
+        """
+        # By default samples have to be AT least a multiple of the overall upsampling factor.
+        # The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
+        # However, the upsampling interpolation output size can be forced to fit any upsampling size
+        # on the fly if necessary.
+        default_overall_up_factor = 2**self.num_upsamplers
+
+        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
+        forward_upsample_size = False
+        upsample_size = None
+
+        if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
+            logger.info("Forward upsample size to force interpolation output size.")
+            forward_upsample_size = True
+
+        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension
+        # expects mask of shape:
+        #   [batch, key_tokens]
+        # adds singleton query_tokens dimension:
+        #   [batch,                    1, key_tokens]
+        # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
+        #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
+        #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
+        if attention_mask is not None:
+            # assume that mask is expressed as:
+            #   (1 = keep,      0 = discard)
+            # convert mask into a bias that can be added to attention scores:
+            #       (keep = +0,     discard = -10000.0)
+            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
+            attention_mask = attention_mask.unsqueeze(1)
+
+        # convert encoder_attention_mask to a bias the same way we do for attention_mask
+        if encoder_attention_mask is not None:
+            encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
+            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
+
+        # 0. center input if necessary
+        if self.config.center_input_sample:
+            sample = 2 * sample - 1.0
+
+        # 1. time
+        timesteps = timestep
+        if not torch.is_tensor(timesteps):
+            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
+            # This would be a good case for the `match` statement (Python 3.10+)
+            is_mps = sample.device.type == "mps"
+            if isinstance(timestep, float):
+                dtype = torch.float32 if is_mps else torch.float64
+            else:
+                dtype = torch.int32 if is_mps else torch.int64
+            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
+        elif len(timesteps.shape) == 0:
+            timesteps = timesteps[None].to(sample.device)
+
+        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+        timesteps = timesteps.expand(sample.shape[0])
+
+        t_emb = self.time_proj(timesteps)
+
+        # `Timesteps` does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=sample.dtype)
+        emb = self.time_embedding(t_emb, timestep_cond)
+        
+        # import pdb; pdb.set_trace()
+        if camera_matrixs is not None:
+            emb = torch.unsqueeze(emb, 1)
+            # came emb
+            cam_emb = self.camera_embedding(camera_matrixs)
+            # cam_emb = self.camera_embedding_2(cam_emb)
+            # import ipdb
+            # ipdb.set_trace()
+            emb = emb.repeat(1,cam_emb.shape[1],1) #torch.Size([32, 4, 1280])
+            emb = emb + cam_emb
+            emb = rearrange(emb, "b f c -> (b f) c", f=emb.shape[1])
+
+        aug_emb = None
+
+        if self.class_embedding is not None and class_labels is not None:
+            if class_labels is None:
+                raise ValueError("class_labels should be provided when num_class_embeds > 0")
+
+            if self.config.class_embed_type == "timestep":
+                class_labels = self.time_proj(class_labels)
+
+                # `Timesteps` does not contain any weights and will always return f32 tensors
+                # there might be better ways to encapsulate this.
+                class_labels = class_labels.to(dtype=sample.dtype)
+
+            class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)
+
+            if self.config.class_embeddings_concat:
+                emb = torch.cat([emb, class_emb], dim=-1)
+            else:
+                emb = emb + class_emb
+
+        if self.config.addition_embed_type == "text":
+            aug_emb = self.add_embedding(encoder_hidden_states)
+        elif self.config.addition_embed_type == "text_image":
+            # Kandinsky 2.1 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
+                )
+
+            image_embs = added_cond_kwargs.get("image_embeds")
+            text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states)
+            aug_emb = self.add_embedding(text_embs, image_embs)
+        elif self.config.addition_embed_type == "text_time":
+            # SDXL - style
+            if "text_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
+                )
+            text_embeds = added_cond_kwargs.get("text_embeds")
+            if "time_ids" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
+                )
+            time_ids = added_cond_kwargs.get("time_ids")
+            time_embeds = self.add_time_proj(time_ids.flatten())
+            time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
+
+            add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
+            add_embeds = add_embeds.to(emb.dtype)
+            aug_emb = self.add_embedding(add_embeds)
+        elif self.config.addition_embed_type == "image":
+            # Kandinsky 2.2 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
+                )
+            image_embs = added_cond_kwargs.get("image_embeds")
+            aug_emb = self.add_embedding(image_embs)
+        elif self.config.addition_embed_type == "image_hint":
+            # Kandinsky 2.2 - style
+            if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`"
+                )
+            image_embs = added_cond_kwargs.get("image_embeds")
+            hint = added_cond_kwargs.get("hint")
+            aug_emb, hint = self.add_embedding(image_embs, hint)
+            sample = torch.cat([sample, hint], dim=1)
+
+        emb = emb + aug_emb if aug_emb is not None else emb
+
+        if self.time_embed_act is not None:
+            emb = self.time_embed_act(emb)
+
+        if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj":
+            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)
+        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj":
+            # Kadinsky 2.1 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
+                )
+
+            image_embeds = added_cond_kwargs.get("image_embeds")
+            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)
+        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj":
+            # Kandinsky 2.2 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
+                )
+            image_embeds = added_cond_kwargs.get("image_embeds")
+            encoder_hidden_states = self.encoder_hid_proj(image_embeds)
+        # 2. pre-process
+        sample = rearrange(sample, "b c f h w -> (b f) c h w", f=sample.shape[2])
+        sample = self.conv_in(sample)
+        # 3. down
+
+        is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None
+        is_adapter = mid_block_additional_residual is None and down_block_additional_residuals is not None
+
+        down_block_res_samples = (sample,)
+        for downsample_block in self.down_blocks:
+            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
+                # For t2i-adapter CrossAttnDownBlock2D
+                additional_residuals = {}
+                if is_adapter and len(down_block_additional_residuals) > 0:
+                    additional_residuals["additional_residuals"] = down_block_additional_residuals.pop(0)
+
+                sample, res_samples = downsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=attention_mask,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    encoder_attention_mask=encoder_attention_mask,
+                    **additional_residuals,
+                )
+            else:
+                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
+
+                if is_adapter and len(down_block_additional_residuals) > 0:
+                    sample += down_block_additional_residuals.pop(0)
+
+            down_block_res_samples += res_samples
+
+        if is_controlnet:
+            new_down_block_res_samples = ()
+
+            for down_block_res_sample, down_block_additional_residual in zip(
+                down_block_res_samples, down_block_additional_residuals
+            ):
+                down_block_res_sample = down_block_res_sample + down_block_additional_residual
+                new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)
+
+            down_block_res_samples = new_down_block_res_samples
+        # print("after down: ", sample.mean(), emb.mean())
+        # 4. mid
+        if self.mid_block is not None:
+            sample = self.mid_block(
+                sample,
+                emb,
+                encoder_hidden_states=encoder_hidden_states,
+                attention_mask=attention_mask,
+                cross_attention_kwargs=cross_attention_kwargs,
+                encoder_attention_mask=encoder_attention_mask,
+            )
+
+        if is_controlnet:
+            sample = sample + mid_block_additional_residual
+
+        # 5. up
+        for i, upsample_block in enumerate(self.up_blocks):
+            is_final_block = i == len(self.up_blocks) - 1
+
+            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
+            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
+
+            # if we have not reached the final block and need to forward the
+            # upsample size, we do it here
+            if not is_final_block and forward_upsample_size:
+                upsample_size = down_block_res_samples[-1].shape[2:]
+
+            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
+                sample = upsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    res_hidden_states_tuple=res_samples,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    upsample_size=upsample_size,
+                    attention_mask=attention_mask,
+                    encoder_attention_mask=encoder_attention_mask,
+                )
+            else:
+                sample = upsample_block(
+                    hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
+                )
+
+        # 6. post-process
+        if self.conv_norm_out:
+            sample = self.conv_norm_out(sample)
+            sample = self.conv_act(sample)
+        sample = self.conv_out(sample)
+
+        if not return_dict:
+            return (sample,)
+
+        return UNetMV2DConditionOutput(sample=sample)
+
+    @classmethod
+    def from_pretrained_2d(
+            cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
+            camera_embedding_type: str, num_views: int, sample_size: int,
+            zero_init_conv_in: bool = True, zero_init_camera_projection: bool = False,
+            projection_class_embeddings_input_dim: int=6, joint_attention: bool = False, 
+            joint_attention_twice: bool = False, multiview_attention: bool = True,
+            cross_domain_attention: bool = False,
+            in_channels: int = 8, out_channels: int = 4, local_crossattn=False,
+            **kwargs
+        ):
+        r"""
+        Instantiate a pretrained PyTorch model from a pretrained model configuration.
+
+        The model is set in evaluation mode - `model.eval()` - by default, and dropout modules are deactivated. To
+        train the model, set it back in training mode with `model.train()`.
+
+        Parameters:
+            pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
+                Can be either:
+
+                    - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
+                      the Hub.
+                    - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
+                      with [`~ModelMixin.save_pretrained`].
+
+            cache_dir (`Union[str, os.PathLike]`, *optional*):
+                Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
+                is not used.
+            torch_dtype (`str` or `torch.dtype`, *optional*):
+                Override the default `torch.dtype` and load the model with another dtype. If `"auto"` is passed, the
+                dtype is automatically derived from the model's weights.
+            force_download (`bool`, *optional*, defaults to `False`):
+                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
+                cached versions if they exist.
+            resume_download (`bool`, *optional*, defaults to `False`):
+                Whether or not to resume downloading the model weights and configuration files. If set to `False`, any
+                incompletely downloaded files are deleted.
+            proxies (`Dict[str, str]`, *optional*):
+                A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
+                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
+            output_loading_info (`bool`, *optional*, defaults to `False`):
+                Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
+            local_files_only(`bool`, *optional*, defaults to `False`):
+                Whether to only load local model weights and configuration files or not. If set to `True`, the model
+                won't be downloaded from the Hub.
+            use_auth_token (`str` or *bool*, *optional*):
+                The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
+                `diffusers-cli login` (stored in `~/.huggingface`) is used.
+            revision (`str`, *optional*, defaults to `"main"`):
+                The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
+                allowed by Git.
+            from_flax (`bool`, *optional*, defaults to `False`):
+                Load the model weights from a Flax checkpoint save file.
+            subfolder (`str`, *optional*, defaults to `""`):
+                The subfolder location of a model file within a larger model repository on the Hub or locally.
+            mirror (`str`, *optional*):
+                Mirror source to resolve accessibility issues if you're downloading a model in China. We do not
+                guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
+                information.
+            device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):
+                A map that specifies where each submodule should go. It doesn't need to be defined for each
+                parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the
+                same device.
+
+                Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For
+                more information about each option see [designing a device
+                map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
+            max_memory (`Dict`, *optional*):
+                A dictionary device identifier for the maximum memory. Will default to the maximum memory available for
+                each GPU and the available CPU RAM if unset.
+            offload_folder (`str` or `os.PathLike`, *optional*):
+                The path to offload weights if `device_map` contains the value `"disk"`.
+            offload_state_dict (`bool`, *optional*):
+                If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if
+                the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True`
+                when there is some disk offload.
+            low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
+                Speed up model loading only loading the pretrained weights and not initializing the weights. This also
+                tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
+                Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
+                argument to `True` will raise an error.
+            variant (`str`, *optional*):
+                Load weights from a specified `variant` filename such as `"fp16"` or `"ema"`. This is ignored when
+                loading `from_flax`.
+            use_safetensors (`bool`, *optional*, defaults to `None`):
+                If set to `None`, the `safetensors` weights are downloaded if they're available **and** if the
+                `safetensors` library is installed. If set to `True`, the model is forcibly loaded from `safetensors`
+                weights. If set to `False`, `safetensors` weights are not loaded.
+
+        <Tip>
+
+        To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with
+        `huggingface-cli login`. You can also activate the special
+        ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use this method in a
+        firewalled environment.
+
+        </Tip>
+
+        Example:
+
+        ```py
+        from diffusers import UNet2DConditionModel
+
+        unet = UNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet")
+        ```
+
+        If you get the error message below, you need to finetune the weights for your downstream task:
+
+        ```bash
+        Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
+        - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated
+        You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+        ```
+        """
+        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
+        ignore_mismatched_sizes = kwargs.pop("ignore_mismatched_sizes", False)
+        force_download = kwargs.pop("force_download", False)
+        from_flax = kwargs.pop("from_flax", False)
+        resume_download = kwargs.pop("resume_download", False)
+        proxies = kwargs.pop("proxies", None)
+        output_loading_info = kwargs.pop("output_loading_info", False)
+        local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE)
+        use_auth_token = kwargs.pop("use_auth_token", None)
+        revision = kwargs.pop("revision", None)
+        torch_dtype = kwargs.pop("torch_dtype", None)
+        subfolder = kwargs.pop("subfolder", None)
+        device_map = kwargs.pop("device_map", None)
+        max_memory = kwargs.pop("max_memory", None)
+        offload_folder = kwargs.pop("offload_folder", None)
+        offload_state_dict = kwargs.pop("offload_state_dict", False)
+        variant = kwargs.pop("variant", None)
+        use_safetensors = kwargs.pop("use_safetensors", None)
+
+        # if use_safetensors and not is_safetensors_available():
+        #     raise ValueError(
+        #         "`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetensors"
+        #     )
+
+        allow_pickle = False
+        if use_safetensors is None:
+            # use_safetensors = is_safetensors_available()
+            use_safetensors = False
+            allow_pickle = True
+
+        if device_map is not None and not is_accelerate_available():
+            raise NotImplementedError(
+                "Loading and dispatching requires `accelerate`. Please make sure to install accelerate or set"
+                " `device_map=None`. You can install accelerate with `pip install accelerate`."
+            )
+
+        # Check if we can handle device_map and dispatching the weights
+        if device_map is not None and not is_torch_version(">=", "1.9.0"):
+            raise NotImplementedError(
+                "Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set"
+                " `device_map=None`."
+            )
+
+        # Load config if we don't provide a configuration
+        config_path = pretrained_model_name_or_path
+
+        user_agent = {
+            "diffusers": __version__,
+            "file_type": "model",
+            "framework": "pytorch",
+        }
+
+        # load config
+        config, unused_kwargs, commit_hash = cls.load_config(
+            config_path,
+            cache_dir=cache_dir,
+            return_unused_kwargs=True,
+            return_commit_hash=True,
+            force_download=force_download,
+            resume_download=resume_download,
+            proxies=proxies,
+            local_files_only=local_files_only,
+            use_auth_token=use_auth_token,
+            revision=revision,
+            subfolder=subfolder,
+            device_map=device_map,
+            max_memory=max_memory,
+            offload_folder=offload_folder,
+            offload_state_dict=offload_state_dict,
+            user_agent=user_agent,
+            **kwargs,
+        )
+
+        # modify config
+        config["_class_name"] = cls.__name__
+        config['in_channels'] = in_channels
+        config['out_channels'] = out_channels
+        config['sample_size'] = sample_size # training resolution
+        config['num_views'] = num_views
+        config['joint_attention'] = joint_attention
+        config['joint_attention_twice'] = joint_attention_twice
+        config['multiview_attention'] = multiview_attention
+        config['cross_domain_attention'] = cross_domain_attention
+        config["down_block_types"] = [
+            "CrossAttnDownBlockMV2D",
+            "CrossAttnDownBlockMV2D",
+            "CrossAttnDownBlockMV2D",
+            "DownBlock2D"
+        ]
+        config['mid_block_type'] = "UNetMidBlockMV2DCrossAttn"
+        config["up_block_types"] = [
+            "UpBlock2D",
+            "CrossAttnUpBlockMV2D",
+            "CrossAttnUpBlockMV2D",
+            "CrossAttnUpBlockMV2D"
+        ]        
+        config['class_embed_type'] = 'projection'
+        if camera_embedding_type == 'e_de_da_sincos':
+            config['projection_class_embeddings_input_dim'] = projection_class_embeddings_input_dim # default 6
+        else:
+            raise NotImplementedError
+
+        # load model
+        model_file = None
+        if from_flax:
+            raise NotImplementedError
+        else:
+            if use_safetensors:
+                try:
+                    model_file = _get_model_file(
+                        pretrained_model_name_or_path,
+                        weights_name=_add_variant(SAFETENSORS_WEIGHTS_NAME, variant),
+                        cache_dir=cache_dir,
+                        force_download=force_download,
+                        resume_download=resume_download,
+                        proxies=proxies,
+                        local_files_only=local_files_only,
+                        use_auth_token=use_auth_token,
+                        revision=revision,
+                        subfolder=subfolder,
+                        user_agent=user_agent,
+                        commit_hash=commit_hash,
+                    )
+                except IOError as e:
+                    if not allow_pickle:
+                        raise e
+                    pass
+            if model_file is None:
+                model_file = _get_model_file(
+                    pretrained_model_name_or_path,
+                    weights_name=_add_variant(WEIGHTS_NAME, variant),
+                    cache_dir=cache_dir,
+                    force_download=force_download,
+                    resume_download=resume_download,
+                    proxies=proxies,
+                    local_files_only=local_files_only,
+                    use_auth_token=use_auth_token,
+                    revision=revision,
+                    subfolder=subfolder,
+                    user_agent=user_agent,
+                    commit_hash=commit_hash,
+                )
+
+            model = cls.from_config(config, **unused_kwargs)
+            if local_crossattn:
+                unet_lora_attn_procs = dict()
+                for name, _ in model.attn_processors.items():
+                    if not name.endswith("attn1.processor"):
+                        default_attn_proc = AttnProcessor()
+                    elif is_xformers_available():
+                        default_attn_proc = XFormersMVAttnProcessor()
+                    else:
+                        default_attn_proc = MVAttnProcessor()
+                    unet_lora_attn_procs[name] = ReferenceOnlyAttnProc(
+                        default_attn_proc, enabled=name.endswith("attn1.processor"), name=name
+                    )
+                model.set_attn_processor(unet_lora_attn_procs)
+            state_dict = load_state_dict(model_file, variant=variant)
+            model._convert_deprecated_attention_blocks(state_dict)
+
+            conv_in_weight = state_dict['conv_in.weight']
+            conv_out_weight = state_dict['conv_out.weight']
+            model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model_2d(
+                model,
+                state_dict,
+                model_file,
+                pretrained_model_name_or_path,
+                ignore_mismatched_sizes=True,
+            )
+            if any([key == 'conv_in.weight' for key, _, _ in mismatched_keys]):
+                # initialize from the original SD structure
+                model.conv_in.weight.data[:,:4] = conv_in_weight
+
+            # whether to place all zero to new layers?
+            if zero_init_conv_in:
+                model.conv_in.weight.data[:,4:] = 0.
+
+            if any([key == 'conv_out.weight' for key, _, _ in mismatched_keys]):
+                # initialize from the original SD structure
+                model.conv_out.weight.data[:,:4] = conv_out_weight
+                if out_channels == 8: # copy for the last 4 channels
+                    model.conv_out.weight.data[:, 4:] = conv_out_weight
+            
+            if zero_init_camera_projection:
+                for p in model.class_embedding.parameters():
+                    torch.nn.init.zeros_(p)
+
+            loading_info = {
+                "missing_keys": missing_keys,
+                "unexpected_keys": unexpected_keys,
+                "mismatched_keys": mismatched_keys,
+                "error_msgs": error_msgs,
+            }
+
+        if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype):
+            raise ValueError(
+                f"{torch_dtype} needs to be of type `torch.dtype`, e.g. `torch.float16`, but is {type(torch_dtype)}."
+            )
+        elif torch_dtype is not None:
+            model = model.to(torch_dtype)
+
+        model.register_to_config(_name_or_path=pretrained_model_name_or_path)
+
+        # Set model in evaluation mode to deactivate DropOut modules by default
+        model.eval()
+        if output_loading_info:
+            return model, loading_info
+
+        return model
+
+    @classmethod
+    def _load_pretrained_model_2d(
+        cls,
+        model,
+        state_dict,
+        resolved_archive_file,
+        pretrained_model_name_or_path,
+        ignore_mismatched_sizes=False,
+    ):
+        # Retrieve missing & unexpected_keys
+        model_state_dict = model.state_dict()
+        loaded_keys = list(state_dict.keys())
+
+        expected_keys = list(model_state_dict.keys())
+
+        original_loaded_keys = loaded_keys
+
+        missing_keys = list(set(expected_keys) - set(loaded_keys))
+        unexpected_keys = list(set(loaded_keys) - set(expected_keys))
+
+        # Make sure we are able to load base models as well as derived models (with heads)
+        model_to_load = model
+
+        def _find_mismatched_keys(
+            state_dict,
+            model_state_dict,
+            loaded_keys,
+            ignore_mismatched_sizes,
+        ):
+            mismatched_keys = []
+            if ignore_mismatched_sizes:
+                for checkpoint_key in loaded_keys:
+                    model_key = checkpoint_key
+
+                    if (
+                        model_key in model_state_dict
+                        and state_dict[checkpoint_key].shape != model_state_dict[model_key].shape
+                    ):
+                        mismatched_keys.append(
+                            (checkpoint_key, state_dict[checkpoint_key].shape, model_state_dict[model_key].shape)
+                        )
+                        del state_dict[checkpoint_key]
+            return mismatched_keys
+
+        if state_dict is not None:
+            # Whole checkpoint
+            mismatched_keys = _find_mismatched_keys(
+                state_dict,
+                model_state_dict,
+                original_loaded_keys,
+                ignore_mismatched_sizes,
+            )
+            error_msgs = _load_state_dict_into_model(model_to_load, state_dict)
+
+        if len(error_msgs) > 0:
+            error_msg = "\n\t".join(error_msgs)
+            if "size mismatch" in error_msg:
+                error_msg += (
+                    "\n\tYou may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method."
+                )
+            raise RuntimeError(f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}")
+
+        if len(unexpected_keys) > 0:
+            logger.warning(
+                f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when"
+                f" initializing {model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are"
+                f" initializing {model.__class__.__name__} from the checkpoint of a model trained on another task"
+                " or with another architecture (e.g. initializing a BertForSequenceClassification model from a"
+                " BertForPreTraining model).\n- This IS NOT expected if you are initializing"
+                f" {model.__class__.__name__} from the checkpoint of a model that you expect to be exactly"
+                " identical (initializing a BertForSequenceClassification model from a"
+                " BertForSequenceClassification model)."
+            )
+        else:
+            logger.info(f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n")
+        if len(missing_keys) > 0:
+            logger.warning(
+                f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at"
+                f" {pretrained_model_name_or_path} and are newly initialized: {missing_keys}\nYou should probably"
+                " TRAIN this model on a down-stream task to be able to use it for predictions and inference."
+            )
+        elif len(mismatched_keys) == 0:
+            logger.info(
+                f"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at"
+                f" {pretrained_model_name_or_path}.\nIf your task is similar to the task the model of the"
+                f" checkpoint was trained on, you can already use {model.__class__.__name__} for predictions"
+                " without further training."
+            )
+        if len(mismatched_keys) > 0:
+            mismatched_warning = "\n".join(
+                [
+                    f"- {key}: found shape {shape1} in the checkpoint and {shape2} in the model instantiated"
+                    for key, shape1, shape2 in mismatched_keys
+                ]
+            )
+            logger.warning(
+                f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at"
+                f" {pretrained_model_name_or_path} and are newly initialized because the shapes did not"
+                f" match:\n{mismatched_warning}\nYou should probably TRAIN this model on a down-stream task to be"
+                " able to use it for predictions and inference."
+            )
+
+        return model, missing_keys, unexpected_keys, mismatched_keys, error_msgs
+

2D_Stage/tuneavideo/models/unet_mv2d_ref.py

@@ -0,0 +1,1570 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Union
+import os
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+from einops import rearrange
+
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders import UNet2DConditionLoadersMixin
+from diffusers.utils import BaseOutput, logging
+from diffusers.models.activations import get_activation
