genphoto/models/unet.py

# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py
import os
import json
import safetensors
import logging
import torch
import torch.nn as nn
import torch.utils.checkpoint

from einops import repeat, rearrange
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union, Dict, Any

from ddiffusers.configuration_utils import ConfigMixin, register_to_config
from ddiffusers.models.attention_processor import AttentionProcessor
from ddiffusers.utils import SAFETENSORS_WEIGHTS_NAME

from ddiffusers.models.modeling_utils import ModelMixin
from ddiffusers.utils import BaseOutput, logging
from ddiffusers.models.embeddings import TimestepEmbedding, Timesteps
from ddiffusers.models.attention_processor import LoRAAttnProcessor
from ddiffusers.loaders import AttnProcsLayers, UNet2DConditionLoadersMixin

from genphoto.models.unet_blocks import (
    CrossAttnDownBlock3D,
    CrossAttnUpBlock3D,
    DownBlock3D,
    UNetMidBlock3DCrossAttn,
    UpBlock3D,
    get_down_block,
    get_up_block,
)
from genphoto.models.attention_processor import (
    LORACameraAdaptorAttnProcessor,
    CameraAdaptorAttnProcessor
)
from genphoto.models.attention_processor import LoRAAttnProcessor as CustomizedLoRAAttnProcessor
from genphoto.models.attention_processor import AttnProcessor as CustomizedAttnProcessor
from genphoto.models.resnet import (
    InflatedConv3d,
    FusionBlock2D
)

@dataclass
class UNet3DConditionOutput(BaseOutput):
    sample: torch.FloatTensor


class UNet3DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
    _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,
            addition_embed_type: Optional[str] = None,
            num_class_embeds: Optional[int] = None,
            upcast_attention: bool = False,
            resnet_time_scale_shift: str = "default",

            # Additional
            use_motion_module=False,
            motion_module_resolutions=(1, 2, 4, 8),
            motion_module_mid_block=False,
            motion_module_type=None,
            motion_module_kwargs={},

            # whether fuse first frame's feature
            fuse_first_frame: bool = False,
    ):
        super().__init__()
        self.logger = logging.get_logger(__name__)

        self.sample_size = sample_size
        time_embed_dim = block_out_channels[0] * 4

        # input
        self.conv_in = InflatedConv3d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))

        # time
        self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
        timestep_input_dim = block_out_channels[0]

        self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)

        # class embedding
        if class_embed_type is None and num_class_embeds is not None:
            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
        elif class_embed_type == "timestep":
            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
        elif class_embed_type == "identity":
            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
        else:
            self.class_embedding = None

        self.down_blocks = nn.ModuleList([])
        self.mid_block = None
        self.up_blocks = nn.ModuleList([])

        self.down_fusers = nn.ModuleList([])
        self.mid_fuser = None
        self.down_fusers.append(
            FusionBlock2D(
                in_channels=block_out_channels[0],
                out_channels=block_out_channels[0],
                temb_channels=time_embed_dim,
                eps=norm_eps,
                groups=norm_num_groups,
                time_embedding_norm=resnet_time_scale_shift,
                non_linearity=act_fn,
            ) if fuse_first_frame else None
        )

        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):
            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,

                use_motion_module=use_motion_module and (res in motion_module_resolutions),
                motion_module_type=motion_module_type,
                motion_module_kwargs=motion_module_kwargs,
            )

            down_fuser = nn.ModuleList(
                [
                    FusionBlock2D(
                        in_channels=output_channel,
                        out_channels=output_channel,
                        temb_channels=time_embed_dim,
                        eps=norm_eps,
                        groups=norm_num_groups,
                        time_embedding_norm=resnet_time_scale_shift,
                        non_linearity=act_fn,
                    ) if fuse_first_frame else None for _ in
                    range(layers_per_block if is_final_block else layers_per_block + 1)
                ]
            )

            self.down_blocks.append(down_block)
            self.down_fusers.append(down_fuser)

