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1. FangSen9000 commented on Oct 12, 2024

   

   FangSen9000

   on Oct 12, 2024

   Author

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   After a few hours of hard work, if anyone has the same problem as me, just replace 'MusePose/src/models/unet_3d.py' with the following:

   Adapted from https://github.com/guoyww/AnimateDiff/blob/main/animatediff/models/unet_blocks.py

   from collections import OrderedDict
   from dataclasses import dataclass
   from os import PathLike
   from pathlib import Path
   from typing import Dict, List, Optional, Tuple, Union

   import torch
   import torch.nn as nn
   import torch.nn.functional as F
   import torch.utils.checkpoint
   from diffusers.configuration_utils import ConfigMixin, register_to_config
   from diffusers.models.attention_processor import AttentionProcessor
   from diffusers.models.embeddings import TimestepEmbedding, Timesteps
   try:
   from diffusers.modeling_utils import ModelMixin
   except:
   from diffusers.models.modeling_utils import ModelMixin
   from diffusers.utils import SAFETENSORS_WEIGHTS_NAME, WEIGHTS_NAME, BaseOutput, logging
   from safetensors.torch import load_file

   from .resnet import InflatedConv3d, InflatedGroupNorm
   from .unet_3d_blocks import UNetMidBlock3DCrossAttn, get_down_block, get_up_block

   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_inflated_groupnorm=False,
       # Additional
       use_motion_module=False,
       motion_module_resolutions=(1, 2, 4, 8),
       motion_module_mid_block=False,
       motion_module_decoder_only=False,
       motion_module_type=None,
       motion_module_kwargs={},
       unet_use_cross_frame_attention=None,
       unet_use_temporal_attention=None,
       mode=None,
       task_type="action",
   ):
       super().__init__()
   
       self.sample_size = sample_size
       time_embed_dim = block_out_channels[0] * 4
   
       # Add this line in the __init__ method
       self.pose_projector = nn.Conv3d(4, 320, kernel_size=1)
       
       # 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
   
       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):
           if task_type == "action":
               name_index, mid_name = None, None
           else:
               name_index, mid_name = i, "MidBlock"
           res = 2**i
           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,
               unet_use_cross_frame_attention=unet_use_cross_frame_attention,
               unet_use_temporal_attention=unet_use_temporal_attention,
               use_inflated_groupnorm=use_inflated_groupnorm,
               use_motion_module=use_motion_module
               and (res in motion_module_resolutions)
               and (not motion_module_decoder_only),
               motion_module_type=motion_module_type,
               motion_module_kwargs=motion_module_kwargs,
               name_index=name_index,
           )
           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,
               unet_use_cross_frame_attention=unet_use_cross_frame_attention,
               unet_use_temporal_attention=unet_use_temporal_attention,
               use_inflated_groupnorm=use_inflated_groupnorm,
               use_motion_module=use_motion_module and motion_module_mid_block,
               motion_module_type=motion_module_type,
               motion_module_kwargs=motion_module_kwargs,
               name=mid_name,
           )
       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):
           res = 2 ** (3 - i)
           is_final_block = i == len(block_out_channels) - 1
           
           if task_type == "action":
               name_index = None
           else:
               name_index = i
           
           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,
               unet_use_cross_frame_attention=unet_use_cross_frame_attention,
               unet_use_temporal_attention=unet_use_temporal_attention,
               use_inflated_groupnorm=use_inflated_groupnorm,
               use_motion_module=use_motion_module
               and (res in motion_module_resolutions),
               motion_module_type=motion_module_type,
               motion_module_kwargs=motion_module_kwargs,
               name_index=name_index,
           )
           self.up_blocks.append(up_block)
           prev_output_channel = output_channel
   
       # out
       if use_inflated_groupnorm:
           self.conv_norm_out = InflatedGroupNorm(
               num_channels=block_out_channels[0],
               num_groups=norm_num_groups,
               eps=norm_eps,
           )
       else:
           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
       )
       
       self.mode = mode
   
   @property
   # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
   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():
               if "temporal_transformer" not in sub_name:
                   fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
   
           return processors
   
       for name, module in self.named_children():
           if "temporal_transformer" not in name:
               fn_recursive_add_processors(name, module, processors)
   
       return processors
   
   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 hasattr(module, "gradient_checkpointing"):
           module.gradient_checkpointing = value
   
   # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor
   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():
               if "temporal_transformer" not in sub_name:
                   fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
   
       for name, module in self.named_children():
           if "temporal_transformer" not in name:
               fn_recursive_attn_processor(name, module, processor)
   
   def forward(
       self,
       sample: torch.FloatTensor,
       timestep: Union[torch.Tensor, float, int],
       encoder_hidden_states: torch.Tensor,
       class_labels: Optional[torch.Tensor] = None,
       pose_cond_fea: Optional[torch.Tensor] = None,
       attention_mask: Optional[torch.Tensor] = None,
       down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
       mid_block_additional_residual: Optional[torch.Tensor] = None,
       return_dict: bool = True,
       self_attention_additional_feats = None,
   ) -> 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)
   
       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 self.config.class_embed_type == "timestep":
               class_labels = self.time_proj(class_labels)
   
           class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
           emb = emb + class_emb
   
           
       # 1. Check the shapes of both tensors
       print(f"sample shape: {sample.shape}")
       print(f"pose_cond_fea shape: {pose_cond_fea.shape}")
       
       
       # pre-process
       #sample = self.conv_in(sample)
       #if pose_cond_fea is not None:
       #    sample = sample + pose_cond_fea
           
       # pre-process
       if sample is not None:
           # Store original height of sample
           original_height = sample.shape[3]
   
           # Project sample to match pose_cond_fea's channel dimension
           sample = self.pose_projector(sample)
   
           # Adjust height of pose_cond_fea to match original sample height
           pose_cond_fea = F.interpolate(pose_cond_fea, 
                                         size=(pose_cond_fea.shape[2], original_height, pose_cond_fea.shape[4]),
                                         mode='trilinear', 
                                         align_corners=False)
   
           # Add debug prints
           print(f"Adjusted sample shape: {sample.shape}")
           print(f"Adjusted pose_cond_fea shape: {pose_cond_fea.shape}")
           
           # Ensure the shapes match before adding
           assert pose_cond_fea.shape == sample.shape, "Shapes still don't match after adjustment"
           
           sample = sample + pose_cond_fea
   
   
       # 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,
                   self_attention_additional_feats=self_attention_additional_feats,
                   mode=self.mode,
               )
           else:
               sample, res_samples = downsample_block(
                   hidden_states=sample,
                   temb=emb,
                   encoder_hidden_states=encoder_hidden_states,
               )
   
           down_block_res_samples += res_samples
   
       if down_block_additional_residuals is not None:
           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 += (down_block_res_sample,)
   
           down_block_res_samples = new_down_block_res_samples
   
       # mid
       sample = self.mid_block(
           sample,
           emb,
           encoder_hidden_states=encoder_hidden_states,
           attention_mask=attention_mask,
           self_attention_additional_feats=self_attention_additional_feats,
           mode=self.mode,
       )
   
       if mid_block_additional_residual is not None:
           sample = sample + mid_block_additional_residual
   
       # 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,
                   self_attention_additional_feats=self_attention_additional_feats,
                   mode=self.mode,
               )
           else:
               sample = upsample_block(
                   hidden_states=sample,
                   temb=emb,
                   res_hidden_states_tuple=res_samples,
                   upsample_size=upsample_size,
                   encoder_hidden_states=encoder_hidden_states,
               )
   
       # 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: PathLike,
       motion_module_path: PathLike,
       subfolder=None,
       unet_additional_kwargs=None,
       mm_zero_proj_out=False,
   ):
       pretrained_model_path = Path(pretrained_model_path)
       motion_module_path = Path(motion_module_path)
       if subfolder is not None:
           pretrained_model_path = pretrained_model_path.joinpath(subfolder)
       logger.info(
           f"loaded temporal unet's pretrained weights from {pretrained_model_path} ..."
       )
   
       config_file = pretrained_model_path / "config.json"
       if not (config_file.exists() and config_file.is_file()):
           raise RuntimeError(f"{config_file} does not exist or is not a file")
   
       unet_config = cls.load_config(config_file)
       unet_config["_class_name"] = cls.__name__
       unet_config["down_block_types"] = [
           "CrossAttnDownBlock3D",
           "CrossAttnDownBlock3D",
           "CrossAttnDownBlock3D",
           "DownBlock3D",
       ]
       unet_config["up_block_types"] = [
           "UpBlock3D",
           "CrossAttnUpBlock3D",
           "CrossAttnUpBlock3D",
           "CrossAttnUpBlock3D",
       ]
       unet_config["mid_block_type"] = "UNetMidBlock3DCrossAttn"
   