+from diffusers.models.attention_processor import AttentionProcessor, AttnProcessor
+from diffusers.models.embeddings import (
+    GaussianFourierProjection,
+    ImageHintTimeEmbedding,
+    ImageProjection,
+    ImageTimeEmbedding,
+    TextImageProjection,
+    TextImageTimeEmbedding,
+    TextTimeEmbedding,
+    TimestepEmbedding,
+    Timesteps,
+)
+from diffusers.models.lora import LoRALinearLayer
+
+from diffusers.models.modeling_utils import ModelMixin, load_state_dict, _load_state_dict_into_model
+from diffusers.models.unet_2d_blocks import (
+    CrossAttnDownBlock2D,
+    CrossAttnUpBlock2D,
+    DownBlock2D,
+    UNetMidBlock2DCrossAttn,
+    UNetMidBlock2DSimpleCrossAttn,
+    UpBlock2D,
+)
+from diffusers.utils import (
+    CONFIG_NAME,
+    DIFFUSERS_CACHE,
+    FLAX_WEIGHTS_NAME,
+    HF_HUB_OFFLINE,
+    SAFETENSORS_WEIGHTS_NAME,
+    WEIGHTS_NAME,
+    _add_variant,
+    _get_model_file,
+    deprecate,
+    is_accelerate_available,
+    is_torch_version,
+    logging,
+)
+from diffusers import __version__
+from tuneavideo.models.unet_mv2d_blocks import (
+    CrossAttnDownBlockMV2D,
+    CrossAttnUpBlockMV2D,
+    UNetMidBlockMV2DCrossAttn,
+    get_down_block,
+    get_up_block,
+)
+from diffusers.models.attention_processor import Attention, AttnProcessor
+from diffusers.utils.import_utils import is_xformers_available
+from tuneavideo.models.transformer_mv2d import XFormersMVAttnProcessor, MVAttnProcessor
+from tuneavideo.models.refunet import ReferenceOnlyAttnProc
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@dataclass
+class UNetMV2DRefOutput(BaseOutput):
+    """
+    The output of [`UNet2DConditionModel`].
+
+    Args:
+        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+            The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
+    """
+
+    sample: torch.FloatTensor = None
+
+class Identity(torch.nn.Module):
+    r"""A placeholder identity operator that is argument-insensitive.
+
+    Args:
+        args: any argument (unused)
+        kwargs: any keyword argument (unused)
+
+    Shape:
+        - Input: :math:`(*)`, where :math:`*` means any number of dimensions.
+        - Output: :math:`(*)`, same shape as the input.
+
+    Examples::
+
+        >>> m = nn.Identity(54, unused_argument1=0.1, unused_argument2=False)
+        >>> input = torch.randn(128, 20)
+        >>> output = m(input)
+        >>> print(output.size())
+        torch.Size([128, 20])
+
+    """
+    def __init__(self, scale=None, *args, **kwargs) -> None:
+        super(Identity, self).__init__()
+
+    def forward(self, input, *args, **kwargs):
+        return input
+
+
+
+class _LoRACompatibleLinear(nn.Module):
+    """
+    A Linear layer that can be used with LoRA.
+    """
+
+    def __init__(self, *args, lora_layer: Optional[LoRALinearLayer] = None, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.lora_layer = lora_layer
+
+    def set_lora_layer(self, lora_layer: Optional[LoRALinearLayer]):
+        self.lora_layer = lora_layer
+
+    def _fuse_lora(self):
+        pass
+
+    def _unfuse_lora(self):
+        pass
+
+    def forward(self, hidden_states, scale=None, lora_scale: int = 1):
+        return hidden_states
+
+class UNetMV2DRefModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
+    r"""
+    A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
+    shaped output.
+
+    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
+    for all models (such as downloading or saving).
+
+    Parameters:
+        sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
+            Height and width of input/output sample.
+        in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.
+        out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
+        center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
+        flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
+            Whether to flip the sin to cos in the time embedding.
+        freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
+        down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
+            The tuple of downsample blocks to use.
+        mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`):
+            Block type for middle of UNet, it can be either `UNetMidBlock2DCrossAttn` or
+            `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped.
+        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`):
+            The tuple of upsample blocks to use.
+        only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):
+            Whether to include self-attention in the basic transformer blocks, see
+            [`~models.attention.BasicTransformerBlock`].
+        block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
+            The tuple of output channels for each block.
+        layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
+        downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
+        mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
+        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
+        norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
+            If `None`, normalization and activation layers is skipped in post-processing.
+        norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
+        cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
+            The dimension of the cross attention features.
+        transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1):
+            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
+            [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
+            [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
+        encoder_hid_dim (`int`, *optional*, defaults to None):
+            If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
+            dimension to `cross_attention_dim`.
+        encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
+            If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
+            embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
+        attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
+        num_attention_heads (`int`, *optional*):
+            The number of attention heads. If not defined, defaults to `attention_head_dim`
+        resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
+            for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
+        class_embed_type (`str`, *optional*, defaults to `None`):
+            The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
+            `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
+        addition_embed_type (`str`, *optional*, defaults to `None`):
+            Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
+            "text". "text" will use the `TextTimeEmbedding` layer.
+        addition_time_embed_dim: (`int`, *optional*, defaults to `None`):
+            Dimension for the timestep embeddings.
+        num_class_embeds (`int`, *optional*, defaults to `None`):
+            Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
+            class conditioning with `class_embed_type` equal to `None`.
+        time_embedding_type (`str`, *optional*, defaults to `positional`):
+            The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
+        time_embedding_dim (`int`, *optional*, defaults to `None`):
+            An optional override for the dimension of the projected time embedding.
+        time_embedding_act_fn (`str`, *optional*, defaults to `None`):
+            Optional activation function to use only once on the time embeddings before they are passed to the rest of
+            the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.
+        timestep_post_act (`str`, *optional*, defaults to `None`):
+            The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
+        time_cond_proj_dim (`int`, *optional*, defaults to `None`):
+            The dimension of `cond_proj` layer in the timestep embedding.
+        conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer.
+        conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer.
+        projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when
+            `class_embed_type="projection"`. Required when `class_embed_type="projection"`.
+        class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time
+            embeddings with the class embeddings.
+        mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):
+            Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If
+            `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the
+            `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`
+            otherwise.
+    """
+
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    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] = (
+            "CrossAttnDownBlockMV2D",
+            "CrossAttnDownBlockMV2D",
+            "CrossAttnDownBlockMV2D",
+            "DownBlock2D",
+        ),
+        mid_block_type: Optional[str] = "UNetMidBlockMV2DCrossAttn",
+        up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlockMV2D", "CrossAttnUpBlockMV2D", "CrossAttnUpBlockMV2D"),
+        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,
+        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]] = 1,
+        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: int = 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,
+        class_embeddings_concat: bool = False,
+        mid_block_only_cross_attention: Optional[bool] = None,
+        cross_attention_norm: Optional[str] = None,
+        addition_embed_type_num_heads=64,
+        num_views: int = 1,
+        joint_attention: bool = False,
+        joint_attention_twice: bool = False,
+        multiview_attention: bool = True,
+        cross_domain_attention: bool = False,
+        camera_input_dim: int = 12,
+        camera_hidden_dim: int = 320,
+        camera_output_dim: int = 1280,
+        
+    ):
+        super().__init__()
+
+        self.sample_size = sample_size
+
+        if num_attention_heads is not None:
+            raise ValueError(
+                "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."
+            )
+
+        # If `num_attention_heads` is not defined (which is the case for most models)
+        # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
+        # The reason for this behavior is to correct for incorrectly named variables that were introduced
+        # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
+        # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
+        # which is why we correct for the naming here.
+        num_attention_heads = num_attention_heads or attention_head_dim
+
+        # Check inputs
+        if len(down_block_types) != len(up_block_types):
+            raise ValueError(
+                f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
+            )
+
+        if len(block_out_channels) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}."
+            )
+
+        if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
+            )
+
+        # input
+        conv_in_padding = (conv_in_kernel - 1) // 2
+        self.conv_in = nn.Conv2d(
+            in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
+        )
+
+        # time
+        if time_embedding_type == "fourier":
+            time_embed_dim = time_embedding_dim or block_out_channels[0] * 2
+            if time_embed_dim % 2 != 0:
+                raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.")
+            self.time_proj = GaussianFourierProjection(
+                time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
+            )
+            timestep_input_dim = time_embed_dim
+        elif time_embedding_type == "positional":
+            time_embed_dim = time_embedding_dim or block_out_channels[0] * 4
+
+            self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
+            timestep_input_dim = block_out_channels[0]
+        else:
+            raise ValueError(
+                f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
+            )
+
+        self.time_embedding = TimestepEmbedding(
+            timestep_input_dim,
+            time_embed_dim,
+            act_fn=act_fn,
+            post_act_fn=timestep_post_act,
+            cond_proj_dim=time_cond_proj_dim,
+        )
+
+        if encoder_hid_dim_type is None and encoder_hid_dim is not None:
+            encoder_hid_dim_type = "text_proj"
+            self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
+            logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")
+
+        if encoder_hid_dim is None and encoder_hid_dim_type is not None:
+            raise ValueError(
+                f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
+            )
+
+        if encoder_hid_dim_type == "text_proj":
+            self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
+        elif encoder_hid_dim_type == "text_image_proj":
+            # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
+            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
+            # case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)`
+            self.encoder_hid_proj = TextImageProjection(
+                text_embed_dim=encoder_hid_dim,
+                image_embed_dim=cross_attention_dim,
+                cross_attention_dim=cross_attention_dim,
+            )
+        elif encoder_hid_dim_type == "image_proj":
+            # Kandinsky 2.2
+            self.encoder_hid_proj = ImageProjection(
+                image_embed_dim=encoder_hid_dim,
+                cross_attention_dim=cross_attention_dim,
+            )
+        elif encoder_hid_dim_type is not None:
+            raise ValueError(
+                f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
+            )
+        else:
+            self.encoder_hid_proj = None
+
+        # class embedding
+        if class_embed_type is None and num_class_embeds is not None:
+            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
+        elif class_embed_type == "timestep":
+            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)
+        elif class_embed_type == "identity":
+            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
+        elif class_embed_type == "projection":
+            if projection_class_embeddings_input_dim is None:
+                raise ValueError(
+                    "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
+                )
+            # The projection `class_embed_type` is the same as the timestep `class_embed_type` except
+            # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
+            # 2. it projects from an arbitrary input dimension.
+            #
+            # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
+            # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
+            # As a result, `TimestepEmbedding` can be passed arbitrary vectors.
+            self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
+        elif class_embed_type == "simple_projection":
+            if projection_class_embeddings_input_dim is None:
+                raise ValueError(
+                    "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
+                )
+            self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)
+        else:
+            self.class_embedding = None
+
+        if addition_embed_type == "text":
+            if encoder_hid_dim is not None:
+                text_time_embedding_from_dim = encoder_hid_dim
+            else:
+                text_time_embedding_from_dim = cross_attention_dim
+
+            self.add_embedding = TextTimeEmbedding(
+                text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
+            )
+        elif addition_embed_type == "text_image":
+            # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
+            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
+            # case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`
+            self.add_embedding = TextImageTimeEmbedding(
+                text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
+            )
+        elif addition_embed_type == "text_time":
+            self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
+            self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
+        elif addition_embed_type == "image":
+            # Kandinsky 2.2
+            self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
+        elif addition_embed_type == "image_hint":
+            # Kandinsky 2.2 ControlNet
+            self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
+        elif addition_embed_type is not None:
+            raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
+
+        if time_embedding_act_fn is None:
+            self.time_embed_act = None
+        else:
+            self.time_embed_act = get_activation(time_embedding_act_fn)
+
+        self.camera_embedding = nn.Sequential(
+            nn.Linear(camera_input_dim, time_embed_dim),
+            nn.SiLU(),
+            nn.Linear(time_embed_dim, time_embed_dim),
+        )
+
+        self.down_blocks = nn.ModuleList([])
+        self.up_blocks = nn.ModuleList([])
+
+        if isinstance(only_cross_attention, bool):
+            if mid_block_only_cross_attention is None:
+                mid_block_only_cross_attention = only_cross_attention
+
+            only_cross_attention = [only_cross_attention] * len(down_block_types)
+
+        if mid_block_only_cross_attention is None:
+            mid_block_only_cross_attention = False
+
+        if isinstance(num_attention_heads, int):
+            num_attention_heads = (num_attention_heads,) * len(down_block_types)
+
+        if isinstance(attention_head_dim, int):
+            attention_head_dim = (attention_head_dim,) * len(down_block_types)
+
+        if isinstance(cross_attention_dim, int):
+            cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
+
+        if isinstance(layers_per_block, int):
+            layers_per_block = [layers_per_block] * len(down_block_types)
+
+        if isinstance(transformer_layers_per_block, int):
+            transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
+
+        if class_embeddings_concat:
+            # The time embeddings are concatenated with the class embeddings. The dimension of the
+            # time embeddings passed to the down, middle, and up blocks is twice the dimension of the
+            # regular time embeddings
+            blocks_time_embed_dim = time_embed_dim * 2
+        else:
+            blocks_time_embed_dim = time_embed_dim
+
+        # down
+        output_channel = block_out_channels[0]
+        for i, down_block_type in enumerate(down_block_types):
+            input_channel = output_channel
+            output_channel = block_out_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+
+            down_block = get_down_block(
+                down_block_type,
+                num_layers=layers_per_block[i],
+                transformer_layers_per_block=transformer_layers_per_block[i],
+                in_channels=input_channel,
+                out_channels=output_channel,
+                temb_channels=blocks_time_embed_dim,
+                add_downsample=not is_final_block,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=cross_attention_dim[i],
+                num_attention_heads=num_attention_heads[i],
+                downsample_padding=downsample_padding,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                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,
+                cross_attention_norm=cross_attention_norm,
+                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
+                num_views=num_views,
+                joint_attention=joint_attention,
+                joint_attention_twice=joint_attention_twice,
+                multiview_attention=multiview_attention,
+                cross_domain_attention=cross_domain_attention
+            )
+            self.down_blocks.append(down_block)
+
+        # mid
+        if mid_block_type == "UNetMidBlock2DCrossAttn":
+            self.mid_block = UNetMidBlock2DCrossAttn(
+                transformer_layers_per_block=transformer_layers_per_block[-1],
+                in_channels=block_out_channels[-1],
+                temb_channels=blocks_time_embed_dim,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                cross_attention_dim=cross_attention_dim[-1],
+                num_attention_heads=num_attention_heads[-1],
+                resnet_groups=norm_num_groups,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                upcast_attention=upcast_attention,
+            )
+        # custom MV2D attention block  
+        elif mid_block_type == "UNetMidBlockMV2DCrossAttn":
+            self.mid_block = UNetMidBlockMV2DCrossAttn(
+                transformer_layers_per_block=transformer_layers_per_block[-1],
+                in_channels=block_out_channels[-1],
+                temb_channels=blocks_time_embed_dim,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                cross_attention_dim=cross_attention_dim[-1],
+                num_attention_heads=num_attention_heads[-1],
+                resnet_groups=norm_num_groups,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                upcast_attention=upcast_attention,
+                num_views=num_views,
+                joint_attention=joint_attention,
+                joint_attention_twice=joint_attention_twice,
+                multiview_attention=multiview_attention,
+                cross_domain_attention=cross_domain_attention
+            )
+        elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn":
+            self.mid_block = UNetMidBlock2DSimpleCrossAttn(
+                in_channels=block_out_channels[-1],
+                temb_channels=blocks_time_embed_dim,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                cross_attention_dim=cross_attention_dim[-1],
+                attention_head_dim=attention_head_dim[-1],
+                resnet_groups=norm_num_groups,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                skip_time_act=resnet_skip_time_act,
+                only_cross_attention=mid_block_only_cross_attention,
+                cross_attention_norm=cross_attention_norm,
+            )
+        elif mid_block_type is None:
+            self.mid_block = None
+        else:
+            raise ValueError(f"unknown mid_block_type : {mid_block_type}")
+
+        # count how many layers upsample the images
+        self.num_upsamplers = 0
+
+        # up
+        reversed_block_out_channels = list(reversed(block_out_channels))
+        reversed_num_attention_heads = list(reversed(num_attention_heads))
+        reversed_layers_per_block = list(reversed(layers_per_block))
+        reversed_cross_attention_dim = list(reversed(cross_attention_dim))
+        reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block))
+        only_cross_attention = list(reversed(only_cross_attention))
+
+        output_channel = reversed_block_out_channels[0]
+        for i, up_block_type in enumerate(up_block_types):
+            is_final_block = i == len(block_out_channels) - 1
+
+            prev_output_channel = output_channel
+            output_channel = reversed_block_out_channels[i]
+            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
+
+            # add upsample block for all BUT final layer
+            if not is_final_block:
+                add_upsample = True
+                self.num_upsamplers += 1
+            else:
+                add_upsample = False
+
+            up_block = get_up_block(
+                up_block_type,
+                num_layers=reversed_layers_per_block[i] + 1,
+                transformer_layers_per_block=reversed_transformer_layers_per_block[i],
+                in_channels=input_channel,
+                out_channels=output_channel,
+                prev_output_channel=prev_output_channel,
+                temb_channels=blocks_time_embed_dim,
+                add_upsample=add_upsample,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=reversed_cross_attention_dim[i],
+                num_attention_heads=reversed_num_attention_heads[i],
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                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,
+                cross_attention_norm=cross_attention_norm,
+                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
+                num_views=num_views,
+                joint_attention=joint_attention,
+                joint_attention_twice=joint_attention_twice,
+                multiview_attention=multiview_attention,
+                cross_domain_attention=cross_domain_attention
+            )
+            self.up_blocks.append(up_block)
+            prev_output_channel = output_channel
+
+        # out
+        # if norm_num_groups is not None:
+        #     self.conv_norm_out = nn.GroupNorm(
+        #         num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
+        #     )
+
+        #     self.conv_act = get_activation(act_fn)
+
+        # else:
+        #     self.conv_norm_out = None
+        #     self.conv_act = None
+
+        # conv_out_padding = (conv_out_kernel - 1) // 2
+        # self.conv_out = nn.Conv2d(
+        #     block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding
+        # )
+
+        self.up_blocks[3].attentions[2].transformer_blocks[0].attn1.to_q = _LoRACompatibleLinear()
+        self.up_blocks[3].attentions[2].transformer_blocks[0].attn1.to_k = _LoRACompatibleLinear()
+        self.up_blocks[3].attentions[2].transformer_blocks[0].attn1.to_v = _LoRACompatibleLinear()
+        self.up_blocks[3].attentions[2].transformer_blocks[0].attn1.to_out = nn.ModuleList([Identity(), Identity()])
+        self.up_blocks[3].attentions[2].transformer_blocks[0].norm2 = Identity()
+        self.up_blocks[3].attentions[2].transformer_blocks[0].attn2 = None
+        self.up_blocks[3].attentions[2].transformer_blocks[0].norm3 = Identity()
+        self.up_blocks[3].attentions[2].transformer_blocks[0].ff = Identity()
+        self.up_blocks[3].attentions[2].proj_out = Identity()
+
+    @property
+    def attn_processors(self) -> Dict[str, AttentionProcessor]:
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        # set recursively
+        processors = {}
+
+        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
+            if hasattr(module, "set_processor"):
+                processors[f"{name}.processor"] = module.processor
+
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
+        r"""
+        Sets the attention processor to use to compute attention.
+
+        Parameters:
+            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                for **all** `Attention` layers.
+
+                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+                processor. This is strongly recommended when setting trainable attention processors.
+
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"))
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
+
+    def set_default_attn_processor(self):
+        """
+        Disables custom attention processors and sets the default attention implementation.
+        """
+        self.set_attn_processor(AttnProcessor())
+
+    def set_attention_slice(self, slice_size):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module splits the input tensor in slices to compute attention in
+        several steps. This is useful for saving some memory in exchange for a small decrease in speed.
+
+        Args:
+            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
+                When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
+                `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
+                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
+                must be a multiple of `slice_size`.
+        """
+        sliceable_head_dims = []
+
+        def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
+            if hasattr(module, "set_attention_slice"):
+                sliceable_head_dims.append(module.sliceable_head_dim)
+
+            for child in module.children():
+                fn_recursive_retrieve_sliceable_dims(child)
+
+        # retrieve number of attention layers
+        for module in self.children():
+            fn_recursive_retrieve_sliceable_dims(module)
+
+        num_sliceable_layers = len(sliceable_head_dims)
+
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = [dim // 2 for dim in sliceable_head_dims]
+        elif slice_size == "max":
+            # make smallest slice possible
+            slice_size = num_sliceable_layers * [1]
+
+        slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
+
+        if len(slice_size) != len(sliceable_head_dims):
+            raise ValueError(
+                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
+                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
+            )
+
+        for i in range(len(slice_size)):
+            size = slice_size[i]
+            dim = sliceable_head_dims[i]
+            if size is not None and size > dim:
+                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
+
+        # Recursively walk through all the children.
+        # Any children which exposes the set_attention_slice method
+        # gets the message
+        def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
+            if hasattr(module, "set_attention_slice"):
+                module.set_attention_slice(slice_size.pop())
+
+            for child in module.children():
+                fn_recursive_set_attention_slice(child, slice_size)
+
+        reversed_slice_size = list(reversed(slice_size))
+        for module in self.children():
+            fn_recursive_set_attention_slice(module, reversed_slice_size)
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, (CrossAttnDownBlock2D, CrossAttnDownBlockMV2D, DownBlock2D, CrossAttnUpBlock2D, CrossAttnUpBlockMV2D, UpBlock2D)):
+            module.gradient_checkpointing = value
+
+    def forward(
+        self,
+        sample: torch.FloatTensor,
+        timestep: Union[torch.Tensor, float, int],
+        encoder_hidden_states: torch.Tensor,
+        camera_matrixs: Optional[torch.Tensor] = None,
+        class_labels: Optional[torch.Tensor] = None,
+        timestep_cond: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
+        down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
+        mid_block_additional_residual: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        return_dict: bool = True,
+    ) -> Union[UNetMV2DRefOutput, Tuple]:
+        r"""
+        The [`UNet2DConditionModel`] forward method.
+
+        Args:
+            sample (`torch.FloatTensor`):
+                The noisy input tensor with the following shape `(batch, channel, height, width)`.
+            timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
+            encoder_hidden_states (`torch.FloatTensor`):
+                The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
+            encoder_attention_mask (`torch.Tensor`):
+                A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
+                `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
+                which adds large negative values to the attention scores corresponding to "discard" tokens.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
+                tuple.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].