        # 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,

                use_motion_module=use_motion_module and motion_module_mid_block,
                motion_module_type=motion_module_type,
                motion_module_kwargs=motion_module_kwargs,
            )
        else:
            raise ValueError(f"unknown mid_block_type : {mid_block_type}")

        self.mid_fuser = FusionBlock2D(
            in_channels=block_out_channels[-1],
            out_channels=block_out_channels[-1],
            temb_channels=time_embed_dim,
            eps=norm_eps,
            groups=norm_num_groups,
            time_embedding_norm=resnet_time_scale_shift,
            non_linearity=act_fn,
        ) if fuse_first_frame else None

        # 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

            prev_output_channel = output_channel
            output_channel = reversed_block_out_channels[i]
            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]

            # add upsample block for all BUT final layer
            if not is_final_block:
                add_upsample = True
                self.num_upsamplers += 1
            else:
                add_upsample = False

            up_block = get_up_block(
                up_block_type,
                num_layers=layers_per_block + 1,
                in_channels=input_channel,
                out_channels=output_channel,
                prev_output_channel=prev_output_channel,
                temb_channels=time_embed_dim,
                add_upsample=add_upsample,
                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                resnet_groups=norm_num_groups,
                cross_attention_dim=cross_attention_dim,
                attn_num_head_channels=reversed_attention_head_dim[i],
                dual_cross_attention=dual_cross_attention,
                use_linear_projection=use_linear_projection,
                only_cross_attention=only_cross_attention[i],
                upcast_attention=upcast_attention,
                resnet_time_scale_shift=resnet_time_scale_shift,

                use_motion_module=use_motion_module and (res in motion_module_resolutions),
                motion_module_type=motion_module_type,
                motion_module_kwargs=motion_module_kwargs,
            )
            self.up_blocks.append(up_block)
            prev_output_channel = output_channel

        # out
        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)
        self.conv_act = nn.SiLU()
        self.conv_out = InflatedConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1)

    def set_image_layer_lora(self, image_layer_lora_rank: int = 128):
        lora_attn_procs = {}
        for name in self.attn_processors.keys():
            self.logger.info(f"(add lora) {name}")
            cross_attention_dim = None if name.endswith("attn1.processor") else self.config.cross_attention_dim
            if name.startswith("mid_block"):
                hidden_size = self.config.block_out_channels[-1]
            elif name.startswith("up_blocks"):
                block_id = int(name[len("up_blocks.")])
                hidden_size = list(reversed(self.config.block_out_channels))[block_id]
            elif name.startswith("down_blocks"):
                block_id = int(name[len("down_blocks.")])
                hidden_size = self.config.block_out_channels[block_id]

            lora_attn_procs[name] = LoRAAttnProcessor(
                hidden_size=hidden_size,
                cross_attention_dim=cross_attention_dim,
                rank=image_layer_lora_rank if image_layer_lora_rank > 16 else hidden_size // image_layer_lora_rank,
            )
        self.set_attn_processor(lora_attn_procs)

        lora_layers = AttnProcsLayers(self.attn_processors)
        self.logger.info(f"(lora parameters): {sum(p.numel() for p in lora_layers.parameters()) / 1e6:.3f} M")
        del lora_layers

    def set_image_layer_lora_scale(self, lora_scale: float = 1.0):
        for block in self.down_blocks: setattr(block, "lora_scale", lora_scale)
        for block in self.up_blocks:   setattr(block, "lora_scale", lora_scale)
        setattr(self.mid_block, "lora_scale", lora_scale)

    def set_motion_module_lora_scale(self, lora_scale: float = 1.0):
        for block in self.down_blocks: setattr(block, "motion_lora_scale", lora_scale)
        for block in self.up_blocks: setattr(block, "motion_lora_scale", lora_scale)
        setattr(self.mid_block, "motion_lora_scale", lora_scale)

    @property
    def attn_processors(self) -> Dict[str, AttentionProcessor]:
        r"""
        Returns:
            `dict` of attention processors: A dictionary containing all attention processors used in the model with
            indexed by its weight name.
        """
        # set recursively
        processors = {}

        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
            # filter out processors in motion module
            if hasattr(module, "set_processor"):
                if not "motion_modules." in name:
                    processors[f"{name}.processor"] = module.processor

            for sub_name, child in module.named_children():
                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)

            return processors

        for name, module in self.named_children():
            fn_recursive_add_processors(name, module, processors)

        return processors

    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
        r"""
        Sets the attention processor to use to compute attention.