       model = cls.from_config(unet_config, **unet_additional_kwargs)
       # load the vanilla weights
       if pretrained_model_path.joinpath(SAFETENSORS_WEIGHTS_NAME).exists():
           logger.debug(
               f"loading safeTensors weights from {pretrained_model_path} ..."
           )
           state_dict = load_file(
               pretrained_model_path.joinpath(SAFETENSORS_WEIGHTS_NAME), device="cpu"
           )
   
       elif pretrained_model_path.joinpath(WEIGHTS_NAME).exists():
           logger.debug(f"loading weights from {pretrained_model_path} ...")
           state_dict = torch.load(
               pretrained_model_path.joinpath(WEIGHTS_NAME),
               map_location="cpu",
               weights_only=True,
           )
       else:
           raise FileNotFoundError(f"no weights file found in {pretrained_model_path}")
   
       # load the motion module weights
       if motion_module_path.exists() and motion_module_path.is_file():
           if motion_module_path.suffix.lower() in [".pth", ".pt", ".ckpt"]:
               logger.info(f"Load motion module params from {motion_module_path}")
               motion_state_dict = torch.load(
                   motion_module_path, map_location="cpu", weights_only=True
               )
           elif motion_module_path.suffix.lower() == ".safetensors":
               motion_state_dict = load_file(motion_module_path, device="cpu")
           else:
               raise RuntimeError(
                   f"unknown file format for motion module weights: {motion_module_path.suffix}"
               )
           if mm_zero_proj_out:
               logger.info(f"Zero initialize proj_out layers in motion module...")
               new_motion_state_dict = OrderedDict()
               for k in motion_state_dict:
                   if "proj_out" in k:
                       continue
                   new_motion_state_dict[k] = motion_state_dict[k]
               motion_state_dict = new_motion_state_dict
   
           # merge the state dicts
           state_dict.update(motion_state_dict)
   
       # load the weights into the model
       m, u = model.load_state_dict(state_dict, strict=False)
       logger.debug(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()
       ]
       logger.info(f"Loaded {sum(params) / 1e6}M-parameter motion module")
   
       return model
   
2. Xushuolin commented on Dec 11, 2024

   

   Xushuolin

   on Dec 11, 2024

   More actions

   you are my god bro!

   😄React with 😄1Reacted by 房森

   ❤️React with ❤️1Reacted by 房森
3. Zhao-Xinda commented on Dec 27, 2024

   

   Zhao-Xinda

   on Dec 27, 2024

   More actions

   After a few hours of hard work, if anyone has the same problem as me, just replace 'MusePose/src/models/unet_3d.py' with the following:

   Adapted from https://github.com/guoyww/AnimateDiff/blob/main/animatediff/models/unet_blocks.py

   from collections import OrderedDict
   from dataclasses import dataclass
   from os import PathLike
   from pathlib import Path
   from typing import Dict, List, Optional, Tuple, Union
   import torch
   import torch.nn as nn
   import torch.nn.functional as F
   import torch.utils.checkpoint
   from diffusers.configuration_utils import ConfigMixin, register_to_config
   from diffusers.models.attention_processor import AttentionProcessor
   from diffusers.models.embeddings import TimestepEmbedding, Timesteps
   try:
   from diffusers.modeling_utils import ModelMixin
   except:
   from diffusers.models.modeling_utils import ModelMixin
   from diffusers.utils import SAFETENSORS_WEIGHTS_NAME, WEIGHTS_NAME, BaseOutput, logging
   from safetensors.torch import load_file
   from .resnet import InflatedConv3d, InflatedGroupNorm
   from .unet_3d_blocks import UNetMidBlock3DCrossAttn, get_down_block, get_up_block
   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_inflated_groupnorm=False,
       # Additional
       use_motion_module=False,
       motion_module_resolutions=(1, 2, 4, 8),
       motion_module_mid_block=False,
       motion_module_decoder_only=False,
       motion_module_type=None,
       motion_module_kwargs={},
       unet_use_cross_frame_attention=None,
       unet_use_temporal_attention=None,
       mode=None,
       task_type="action",
   ):
       super().__init__()
   