+            added_cond_kwargs: (`dict`, *optional*):
+                A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that
+                are passed along to the UNet blocks.
+
+        Returns:
+            [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
+                If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise
+                a `tuple` is returned where the first element is the sample tensor.
+        """
+        # By default samples have to be AT least a multiple of the overall upsampling factor.
+        # The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
+        # However, the upsampling interpolation output size can be forced to fit any upsampling size
+        # on the fly if necessary.
+        default_overall_up_factor = 2**self.num_upsamplers
+
+        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
+        forward_upsample_size = False
+        upsample_size = None
+
+        if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
+            logger.info("Forward upsample size to force interpolation output size.")
+            forward_upsample_size = True
+
+        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension
+        # expects mask of shape:
+        #   [batch, key_tokens]
+        # adds singleton query_tokens dimension:
+        #   [batch,                    1, key_tokens]
+        # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
+        #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
+        #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
+        if attention_mask is not None:
+            # assume that mask is expressed as:
+            #   (1 = keep,      0 = discard)
+            # convert mask into a bias that can be added to attention scores:
+            #       (keep = +0,     discard = -10000.0)
+            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
+            attention_mask = attention_mask.unsqueeze(1)
+
+        # convert encoder_attention_mask to a bias the same way we do for attention_mask
+        if encoder_attention_mask is not None:
+            encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
+            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
+
+        # 0. center input if necessary
+        if self.config.center_input_sample:
+            sample = 2 * sample - 1.0
+
+        # 1. time
+        timesteps = timestep
+        if not torch.is_tensor(timesteps):
+            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
+            # This would be a good case for the `match` statement (Python 3.10+)
+            is_mps = sample.device.type == "mps"
+            if isinstance(timestep, float):
+                dtype = torch.float32 if is_mps else torch.float64
+            else:
+                dtype = torch.int32 if is_mps else torch.int64
+            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
+        elif len(timesteps.shape) == 0:
+            timesteps = timesteps[None].to(sample.device)
+
+        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+        timesteps = timesteps.expand(sample.shape[0])
+
+        t_emb = self.time_proj(timesteps)
+
+        # `Timesteps` does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=sample.dtype)
+        emb = self.time_embedding(t_emb, timestep_cond)
+        
+        # import pdb; pdb.set_trace()
+        if camera_matrixs is not None:
+            emb = torch.unsqueeze(emb, 1)
+            # came emb
+            cam_emb = self.camera_embedding(camera_matrixs)
+            # cam_emb = self.camera_embedding_2(cam_emb)
+            emb = emb.repeat(1,cam_emb.shape[1],1) #torch.Size([32, 4, 1280])
+            emb = emb + cam_emb
+            emb = rearrange(emb, "b f c -> (b f) c", f=emb.shape[1])
+
+        aug_emb = None
+
+        if self.class_embedding is not None and class_labels is not None:
+            if class_labels is None:
+                raise ValueError("class_labels should be provided when num_class_embeds > 0")
+
+            if self.config.class_embed_type == "timestep":
+                class_labels = self.time_proj(class_labels)
+
+                # `Timesteps` does not contain any weights and will always return f32 tensors
+                # there might be better ways to encapsulate this.
+                class_labels = class_labels.to(dtype=sample.dtype)
+
+            class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)
+
+            if self.config.class_embeddings_concat:
+                emb = torch.cat([emb, class_emb], dim=-1)
+            else:
+                emb = emb + class_emb
+
+        if self.config.addition_embed_type == "text":
+            aug_emb = self.add_embedding(encoder_hidden_states)
+        elif self.config.addition_embed_type == "text_image":
+            # Kandinsky 2.1 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
+                )
+
+            image_embs = added_cond_kwargs.get("image_embeds")
+            text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states)
+            aug_emb = self.add_embedding(text_embs, image_embs)
+        elif self.config.addition_embed_type == "text_time":
+            # SDXL - style
+            if "text_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
+                )
+            text_embeds = added_cond_kwargs.get("text_embeds")
+            if "time_ids" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
+                )
+            time_ids = added_cond_kwargs.get("time_ids")
+            time_embeds = self.add_time_proj(time_ids.flatten())
+            time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
+
+            add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
+            add_embeds = add_embeds.to(emb.dtype)
+            aug_emb = self.add_embedding(add_embeds)
+        elif self.config.addition_embed_type == "image":
+            # Kandinsky 2.2 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
+                )
+            image_embs = added_cond_kwargs.get("image_embeds")
+            aug_emb = self.add_embedding(image_embs)
+        elif self.config.addition_embed_type == "image_hint":
+            # Kandinsky 2.2 - style
+            if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`"
+                )
+            image_embs = added_cond_kwargs.get("image_embeds")
+            hint = added_cond_kwargs.get("hint")
+            aug_emb, hint = self.add_embedding(image_embs, hint)
+            sample = torch.cat([sample, hint], dim=1)
+
+        emb = emb + aug_emb if aug_emb is not None else emb
+
+        if self.time_embed_act is not None:
+            emb = self.time_embed_act(emb)
+
+        if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj":
+            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)
+        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj":
+            # Kadinsky 2.1 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
+                )
+
+            image_embeds = added_cond_kwargs.get("image_embeds")
+            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)
+        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj":
+            # Kandinsky 2.2 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
+                )
+            image_embeds = added_cond_kwargs.get("image_embeds")
+            encoder_hidden_states = self.encoder_hid_proj(image_embeds)
+        # 2. pre-process
+        sample = rearrange(sample, "b c f h w -> (b f) c h w", f=sample.shape[2])
+        sample = self.conv_in(sample)
+        # 3. down
+
+        is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None
+        is_adapter = mid_block_additional_residual is None and down_block_additional_residuals is not None
+
+        down_block_res_samples = (sample,)
+        for downsample_block in self.down_blocks:
+            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
+                # For t2i-adapter CrossAttnDownBlock2D
+                additional_residuals = {}
+                if is_adapter and len(down_block_additional_residuals) > 0:
+                    additional_residuals["additional_residuals"] = down_block_additional_residuals.pop(0)
+
+                sample, res_samples = downsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=attention_mask,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    encoder_attention_mask=encoder_attention_mask,
+                    **additional_residuals,
+                )
+            else:
+                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
+
+                if is_adapter and len(down_block_additional_residuals) > 0:
+                    sample += down_block_additional_residuals.pop(0)
+
+            down_block_res_samples += res_samples
+
+        if is_controlnet:
+            new_down_block_res_samples = ()
+
+            for down_block_res_sample, down_block_additional_residual in zip(
+                down_block_res_samples, down_block_additional_residuals
+            ):
+                down_block_res_sample = down_block_res_sample + down_block_additional_residual
+                new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)
+
+            down_block_res_samples = new_down_block_res_samples
+        # print("after down: ", sample.mean(), emb.mean())
+
+        # 4. mid
+        if self.mid_block is not None:
+            sample = self.mid_block(
+                sample,
+                emb,
+                encoder_hidden_states=encoder_hidden_states,
+                attention_mask=attention_mask,
+                cross_attention_kwargs=cross_attention_kwargs,
+                encoder_attention_mask=encoder_attention_mask,
+            )
+
+        if is_controlnet:
+            sample = sample + mid_block_additional_residual
+
+        # print("after mid: ", sample.mean())
+        # 5. up
+        for i, upsample_block in enumerate(self.up_blocks):
+            is_final_block = i == len(self.up_blocks) - 1
+
+            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
+            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
+
+            # if we have not reached the final block and need to forward the
+            # upsample size, we do it here
+            if not is_final_block and forward_upsample_size:
+                upsample_size = down_block_res_samples[-1].shape[2:]
+
+            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
+                sample = upsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    res_hidden_states_tuple=res_samples,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    upsample_size=upsample_size,
+                    attention_mask=attention_mask,
+                    encoder_attention_mask=encoder_attention_mask,
+                )
+            else:
+                sample = upsample_block(
+                    hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
+                )
+
+        # 6. post-process
+        # if self.conv_norm_out:
+        #     sample = self.conv_norm_out(sample)
+        #     sample = self.conv_act(sample)
+        # sample = self.conv_out(sample)
+
+        if not return_dict:
+            return (sample,)
+
+        return UNetMV2DRefOutput(sample=sample)
+
+    @classmethod
+    def from_pretrained_2d(
+            cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
+            camera_embedding_type: str, num_views: int, sample_size: int,
+            zero_init_conv_in: bool = True, zero_init_camera_projection: bool = False,
+            projection_class_embeddings_input_dim: int=6, joint_attention: bool = False, 
+            joint_attention_twice: bool = False, multiview_attention: bool = True,
+            cross_domain_attention: bool = False,
+            in_channels: int = 8, out_channels: int = 4, local_crossattn=False,
+            **kwargs
+        ):
+        r"""
+        Instantiate a pretrained PyTorch model from a pretrained model configuration.
+
+        The model is set in evaluation mode - `model.eval()` - by default, and dropout modules are deactivated. To
+        train the model, set it back in training mode with `model.train()`.
+
+        Parameters:
+            pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
+                Can be either:
+
+                    - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
+                      the Hub.
+                    - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
+                      with [`~ModelMixin.save_pretrained`].
+
+            cache_dir (`Union[str, os.PathLike]`, *optional*):
+                Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
+                is not used.
+            torch_dtype (`str` or `torch.dtype`, *optional*):
+                Override the default `torch.dtype` and load the model with another dtype. If `"auto"` is passed, the
+                dtype is automatically derived from the model's weights.
+            force_download (`bool`, *optional*, defaults to `False`):
+                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
+                cached versions if they exist.
+            resume_download (`bool`, *optional*, defaults to `False`):
+                Whether or not to resume downloading the model weights and configuration files. If set to `False`, any
+                incompletely downloaded files are deleted.
+            proxies (`Dict[str, str]`, *optional*):
+                A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
+                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
+            output_loading_info (`bool`, *optional*, defaults to `False`):
+                Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
+            local_files_only(`bool`, *optional*, defaults to `False`):
+                Whether to only load local model weights and configuration files or not. If set to `True`, the model
+                won't be downloaded from the Hub.
+            use_auth_token (`str` or *bool*, *optional*):
+                The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
+                `diffusers-cli login` (stored in `~/.huggingface`) is used.
+            revision (`str`, *optional*, defaults to `"main"`):
+                The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
+                allowed by Git.
+            from_flax (`bool`, *optional*, defaults to `False`):
+                Load the model weights from a Flax checkpoint save file.
+            subfolder (`str`, *optional*, defaults to `""`):
+                The subfolder location of a model file within a larger model repository on the Hub or locally.
+            mirror (`str`, *optional*):
+                Mirror source to resolve accessibility issues if you're downloading a model in China. We do not
+                guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
+                information.
+            device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):
+                A map that specifies where each submodule should go. It doesn't need to be defined for each
+                parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the
+                same device.
+
+                Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For
+                more information about each option see [designing a device
+                map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
+            max_memory (`Dict`, *optional*):
+                A dictionary device identifier for the maximum memory. Will default to the maximum memory available for
+                each GPU and the available CPU RAM if unset.
+            offload_folder (`str` or `os.PathLike`, *optional*):
+                The path to offload weights if `device_map` contains the value `"disk"`.
+            offload_state_dict (`bool`, *optional*):
+                If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if
+                the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True`
+                when there is some disk offload.
+            low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
+                Speed up model loading only loading the pretrained weights and not initializing the weights. This also
+                tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
+                Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
+                argument to `True` will raise an error.
+            variant (`str`, *optional*):
+                Load weights from a specified `variant` filename such as `"fp16"` or `"ema"`. This is ignored when
+                loading `from_flax`.
+            use_safetensors (`bool`, *optional*, defaults to `None`):
+                If set to `None`, the `safetensors` weights are downloaded if they're available **and** if the
+                `safetensors` library is installed. If set to `True`, the model is forcibly loaded from `safetensors`
+                weights. If set to `False`, `safetensors` weights are not loaded.
+
+        <Tip>
+
+        To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with
+        `huggingface-cli login`. You can also activate the special
+        ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use this method in a
+        firewalled environment.
+
+        </Tip>
+
+        Example:
+
+        ```py
+        from diffusers import UNet2DConditionModel
+
+        unet = UNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet")
+        ```
+
+        If you get the error message below, you need to finetune the weights for your downstream task:
+
+        ```bash
+        Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
+        - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated
+        You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+        ```
+        """
+        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
+        ignore_mismatched_sizes = kwargs.pop("ignore_mismatched_sizes", False)
+        force_download = kwargs.pop("force_download", False)
+        from_flax = kwargs.pop("from_flax", False)
+        resume_download = kwargs.pop("resume_download", False)
+        proxies = kwargs.pop("proxies", None)
+        output_loading_info = kwargs.pop("output_loading_info", False)
+        local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE)
+        use_auth_token = kwargs.pop("use_auth_token", None)
+        revision = kwargs.pop("revision", None)
+        torch_dtype = kwargs.pop("torch_dtype", None)
+        subfolder = kwargs.pop("subfolder", None)
+        device_map = kwargs.pop("device_map", None)
+        max_memory = kwargs.pop("max_memory", None)
+        offload_folder = kwargs.pop("offload_folder", None)
+        offload_state_dict = kwargs.pop("offload_state_dict", False)
+        variant = kwargs.pop("variant", None)
+        use_safetensors = kwargs.pop("use_safetensors", None)
+
+        # if use_safetensors and not is_safetensors_available():
+        #     raise ValueError(
+        #         "`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetensors"
+        #     )
+
+        allow_pickle = False
+        if use_safetensors is None:
+            # use_safetensors = is_safetensors_available()
+            use_safetensors = False
+            allow_pickle = True
+
+        if device_map is not None and not is_accelerate_available():
+            raise NotImplementedError(
+                "Loading and dispatching requires `accelerate`. Please make sure to install accelerate or set"
+                " `device_map=None`. You can install accelerate with `pip install accelerate`."
+            )
+
+        # Check if we can handle device_map and dispatching the weights
+        if device_map is not None and not is_torch_version(">=", "1.9.0"):
+            raise NotImplementedError(
+                "Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set"
+                " `device_map=None`."
+            )
+
+        # Load config if we don't provide a configuration
+        config_path = pretrained_model_name_or_path
+
+        user_agent = {
+            "diffusers": __version__,
+            "file_type": "model",
+            "framework": "pytorch",
+        }
+
+        # load config
+        config, unused_kwargs, commit_hash = cls.load_config(
+            config_path,
+            cache_dir=cache_dir,
+            return_unused_kwargs=True,
+            return_commit_hash=True,
+            force_download=force_download,
+            resume_download=resume_download,
+            proxies=proxies,
+            local_files_only=local_files_only,
+            use_auth_token=use_auth_token,
+            revision=revision,
+            subfolder=subfolder,
+            device_map=device_map,
+            max_memory=max_memory,
+            offload_folder=offload_folder,
+            offload_state_dict=offload_state_dict,
+            user_agent=user_agent,
+            **kwargs,
+        )
+
+        # modify config
+        config["_class_name"] = cls.__name__
+        config['in_channels'] = in_channels
+        config['out_channels'] = out_channels
+        config['sample_size'] = sample_size # training resolution
+        config['num_views'] = num_views
+        config['joint_attention'] = joint_attention
+        config['joint_attention_twice'] = joint_attention_twice
+        config['multiview_attention'] = multiview_attention
+        config['cross_domain_attention'] = cross_domain_attention
+        config["down_block_types"] = [
+            "CrossAttnDownBlockMV2D",
+            "CrossAttnDownBlockMV2D",
+            "CrossAttnDownBlockMV2D",
+            "DownBlock2D"
+        ]
+        config['mid_block_type'] = "UNetMidBlockMV2DCrossAttn"
+        config["up_block_types"] = [
+            "UpBlock2D",
+            "CrossAttnUpBlockMV2D",
+            "CrossAttnUpBlockMV2D",
+            "CrossAttnUpBlockMV2D"
+        ]        
+        config['class_embed_type'] = 'projection'
+        if camera_embedding_type == 'e_de_da_sincos':
+            config['projection_class_embeddings_input_dim'] = projection_class_embeddings_input_dim # default 6
+        else:
+            raise NotImplementedError
+
+        # load model
+        model_file = None
+        if from_flax:
+            raise NotImplementedError
+        else:
+            if use_safetensors:
+                try:
+                    model_file = _get_model_file(
+                        pretrained_model_name_or_path,
+                        weights_name=_add_variant(SAFETENSORS_WEIGHTS_NAME, variant),
+                        cache_dir=cache_dir,
+                        force_download=force_download,
+                        resume_download=resume_download,
+                        proxies=proxies,
+                        local_files_only=local_files_only,
+                        use_auth_token=use_auth_token,
+                        revision=revision,
+                        subfolder=subfolder,
+                        user_agent=user_agent,
+                        commit_hash=commit_hash,
+                    )
+                except IOError as e:
+                    if not allow_pickle:
+                        raise e
+                    pass
+            if model_file is None:
+                model_file = _get_model_file(
+                    pretrained_model_name_or_path,
+                    weights_name=_add_variant(WEIGHTS_NAME, variant),
+                    cache_dir=cache_dir,
+                    force_download=force_download,
+                    resume_download=resume_download,
+                    proxies=proxies,
+                    local_files_only=local_files_only,
+                    use_auth_token=use_auth_token,
+                    revision=revision,
+                    subfolder=subfolder,
+                    user_agent=user_agent,
+                    commit_hash=commit_hash,
+                )
+
+            model = cls.from_config(config, **unused_kwargs)
+            if local_crossattn:
+                unet_lora_attn_procs = dict()
+                for name, _ in model.attn_processors.items():
+                    if not name.endswith("attn1.processor"):
+                        default_attn_proc = AttnProcessor()
+                    elif is_xformers_available():
+                        default_attn_proc = XFormersMVAttnProcessor()
+                    else:
+                        default_attn_proc = MVAttnProcessor()
+                    unet_lora_attn_procs[name] = ReferenceOnlyAttnProc(
+                        default_attn_proc, enabled=name.endswith("attn1.processor"), name=name
+                    )
+                model.set_attn_processor(unet_lora_attn_procs)
+            state_dict = load_state_dict(model_file, variant=variant)
+            model._convert_deprecated_attention_blocks(state_dict)
+
+            conv_in_weight = state_dict['conv_in.weight']
+            conv_out_weight = state_dict['conv_out.weight']
+            model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model_2d(
+                model,
+                state_dict,
+                model_file,
+                pretrained_model_name_or_path,
+                ignore_mismatched_sizes=True,
+            )
+            if any([key == 'conv_in.weight' for key, _, _ in mismatched_keys]):
+                # initialize from the original SD structure
+                model.conv_in.weight.data[:,:4] = conv_in_weight
+
+            # whether to place all zero to new layers?
+            if zero_init_conv_in:
+                model.conv_in.weight.data[:,4:] = 0.
+
+            if any([key == 'conv_out.weight' for key, _, _ in mismatched_keys]):
+                # initialize from the original SD structure
+                model.conv_out.weight.data[:,:4] = conv_out_weight
+                if out_channels == 8: # copy for the last 4 channels
+                    model.conv_out.weight.data[:, 4:] = conv_out_weight
+            
+            if zero_init_camera_projection:
+                for p in model.class_embedding.parameters():
+                    torch.nn.init.zeros_(p)
+
+            loading_info = {
+                "missing_keys": missing_keys,
+                "unexpected_keys": unexpected_keys,
+                "mismatched_keys": mismatched_keys,
+                "error_msgs": error_msgs,
+            }
+
+        if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype):
+            raise ValueError(
+                f"{torch_dtype} needs to be of type `torch.dtype`, e.g. `torch.float16`, but is {type(torch_dtype)}."
+            )
+        elif torch_dtype is not None:
+            model = model.to(torch_dtype)
+
+        model.register_to_config(_name_or_path=pretrained_model_name_or_path)
+
+        # Set model in evaluation mode to deactivate DropOut modules by default
+        model.eval()
+        if output_loading_info:
+            return model, loading_info
+
+        return model
+
+    @classmethod
+    def _load_pretrained_model_2d(
+        cls,
+        model,
+        state_dict,
+        resolved_archive_file,
+        pretrained_model_name_or_path,
+        ignore_mismatched_sizes=False,
+    ):
+        # Retrieve missing & unexpected_keys
+        model_state_dict = model.state_dict()
+        loaded_keys = list(state_dict.keys())
+
+        expected_keys = list(model_state_dict.keys())
+
+        original_loaded_keys = loaded_keys
+
+        missing_keys = list(set(expected_keys) - set(loaded_keys))
+        unexpected_keys = list(set(loaded_keys) - set(expected_keys))
+
+        # Make sure we are able to load base models as well as derived models (with heads)
+        model_to_load = model
+
+        def _find_mismatched_keys(
+            state_dict,
+            model_state_dict,
+            loaded_keys,
+            ignore_mismatched_sizes,
+        ):
+            mismatched_keys = []
+            if ignore_mismatched_sizes:
+                for checkpoint_key in loaded_keys:
+                    model_key = checkpoint_key
+
+                    if (
+                        model_key in model_state_dict
+                        and state_dict[checkpoint_key].shape != model_state_dict[model_key].shape
+                    ):
+                        mismatched_keys.append(
+                            (checkpoint_key, state_dict[checkpoint_key].shape, model_state_dict[model_key].shape)
+                        )
+                        del state_dict[checkpoint_key]
+            return mismatched_keys
+
+        if state_dict is not None:
+            # Whole checkpoint
+            mismatched_keys = _find_mismatched_keys(
+                state_dict,
+                model_state_dict,
+                original_loaded_keys,
+                ignore_mismatched_sizes,
+            )
+            error_msgs = _load_state_dict_into_model(model_to_load, state_dict)
+
+        if len(error_msgs) > 0:
+            error_msg = "\n\t".join(error_msgs)
+            if "size mismatch" in error_msg:
+                error_msg += (
+                    "\n\tYou may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method."
+                )
+            raise RuntimeError(f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}")
+
+        if len(unexpected_keys) > 0:
+            logger.warning(
+                f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when"
+                f" initializing {model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are"
+                f" initializing {model.__class__.__name__} from the checkpoint of a model trained on another task"
+                " or with another architecture (e.g. initializing a BertForSequenceClassification model from a"
+                " BertForPreTraining model).\n- This IS NOT expected if you are initializing"
+                f" {model.__class__.__name__} from the checkpoint of a model that you expect to be exactly"
+                " identical (initializing a BertForSequenceClassification model from a"
+                " BertForSequenceClassification model)."
+            )
+        else:
+            logger.info(f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n")
+        if len(missing_keys) > 0:
+            logger.warning(
+                f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at"
+                f" {pretrained_model_name_or_path} and are newly initialized: {missing_keys}\nYou should probably"
+                " TRAIN this model on a down-stream task to be able to use it for predictions and inference."
+            )
+        elif len(mismatched_keys) == 0:
+            logger.info(
+                f"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at"
+                f" {pretrained_model_name_or_path}.\nIf your task is similar to the task the model of the"
+                f" checkpoint was trained on, you can already use {model.__class__.__name__} for predictions"
+                " without further training."
+            )
+        if len(mismatched_keys) > 0:
+            mismatched_warning = "\n".join(
+                [
+                    f"- {key}: found shape {shape1} in the checkpoint and {shape2} in the model instantiated"
+                    for key, shape1, shape2 in mismatched_keys
+                ]
+            )
+            logger.warning(
+                f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at"
+                f" {pretrained_model_name_or_path} and are newly initialized because the shapes did not"
+                f" match:\n{mismatched_warning}\nYou should probably TRAIN this model on a down-stream task to be"
+                " able to use it for predictions and inference."
+            )
+
+        return model, missing_keys, unexpected_keys, mismatched_keys, error_msgs
+