        Parameters:
            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
                The instantiated processor class or a dictionary of processor classes that will be set as the processor
                for **all** `Attention` layers.

                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
                processor. This is strongly recommended when setting trainable attention processors.

        """
        count = len(self.attn_processors.keys())

        if isinstance(processor, dict) and len(processor) != count:
            raise ValueError(
                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
            )

        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
            if hasattr(module, "set_processor"):
                if not "motion_modules." in name:
                    if not isinstance(processor, dict):
                        module.set_processor(processor)
                    else:
                        module.set_processor(processor.pop(f"{name}.processor"))

            for sub_name, child in module.named_children():
                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)

        for name, module in self.named_children():
            fn_recursive_attn_processor(name, module, processor)

    def set_motion_module_lora_layers(self, motion_module_lora_rank: int = 32):
        lora_attn_procs = {}
        for name in self.mm_attn_processors.keys():
            self.logger.info(f"(add lora) {name}")
            cross_attention_dim = None
            if name.startswith("mid_block"):
                hidden_size = self.config.block_out_channels[-1]
            elif name.startswith("up_blocks"):
                block_id = int(name[len("up_blocks.")])
                hidden_size = list(reversed(self.config.block_out_channels))[block_id]
            elif name.startswith("down_blocks"):
                block_id = int(name[len("down_blocks.")])
                hidden_size = self.config.block_out_channels[block_id]

            lora_attn_procs[name] = LoRAAttnProcessor(
                hidden_size=hidden_size,
                cross_attention_dim=cross_attention_dim,
                rank=motion_module_lora_rank if motion_module_lora_rank > 16 else hidden_size // motion_module_lora_rank,
            )
        self.set_mm_attn_processor(lora_attn_procs)

        lora_layers = AttnProcsLayers(self.mm_attn_processors)
        return lora_layers

    @property
    def mm_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]):
            # filter out processors in motion module
            if hasattr(module, "set_processor"):
                if "motion_modules." in name:
                    processors[f"{name}.processor"] = module.processor

            for sub_name, child in module.named_children():
                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)

            return processors

        for name, module in self.named_children():
            fn_recursive_add_processors(name, module, processors)

        return processors

    def set_mm_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.mm_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 "motion_modules." in name:
                    if not isinstance(processor, dict):
                        module.set_processor(processor)
                    else:
                        module.set_processor(processor.pop(f"{name}.processor"))

            for sub_name, child in module.named_children():
                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)

        for name, module in self.named_children():
            fn_recursive_attn_processor(name, module, processor)

    def set_attention_slice(self, slice_size):
        r"""
        Enable sliced attention computation.

        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
        in several steps. This is useful to save some memory in exchange for a small speed decrease.

        Args:
            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
                `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is
                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
                must be a multiple of `slice_size`.
        """
        sliceable_head_dims = []

        def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):
            if hasattr(module, "set_attention_slice"):
                sliceable_head_dims.append(module.sliceable_head_dim)

            for child in module.children():
                fn_recursive_retrieve_slicable_dims(child)

        # retrieve number of attention layers
        for module in self.children():
            fn_recursive_retrieve_slicable_dims(module)

        num_slicable_layers = len(sliceable_head_dims)

        if slice_size == "auto":
            # half the attention head size is usually a good trade-off between
            # speed and memory
            slice_size = [dim // 2 for dim in sliceable_head_dims]
        elif slice_size == "max":
            # make smallest slice possible
            slice_size = num_slicable_layers * [1]

        slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size

        if len(slice_size) != len(sliceable_head_dims):
            raise ValueError(
                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
            )

        for i in range(len(slice_size)):
            size = slice_size[i]
            dim = sliceable_head_dims[i]
            if size is not None and size > dim:
                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")