       self.sample_size = sample_size
       time_embed_dim = block_out_channels[0] * 4
   
       # Add this line in the __init__ method
       self.pose_projector = nn.Conv3d(4, 320, kernel_size=1)
       
       # 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
   
       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):
           if task_type == "action":
               name_index, mid_name = None, None
           else:
               name_index, mid_name = i, "MidBlock"
           res = 2**i
           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,
               unet_use_cross_frame_attention=unet_use_cross_frame_attention,
               unet_use_temporal_attention=unet_use_temporal_attention,
               use_inflated_groupnorm=use_inflated_groupnorm,
               use_motion_module=use_motion_module
               and (res in motion_module_resolutions)
               and (not motion_module_decoder_only),
               motion_module_type=motion_module_type,
               motion_module_kwargs=motion_module_kwargs,
               name_index=name_index,
           )
           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,
               unet_use_cross_frame_attention=unet_use_cross_frame_attention,
               unet_use_temporal_attention=unet_use_temporal_attention,
               use_inflated_groupnorm=use_inflated_groupnorm,
               use_motion_module=use_motion_module and motion_module_mid_block,
               motion_module_type=motion_module_type,
               motion_module_kwargs=motion_module_kwargs,
               name=mid_name,
           )
       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):
           res = 2 ** (3 - i)
           is_final_block = i == len(block_out_channels) - 1
           
           if task_type == "action":
               name_index = None
           else:
               name_index = i
           
           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,
               unet_use_cross_frame_attention=unet_use_cross_frame_attention,
               unet_use_temporal_attention=unet_use_temporal_attention,
               use_inflated_groupnorm=use_inflated_groupnorm,
               use_motion_module=use_motion_module
               and (res in motion_module_resolutions),
               motion_module_type=motion_module_type,
               motion_module_kwargs=motion_module_kwargs,
               name_index=name_index,
           )
           self.up_blocks.append(up_block)
           prev_output_channel = output_channel
   
       # out
       if use_inflated_groupnorm:
           self.conv_norm_out = InflatedGroupNorm(
               num_channels=block_out_channels[0],
               num_groups=norm_num_groups,
               eps=norm_eps,
           )
       else:
           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
       )
       
       self.mode = mode
   
   @property
   # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
   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():
               if "temporal_transformer" not in sub_name:
                   fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
   
           return processors
   
       for name, module in self.named_children():
           if "temporal_transformer" not in name:
               fn_recursive_add_processors(name, module, processors)
   
       return processors
   
   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 hasattr(module, "gradient_checkpointing"):
           module.gradient_checkpointing = value
   
   # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor
   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():
               if "temporal_transformer" not in sub_name:
                   fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
   
       for name, module in self.named_children():
           if "temporal_transformer" not in name:
               fn_recursive_attn_processor(name, module, processor)
   
   def forward(
       self,
       sample: torch.FloatTensor,
       timestep: Union[torch.Tensor, float, int],
       encoder_hidden_states: torch.Tensor,
       class_labels: Optional[torch.Tensor] = None,
       pose_cond_fea: Optional[torch.Tensor] = None,
       attention_mask: Optional[torch.Tensor] = None,
       down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
       mid_block_additional_residual: Optional[torch.Tensor] = None,
       return_dict: bool = True,
       self_attention_additional_feats = None,
   ) -> 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)
   
       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 self.config.class_embed_type == "timestep":
               class_labels = self.time_proj(class_labels)
   
           class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
           emb = emb + class_emb
   
           
       # 1. Check the shapes of both tensors
       print(f"sample shape: {sample.shape}")
       print(f"pose_cond_fea shape: {pose_cond_fea.shape}")
       
       
       # pre-process
       #sample = self.conv_in(sample)
       #if pose_cond_fea is not None:
       #    sample = sample + pose_cond_fea
           
       # pre-process
       if sample is not None:
           # Store original height of sample
           original_height = sample.shape[3]
   
           # Project sample to match pose_cond_fea's channel dimension
           sample = self.pose_projector(sample)
   
           # Adjust height of pose_cond_fea to match original sample height
           pose_cond_fea = F.interpolate(pose_cond_fea, 
                                         size=(pose_cond_fea.shape[2], original_height, pose_cond_fea.shape[4]),
                                         mode='trilinear', 
                                         align_corners=False)
   
           # Add debug prints
           print(f"Adjusted sample shape: {sample.shape}")
           print(f"Adjusted pose_cond_fea shape: {pose_cond_fea.shape}")
           