2D_Stage/tuneavideo/pipelines/pipeline_tuneavideo.py

@@ -0,0 +1,585 @@
+# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
+
+import tqdm
+
+import inspect
+from typing import Callable, List, Optional, Union
+from dataclasses import dataclass
+
+import numpy as np
+import torch
+
+from diffusers.utils import is_accelerate_available
+from packaging import version
+from transformers import CLIPTextModel, CLIPTokenizer
+import torchvision.transforms.functional as TF
+
+from diffusers.configuration_utils import FrozenDict
+from diffusers.models import AutoencoderKL
+from diffusers import DiffusionPipeline
+from diffusers.schedulers import (
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+)
+from diffusers.utils import deprecate, logging, BaseOutput
+
+from einops import rearrange
+
+from ..models.unet import UNet3DConditionModel
+from torchvision.transforms import InterpolationMode
+
+import ipdb
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@dataclass
+class TuneAVideoPipelineOutput(BaseOutput):
+    videos: Union[torch.Tensor, np.ndarray]
+
+
+class TuneAVideoPipeline(DiffusionPipeline):
+    _optional_components = []
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet3DConditionModel,
+        
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+        ],
+        ref_unet = None,
+        feature_extractor=None,
+        image_encoder=None
+    ):
+        super().__init__()
+        self.ref_unet = ref_unet
+        self.feature_extractor = feature_extractor
+        self.image_encoder = image_encoder
+        
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
+                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
+                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
+                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
+                " file"
+            )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
+                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
+                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
+                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
+                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
+            )
+            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["clip_sample"] = False
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
+            version.parse(unet.config._diffusers_version).base_version
+        ) < version.parse("0.9.0.dev0")
+        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
+            deprecation_message = (
+                "The configuration file of the unet has set the default `sample_size` to smaller than"
+                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
+                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
+                " in the config might lead to incorrect results in future versions. If you have downloaded this"
+                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
+                " the `unet/config.json` file"
+            )
+            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(unet.config)
+            new_config["sample_size"] = 64
+            unet._internal_dict = FrozenDict(new_config)
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+
+    def enable_vae_slicing(self):
+        self.vae.enable_slicing()
+
+    def disable_vae_slicing(self):
+        self.vae.disable_slicing()
+
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        if is_accelerate_available():
+            from accelerate import cpu_offload
+        else:
+            raise ImportError("Please install accelerate via `pip install accelerate`")
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
+            if cpu_offloaded_model is not None:
+                cpu_offload(cpu_offloaded_model, device)
+
+
+    @property
+    def _execution_device(self):
+        if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
+            return self.device
+        for module in self.unet.modules():
+            if (
+                hasattr(module, "_hf_hook")
+                and hasattr(module._hf_hook, "execution_device")
+                and module._hf_hook.execution_device is not None
+            ):
+                return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    def _encode_image(self, image_pil, device, num_images_per_prompt, do_classifier_free_guidance, img_proj=None):
+        dtype = next(self.image_encoder.parameters()).dtype
+
+        # image_pt = self.feature_extractor(images=image_pil, return_tensors="pt").pixel_values
+        # image_pt = image_pt.to(device=device, dtype=dtype)
+        # image_embeddings = self.image_encoder(image_pt).image_embeds
+        # image_embeddings = image_embeddings.unsqueeze(1)
+
+        # # image encoding
+        clip_image_mean = torch.as_tensor(self.feature_extractor.image_mean)[:,None,None].to(device, dtype=torch.float32)
+        clip_image_std = torch.as_tensor(self.feature_extractor.image_std)[:,None,None].to(device, dtype=torch.float32)
+        imgs_in_proc = TF.resize(image_pil, (self.feature_extractor.crop_size['height'], self.feature_extractor.crop_size['width']), interpolation=InterpolationMode.BICUBIC)
+        # do the normalization in float32 to preserve precision
+        imgs_in_proc = ((imgs_in_proc.float() - clip_image_mean) / clip_image_std).to(dtype)        
+        if img_proj is None:
+            # (B*Nv, 1, 768)
+            image_embeddings = self.image_encoder(imgs_in_proc).image_embeds.unsqueeze(1)
+            # duplicate image embeddings for each generation per prompt, using mps friendly method
+            # Note: repeat differently from official pipelines
+            # B1B2B3B4 -> B1B2B3B4B1B2B3B4
+            bs_embed, seq_len, _ = image_embeddings.shape
+            image_embeddings = image_embeddings.repeat(num_images_per_prompt, 1, 1)
+            if do_classifier_free_guidance:
+                negative_prompt_embeds = torch.zeros_like(image_embeddings)
+
+                # For classifier free guidance, we need to do two forward passes.
+                # Here we concatenate the unconditional and text embeddings into a single batch
+                # to avoid doing two forward passes
+                image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings])
+        else:
+            if do_classifier_free_guidance:
+                negative_image_proc = torch.zeros_like(imgs_in_proc)
+
+                # For classifier free guidance, we need to do two forward passes.
+                # Here we concatenate the unconditional and text embeddings into a single batch
+                # to avoid doing two forward passes
+                imgs_in_proc = torch.cat([negative_image_proc, imgs_in_proc])
+            
+            image_embeds = image_encoder(imgs_in_proc, output_hidden_states=True).hidden_states[-2]
+            image_embeddings = img_proj(image_embeds)
+
+            # image_embeddings_unet = rearrange(image_embeddings_unet, 'B Nv d c -> (B Nv) d c')
+        
+        # image_pt = torch.stack([TF.to_tensor(img) for img in image_pil], dim=0).to(device)
+        # image_pil = image_pil * 2.0 - 1.0
+        image_latents = self.vae.encode(image_pil* 2.0 - 1.0).latent_dist.mode() * self.vae.config.scaling_factor
+
+        # Note: repeat differently from official pipelines
+        # B1B2B3B4 -> B1B2B3B4B1B2B3B4        
+        image_latents = image_latents.repeat(num_images_per_prompt, 1, 1, 1)
+
+        # if do_classifier_free_guidance:
+        #     image_latents = torch.cat([torch.zeros_like(image_latents), image_latents])
+
+        return image_embeddings, image_latents
+
+    def _encode_prompt(self, prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt):
+        batch_size = len(prompt) if isinstance(prompt, list) else 1
+
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
+            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+            )
+
+        if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+            attention_mask = text_inputs.attention_mask.to(device)
+        else:
+            attention_mask = None
+
+        text_embeddings = self.text_encoder(
+            text_input_ids.to(device),
+            attention_mask=attention_mask,
+        )
+        text_embeddings = text_embeddings[0]
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        bs_embed, seq_len, _ = text_embeddings.shape
+        text_embeddings = text_embeddings.repeat(1, num_videos_per_prompt, 1)
+        text_embeddings = text_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = text_input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = uncond_input.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            uncond_embeddings = self.text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            uncond_embeddings = uncond_embeddings[0]
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(1, num_videos_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.view(batch_size * num_videos_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        return text_embeddings
+
+    def decode_latents(self, latents):
+        video_length = latents.shape[2]
+        latents = 1 / 0.18215 * latents
+        latents = rearrange(latents, "b c f h w -> (b f) c h w")
+        video = self.vae.decode(latents).sample
+        video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
+        video = (video / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        video = video.cpu().float().numpy()
+        return video
+
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    def check_inputs(self, prompt, height, width, callback_steps):
+        if not isinstance(prompt, str) and not isinstance(prompt, list):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+    def prepare_latents(self, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator, latents=None):
+        shape = (batch_size, num_channels_latents, video_length, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if latents is None:
+            rand_device = "cpu" if device.type == "mps" else device
+
+            if isinstance(generator, list):
+                shape = (1,) + shape[1:]
+                latents = [
+                    torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype)
+                    for i in range(batch_size)
+                ]
+                latents = torch.cat(latents, dim=0).to(device)
+            else:
+                latents = torch.randn(shape, dtype=dtype).to(device)
+        else:
+            if latents.shape != shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        image: Union[str, List[str]],
+        video_length: Optional[int],
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_videos_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "tensor",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+        camera_matrixs = None,
+        class_labels = None,
+        prompt_ids = None,
+        unet_condition_type = None,
+        pose_guider = None,
+        pose_image = None,
+        img_proj=None,
+        use_noise=True,
+        use_shifted_noise=False,
+        rescale = 0.7,
+        **kwargs,
+    ):
+        # Default height and width to unet
+        height = height or self.unet.config.sample_size * self.vae_scale_factor
+        width = width or self.unet.config.sample_size * self.vae_scale_factor
+
+        # Check inputs. Raise error if not correct
+        self.check_inputs(prompt, height, width, callback_steps)
+        if isinstance(image, list):
+            batch_size = len(image)
+        else:
+            batch_size = image.shape[0]
+        # assert batch_size >= video_length and batch_size % video_length == 0
+        # Define call parameters
+        batch_size = 1 if isinstance(prompt, str) else len(prompt)
+        device = self._execution_device
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        
+        # 3. Encode input image
+        # if isinstance(image, list):
+        #     image_pil = image
+        # elif isinstance(image, torch.Tensor):
+        #     image_pil = [TF.to_pil_image(image[i]) for i in range(image.shape[0])]
+        # encode input reference image
+        image_embeddings, image_latents = self._encode_image(image, device, num_videos_per_prompt, do_classifier_free_guidance, img_proj=img_proj) #torch.Size([64, 1, 768]) torch.Size([64, 4, 32, 32])
+        image_latents = rearrange(image_latents, "(b f) c h w -> b c f h w", f=1) #torch.Size([64, 4, 1, 32, 32])
+
+        # Encode input prompt_id
+        # encoder_hidden_states = self.text_encoder(prompt_ids)[0] #torch.Size([32, 77, 768])
+
+        # Encode input prompt
+        text_embeddings = self._encode_prompt( #torch.Size([64, 77, 768])
+            prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt
+        )
+
+        # Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        # Prepare latent variables
+        num_channels_latents = self.unet.in_channels
+        latents = self.prepare_latents( #torch.Size([32, 4, 4, 32, 32])
+            batch_size * num_videos_per_prompt,
+            num_channels_latents,
+            video_length,
+            height,
+            width,
+            text_embeddings.dtype,
+            device,
+            generator,
+            latents,
+        )
+        latents_dtype = latents.dtype
+        # import ipdb
+        # ipdb.set_trace()
+        # Prepare extra step kwargs.
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+        # prepare camera_matrix
+        if camera_matrixs is not None:
+            camera_matrixs = torch.cat([camera_matrixs] * 2) if do_classifier_free_guidance else camera_matrixs #(64, 4, 12)
+        # Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        if pose_guider is not None:
+            if len(pose_image.shape) == 5:
+                pose_embeds = pose_guider(rearrange(pose_image, "b f c h w -> (b f) c h w"))
+                pose_embeds = rearrange(pose_embeds, "(b f) c h w-> b c f h w ", f=video_length)
+            else:
+                pose_embeds = pose_guider(pose_image).unsqueeze(0)
+            pose_embeds = torch.cat([pose_embeds]*2, dim=0)
+            # import ipdb
+            # ipdb.set_trace()
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(tqdm.tqdm(timesteps)):
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+                if pose_guider is not None:
+                    latent_model_input = latent_model_input + pose_embeds
+
+                noise_cond = torch.randn_like(image_latents)
+                if use_noise:
+                    cond_latents = self.scheduler.add_noise(image_latents, noise_cond, t)
+                else:
+                    cond_latents = image_latents
+                cond_latent_model_input = torch.cat([cond_latents] * 2) if do_classifier_free_guidance else cond_latents
+                cond_latent_model_input = self.scheduler.scale_model_input(cond_latent_model_input, t)
+
+                # predict the noise residual
+                # ref text condition
+                ref_dict = {}
+                if self.ref_unet is not None:
+                    noise_pred_cond = self.ref_unet(
+                        cond_latent_model_input,    #torch.Size([64, 4, 1, 32, 32])
+                        t,             #torch.Size([32])
+                        encoder_hidden_states=text_embeddings.to(torch.float32), #torch.Size([64, 77, 768])
+                        cross_attention_kwargs=dict(mode="w", ref_dict=ref_dict)
+                    ).sample.to(dtype=latents_dtype)
+                
+                # if torch.isnan(noise_pred_cond).any():
+                #     ipdb.set_trace()
+                # Predict the noise residual and compute loss
+                # model_pred = unet(noisy_latents, timesteps, encoder_hidden_states, camera_matrixs).sample
+                # unet
+                #text condition for unet 
+                text_embeddings_unet = text_embeddings.unsqueeze(1).repeat(1,latents.shape[2],1,1)
+                text_embeddings_unet = rearrange(text_embeddings_unet, 'B Nv d c -> (B Nv) d c')
+                #image condition for unet
+                image_embeddings_unet = image_embeddings.unsqueeze(1).repeat(1,latents.shape[2],1, 1)
+                image_embeddings_unet = rearrange(image_embeddings_unet, 'B Nv d c -> (B Nv) d c')
+
+                if unet_condition_type == 'text':
+                    encoder_hidden_states_unet_cond = text_embeddings_unet  
+                elif unet_condition_type == 'image':
+                    encoder_hidden_states_unet_cond = image_embeddings_unet
+                else:
+                    raise('need unet_condition_type')
+                
+                if self.ref_unet is not None:
+                    noise_pred = self.unet(
+                        latent_model_input.to(torch.float32),         #torch.Size([64, 4, 4, 32, 32])
+                        t,
+                        encoder_hidden_states=encoder_hidden_states_unet_cond.to(torch.float32), 
+                        camera_matrixs=camera_matrixs.to(torch.float32),      #torch.Size([64, 4, 12])
+                        cross_attention_kwargs=dict(mode="r", ref_dict=ref_dict, is_cfg_guidance=do_classifier_free_guidance)
+                        # cross_attention_kwargs=dict(mode="n", ref_dict=ref_dict, is_cfg_guidance=do_classifier_free_guidance)
+                    ).sample.to(dtype=latents_dtype)
+                else:
+                    noise_pred = self.unet(
+                        latent_model_input.to(torch.float32),         #torch.Size([64, 4, 4, 32, 32])
+                        t,
+                        encoder_hidden_states=encoder_hidden_states_unet_cond.to(torch.float32), 
+                        camera_matrixs=camera_matrixs.to(torch.float32),      #torch.Size([64, 4, 12])
+                        # cross_attention_kwargs=dict(mode="r", ref_dict=ref_dict, is_cfg_guidance=do_classifier_free_guidance)
+                        cross_attention_kwargs=dict(mode="n", ref_dict=ref_dict, is_cfg_guidance=do_classifier_free_guidance)
+                    ).sample.to(dtype=latents_dtype)
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    if use_shifted_noise:
+                        # Apply regular classifier-free guidance.
+                        cfg = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+                        # Calculate standard deviations.
+                        std_pos = noise_pred_text.std([1,2,3], keepdim=True)
+                        std_cfg = cfg.std([1,2,3], keepdim=True)
+                        # Apply guidance rescale with fused operations.
+                        factor = std_pos / std_cfg
+                        factor = rescale * factor + (1 - rescale)
+                        noise_pred = cfg * factor
+                    else:
+                        noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+                    # noise_pred_uncond_, noise_pred_text_ = noise_pred_cond.chunk(2)
+                    # noise_pred_cond = noise_pred_uncond_ + guidance_scale * (noise_pred_text_ - noise_pred_uncond_)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                noise_pred = rearrange(noise_pred, "(b f) c h w -> b c f h w", f=video_length)
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+                # noise_pred_cond = rearrange(noise_pred_cond, "(b f) c h w -> b c f h w", f=1)
+                # cond_latents = self.scheduler.step(noise_pred_cond, t, cond_latents, **extra_step_kwargs).prev_sample
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        callback(i, t, latents)
+
+        # Post-processing
+        video = self.decode_latents(latents)
+
+        # Convert to tensor
+        if output_type == "tensor":
+            video = torch.from_numpy(video)
+
+        if not return_dict:
+            return video
+
+        return TuneAVideoPipelineOutput(videos=video)

