Stage2 RuntimeError: The size of tensor a (22) must match the size of tensor b (23) at non-singleton dimension 3

[FangSen9000]

~/# accelerate launch train_stage_2.py --config configs/train/stage2.yaml
The following values were not passed to accelerate launch and had defaults used instead:
--num_processes was set to a value of 1
--num_machines was set to a value of 1
--mixed_precision was set to a value of 'no'
--dynamo_backend was set to a value of 'no'
To avoid this warning pass in values for each of the problematic parameters or run accelerate config.
10/12/2024 09:16:18 - INFO - main - Distributed environment: NO
Num processes: 1
Process index: 0
Local process index: 0
Device: cuda

Mixed precision type: fp16

{'scaling_factor', 'force_upcast'} was not found in config. Values will be initialized to default values.
{'addition_time_embed_dim', 'time_embedding_type', 'num_class_embeds', 'encoder_hid_dim', 'encoder_hid_dim_type', 'addition_embed_type_num_heads', 'addition_embed_type', 'dual_cross_attention', 'dropout', 'resnet_out_scale_factor', 'attention_type', 'reverse_transformer_layers_per_block', 'projection_class_embeddings_input_dim', 'mid_block_type', 'conv_out_kernel', 'resnet_skip_time_act', 'use_linear_projection', 'class_embeddings_concat', 'time_embedding_dim', 'timestep_post_act', 'resnet_time_scale_shift', 'only_cross_attention', 'transformer_layers_per_block', 'class_embed_type', 'conv_in_kernel', 'time_cond_proj_dim', 'time_embedding_act_fn', 'mid_block_only_cross_attention', 'num_attention_heads', 'upcast_attention', 'cross_attention_norm'} was not found in config. Values will be initialized to default values.
Some weights of the model checkpoint were not used when initializing UNet2DConditionModel:
['conv_norm_out.weight, conv_norm_out.bias, conv_out.weight, conv_out.bias']
10/12/2024 09:16:25 - INFO - src.models.unet_3d - loaded temporal unet's pretrained weights from pretrained_weights/stable-diffusion-v1-5/unet ...
{'dual_cross_attention', 'use_linear_projection', 'num_class_embeds', 'upcast_attention', 'mode', 'task_type', 'resnet_time_scale_shift', 'only_cross_attention', 'class_embed_type'} was not found in config. Values will be initialized to default values.
10/12/2024 09:16:38 - INFO - src.models.unet_3d - Load motion module params from pretrained_weights/mm_sd_v15_v2.ckpt
10/12/2024 09:16:39 - INFO - src.models.unet_3d - Loaded 453.20928M-parameter motion module
10/12/2024 09:16:44 - INFO - main - Total trainable params 546
10/12/2024 09:16:45 - INFO - main - ***** Running training *****
10/12/2024 09:16:45 - INFO - main - Num examples = 7755
10/12/2024 09:16:45 - INFO - main - Num Epochs = 2
10/12/2024 09:16:45 - INFO - main - Instantaneous batch size per device = 1
10/12/2024 09:16:45 - INFO - main - Total train batch size (w. parallel, distributed & accumulation) = 1
10/12/2024 09:16:45 - INFO - main - Gradient Accumulation steps = 1
10/12/2024 09:16:45 - INFO - main - Total optimization steps = 10000
Steps: 0%| | 0/10000 [00:00<?, ?it/s]10/12/2024 09:16:50 - INFO - src.models.unet_3d - Forward upsample size to force interpolation output size.
Traceback (most recent call last):
File "/root/MusePose/train_stage_2.py", line 773, in
main(config)
File "/root/MusePose/train_stage_2.py", line 602, in main
model_pred = net(
File "/root/miniconda3/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/root/miniconda3/lib/python3.10/site-packages/accelerate/utils/operations.py", line 825, in forward
return model_forward(*args, **kwargs)
File "/root/miniconda3/lib/python3.10/site-packages/accelerate/utils/operations.py", line 813, in call
return convert_to_fp32(self.model_forward(*args, **kwargs))
File "/root/miniconda3/lib/python3.10/site-packages/torch/amp/autocast_mode.py", line 14, in decorate_autocast
return func(*args, **kwargs)
File "/root/MusePose/train_stage_2.py", line 96, in forward
model_pred = self.denoising_unet(
File "/root/miniconda3/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(args, **kwargs)
File "/root//src/models/unet_3d.py", line 505, in forward
sample = sample + pose_cond_fea
RuntimeError: The size of tensor a (22) must match the size of tensor b (23) at non-singleton dimension 3
Steps: 0%| | 0/10000 [00:06<?, ?it/s]
Traceback (most recent call last):
File "/root/miniconda3/bin/accelerate", line 8, in
sys.exit(main())
File "/root/miniconda3/lib/python3.10/site-packages/accelerate/commands/accelerate_cli.py", line 46, in main
args.func(args)
File "/root/miniconda3/lib/python3.10/site-packages/accelerate/commands/launch.py", line 1075, in launch_command
simple_launcher(args)
File "/root/miniconda3/lib/python3.10/site-packages/accelerate/commands/launch.py", line 681, in simple_launcher
raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd)
subprocess.CalledProcessError: Command '['/root/miniconda3/bin/python', 'train_stage_2.py', '--config', 'configs/train/stage2.yaml']' returned non-zero exit status 1.

[FangSen9000]

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

[Xushuolin]

you are my god bro!

[Zhao-Xinda]

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