           # Ensure the shapes match before adding
           assert pose_cond_fea.shape == sample.shape, "Shapes still don't match after adjustment"
           
           sample = sample + pose_cond_fea
   
   
       # 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,
                   self_attention_additional_feats=self_attention_additional_feats,
                   mode=self.mode,
               )
           else:
               sample, res_samples = downsample_block(
                   hidden_states=sample,
                   temb=emb,
                   encoder_hidden_states=encoder_hidden_states,
               )
   
           down_block_res_samples += res_samples
   
       if down_block_additional_residuals is not None:
           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 += (down_block_res_sample,)
   
           down_block_res_samples = new_down_block_res_samples
   
       # mid
       sample = self.mid_block(
           sample,
           emb,
           encoder_hidden_states=encoder_hidden_states,
           attention_mask=attention_mask,
           self_attention_additional_feats=self_attention_additional_feats,
           mode=self.mode,
       )
   
       if mid_block_additional_residual is not None:
           sample = sample + mid_block_additional_residual
   
       # 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,
                   self_attention_additional_feats=self_attention_additional_feats,
                   mode=self.mode,
               )
           else:
               sample = upsample_block(
                   hidden_states=sample,
                   temb=emb,
                   res_hidden_states_tuple=res_samples,
                   upsample_size=upsample_size,
                   encoder_hidden_states=encoder_hidden_states,
               )
   
       # 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: PathLike,
       motion_module_path: PathLike,
       subfolder=None,
       unet_additional_kwargs=None,
       mm_zero_proj_out=False,
   ):
       pretrained_model_path = Path(pretrained_model_path)
       motion_module_path = Path(motion_module_path)
       if subfolder is not None:
           pretrained_model_path = pretrained_model_path.joinpath(subfolder)
       logger.info(
           f"loaded temporal unet's pretrained weights from {pretrained_model_path} ..."
       )
   
       config_file = pretrained_model_path / "config.json"
       if not (config_file.exists() and config_file.is_file()):
           raise RuntimeError(f"{config_file} does not exist or is not a file")
   
       unet_config = cls.load_config(config_file)
       unet_config["_class_name"] = cls.__name__
       unet_config["down_block_types"] = [
           "CrossAttnDownBlock3D",
           "CrossAttnDownBlock3D",
           "CrossAttnDownBlock3D",
           "DownBlock3D",
       ]
       unet_config["up_block_types"] = [
           "UpBlock3D",
           "CrossAttnUpBlock3D",
           "CrossAttnUpBlock3D",
           "CrossAttnUpBlock3D",
       ]
       unet_config["mid_block_type"] = "UNetMidBlock3DCrossAttn"
   
       model = cls.from_config(unet_config, **unet_additional_kwargs)
       # load the vanilla weights
       if pretrained_model_path.joinpath(SAFETENSORS_WEIGHTS_NAME).exists():
           logger.debug(
               f"loading safeTensors weights from {pretrained_model_path} ..."
           )
           state_dict = load_file(
               pretrained_model_path.joinpath(SAFETENSORS_WEIGHTS_NAME), device="cpu"
           )
   
       elif pretrained_model_path.joinpath(WEIGHTS_NAME).exists():
           logger.debug(f"loading weights from {pretrained_model_path} ...")
           state_dict = torch.load(
               pretrained_model_path.joinpath(WEIGHTS_NAME),
               map_location="cpu",
               weights_only=True,
           )
       else:
           raise FileNotFoundError(f"no weights file found in {pretrained_model_path}")
   
       # load the motion module weights
       if motion_module_path.exists() and motion_module_path.is_file():
           if motion_module_path.suffix.lower() in [".pth", ".pt", ".ckpt"]:
               logger.info(f"Load motion module params from {motion_module_path}")
               motion_state_dict = torch.load(
                   motion_module_path, map_location="cpu", weights_only=True
               )
           elif motion_module_path.suffix.lower() == ".safetensors":
               motion_state_dict = load_file(motion_module_path, device="cpu")
           else:
               raise RuntimeError(
                   f"unknown file format for motion module weights: {motion_module_path.suffix}"
               )
           if mm_zero_proj_out:
               logger.info(f"Zero initialize proj_out layers in motion module...")
               new_motion_state_dict = OrderedDict()
               for k in motion_state_dict:
                   if "proj_out" in k:
                       continue
                   new_motion_state_dict[k] = motion_state_dict[k]
               motion_state_dict = new_motion_state_dict
   
           # merge the state dicts
           state_dict.update(motion_state_dict)
   
       # load the weights into the model
       m, u = model.load_state_dict(state_dict, strict=False)
       logger.debug(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()
       ]
       logger.info(f"Loaded {sum(params) / 1e6}M-parameter motion module")
   
       return model
   

   Is this three-dimensional interpolation correct, because one dimension is time, and does denoising_unet need to be retrained after this change? Because the original denoising does not have interpolation, sample=sample+cond