2D_Stage/tuneavideo/util.py

@@ -0,0 +1,128 @@
+import os
+import imageio
+import numpy as np
+from typing import Union
+import cv2
+import torch
+import torchvision
+
+from tqdm import tqdm
+from einops import rearrange
+
+def shifted_noise(betas, image_d=512, noise_d=256, shifted_noise=True):
+    alphas = 1 - betas
+    alphas_bar = torch.cumprod(alphas, dim=0)
+    d = (image_d / noise_d) ** 2
+    if shifted_noise:
+        alphas_bar = alphas_bar / (d - (d - 1) * alphas_bar)
+    alphas_bar_sqrt = torch.sqrt(alphas_bar)
+    alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
+    alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
+    # Shift so last timestep is zero.
+    alphas_bar_sqrt -= alphas_bar_sqrt_T
+    # Scale so first timestep is back to old value.
+    alphas_bar_sqrt *= alphas_bar_sqrt_0 / (
+    alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
+
+    # Convert alphas_bar_sqrt to betas
+    alphas_bar = alphas_bar_sqrt ** 2
+    alphas = alphas_bar[1:] / alphas_bar[:-1]
+    alphas = torch.cat([alphas_bar[0:1], alphas])
+    betas = 1 - alphas
+    return betas
+
+def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=4, fps=8):
+    videos = rearrange(videos, "b c t h w -> t b c h w")
+    outputs = []
+    for x in videos:
+        x = torchvision.utils.make_grid(x, nrow=n_rows)
+        x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
+        if rescale:
+            x = (x + 1.0) / 2.0  # -1,1 -> 0,1
+        x = (x * 255).numpy().astype(np.uint8)
+        outputs.append(x)
+
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+    imageio.mimsave(path, outputs, duration=1000/fps)
+
+def save_imgs_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=4, fps=8):
+    videos = rearrange(videos, "b c t h w -> t b c h w")
+    for i, x in enumerate(videos):
+        x = torchvision.utils.make_grid(x, nrow=n_rows)
+        x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
+        if rescale:
+            x = (x + 1.0) / 2.0  # -1,1 -> 0,1
+        x = (x * 255).numpy().astype(np.uint8)
+        os.makedirs(os.path.dirname(path), exist_ok=True)
+        cv2.imwrite(os.path.join(path, f'view_{i}.png'), x[:,:,::-1])
+
+def imgs_grid(videos: torch.Tensor, rescale=False, n_rows=4, fps=8):
+    videos = rearrange(videos, "b c t h w -> t b c h w")
+    image_list = []
+    for i, x in enumerate(videos):
+        x = torchvision.utils.make_grid(x, nrow=n_rows)
+        x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
+        if rescale:
+            x = (x + 1.0) / 2.0  # -1,1 -> 0,1
+        x = (x * 255).numpy().astype(np.uint8)
+        # image_list.append(x[:,:,::-1])
+        image_list.append(x)
+    return image_list
+
+# DDIM Inversion
+@torch.no_grad()
+def init_prompt(prompt, pipeline):
+    uncond_input = pipeline.tokenizer(
+        [""], padding="max_length", max_length=pipeline.tokenizer.model_max_length,
+        return_tensors="pt"
+    )
+    uncond_embeddings = pipeline.text_encoder(uncond_input.input_ids.to(pipeline.device))[0]
+    text_input = pipeline.tokenizer(
+        [prompt],
+        padding="max_length",
+        max_length=pipeline.tokenizer.model_max_length,
+        truncation=True,
+        return_tensors="pt",
+    )
+    text_embeddings = pipeline.text_encoder(text_input.input_ids.to(pipeline.device))[0]
+    context = torch.cat([uncond_embeddings, text_embeddings])
+
+    return context
+
+
+def next_step(model_output: Union[torch.FloatTensor, np.ndarray], timestep: int,
+              sample: Union[torch.FloatTensor, np.ndarray], ddim_scheduler):
+    timestep, next_timestep = min(
+        timestep - ddim_scheduler.config.num_train_timesteps // ddim_scheduler.num_inference_steps, 999), timestep
+    alpha_prod_t = ddim_scheduler.alphas_cumprod[timestep] if timestep >= 0 else ddim_scheduler.final_alpha_cumprod
+    alpha_prod_t_next = ddim_scheduler.alphas_cumprod[next_timestep]
+    beta_prod_t = 1 - alpha_prod_t
+    next_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
+    next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output
+    next_sample = alpha_prod_t_next ** 0.5 * next_original_sample + next_sample_direction
+    return next_sample
+
+
+def get_noise_pred_single(latents, t, context, unet):
+    noise_pred = unet(latents, t, encoder_hidden_states=context)["sample"]
+    return noise_pred
+
+
+@torch.no_grad()
+def ddim_loop(pipeline, ddim_scheduler, latent, num_inv_steps, prompt):
+    context = init_prompt(prompt, pipeline)
+    uncond_embeddings, cond_embeddings = context.chunk(2)
+    all_latent = [latent]
+    latent = latent.clone().detach()
+    for i in tqdm(range(num_inv_steps)):
+        t = ddim_scheduler.timesteps[len(ddim_scheduler.timesteps) - i - 1]
+        noise_pred = get_noise_pred_single(latent.to(torch.float32), t, cond_embeddings.to(torch.float32), pipeline.unet)
+        latent = next_step(noise_pred, t, latent, ddim_scheduler)
+        all_latent.append(latent)
+    return all_latent
+
+
+@torch.no_grad()
+def ddim_inversion(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt=""):
+    ddim_latents = ddim_loop(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt)
+    return ddim_latents

2D_Stage/webui.py

@@ -0,0 +1,323 @@
+import gradio as gr
+from PIL import Image
+import glob
+
+import io
+import argparse
+import inspect
+import os
+import random
+from typing import Dict, Optional, Tuple
+from omegaconf import OmegaConf
+import numpy as np
+
+import torch
+import torch.utils.checkpoint
+
+from accelerate.logging import get_logger
+from accelerate.utils import set_seed
+from diffusers import AutoencoderKL, DDIMScheduler
+from diffusers.utils import check_min_version
+from tqdm.auto import tqdm
+from transformers import CLIPTextModel, CLIPTokenizer, CLIPImageProcessor, CLIPVisionModelWithProjection
+from torchvision import transforms
+
+from tuneavideo.models.unet_mv2d_condition import UNetMV2DConditionModel
+from tuneavideo.models.unet_mv2d_ref import UNetMV2DRefModel
+from tuneavideo.models.PoseGuider import PoseGuider
+from tuneavideo.pipelines.pipeline_tuneavideo import TuneAVideoPipeline
+from tuneavideo.util import shifted_noise
+from einops import rearrange
+import PIL
+from PIL import Image
+from torchvision.utils import save_image
+import json
+import cv2
+
+import onnxruntime as rt
+from huggingface_hub.file_download import hf_hub_download
+from rm_anime_bg.cli import get_mask, SCALE
+
+from huggingface_hub import hf_hub_download, list_repo_files
+
+repo_id = "zjpshadow/CharacterGen"
+all_files = list_repo_files(repo_id, revision="main")
+
+for file in all_files:
+    if os.path.exists("../" + file):
+        continue
+    if file.startswith("2D_Stage"):
+        hf_hub_download(repo_id, file, local_dir="../")
+
+class rm_bg_api:
+
+    def __init__(self, force_cpu: Optional[bool] = True):
+        session_infer_path = hf_hub_download(
+            repo_id="skytnt/anime-seg", filename="isnetis.onnx",
+        )
+        providers: list[str] = ["CPUExecutionProvider"]
+        if not force_cpu and "CUDAExecutionProvider" in rt.get_available_providers():
+            providers = ["CUDAExecutionProvider"]
+
+        self.session_infer = rt.InferenceSession(
+            session_infer_path, providers=providers,
+        )
+
+    def remove_background(
+        self,
+        imgs: list[np.ndarray],
+        alpha_min: float,
+        alpha_max: float,
+    ) -> list:
+        process_imgs = []
+        for img in imgs:
+            # CHANGE to RGB
+            img = cv2.cvtColor(img, cv2.COLOR_RGBA2RGB)
+            mask = get_mask(self.session_infer, img)
+
+            mask[mask < alpha_min] = 0.0  # type: ignore
+            mask[mask > alpha_max] = 1.0  # type: ignore
+
+            img_after = (mask * img + SCALE * (1 - mask)).astype(np.uint8)  # type: ignore
+            mask = (mask * SCALE).astype(np.uint8)  # type: ignore
+            img_after = np.concatenate([img_after, mask], axis=2, dtype=np.uint8)
+            mask = mask.repeat(3, axis=2)
+            process_imgs.append(Image.fromarray(img_after))
+        return process_imgs
+
+check_min_version("0.24.0")
+
+logger = get_logger(__name__, log_level="INFO")
+
+def set_seed(seed):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+
+def get_bg_color(bg_color):
+    if bg_color == 'white':
+        bg_color = np.array([1., 1., 1.], dtype=np.float32)
+    elif bg_color == 'black':
+        bg_color = np.array([0., 0., 0.], dtype=np.float32)
+    elif bg_color == 'gray':
+        bg_color = np.array([0.5, 0.5, 0.5], dtype=np.float32)
+    elif bg_color == 'random':
+        bg_color = np.random.rand(3)
+    elif isinstance(bg_color, float):
+        bg_color = np.array([bg_color] * 3, dtype=np.float32)
+    else:
+        raise NotImplementedError
+    return bg_color
+
+def process_image(image, totensor):
+    if not image.mode == "RGBA":
+        image = image.convert("RGBA")
+
+    # Find non-transparent pixels
+    non_transparent = np.nonzero(np.array(image)[..., 3])
+    min_x, max_x = non_transparent[1].min(), non_transparent[1].max()
+    min_y, max_y = non_transparent[0].min(), non_transparent[0].max()    
+    image = image.crop((min_x, min_y, max_x, max_y))
+
+    # paste to center
+    max_dim = max(image.width, image.height)
+    max_height = max_dim
+    max_width = int(max_dim / 3 * 2)
+    new_image = Image.new("RGBA", (max_width, max_height))
+    left = (max_width - image.width) // 2
+    top = (max_height - image.height) // 2
+    new_image.paste(image, (left, top))
+
+    image = new_image.resize((512, 768), resample=PIL.Image.BICUBIC)
+    image = np.array(image)
+    image = image.astype(np.float32) / 255.
+    assert image.shape[-1] == 4  # RGBA
+    alpha = image[..., 3:4]
+    bg_color = get_bg_color("gray")
+    image = image[..., :3] * alpha + bg_color * (1 - alpha)
+    # save image
+    # new_image = Image.fromarray((image * 255).astype(np.uint8))
+    # new_image.save("input.png")
+    return totensor(image)
+
+class Inference_API:
+
+    def __init__(self):
+        self.validation_pipeline = None
+
+    @torch.no_grad()
+    def inference(self, input_image, vae, feature_extractor, image_encoder, unet, ref_unet, tokenizer, text_encoder, pretrained_model_path, generator, validation, val_width, val_height, unet_condition_type,
+                    pose_guider=None, use_noise=True, use_shifted_noise=False, noise_d=256, crop=False, seed=100, timestep=20):
+        set_seed(seed)
+        # Get the validation pipeline
+        if self.validation_pipeline is None:
+            noise_scheduler = DDIMScheduler.from_pretrained(pretrained_model_path, subfolder="scheduler")
+            if use_shifted_noise:
+                print(f"enable shifted noise for {val_height} to {noise_d}")
+                betas = shifted_noise(noise_scheduler.betas, image_d=val_height, noise_d=noise_d)
+                noise_scheduler.betas = betas
+                noise_scheduler.alphas = 1 - betas
+                noise_scheduler.alphas_cumprod = torch.cumprod(noise_scheduler.alphas, dim=0)
+            self.validation_pipeline = TuneAVideoPipeline(
+                vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, ref_unet=ref_unet,feature_extractor=feature_extractor,image_encoder=image_encoder,
+                scheduler=noise_scheduler
+            )
+            self.validation_pipeline.enable_vae_slicing()
+            self.validation_pipeline.set_progress_bar_config(disable=True)
+
+        totensor = transforms.ToTensor()
+
+        metas = json.load(open("./material/pose.json", "r"))
+        cameras = []
+        pose_images = []
+        input_path = "./material"
+        for lm in metas:
+            cameras.append(torch.tensor(np.array(lm[0]).reshape(4, 4).transpose(1,0)[:3, :4]).reshape(-1))
+            if not crop:
+                pose_images.append(totensor(np.asarray(Image.open(os.path.join(input_path, lm[1])).resize(
+                    (val_height, val_width), resample=PIL.Image.BICUBIC)).astype(np.float32) / 255.))
+            else:
+                pose_image = Image.open(os.path.join(input_path, lm[1]))
+                crop_area = (128, 0, 640, 768)
+                pose_images.append(totensor(np.array(pose_image.crop(crop_area)).astype(np.float32)) / 255.)
+        camera_matrixs = torch.stack(cameras).unsqueeze(0).to("cuda")
+        pose_imgs_in = torch.stack(pose_images).to("cuda")
+        prompts = "high quality, best quality"
+        prompt_ids = tokenizer(
+            prompts, max_length=tokenizer.model_max_length, padding="max_length", truncation=True,
+            return_tensors="pt"
+        ).input_ids[0]
+
+        # (B*Nv, 3, H, W)
+        B = 1
+        weight_dtype = torch.bfloat16
+        imgs_in = process_image(input_image, totensor)
+        imgs_in = rearrange(imgs_in.unsqueeze(0).unsqueeze(0), "B Nv C H W -> (B Nv) C H W")
+                
+        with torch.autocast("cuda", dtype=weight_dtype):
+            imgs_in = imgs_in.to("cuda")
+            # B*Nv images
+            out = self.validation_pipeline(prompt=prompts, image=imgs_in.to(weight_dtype), generator=generator, 
+                                        num_inference_steps=timestep,
+                                        camera_matrixs=camera_matrixs.to(weight_dtype), prompt_ids=prompt_ids, 
+                                        height=val_height, width=val_width, unet_condition_type=unet_condition_type, 
+                                        pose_guider=None, pose_image=pose_imgs_in, use_noise=use_noise, 
+                                        use_shifted_noise=use_shifted_noise, **validation).videos
+            out = rearrange(out, "B C f H W -> (B f) C H W", f=validation.video_length)
+
+        image_outputs = []
+        for bs in range(4):
+            img_buf = io.BytesIO()
+            save_image(out[bs], img_buf, format='PNG')
+            img_buf.seek(0)
+            img = Image.open(img_buf)
+            image_outputs.append(img)
+        torch.cuda.empty_cache()
+        return image_outputs 
+
+@torch.no_grad()
+def main(
+    pretrained_model_path: str,
+    image_encoder_path: str,
+    ckpt_dir: str,
+    validation: Dict,
+    local_crossattn: bool = True,
+    unet_from_pretrained_kwargs=None,
+    unet_condition_type=None,
+    use_pose_guider=False,
+    use_noise=True,
+    use_shifted_noise=False,
+    noise_d=256
+):
+    *_, config = inspect.getargvalues(inspect.currentframe())
+
+    device = "cuda"
+
+    tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
+    text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder")
+    image_encoder = CLIPVisionModelWithProjection.from_pretrained(image_encoder_path)
+    feature_extractor = CLIPImageProcessor()
+    vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae")
+    unet = UNetMV2DConditionModel.from_pretrained_2d(pretrained_model_path, subfolder="unet", local_crossattn=local_crossattn, **unet_from_pretrained_kwargs)
+    ref_unet = UNetMV2DRefModel.from_pretrained_2d(pretrained_model_path, subfolder="unet", local_crossattn=local_crossattn, **unet_from_pretrained_kwargs)
+    if use_pose_guider:
+        pose_guider = PoseGuider(noise_latent_channels=4).to("cuda")
+    else:
+        pose_guider = None
+
+    unet_params = torch.load(os.path.join(ckpt_dir, "pytorch_model.bin"), map_location="cpu")
+    if use_pose_guider:
+        pose_guider_params = torch.load(os.path.join(ckpt_dir, "pytorch_model_1.bin"), map_location="cpu")
+        ref_unet_params = torch.load(os.path.join(ckpt_dir, "pytorch_model_2.bin"), map_location="cpu")
+        pose_guider.load_state_dict(pose_guider_params)
+    else:
+        ref_unet_params = torch.load(os.path.join(ckpt_dir, "pytorch_model_1.bin"), map_location="cpu")
+    unet.load_state_dict(unet_params)
+    ref_unet.load_state_dict(ref_unet_params)
+
+    weight_dtype = torch.float16
+
+    text_encoder.to(device, dtype=weight_dtype)
+    image_encoder.to(device, dtype=weight_dtype)
+    vae.to(device, dtype=weight_dtype)
+    ref_unet.to(device, dtype=weight_dtype)
+    unet.to(device, dtype=weight_dtype)
+
+    vae.requires_grad_(False)
+    unet.requires_grad_(False)
+    ref_unet.requires_grad_(False)
+
+    generator = torch.Generator(device="cuda")
+    inferapi = Inference_API()
+    remove_api = rm_bg_api()
+    def gen4views(image, width, height, seed, timestep, remove_bg):
+        if remove_bg:
+            image = remove_api.remove_background(
+                imgs=[np.array(image)],
+                alpha_min=0.1,
+                alpha_max=0.9,
+            )[0]
+        return inferapi.inference(
+            image, vae, feature_extractor, image_encoder, unet, ref_unet, tokenizer, text_encoder, pretrained_model_path,
+            generator, validation, width, height, unet_condition_type,
+            pose_guider=pose_guider, use_noise=use_noise, use_shifted_noise=use_shifted_noise, noise_d=noise_d,
+            crop=True, seed=seed, timestep=timestep
+        )
+
+    with gr.Blocks() as demo:
+        gr.Markdown("# [SIGGRAPH'24] CharacterGen: Efficient 3D Character Generation from Single Images with Multi-View Pose Calibration")
+        gr.Markdown("# 2D Stage: One Image to Four Views of Character Image")
+        gr.Markdown("**Please Upload the Image without background, and the pictures uploaded should preferably be full-body frontal photos.**")
+        with gr.Row():
+            with gr.Column():
+                img_input = gr.Image(type="pil", label="Upload Image(without background)", image_mode="RGBA", width=768, height=512)
+                gr.Examples(
+                    label="Example Images",
+                    examples=glob.glob("./material/examples/*.png"),
+                    inputs=[img_input]
+                )
+                with gr.Row():
+                    width_input = gr.Number(label="Width", value=512)
+                    height_input = gr.Number(label="Height", value=768)
+                    seed_input = gr.Number(label="Seed", value=2333)
+                    remove_bg = gr.Checkbox(label="Remove Background (with algorithm)", value=False)
+                timestep = gr.Slider(minimum=10, maximum=70, step=1, value=40, label="Timesteps")
+            with gr.Column():
+                button = gr.Button(value="Generate")
+                output = gr.Gallery(label="4 views of Character Image")
+        
+        button.click(
+            fn=gen4views,
+            inputs=[img_input, width_input, height_input, seed_input, timestep, remove_bg],
+            outputs=[output]
+        )
+
+    demo.launch()
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, default="./configs/infer.yaml")
+    args = parser.parse_args()
+
+    main(**OmegaConf.load(args.config))

3D_Stage/configs/infer.yaml

@@ -0,0 +1,104 @@
+system_cls: lrm.systems.multiview_lrm.MultiviewLRM
+data:
+  cond_width: 504
+  cond_height: 504
+
+system:
+  weights: ./models/lrm.ckpt
+
+  weights_ignore_modules:
+    - decoder.heads.density
+
+  check_train_every_n_steps: 100
+
+  camera_embedder_cls: lrm.models.camera.LinearCameraEmbedder
+  camera_embedder:
+    in_channels: 16
+    out_channels: 768
+    conditions:
+      - c2w_cond
+
+  # image tokenizer transforms input images to tokens
+  image_tokenizer_cls: lrm.models.tokenizers.image.DINOV2SingleImageTokenizer
+  image_tokenizer:
+    pretrained_model_name_or_path: "./models/base"
+    freeze_backbone_params: false
+    enable_memory_efficient_attention: true
+    enable_gradient_checkpointing: true
+    # camera modulation to the DINO transformer layers
+    modulation: true
+    modulation_zero_init: true
+    modulation_single_layer: true
+    modulation_cond_dim: ${system.camera_embedder.out_channels}
+
+  # tokenizer gives a tokenized representation for the 3D scene
+  # triplane tokens in this case
+  tokenizer_cls: lrm.models.tokenizers.triplane.TriplaneLearnablePositionalEmbedding
+  tokenizer:
+    plane_size: 32
+    num_channels: 512
+
+  # backbone network is a transformer that takes scene tokens (potentially with conditional image tokens)
+  # and outputs scene tokens of the same size
+  backbone_cls: lrm.models.transformers.transformer_1d.Transformer1D
+  backbone:
+    in_channels: ${system.tokenizer.num_channels}
+    num_attention_heads: 16
+    attention_head_dim: 64
+    num_layers: 12
+    cross_attention_dim: 768 # hard-code, =DINO feature dim
+    # camera modulation to the transformer layers
+    # if not needed, set norm_type=layer_norm and do not specify cond_dim_ada_norm_continuous
+    norm_type: "layer_norm"
+    enable_memory_efficient_attention: true
+    gradient_checkpointing: true
+
+  # post processor takes scene tokens and outputs the final scene parameters that will be used for rendering
+  # in this case, triplanes are upsampled and the features are condensed
+  post_processor_cls: lrm.models.networks.TriplaneUpsampleNetwork
+  post_processor:
+    in_channels: 512
+    out_channels: 80
+
+  renderer_cls: lrm.models.renderers.triplane_dmtet.TriplaneDMTetRenderer
+  renderer:
+    radius: 0.6 # slightly larger than 0.5
+    feature_reduction: concat
+    sdf_bias: -2.
+    tet_dir: "./load/tets/"
+    isosurface_resolution: 256
+    enable_isosurface_grid_deformation: false
+    sdf_activation: negative
+
+  decoder_cls: lrm.models.networks.MultiHeadMLP
+  decoder:
+    in_channels: 240 # 3 * 80
+    n_neurons: 64
+    n_hidden_layers_share: 8
+    heads:
+      - name: sdf
+        out_channels: 1
+        n_hidden_layers: 1
+        output_activation: null
+      - name: features
+        out_channels: 3
+        n_hidden_layers: 1
+        output_activation: null # activate in material
+    activation: silu
+    chunk_mode: deferred
+    chunk_size: 131072      
+
+  exporter:
+    fmt: "obj"
+   #visual: "vertex"
+    visual: "uv"
+    save_uv: True
+    save_texture: True
+    uv_unwrap_method: "open3d"
+    output_path: "./outputs"
+
+  material_cls: lrm.models.materials.no_material.NoMaterial
+
+  background_cls: lrm.models.background.solid_color_background.SolidColorBackground
+  background:
+    color: [0.5, 0.5, 0.5]

3D_Stage/load/tets/generate_tets.py

@@ -0,0 +1,58 @@
+# Copyright (c) 2020-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+
+import os
+
+import numpy as np
+
+"""
+This code segment shows how to use Quartet: https://github.com/crawforddoran/quartet,
+to generate a tet grid
+1) Download, compile and run Quartet as described in the link above. Example usage `quartet meshes/cube.obj 0.5 cube_5.tet`
+2) Run the function below to generate a file `cube_32_tet.tet`
+"""
+
+
+def generate_tetrahedron_grid_file(res=32, root=".."):
+    frac = 1.0 / res
+    command = f"cd {root}; ./quartet meshes/cube.obj {frac} meshes/cube_{res}_tet.tet -s meshes/cube_boundary_{res}.obj"
+    os.system(command)
+
+
+"""
+This code segment shows how to convert from a quartet .tet file to compressed npz file
+"""
+
+
+def convert_from_quartet_to_npz(quartetfile="cube_32_tet.tet", npzfile="32_tets"):
+    file1 = open(quartetfile, "r")
+    header = file1.readline()
+    numvertices = int(header.split(" ")[1])
+    numtets = int(header.split(" ")[2])
+    print(numvertices, numtets)
+
+    # load vertices
+    vertices = np.loadtxt(quartetfile, skiprows=1, max_rows=numvertices)
+    print(vertices.shape)
+
+    # load indices
+    indices = np.loadtxt(
+        quartetfile, dtype=int, skiprows=1 + numvertices, max_rows=numtets
+    )
+    print(indices.shape)
+
+    np.savez_compressed(npzfile, vertices=vertices, indices=indices)
+
+
+root = "/home/gyc/quartet"
+for res in [300, 350, 400]:
+    generate_tetrahedron_grid_file(res, root)
+    convert_from_quartet_to_npz(
+        os.path.join(root, f"meshes/cube_{res}_tet.tet"), npzfile=f"{res}_tets"
+    )

3D_Stage/lrm/__init__.py

@@ -0,0 +1,29 @@
+import importlib
+
+
+def find(cls_string):
+    module_string = ".".join(cls_string.split(".")[:-1])
+    cls_name = cls_string.split(".")[-1]
+    module = importlib.import_module(module_string, package=None)
+    cls = getattr(module, cls_name)
+    return cls
+
+
+###  grammar sugar for logging utilities  ###
+import logging
+
+logger = logging.getLogger("pytorch_lightning")
+
+from pytorch_lightning.utilities.rank_zero import (
+    rank_zero_debug,
+    rank_zero_info,
+    rank_zero_only,
+)
+
+debug = rank_zero_debug
+info = rank_zero_info
+
+
+@rank_zero_only
+def warn(*args, **kwargs):
+    logger.warn(*args, **kwargs)

3D_Stage/lrm/models/background/base.py

@@ -0,0 +1,24 @@
+import random
+from dataclasses import dataclass, field
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import lrm
+from ...utils.base import BaseModule
+from ...utils.typing import *
+
+
+class BaseBackground(BaseModule):
+    @dataclass
+    class Config(BaseModule.Config):
+        pass
+
+    cfg: Config
+
+    def configure(self):
+        pass
+
+    def forward(self, dirs: Float[Tensor, "B H W 3"]) -> Float[Tensor, "B H W Nc"]:
+        raise NotImplementedError

3D_Stage/lrm/models/background/solid_color_background.py

@@ -0,0 +1,58 @@
+import random
+from dataclasses import dataclass, field
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import lrm
+from .base import BaseBackground
+from ...utils.typing import *
+
+
+class SolidColorBackground(BaseBackground):
+    @dataclass
+    class Config(BaseBackground.Config):
+        n_output_dims: int = 3
+        color: Tuple = (1.0, 1.0, 1.0)
+        learned: bool = False
+        random_aug: bool = False
+        random_aug_prob: float = 0.5
+
+    cfg: Config
+
+    def configure(self) -> None:
+        self.env_color: Float[Tensor, "Nc"]
+        if self.cfg.learned:
+            self.env_color = nn.Parameter(
+                torch.as_tensor(self.cfg.color, dtype=torch.float32)
+            )
+        else:
+            self.register_buffer(
+                "env_color", torch.as_tensor(self.cfg.color, dtype=torch.float32)
+            )
+
+    def forward(
+        self,
+        dirs: Float[Tensor, "B H W Nc"],
+        color_spec: Optional[Float[Tensor, "Nc"]] = None,
+    ) -> Float[Tensor, "B H W Nc"]:
+        color = torch.ones(*dirs.shape[:-1], self.cfg.n_output_dims).to(dirs) * (
+            color_spec if color_spec is not None else self.env_color
+        )
+        if (
+            self.training
+            and self.cfg.random_aug
+            and random.random() < self.cfg.random_aug_prob
+        ):
+            # use random background color with probability random_aug_prob
+            # color = color * 0 + (
+            #     torch.ones(*dirs.shape[:-1], self.cfg.n_output_dims).to(dirs) *
+            #     torch.rand(self.cfg.n_output_dims).to(dirs)
+            # )
+            color = color * 0 + (  # prevent checking for unused parameters in DDP
+                torch.rand(dirs.shape[0], 1, 1, self.cfg.n_output_dims)
+                .to(dirs)
+                .expand(*dirs.shape[:-1], -1)
+            )
+        return color

3D_Stage/lrm/models/camera.py

@@ -0,0 +1,33 @@
+from dataclasses import dataclass, field
+
+import torch
+import torch.nn as nn
+
+from ..utils.base import BaseModule
+from ..utils.typing import *
+
+
+class LinearCameraEmbedder(BaseModule):
+    @dataclass
+    class Config(BaseModule.Config):
+        in_channels: int = 0
+        out_channels: int = 0
+        conditions: List[str] = field(default_factory=list)
+
+    cfg: Config
+
+    def configure(self) -> None:
+        super().configure()
+        self.linear = nn.Linear(self.cfg.in_channels, self.cfg.out_channels)
+
+    def forward(self, **kwargs):
+        cond_tensors = []
+        for cond_name in self.cfg.conditions:
+            assert cond_name in kwargs
+            cond = kwargs[cond_name]
+            # cond in shape (B, Nv, ...)
+            cond_tensors.append(cond.view(*cond.shape[:2], -1))
+        cond_tensor = torch.cat(cond_tensors, dim=-1)
+        assert cond_tensor.shape[-1] == self.cfg.in_channels
+        embedding = self.linear(cond_tensor)
+        return embedding

3D_Stage/lrm/models/exporters/base.py

@@ -0,0 +1,33 @@
+from dataclasses import dataclass
+
+import lrm
+from ..renderers.base import BaseRenderer
+from ...utils.base import BaseObject
+from ...utils.typing import *
+
+
+@dataclass
+class ExporterOutput:
+    save_name: str
+    save_type: str
+    params: Dict[str, Any]
+
+
+class Exporter(BaseObject):
+    @dataclass
+    class Config(BaseObject.Config):
+        save_video: bool = False
+
+    cfg: Config
+
+    def configure(self, renderer: BaseRenderer) -> None:
+        self.renderer = renderer
+
+    def __call__(self, *args, **kwargs) -> List[ExporterOutput]:
+        raise NotImplementedError
+
+
+class DummyExporter(Exporter):
+    def __call__(self, *args, **kwargs) -> List[ExporterOutput]:
+        # DummyExporter does not export anything
+        return []

3D_Stage/lrm/models/exporters/mesh_exporter.py

@@ -0,0 +1,263 @@
+from dataclasses import dataclass, field
+import tempfile
+import os
+
+import cv2
+import numpy as np
+import torch
+
+import lrm
+from ..renderers.base import BaseRenderer
+from .base import Exporter, ExporterOutput
+from ..mesh import Mesh
+from ...utils.rasterize import NVDiffRasterizerContext
+from ...utils.typing import *
+from ...utils.misc import time_recorder as tr, time_recorder_enabled
+
+
+def uv_padding_cpu(image, hole_mask, padding):
+    uv_padding_size = padding
+    inpaint_image = (
+        cv2.inpaint(
+            (image.detach().cpu().numpy() * 255).astype(np.uint8),
+            (hole_mask.detach().cpu().numpy() * 255).astype(np.uint8),
+            uv_padding_size,
+            cv2.INPAINT_TELEA,
+        )
+        / 255.0
+    )
+    return torch.from_numpy(inpaint_image).to(image)
+
+
+def uv_padding_cvc(image, hole_mask, padding):
+    import cvcuda
+
+    torch_to_cvc = lambda x, layout: cvcuda.as_tensor(x, layout)
+    cvc_to_torch = lambda x: torch.as_tensor(x.cuda())
+
+    uv_padding_size = padding
+    image_cvc = torch_to_cvc((image.detach() * 255).to(torch.uint8), "HWC")
+    hole_mask_cvc = torch_to_cvc((hole_mask.detach() * 255).to(torch.uint8), "HW")
+    inpaint_image = cvcuda.inpaint(image_cvc, hole_mask_cvc, uv_padding_size)
+    inpaint_image = cvc_to_torch(inpaint_image) / 255.0
+    return inpaint_image.to(image)
+
+
+def uv_padding(image, hole_mask, padding):
+    try:
+        inpaint_image = uv_padding_cvc(image, hole_mask, padding)
+    except:
+        lrm.info(f"CVCUDA not available, fallback to CPU UV padding.")
+        inpaint_image = uv_padding_cpu(image, hole_mask, padding)
+    return inpaint_image
+
+
+class MeshExporter(Exporter):
+    @dataclass
+    class Config(Exporter.Config):
+        fmt: str = "obj"  # in ['obj', 'glb']
+        visual: str = "uv"  # in ['uv', 'vertex']
+        save_name: str = "model"
+        save_normal: bool = False
+        save_uv: bool = True
+        save_texture: bool = True
+        texture_size: int = 1024
+        texture_format: str = "jpg"
+        uv_unwrap_method: str = "xatlas"
+        xatlas_chart_options: dict = field(default_factory=dict)
+        xatlas_pack_options: dict = field(default_factory=dict)
+        smartuv_options: dict = field(default_factory=dict)
+        uv_padding_size: int = 2
+        subdivide: bool = False
+        post_process: bool = False
+        post_process_options: dict = field(default_factory=dict)
+        context_type: str = "gl"
+        output_path: str = "outputs"
+
+    cfg: Config
+
+    def configure(self, renderer: BaseRenderer) -> None:
+        super().configure(renderer)
+        self.ctx = NVDiffRasterizerContext(self.cfg.context_type, self.device)
+        if self.cfg.fmt == "obj-mtl":
+            lrm.warn(
+                f"fmt=obj-mtl is deprecated, please us fmt=obj and visual=uv instead."
+            )
+            self.cfg.fmt = "obj"
+            self.cfg.visual = "uv"
+
+        if self.cfg.fmt == "glb":
+            assert self.cfg.visual in [
+                "vertex",
+                "uv-blender",
+            ], "GLB format only supports visual=vertex and visual=uv-blender!"
+
+    def get_geometry(self, scene_code: torch.Tensor) -> Mesh:
+        tr.start("Surface extraction")
+        mesh: Mesh = self.renderer.isosurface(scene_code)
+        tr.end("Surface extraction")
+        return mesh
+
+    def get_texture_maps(
+        self, scene_code: torch.Tensor, mesh: Mesh
+    ) -> Dict[str, torch.Tensor]:
+        assert mesh.has_uv
+        # clip space transform
+        uv_clip = mesh.v_tex * 2.0 - 1.0
+        # pad to four component coordinate
+        uv_clip4 = torch.cat(
+            (
+                uv_clip,
+                torch.zeros_like(uv_clip[..., 0:1]),
+                torch.ones_like(uv_clip[..., 0:1]),
+            ),
+            dim=-1,
+        )
+        # rasterize
+        rast, _ = self.ctx.rasterize_one(
+            uv_clip4,
+            mesh.t_tex_idx,
+            (self.cfg.texture_size, self.cfg.texture_size),
+        )
+
+        hole_mask = ~(rast[:, :, 3] > 0)
+
+        # Interpolate world space position
+        gb_pos, _ = self.ctx.interpolate_one(
+            mesh.v_pos, rast[None, ...], mesh.t_pos_idx
+        )
+        gb_pos = gb_pos[0]
+
+        # Sample out textures from MLP
+        tr.start("Query color")
+        geo_out = self.renderer.query(scene_code, points=gb_pos)
+        tr.end("Query color")
+        mat_out = self.renderer.material.export(points=gb_pos, **geo_out)
+
+        textures = {}
+        tr.start("UV padding")
+        if "albedo" in mat_out:
+            textures["map_Kd"] = uv_padding(
+                mat_out["albedo"], hole_mask, self.cfg.uv_padding_size
+            )
+        else:
+            lrm.warn(
+                "save_texture is True but no albedo texture found, using default white texture"
+            )
+        if "metallic" in mat_out:
+            textures["map_Pm"] = uv_padding(
+                mat_out["metallic"], hole_mask, self.cfg.uv_padding_size
+            )
+        if "roughness" in mat_out:
+            textures["map_Pr"] = uv_padding(
+                mat_out["roughness"], hole_mask, self.cfg.uv_padding_size
+            )
+        if "bump" in mat_out:
+            textures["map_Bump"] = uv_padding(
+                mat_out["bump"], hole_mask, self.cfg.uv_padding_size
+            )
+        tr.end("UV padding")
+        return textures
+
+    def __call__(self, names, scene_codes) -> List[ExporterOutput]:
+        outputs = []
+        for name, scene_code in zip(names, scene_codes):
+            mesh = self.get_geometry(scene_code)
+            if self.cfg.post_process:
+                tr.start("Mesh post-processing")
+                mesh = mesh.post_process(self.cfg.post_process_options)
+                tr.end("Mesh post-processing")
+            if self.cfg.visual == "uv":
+                output = self.export_model_with_mtl(
+                    name, self.cfg.fmt, scene_code, mesh
+                )
+            elif self.cfg.visual == "vertex":
+                output = self.export_model(name, self.cfg.fmt, scene_code, mesh)
+            elif self.cfg.visual == "uv-blender":
+                output = self.export_model_blender(name, self.cfg.fmt, scene_code, mesh)
+            else:
+                raise ValueError(f"Unsupported visual format: {self.cfg.visual}")
+            outputs.append(output)
+        return outputs
+
+    def export_model_with_mtl(
+        self, name: str, fmt: str, scene_code: torch.Tensor, mesh: Mesh
+    ) -> ExporterOutput:
+        params = {
+            "mesh": mesh,
+            "save_mat": True,
+            "save_normal": self.cfg.save_normal,
+            "save_uv": self.cfg.save_uv,
+            "save_vertex_color": False,
+            "map_Kd": None,  # Base Color
+            "map_Ks": None,  # Specular
+            "map_Bump": None,  # Normal
+            # ref: https://en.wikipedia.org/wiki/Wavefront_.obj_file#Physically-based_Rendering
+            "map_Pm": None,  # Metallic
+            "map_Pr": None,  # Roughness
+            "map_format": self.cfg.texture_format,
+        }
+
+        if self.cfg.save_uv:
+            mesh.unwrap_uv(
+                self.cfg.uv_unwrap_method,
+                self.cfg.xatlas_chart_options,
+                self.cfg.xatlas_pack_options,
+                self.cfg.smartuv_options,
+            )
+
+        if self.cfg.save_texture:
+            lrm.info("Exporting textures ...")
+            assert self.cfg.save_uv, "save_uv must be True when save_texture is True"
+
+            with time_recorder_enabled():
+                textures = self.get_texture_maps(scene_code, mesh)
+                params.update(textures)
+        os.makedirs(self.cfg.output_path, exist_ok=True)
+        np.savez(f"{self.cfg.output_path}/tex_info.npz", v_tex=mesh.v_tex.cpu().numpy(), t_tex_idx=mesh.t_tex_idx.cpu().numpy())
+        return ExporterOutput(
+            save_name=f"{self.cfg.save_name}-{name}.{fmt}", save_type=fmt, params=params
+        )
+
+    def export_model(
+        self, name: str, fmt: str, scene_code, mesh: Mesh
+    ) -> ExporterOutput:
+        params = {
+            "mesh": mesh,
+            "save_mat": False,
+            "save_normal": self.cfg.save_normal,
+            "save_uv": self.cfg.save_uv,
+            "save_vertex_color": False,
+            "map_Kd": None,  # Base Color
+            "map_Ks": None,  # Specular
+            "map_Bump": None,  # Normal
+            # ref: https://en.wikipedia.org/wiki/Wavefront_.obj_file#Physically-based_Rendering
+            "map_Pm": None,  # Metallic
+            "map_Pr": None,  # Roughness
+            "map_format": self.cfg.texture_format,
+        }
+
+        if self.cfg.save_uv:
+            mesh.unwrap_uv(
+                self.cfg.uv_unwrap_method,
+                self.cfg.xatlas_chart_options,
+                self.cfg.xatlas_pack_options,
+                self.cfg.smartuv_options,
+            )
+
+        if self.cfg.save_texture:
+            lrm.info("Exporting textures ...")
+            geo_out = self.renderer.query(scene_code, points=mesh.v_pos)
+            mat_out = self.renderer.material.export(points=mesh.v_pos, **geo_out)
+
+            if "albedo" in mat_out:
+                mesh.set_vertex_color(mat_out["albedo"])
+                params["save_vertex_color"] = True
+            else:
+                lrm.warn(
+                    "save_texture is True but no albedo texture found, not saving vertex color"
+                )
+
+        return ExporterOutput(
+            save_name=f"{self.cfg.save_name}-{name}.{fmt}", save_type=fmt, params=params
+        )

3D_Stage/lrm/models/isosurface.py

@@ -0,0 +1,272 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import lrm
+from ..models.mesh import Mesh
+from ..utils.typing import *
+from ..utils.ops import scale_tensor
+
+
+class IsosurfaceHelper(nn.Module):
+    points_range: Tuple[float, float] = (0, 1)
+
+    @property
+    def grid_vertices(self) -> Float[Tensor, "N 3"]:
+        raise NotImplementedError
+
+
+class MarchingCubeCPUHelper(IsosurfaceHelper):
+    def __init__(self, resolution: int) -> None:
+        super().__init__()
+        self.resolution = resolution
+        import mcubes
+
+        self.mc_func: Callable = mcubes.marching_cubes
+        self._grid_vertices: Optional[Float[Tensor, "N3 3"]] = None
+        self._dummy: Float[Tensor, "..."]
+        self.register_buffer(
+            "_dummy", torch.zeros(0, dtype=torch.float32), persistent=False
+        )
+
+    @property
+    def grid_vertices(self) -> Float[Tensor, "N3 3"]:
+        if self._grid_vertices is None:
+            # keep the vertices on CPU so that we can support very large resolution
+            x, y, z = (
+                torch.linspace(*self.points_range, self.resolution),
+                torch.linspace(*self.points_range, self.resolution),
+                torch.linspace(*self.points_range, self.resolution),
+            )
+            x, y, z = torch.meshgrid(x, y, z, indexing="ij")
+            verts = torch.cat(
+                [x.reshape(-1, 1), y.reshape(-1, 1), z.reshape(-1, 1)], dim=-1
+            ).reshape(-1, 3)
+            self._grid_vertices = verts
+        return self._grid_vertices
+
+    def forward(
+        self,
+        level: Float[Tensor, "N3 1"],
+        deformation: Optional[Float[Tensor, "N3 3"]] = None,
+    ) -> Mesh:
+        if deformation is not None:
+            lrm.warn(
+                f"{self.__class__.__name__} does not support deformation. Ignoring."
+            )
+        level = -level.view(self.resolution, self.resolution, self.resolution)
+        print(level.shape, level.min(), level.max())
+        v_pos, t_pos_idx = self.mc_func(
+            level.detach().cpu().numpy(), 0.0
+        )  # transform to numpy
+        # test
+        v_pos, t_pos_idx = (
+            torch.from_numpy(v_pos).float().to(self._dummy.device),
+            torch.from_numpy(t_pos_idx.astype(np.int64)).long().to(self._dummy.device),
+        )  # transform back to torch tensor on CUDA
+        v_pos = v_pos / (self.resolution - 1.0)
+        return Mesh(v_pos=v_pos, t_pos_idx=t_pos_idx)
+
+
+def get_center_boundary_index(verts):
+    # Assuming the verts are in range [-1.0, 1.0]
+    length_ = torch.linalg.norm(verts**2, ord=2, dim=-1, keepdim=False)
+    center_idx = torch.argmin(length_)
+    # center_idx = torch.where(length_ < 0.1)[0]
+    boundary_neg = verts == verts.max()
+    boundary_pos = verts == verts.min()
+    boundary = torch.bitwise_or(boundary_pos, boundary_neg)
+    boundary = torch.sum(boundary.float(), dim=-1)
+    boundary_idx = torch.nonzero(boundary)
+    return center_idx.unsqueeze(0), boundary_idx.squeeze(dim=-1)
+
+
+class MarchingTetrahedraHelper(IsosurfaceHelper):
+    def __init__(self, resolution: int, tets_path: str):
+        super().__init__()
+        self.resolution = resolution
+        self.tets_path = tets_path
+
+        self.triangle_table: Float[Tensor, "..."]
+        self.register_buffer(
+            "triangle_table",
+            torch.as_tensor(
+                [
+                    [-1, -1, -1, -1, -1, -1],
+                    [1, 0, 2, -1, -1, -1],
+                    [4, 0, 3, -1, -1, -1],
+                    [1, 4, 2, 1, 3, 4],
+                    [3, 1, 5, -1, -1, -1],
+                    [2, 3, 0, 2, 5, 3],
+                    [1, 4, 0, 1, 5, 4],
+                    [4, 2, 5, -1, -1, -1],
+                    [4, 5, 2, -1, -1, -1],
+                    [4, 1, 0, 4, 5, 1],
+                    [3, 2, 0, 3, 5, 2],
+                    [1, 3, 5, -1, -1, -1],
+                    [4, 1, 2, 4, 3, 1],
+                    [3, 0, 4, -1, -1, -1],
+                    [2, 0, 1, -1, -1, -1],
+                    [-1, -1, -1, -1, -1, -1],
+                ],
+                dtype=torch.long,
+            ),
+            persistent=False,
+        )
+        self.num_triangles_table: Integer[Tensor, "..."]
+        self.register_buffer(
+            "num_triangles_table",
+            torch.as_tensor(
+                [0, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 0], dtype=torch.long
+            ),
+            persistent=False,
+        )
+        self.base_tet_edges: Integer[Tensor, "..."]
+        self.register_buffer(
+            "base_tet_edges",
+            torch.as_tensor([0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3], dtype=torch.long),
+            persistent=False,
+        )
+
+        tets = np.load(self.tets_path)
+        self._grid_vertices: Float[Tensor, "..."]
+        self.register_buffer(
+            "_grid_vertices",
+            torch.from_numpy(tets["vertices"]).float(),
+            persistent=False,
+        )
+        self.indices: Integer[Tensor, "..."]
+        self.register_buffer(
+            "indices", torch.from_numpy(tets["indices"]).long(), persistent=False
+        )
+
+        self._all_edges: Optional[Integer[Tensor, "Ne 2"]] = None
+        self.center_indices, self.boundary_indices = get_center_boundary_index(
+            scale_tensor(self.grid_vertices, self.points_range, (-1.0, 1.0))
+        )
+
+    def normalize_grid_deformation(
+        self, grid_vertex_offsets: Float[Tensor, "Nv 3"]
+    ) -> Float[Tensor, "Nv 3"]:
+        return (
+            (self.points_range[1] - self.points_range[0])
+            / (self.resolution)  # half tet size is approximately 1 / self.resolution
+            * torch.tanh(grid_vertex_offsets)
+        )  # FIXME: hard-coded activation
+
+    @property
+    def grid_vertices(self) -> Float[Tensor, "Nv 3"]:
+        return self._grid_vertices
+
+    @property
+    def all_edges(self) -> Integer[Tensor, "Ne 2"]:
+        if self._all_edges is None:
+            # compute edges on GPU, or it would be VERY SLOW (basically due to the unique operation)
+            edges = torch.tensor(
+                [0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3],
+                dtype=torch.long,
+                device=self.indices.device,
+            )
+            _all_edges = self.indices[:, edges].reshape(-1, 2)
+            _all_edges_sorted = torch.sort(_all_edges, dim=1)[0]
+            _all_edges = torch.unique(_all_edges_sorted, dim=0)
+            self._all_edges = _all_edges
+        return self._all_edges
+
+    def sort_edges(self, edges_ex2):
+        with torch.no_grad():
+            order = (edges_ex2[:, 0] > edges_ex2[:, 1]).long()
+            order = order.unsqueeze(dim=1)
+
+            a = torch.gather(input=edges_ex2, index=order, dim=1)
+            b = torch.gather(input=edges_ex2, index=1 - order, dim=1)
+
+        return torch.stack([a, b], -1)
+
+    def _forward(self, pos_nx3, sdf_n, tet_fx4):
+        with torch.no_grad():
+            occ_n = sdf_n > 0
+            occ_fx4 = occ_n[tet_fx4.reshape(-1)].reshape(-1, 4)
+            occ_sum = torch.sum(occ_fx4, -1)
+            valid_tets = (occ_sum > 0) & (occ_sum < 4)
+            occ_sum = occ_sum[valid_tets]
+
+            # find all vertices
+            all_edges = tet_fx4[valid_tets][:, self.base_tet_edges].reshape(-1, 2)
+            all_edges = self.sort_edges(all_edges)
+            unique_edges, idx_map = torch.unique(all_edges, dim=0, return_inverse=True)
+
+            unique_edges = unique_edges.long()
+            mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 1
+            mapping = (
+                torch.ones(
+                    (unique_edges.shape[0]), dtype=torch.long, device=pos_nx3.device
+                )
+                * -1
+            )
+            mapping[mask_edges] = torch.arange(
+                mask_edges.sum(), dtype=torch.long, device=pos_nx3.device
+            )
+            idx_map = mapping[idx_map]  # map edges to verts
+
+            interp_v = unique_edges[mask_edges]
+        edges_to_interp = pos_nx3[interp_v.reshape(-1)].reshape(-1, 2, 3)
+        edges_to_interp_sdf = sdf_n[interp_v.reshape(-1)].reshape(-1, 2, 1)
+        edges_to_interp_sdf[:, -1] *= -1
+
+        denominator = edges_to_interp_sdf.sum(1, keepdim=True)
+
+        edges_to_interp_sdf = torch.flip(edges_to_interp_sdf, [1]) / denominator
+        verts = (edges_to_interp * edges_to_interp_sdf).sum(1)
+
+        idx_map = idx_map.reshape(-1, 6)
+
+        v_id = torch.pow(2, torch.arange(4, dtype=torch.long, device=pos_nx3.device))
+        tetindex = (occ_fx4[valid_tets] * v_id.unsqueeze(0)).sum(-1)
+        num_triangles = self.num_triangles_table[tetindex]
+
+        # Generate triangle indices
+        faces = torch.cat(
+            (
+                torch.gather(
+                    input=idx_map[num_triangles == 1],
+                    dim=1,
+                    index=self.triangle_table[tetindex[num_triangles == 1]][:, :3],
+                ).reshape(-1, 3),
+                torch.gather(
+                    input=idx_map[num_triangles == 2],
+                    dim=1,
+                    index=self.triangle_table[tetindex[num_triangles == 2]][:, :6],
+                ).reshape(-1, 3),
+            ),
+            dim=0,
+        )
+
+        return verts, faces
+
+    def forward(
+        self,
+        level: Float[Tensor, "N3 1"],
+        deformation: Optional[Float[Tensor, "N3 3"]] = None,
+    ) -> Mesh:
+        if deformation is not None:
+            grid_vertices = self.grid_vertices + self.normalize_grid_deformation(
+                deformation
+            )
+        else:
+            grid_vertices = self.grid_vertices
+
+        v_pos, t_pos_idx = self._forward(grid_vertices, level, self.indices)
+
+        mesh = Mesh(
+            v_pos=v_pos,
+            t_pos_idx=t_pos_idx,
+            # extras
+            grid_vertices=grid_vertices,
+            tet_edges=self.all_edges,
+            grid_level=level,
+            grid_deformation=deformation,
+        )
+
+        return mesh

3D_Stage/lrm/models/lpips.py

@@ -0,0 +1,20 @@
+from dataclasses import dataclass
+from typing import Any
+import lpips
+
+from ..utils.ops import scale_tensor
+from ..utils.misc import get_device
+
+
+class LPIPS:
+    def __init__(self):
+        self.model = lpips.LPIPS(net="vgg").to(get_device())
+        self.model.eval()
+        for params in self.model.parameters():
+            params.requires_grad = False
+        self.model_input_range = (-1, 1)
+
+    def __call__(self, x1, x2, return_layers=False, input_range=(0, 1)):
+        x1 = scale_tensor(x1, input_range, self.model_input_range)
+        x2 = scale_tensor(x2, input_range, self.model_input_range)
+        return self.model(x1, x2, retPerLayer=return_layers, normalize=False)

3D_Stage/lrm/models/materials/base.py

@@ -0,0 +1,29 @@
+import random
+from dataclasses import dataclass, field
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import lrm
+from ...utils.base import BaseModule
+from ...utils.typing import *
+
+
+class BaseMaterial(BaseModule):
+    @dataclass
+    class Config(BaseModule.Config):
+        pass
+
+    cfg: Config
+    requires_normal: bool = False
+    requires_tangent: bool = False
+
+    def configure(self):
+        pass
+
+    def forward(self, *args, **kwargs) -> Float[Tensor, "*B 3"]:
+        raise NotImplementedError
+
+    def export(self, *args, **kwargs) -> Dict[str, Any]:
+        return {}

3D_Stage/lrm/models/materials/no_material.py

@@ -0,0 +1,60 @@
+import random
+from dataclasses import dataclass, field
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import lrm
+from .base import BaseMaterial
+from ..networks import get_encoding, get_mlp
+from ...utils.ops import dot, get_activation
+from ...utils.typing import *
+
+
+class NoMaterial(BaseMaterial):
+    @dataclass
+    class Config(BaseMaterial.Config):
+        n_output_dims: int = 3
+        color_activation: str = "sigmoid"
+        input_feature_dims: Optional[int] = None
+        mlp_network_config: Optional[dict] = None
+        requires_normal: bool = False
+
+    cfg: Config
+
+    def configure(self) -> None:
+        self.use_network = False
+        if (
+            self.cfg.input_feature_dims is not None
+            and self.cfg.mlp_network_config is not None
+        ):
+            self.network = get_mlp(
+                self.cfg.input_feature_dims,
+                self.cfg.n_output_dims,
+                self.cfg.mlp_network_config,
+            )
+            self.use_network = True
+        self.requires_normal = self.cfg.requires_normal
+
+    def forward(
+        self, features: Float[Tensor, "B ... Nf"], **kwargs
+    ) -> Float[Tensor, "B ... Nc"]:
+        if not self.use_network:
+            assert (
+                features.shape[-1] == self.cfg.n_output_dims
+            ), f"Expected {self.cfg.n_output_dims} output dims, only got {features.shape[-1]} dims input."
+            color = get_activation(self.cfg.color_activation)(features)
+        else:
+            color = self.network(features.view(-1, features.shape[-1])).view(
+                *features.shape[:-1], self.cfg.n_output_dims
+            )
+            color = get_activation(self.cfg.color_activation)(color)
+        return color
+
+    def export(self, features: Float[Tensor, "*N Nf"], **kwargs) -> Dict[str, Any]:
+        color = self(features, **kwargs).clamp(0, 1)
+        assert color.shape[-1] >= 3, "Output color must have at least 3 channels"
+        if color.shape[-1] > 3:
+            lrm.warn("Output color has >3 channels, treating the first 3 as RGB")
+        return {"albedo": color[..., :3]}

3D_Stage/lrm/models/mesh.py

@@ -0,0 +1,471 @@
+from __future__ import annotations
+
+import functools
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+import lrm
+from ..utils.ops import dot
+from ..utils.typing import *
+from ..utils.misc import time_recorder as tr, time_recorder_enabled
+
+
+class Mesh:
+    def __init__(
+        self, v_pos: Float[Tensor, "Nv 3"], t_pos_idx: Integer[Tensor, "Nf 3"], **kwargs
+    ) -> None:
+        self.v_pos: Float[Tensor, "Nv 3"] = v_pos
+        self.t_pos_idx: Integer[Tensor, "Nf 3"] = t_pos_idx
+        self._v_nrm: Optional[Float[Tensor, "Nv 3"]] = None
+        self._v_tng: Optional[Float[Tensor, "Nv 3"]] = None
+        self._v_tex: Optional[Float[Tensor, "Nt 3"]] = None
+        self._t_tex_idx: Optional[Float[Tensor, "Nf 3"]] = None
+        self._v_rgb: Optional[Float[Tensor, "Nv 3"]] = None
+        self._edges: Optional[Integer[Tensor, "Ne 2"]] = None
+        self.extras: Dict[str, Any] = {}
+        for k, v in kwargs.items():
+            self.add_extra(k, v)
+
+    def add_extra(self, k, v) -> None:
+        self.extras[k] = v
+
+    def remove_outlier(self, outlier_n_faces_threshold: Union[int, float]) -> Mesh:
+        if self.requires_grad:
+            lrm.debug("Mesh is differentiable, not removing outliers")
+            return self
+
+        # use trimesh to first split the mesh into connected components
+        # then remove the components with less than n_face_threshold faces
+        import trimesh
+
+        # construct a trimesh object
+        mesh = trimesh.Trimesh(
+            vertices=self.v_pos.detach().cpu().numpy(),
+            faces=self.t_pos_idx.detach().cpu().numpy(),
+        )
+
+        # split the mesh into connected components
+        components = mesh.split(only_watertight=False)
+        # log the number of faces in each component
+        lrm.debug(
+            "Mesh has {} components, with faces: {}".format(
+                len(components), [c.faces.shape[0] for c in components]
+            )
+        )
+
+        n_faces_threshold: int
+        if isinstance(outlier_n_faces_threshold, float):
+            # set the threshold to the number of faces in the largest component multiplied by outlier_n_faces_threshold
+            n_faces_threshold = int(
+                max([c.faces.shape[0] for c in components]) * outlier_n_faces_threshold
+            )
+        else:
+            # set the threshold directly to outlier_n_faces_threshold
+            n_faces_threshold = outlier_n_faces_threshold
+
+        # log the threshold
+        lrm.debug(
+            "Removing components with less than {} faces".format(n_faces_threshold)
+        )
+
+        # remove the components with less than n_face_threshold faces
+        components = [c for c in components if c.faces.shape[0] >= n_faces_threshold]
+
+        # log the number of faces in each component after removing outliers
+        lrm.debug(
+            "Mesh has {} components after removing outliers, with faces: {}".format(
+                len(components), [c.faces.shape[0] for c in components]
+            )
+        )
+        # merge the components
+        mesh = trimesh.util.concatenate(components)
+
+        # convert back to our mesh format
+        v_pos = torch.from_numpy(mesh.vertices).to(self.v_pos)
+        t_pos_idx = torch.from_numpy(mesh.faces).to(self.t_pos_idx)
+
+        clean_mesh = Mesh(v_pos, t_pos_idx)
+        # keep the extras unchanged
+
+        if len(self.extras) > 0:
+            clean_mesh.extras = self.extras
+            lrm.debug(
+                f"The following extra attributes are inherited from the original mesh unchanged: {list(self.extras.keys())}"
+            )
+        return clean_mesh
+
+    def subdivide(self):
+        if self.requires_grad:
+            lrm.debug("Mesh is differentiable, not performing subdivision")
+            return self
+
+        import trimesh
+
+        mesh = trimesh.Trimesh(
+            vertices=self.v_pos.detach().cpu().numpy(),
+            faces=self.t_pos_idx.detach().cpu().numpy(),
+        )
+
+        mesh.subdivide_loop()
+
+        v_pos = torch.from_numpy(mesh.vertices).to(self.v_pos)
+        t_pos_idx = torch.from_numpy(mesh.faces).to(self.t_pos_idx)
+
+        subdivided_mesh = Mesh(v_pos, t_pos_idx)
+
+        if len(self.extras) > 0:
+            subdivided_mesh.extras = self.extras
+            lrm.debug(
+                f"The following extra attributes are inherited from the original mesh unchanged: {list(self.extras.keys())}"
+            )
+
+        return subdivided_mesh
+
+    def post_process(self, options):
+        if self.requires_grad:
+            lrm.debug("Mesh is differentiable, not performing post processing")
+            return self
+
+        from extern.mesh_process.MeshProcess import process_mesh
+
+        v_pos, t_pos_idx = process_mesh(
+            vertices=self.v_pos.detach().cpu().numpy(),
+            faces=self.t_pos_idx.detach().cpu().numpy(),
+            **options,
+        )
+
+        v_pos = torch.from_numpy(v_pos).to(self.v_pos).contiguous()
+        t_pos_idx = torch.from_numpy(t_pos_idx).to(self.t_pos_idx).contiguous()
+
+        processed_mesh = Mesh(v_pos, t_pos_idx)
+
+        if len(self.extras) > 0:
+            processed_mesh.extras = self.extras
+            lrm.debug(
+                f"The following extra attributes are inherited from the original mesh unchanged: {list(self.extras.keys())}"
+            )
+
+        return processed_mesh
+
+    @property
+    def requires_grad(self):
+        return self.v_pos.requires_grad
+
+    @property
+    def v_nrm(self):
+        if self._v_nrm is None:
+            self._v_nrm = self._compute_vertex_normal()
+        return self._v_nrm
+
+    @property
+    def v_tng(self):
+        if self._v_tng is None:
+            self._v_tng = self._compute_vertex_tangent()
+        return self._v_tng
+
+    @property
+    def v_tex(self):
+        if self._v_tex is None:
+            self._v_tex, self._t_tex_idx = self._unwrap_uv()
+        return self._v_tex
+
+    @property
+    def t_tex_idx(self):
+        if self._t_tex_idx is None:
+            self._v_tex, self._t_tex_idx = self._unwrap_uv()
+        return self._t_tex_idx
+
+    @property
+    def v_rgb(self):
+        return self._v_rgb
+
+    @property
+    def edges(self):
+        if self._edges is None:
+            self._edges = self._compute_edges()
+        return self._edges
+
+    def _compute_vertex_normal(self):
+        i0 = self.t_pos_idx[:, 0]
+        i1 = self.t_pos_idx[:, 1]
+        i2 = self.t_pos_idx[:, 2]
+
+        v0 = self.v_pos[i0, :]
+        v1 = self.v_pos[i1, :]
+        v2 = self.v_pos[i2, :]
+
+        face_normals = torch.cross(v1 - v0, v2 - v0)
+
+        # Splat face normals to vertices
+        v_nrm = torch.zeros_like(self.v_pos)
+        v_nrm.scatter_add_(0, i0[:, None].repeat(1, 3), face_normals)
+        v_nrm.scatter_add_(0, i1[:, None].repeat(1, 3), face_normals)
+        v_nrm.scatter_add_(0, i2[:, None].repeat(1, 3), face_normals)
+
+        # Normalize, replace zero (degenerated) normals with some default value
+        v_nrm = torch.where(
+            dot(v_nrm, v_nrm) > 1e-20, v_nrm, torch.as_tensor([0.0, 0.0, 1.0]).to(v_nrm)
+        )
+        v_nrm = F.normalize(v_nrm, dim=1)
+
+        if torch.is_anomaly_enabled():
+            assert torch.all(torch.isfinite(v_nrm))
+
+        return v_nrm
+
+    def _compute_vertex_tangent(self):
+        vn_idx = [None] * 3
+        pos = [None] * 3
+        tex = [None] * 3
+        for i in range(0, 3):
+            pos[i] = self.v_pos[self.t_pos_idx[:, i]]
+            tex[i] = self.v_tex[self.t_tex_idx[:, i]]
+            # t_nrm_idx is always the same as t_pos_idx
+            vn_idx[i] = self.t_pos_idx[:, i]
+
+        tangents = torch.zeros_like(self.v_nrm)
+        tansum = torch.zeros_like(self.v_nrm)
+
+        # Compute tangent space for each triangle
+        uve1 = tex[1] - tex[0]
+        uve2 = tex[2] - tex[0]
+        pe1 = pos[1] - pos[0]
+        pe2 = pos[2] - pos[0]
+
+        nom = pe1 * uve2[..., 1:2] - pe2 * uve1[..., 1:2]
+        denom = uve1[..., 0:1] * uve2[..., 1:2] - uve1[..., 1:2] * uve2[..., 0:1]
+
+        # Avoid division by zero for degenerated texture coordinates
+        tang = nom / torch.where(
+            denom > 0.0, torch.clamp(denom, min=1e-6), torch.clamp(denom, max=-1e-6)
+        )
+
+        # Update all 3 vertices
+        for i in range(0, 3):
+            idx = vn_idx[i][:, None].repeat(1, 3)
+            tangents.scatter_add_(0, idx, tang)  # tangents[n_i] = tangents[n_i] + tang
+            tansum.scatter_add_(
+                0, idx, torch.ones_like(tang)
+            )  # tansum[n_i] = tansum[n_i] + 1
+        tangents = tangents / tansum
+
+        # Normalize and make sure tangent is perpendicular to normal
+        tangents = F.normalize(tangents, dim=1)
+        tangents = F.normalize(tangents - dot(tangents, self.v_nrm) * self.v_nrm)
+
+        if torch.is_anomaly_enabled():
+            assert torch.all(torch.isfinite(tangents))
+
+        return tangents
+
+    def _unwrap_uv_open3d(
+        self
+    ):
+        import open3d as o3d
+        mesh = o3d.t.geometry.TriangleMesh()
+        mesh.vertex.positions = o3d.core.Tensor(self.v_pos.detach().cpu().numpy())
+        mesh.triangle.indices = o3d.core.Tensor(self.t_pos_idx.cpu().numpy())
+        mesh.compute_uvatlas(size=1024)
+        texture_uvs = torch.from_numpy(mesh.triangle.texture_uvs.numpy()).reshape(-1, 2).cuda()
+        indices = torch.arange(self.t_pos_idx.shape[0] * 3).reshape(-1, 3).to(torch.int64).cuda()
+        # Add a wood texture and visualize
+        return texture_uvs, indices
+    
+    def _unwrap_uv_xatlas(
+        self, xatlas_chart_options: dict = {}, xatlas_pack_options: dict = {}
+    ):
+        lrm.info("Using xatlas to perform UV unwrapping, may take a while ...")
+
+        import xatlas
+
+        atlas = xatlas.Atlas()
+        atlas.add_mesh(
+            self.v_pos.detach().cpu().numpy(),
+            self.t_pos_idx.cpu().numpy(),
+        )
+        co = xatlas.ChartOptions()
+        po = xatlas.PackOptions()
+        for k, v in xatlas_chart_options.items():
+            setattr(co, k, v)
+        for k, v in xatlas_pack_options.items():
+            setattr(po, k, v)
+        atlas.generate(co, po)
+        vmapping, indices, uvs = atlas.get_mesh(0)
+        vmapping = (
+            torch.from_numpy(
+                vmapping.astype(np.uint64, casting="same_kind").view(np.int64)
+            )
+            .to(self.v_pos.device)
+            .long()
+        )
+        uvs = torch.from_numpy(uvs).to(self.v_pos.device).float()
+        indices = (
+            torch.from_numpy(
+                indices.astype(np.uint64, casting="same_kind").view(np.int64)
+            )
+            .to(self.v_pos.device)
+            .long()
+        )
+        return uvs, indices
+
+    def _unwrap_uv_smartuv(self, options: dict = {}):
+        from extern.mesh_process.MeshProcess import (
+            mesh_to_bpy,
+            get_uv_from_bpy,
+            bpy_context,
+            bpy_export,
+        )
+        from lrm.utils.misc import time_recorder as tr
+
+        v_pos, t_pos_idx = self.v_pos.cpu().numpy(), self.t_pos_idx.cpu().numpy()
+        with bpy_context():
+            mesh_bpy = mesh_to_bpy("_", v_pos, t_pos_idx)
+            v_tex = get_uv_from_bpy(mesh_bpy, **options).astype(np.float32)
+
+        assert v_tex.shape[0] == self.t_pos_idx.shape[0] * 3
+
+        t_tex_idx = torch.arange(
+            self.t_pos_idx.shape[0] * 3, device=self.t_pos_idx.device, dtype=torch.long
+        ).reshape(-1, 3)
+
+        """
+        # super efficient de-duplication
+        v_tex_u_uint32 = v_tex[..., 0].view(np.uint32)
+        v_tex_v_uint32 = v_tex[..., 1].view(np.uint32)
+        v_hashed = (v_tex_u_uint32.astype(np.uint64) << 32) | v_tex_v_uint32
+        v_hashed = torch.from_numpy(v_hashed.view(np.int64)).to(self.v_pos.device)
+
+        v_tex = torch.from_numpy(v_tex).to(
+            device=self.v_pos.device, dtype=torch.float32
+        )
+        t_pos_idx_f3 = torch.arange(
+            self.t_pos_idx.shape[0] * 3, device=self.t_pos_idx.device, dtype=torch.long
+        ).reshape(-1, 3)
+        v_pos_f3 = self.v_pos[self.t_pos_idx].reshape(-1, 3)
+
+        # super efficient de-duplication
+        v_hashed_dedup, inverse_indices = torch.unique(v_hashed, return_inverse=True)
+        dedup_size, full_size = v_hashed_dedup.shape[0], inverse_indices.shape[0]
+        indices = torch.scatter_reduce(
+            torch.full(
+                [dedup_size],
+                fill_value=full_size,
+                device=inverse_indices.device,
+                dtype=torch.long,
+            ),
+            index=inverse_indices,
+            src=torch.arange(
+                full_size, device=inverse_indices.device, dtype=torch.int64
+            ),
+            dim=0,
+            reduce="amin",
+        )
+        v_tex = v_tex[indices]
+        t_tex_idx = inverse_indices.reshape(-1, 3)
+
+        v_pos = v_pos_f3[indices]
+        t_pos_idx = inverse_indices[t_pos_idx_f3]
+        """
+
+        return self.v_pos, self.t_pos_idx, v_tex, t_tex_idx
+
+    def unwrap_uv(
+        self,
+        method: str,
+        xatlas_chart_options: dict = {},
+        xatlas_pack_options: dict = {},
+        smartuv_options: dict = {},
+    ):
+        if method == "xatlas":
+            with time_recorder_enabled():
+                tr.start("UV unwrapping xatlas")
+                self._v_tex, self._t_tex_idx = self._unwrap_uv_xatlas(
+                    xatlas_chart_options, xatlas_pack_options
+                )
+                tr.end("UV unwrapping xatlas")
+        elif method == "open3d":
+            with time_recorder_enabled():
+                tr.start("UV unwrapping o3d")
+                self._v_tex, self._t_tex_idx = self._unwrap_uv_open3d()
+                tr.end("UV unwrapping o3d")
+        elif method == "smartuv":
+            with time_recorder_enabled():
+                tr.start("UV unwrapping smartuv")
+                (
+                    self.v_pos,
+                    self.t_pos_idx,
+                    self._v_tex,
+                    self._t_tex_idx,
+                ) = self._unwrap_uv_smartuv(smartuv_options)
+                tr.end("UV unwrapping smartuv")
+        else:
+            raise NotImplementedError
+
+    def set_vertex_color(self, v_rgb):
+        assert v_rgb.shape[0] == self.v_pos.shape[0]
+        self._v_rgb = v_rgb
+    
+    def set_uv(self, v_tex, t_tex_idx):
+        self._v_tex = v_tex
+        self._t_tex_idx = t_tex_idx
+    
+    @property
+    def has_uv(self):
+        return self._v_tex is not None and self._t_tex_idx is not None
+
+    def _compute_edges(self):
+        # Compute edges
+        edges = torch.cat(
+            [
+                self.t_pos_idx[:, [0, 1]],
+                self.t_pos_idx[:, [1, 2]],
+                self.t_pos_idx[:, [2, 0]],
+            ],
+            dim=0,
+        )
+        edges = edges.sort()[0]
+        edges = torch.unique(edges, dim=0)
+        return edges
+
+    def normal_consistency(self) -> Float[Tensor, ""]:
+        edge_nrm: Float[Tensor, "Ne 2 3"] = self.v_nrm[self.edges]
+        nc = (
+            1.0 - torch.cosine_similarity(edge_nrm[:, 0], edge_nrm[:, 1], dim=-1)
+        ).mean()
+        return nc
+
+    def _laplacian_uniform(self):
+        # from stable-dreamfusion
+        # https://github.com/ashawkey/stable-dreamfusion/blob/8fb3613e9e4cd1ded1066b46e80ca801dfb9fd06/nerf/renderer.py#L224
+        verts, faces = self.v_pos, self.t_pos_idx
+
+        V = verts.shape[0]
+        F = faces.shape[0]
+
+        # Neighbor indices
+        ii = faces[:, [1, 2, 0]].flatten()
+        jj = faces[:, [2, 0, 1]].flatten()
+        adj = torch.stack([torch.cat([ii, jj]), torch.cat([jj, ii])], dim=0).unique(
+            dim=1
+        )
+        adj_values = torch.ones(adj.shape[1]).to(verts)
+
+        # Diagonal indices
+        diag_idx = adj[0]
+
+        # Build the sparse matrix
+        idx = torch.cat((adj, torch.stack((diag_idx, diag_idx), dim=0)), dim=1)
+        values = torch.cat((-adj_values, adj_values))
+
+        # The coalesce operation sums the duplicate indices, resulting in the
+        # correct diagonal
+        return torch.sparse_coo_tensor(idx, values, (V, V)).coalesce()
+
+    def laplacian(self) -> Float[Tensor, ""]:
+        with torch.no_grad():
+            L = self._laplacian_uniform()
+        loss = L.mm(self.v_pos)
+        loss = loss.norm(dim=1)
+        loss = loss.mean()
+        return loss

3D_Stage/lrm/models/networks.py

@@ -0,0 +1,390 @@
+from dataclasses import dataclass, field
+from copy import deepcopy
+
+import torch
+import torch.nn as nn
+from einops import rearrange
+
+from ..utils.base import BaseModule
+from ..utils.ops import get_activation
+from ..utils.typing import *
+
+
+class TriplaneUpsampleNetwork(BaseModule):
+    @dataclass
+    class Config(BaseModule.Config):
+        in_channels: int = 1024
+        out_channels: int = 80
+
+    cfg: Config
+
+    def configure(self) -> None:
+        super().configure()
+        self.upsample = nn.ConvTranspose2d(
+            self.cfg.in_channels, self.cfg.out_channels, kernel_size=2, stride=2
+        )
+
+    def forward(
+        self, triplanes: Float[Tensor, "B 3 Ci Hp Wp"]
+    ) -> Float[Tensor, "B 3 Co Hp2 Wp2"]:
+        triplanes_up = rearrange(
+            self.upsample(
+                rearrange(triplanes, "B Np Ci Hp Wp -> (B Np) Ci Hp Wp", Np=3)
+            ),
+            "(B Np) Co Hp Wp -> B Np Co Hp Wp",
+            Np=3,
+        )
+        return triplanes_up
+
+
+class MLP(nn.Module):
+    def __init__(
+        self,
+        dim_in: int,
+        dim_out: int,
+        n_neurons: int,
+        n_hidden_layers: int,
+        activation: str = "relu",
+        output_activation: Optional[str] = None,
+        bias: bool = True,
+        weight_init: Optional[str] = "kaiming_uniform",
+        bias_init: Optional[str] = None,
+    ):
+        super().__init__()
+        layers = [
+            self.make_linear(
+                dim_in,
+                n_neurons,
+                is_first=True,
+                is_last=False,
+                bias=bias,
+                weight_init=weight_init,
+                bias_init=bias_init,
+            ),
+            self.make_activation(activation),
+        ]
+        for i in range(n_hidden_layers - 1):
+            layers += [
+                self.make_linear(
+                    n_neurons,
+                    n_neurons,
+                    is_first=False,
+                    is_last=False,
+                    bias=bias,
+                    weight_init=weight_init,
+                    bias_init=bias_init,
+                ),
+                self.make_activation(activation),
+            ]
+        layers += [
+            self.make_linear(
+                n_neurons,
+                dim_out,
+                is_first=False,
+                is_last=True,
+                bias=bias,
+                weight_init=weight_init,
+                bias_init=bias_init,
+            )
+        ]
+        self.layers = nn.Sequential(*layers)
+        self.output_activation = get_activation(output_activation)
+
+    def forward(self, x):
+        x = self.layers(x)
+        x = self.output_activation(x)
+        return x
+
+    def make_linear(
+        self,
+        dim_in,
+        dim_out,
+        is_first,
+        is_last,
+        bias=True,
+        weight_init=None,
+        bias_init=None,
+    ):
+        layer = nn.Linear(dim_in, dim_out, bias=bias)
+
+        if weight_init is None:
+            pass
+        elif weight_init == "kaiming_uniform":
+            torch.nn.init.kaiming_uniform_(layer.weight, nonlinearity="relu")
+        else:
+            raise NotImplementedError
+
+        if bias:
+            if bias_init is None:
+                pass
+            elif bias_init == "zero":
+                torch.nn.init.zeros_(layer.bias)
+            else:
+                raise NotImplementedError
+
+        return layer
+
+    def make_activation(self, activation):
+        if activation == "relu":
+            return nn.ReLU(inplace=True)
+        elif activation == "silu":
+            return nn.SiLU(inplace=True)
+        else:
+            raise NotImplementedError
+
+
+@dataclass
+class HeadSpec:
+    name: str
+    out_channels: int
+    n_hidden_layers: int
+    output_activation: Optional[str] = None
+
+
+class MultiHeadMLP(BaseModule):
+    @dataclass
+    class Config(BaseModule.Config):
+        in_channels: int = 0
+        n_neurons: int = 0
+        n_hidden_layers_share: int = 0
+        heads: List[HeadSpec] = field(default_factory=lambda: [])
+        activation: str = "relu"
+        bias: bool = True
+        weight_init: Optional[str] = "kaiming_uniform"
+        bias_init: Optional[str] = None
+        chunk_mode: Optional[str] = None
+        chunk_size: int = -1
+
+    cfg: Config
+
+    def configure(self) -> None:
+        super().configure()
+        shared_layers = [
+            self.make_linear(
+                self.cfg.in_channels,
+                self.cfg.n_neurons,
+                bias=self.cfg.bias,
+                weight_init=self.cfg.weight_init,
+                bias_init=self.cfg.bias_init,
+            ),
+            self.make_activation(self.cfg.activation),
+        ]
+        for i in range(self.cfg.n_hidden_layers_share - 1):
+            shared_layers += [
+                self.make_linear(
+                    self.cfg.n_neurons,
+                    self.cfg.n_neurons,
+                    bias=self.cfg.bias,
+                    weight_init=self.cfg.weight_init,
+                    bias_init=self.cfg.bias_init,
+                ),
+                self.make_activation(self.cfg.activation),
+            ]
+        self.shared_layers = nn.Sequential(*shared_layers)
+
+        assert len(self.cfg.heads) > 0
+        heads = {}
+        for head in self.cfg.heads:
+            head_layers = []
+            for i in range(head.n_hidden_layers):
+                head_layers += [
+                    self.make_linear(
+                        self.cfg.n_neurons,
+                        self.cfg.n_neurons,
+                        bias=self.cfg.bias,
+                        weight_init=self.cfg.weight_init,
+                        bias_init=self.cfg.bias_init,
+                    ),
+                    self.make_activation(self.cfg.activation),
+                ]
+            head_layers += [
+                self.make_linear(
+                    self.cfg.n_neurons,
+                    head.out_channels,
+                    bias=self.cfg.bias,
+                    weight_init=self.cfg.weight_init,
+                    bias_init=self.cfg.bias_init,
+                ),
+            ]
+            heads[head.name] = nn.Sequential(*head_layers)
+        self.heads = nn.ModuleDict(heads)
+
+        if self.cfg.chunk_mode is not None:
+            assert self.cfg.chunk_size > 0
+
+    def make_linear(
+        self,
+        dim_in,
+        dim_out,
+        bias=True,
+        weight_init=None,
+        bias_init=None,
+    ):
+        layer = nn.Linear(dim_in, dim_out, bias=bias)
+
+        if weight_init is None:
+            pass
+        elif weight_init == "kaiming_uniform":
+            torch.nn.init.kaiming_uniform_(layer.weight, nonlinearity="relu")
+        else:
+            raise NotImplementedError
+
+        if bias:
+            if bias_init is None:
+                pass
+            elif bias_init == "zero":
+                torch.nn.init.zeros_(layer.bias)
+            else:
+                raise NotImplementedError
+
+        return layer
+
+    def make_activation(self, activation):
+        if activation == "relu":
+            return nn.ReLU(inplace=True)
+        elif activation == "silu":
+            return nn.SiLU(inplace=True)
+        else:
+            raise NotImplementedError
+
+    def forward(
+        self, x, include: Optional[List] = None, exclude: Optional[List] = None
+    ):
+        inp_shape = x.shape[:-1]
+        x = x.reshape(-1, x.shape[-1])
+
+        if self.cfg.chunk_mode is None:
+            shared_features = self.shared_layers(x)
+        elif self.cfg.chunk_mode == "deferred":
+            shared_features = DeferredFunc.apply(
+                self.shared_layers, x, self.cfg.chunk_size
+            )
+        elif self.cfg.chunk_mode == "checkpointing":
+            shared_features = apply_batch_checkpointing(
+                self.shared_layers, x, self.cfg.chunk_size
+            )
+        else:
+            raise NotImplementedError
+
+        shared_features = shared_features.reshape(*inp_shape, -1)
+
+        if include is not None and exclude is not None:
+            raise ValueError("Cannot specify both include and exclude.")
+        if include is not None:
+            heads = [h for h in self.cfg.heads if h.name in include]
+        elif exclude is not None:
+            heads = [h for h in self.cfg.heads if h.name not in exclude]
+        else:
+            heads = self.cfg.heads
+
+        out = {
+            head.name: get_activation(head.output_activation)(
+                self.heads[head.name](shared_features)
+            )
+            for head in heads
+        }
+        """
+        # TypeError
+        if self.cfg.chunk_mode is None:
+            out = {
+                head.name: get_activation(head.output_activation)(
+                    self.heads[head.name](shared_features)
+                )
+                for head in heads
+            }
+        elif self.cfg.chunk_mode  == "deferred":
+            out = {
+                head.name: get_activation(head.output_activation)(
+                    DeferredFunc.apply(self.heads[head.name], shared_features, self.cfg.chunk_size)
+                )
+                for head in heads
+            }
+        else:
+            raise NotImplementedError
+        """
+        return out
+
+
+class DeferredFunc(torch.autograd.Function):
+    # Note that forward, setup_context, and backward are @staticmethods
+    @staticmethod
+    def forward(ctx, model, x, chunk_size):
+        model_copy = deepcopy(model)
+        model_copy.requires_grad_(False)
+
+        ret = []
+        x_split = torch.split(x, chunk_size, dim=0)
+
+        with torch.no_grad():
+            for cur_x in x_split:
+                ret.append(model_copy(cur_x))
+
+        ctx.model = model
+        ctx.save_for_backward(x.detach(), torch.as_tensor(chunk_size))
+
+        ret = torch.cat(ret, dim=0)
+
+        return ret
+
+    # This function has only a single output, so it gets only one gradient
+    @staticmethod
+    def backward(ctx, grad_output):
+        model = ctx.model
+        x, chunk_size = ctx.saved_tensors
+        chunk_size = chunk_size.item()
+
+        model_copy = deepcopy(model)
+
+        x_split = torch.split(x, chunk_size, dim=0)
+        grad_output_split = torch.split(grad_output, chunk_size, 0)
+        grad_input_split = []
+
+        with torch.set_grad_enabled(True):
+            model_copy.requires_grad_(True)
+            model_copy.zero_grad()
+            for cur_x, cur_grad_output in zip(x_split, grad_output_split):
+                cur_x.requires_grad_(True)
+                cur_y = model_copy(cur_x)
+                cur_y.backward(cur_grad_output)
+
+                grad_input_split.append(cur_x.grad.clone())
+
+        grad_input = torch.cat(grad_input_split, dim=0)
+
+        model_copy_params = list(model_copy.parameters())
+        model_params = list(model.parameters())
+
+        for param, param_copy in zip(model_params, model_copy_params):
+            if param.grad is None:
+                param.grad = param_copy.grad.clone()
+            else:
+                param.grad.add_(param_copy.grad)
+
+        return None, grad_input, None
+
+
+def apply_batch_checkpointing(func, x, chunk_size):
+    if chunk_size >= len(x):
+        # return func(x)
+        return torch.utils.checkpoint.checkpoint(func, x, use_reentrant=False)
+
+    x_split = torch.split(x, chunk_size, dim=0)
+
+    def cat_and_query(y_all, x):
+        return torch.cat([y_all, func(x)])
+
+    y_all = func(x_split[0])
+    for cur_x in x_split[1:]:
+        y_all = torch.utils.checkpoint.checkpoint(
+            cat_and_query, y_all, cur_x, use_reentrant=False
+        )
+
+    return y_all
+
+
+def get_encoding(n_input_dims: int, config) -> nn.Module:
+    raise NotImplementedError
+
+
+def get_mlp(n_input_dims, n_output_dims, config) -> nn.Module:
+    raise NotImplementedError

3D_Stage/lrm/models/renderers/base.py

@@ -0,0 +1,68 @@
+from dataclasses import dataclass
+
+import torch
+import torch.nn.functional as F
+
+import lrm
+from ..networks import MultiHeadMLP
+from ..background.base import BaseBackground
+from ..materials.base import BaseMaterial
+from ...utils.base import BaseModule
+from ...utils.typing import *
+
+
+class BaseRenderer(BaseModule):
+    @dataclass
+    class Config(BaseModule.Config):
+        radius: float = 1.0
+
+    cfg: Config
+
+    def configure(
+        self,
+        decoder: MultiHeadMLP,
+        material: BaseMaterial,
+        background: BaseBackground,
+    ) -> None:
+        super().configure()
+
+        self.set_decoder(decoder)
+        self.set_material(material)
+        self.set_background(background)
+
+        # set up bounding box
+        self.bbox: Float[Tensor, "2 3"]
+        self.register_buffer(
+            "bbox",
+            torch.as_tensor(
+                [
+                    [-self.cfg.radius, -self.cfg.radius, -self.cfg.radius],
+                    [self.cfg.radius, self.cfg.radius, self.cfg.radius],
+                ],
+                dtype=torch.float32,
+            ),
+        )
+
+    def forward(self, *args, **kwargs) -> Dict[str, Any]:
+        raise NotImplementedError
+
+    @property
+    def decoder(self) -> MultiHeadMLP:
+        return self.non_module("decoder")
+
+    @property
+    def material(self) -> BaseMaterial:
+        return self.non_module("material")
+
+    @property
+    def background(self) -> BaseBackground:
+        return self.non_module("background")
+
+    def set_decoder(self, decoder: MultiHeadMLP) -> None:
+        self.register_non_module("decoder", decoder)
+
+    def set_material(self, material: BaseMaterial) -> None:
+        self.register_non_module("material", material)
+
+    def set_background(self, background: BaseBackground) -> None:
+        self.register_non_module("background", background)