        # Recursively walk through all the children.
        # Any children which exposes the set_attention_slice method
        # gets the message
        def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
            if hasattr(module, "set_attention_slice"):
                module.set_attention_slice(slice_size.pop())

            for child in module.children():
                fn_recursive_set_attention_slice(child, slice_size)

        reversed_slice_size = list(reversed(slice_size))
        for module in self.children():
            fn_recursive_set_attention_slice(module, reversed_slice_size)

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):
            module.gradient_checkpointing = value

    def forward(
            self,
            sample: torch.FloatTensor,
            timestep: Union[torch.Tensor, float, int],
            encoder_hidden_states: Union[torch.Tensor, List[torch.Tensor]],
            class_labels: Optional[torch.Tensor] = None,
            attention_mask: Optional[torch.Tensor] = None,
            cross_attention_kwargs: Optional[Dict[str, Any]] = None,
            return_dict: bool = True,

            # support controlnet
            down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
            mid_block_additional_residual: Optional[torch.Tensor] = None,

            # other features
            motion_module_alphas: Union[tuple, float] = 1.0,
            debug: bool = False,
    ) -> Union[UNet3DConditionOutput, Tuple]:

        activations = {}

        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:]):
            self.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 necessary1
        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

        # extend encoder_hidden_states
        video_length = sample.shape[2]
        encoder_hidden_states = repeat(encoder_hidden_states, "b n c -> (b f) n c", f=video_length)

        # emb_single = emb
        # emb = repeat(emb, "b c -> (b f) c", f=video_length)

        # pre-process
        sample = self.conv_in(sample)
        activations["conv_in_out"] = sample

        # to be fused
        if self.down_fusers[0] != None:
            # scale, shift   = self.down_fusers[0](sample[:,:,0].contiguous(), emb_single).unsqueeze(2).chunk(2, dim=1)
            # sample[:,:,1:] = (1 + scale) * sample[:,:,1:].contiguous() + shift
            fused_sample = self.down_fusers[0](
                init_hidden_state=sample[:, :, :1].contiguous(),
                post_hidden_states=sample[:, :, 1:].contiguous(),
                temb=emb_single,
            )
            sample = torch.cat([sample[:, :, :1], fused_sample], dim=2)

        activations["conv_in_fuse_out"] = sample

        # down
        down_block_res_samples = (sample,)

        # motion module alpha
        if isinstance(motion_module_alphas, float):
            motion_module_alphas = (motion_module_alphas,) * 5

        for downsample_block, down_fuser, motion_module_alpha in zip(self.down_blocks, self.down_fusers[1:],
                                                                     motion_module_alphas[:-1]):
            sample, res_samples = downsample_block(
                hidden_states=sample,
                temb=emb,
                encoder_hidden_states=encoder_hidden_states,
                attention_mask=attention_mask,
                motion_module_alpha=motion_module_alpha,
                cross_attention_kwargs=cross_attention_kwargs
            )
            # to be fused
            for sample_idx, fuser in enumerate(down_fuser):
                if fuser != None:
                    fused_sample = fuser(
                        init_hidden_state=res_samples[sample_idx][:, :, :1].contiguous(),
                        post_hidden_states=res_samples[sample_idx][:, :, 1:].contiguous(),
                        temb=emb_single,
                    )
                    res_samples = list(res_samples)
                    res_samples[sample_idx] = torch.cat([res_samples[sample_idx][:, :, :1], fused_sample], dim=2)
                    res_samples = tuple(res_samples)

            down_block_res_samples += res_samples

        # support controlnet
        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
            ):
                if len(down_block_additional_residual.shape) == 4:
                    # b c h w
                    # if input single condition, apply it to all frames
                    down_block_additional_residual = down_block_additional_residual.unsqueeze(2)
                    # boardcast will solve the problem
                    # down_block_additional_residual = repeat(down_block_additional_residual, "b c f h w -> b c (f n) h w", n=video_length)

                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,
            motion_module_alpha=motion_module_alphas[-1], cross_attention_kwargs=cross_attention_kwargs
        )

        # mid block fuser
        if self.mid_fuser != None:
            fused_sample = self.mid_fuser(
                init_hidden_state=sample[:, :, :1],
                post_hidden_states=sample[:, :, 1:],
                temb=emb_single,
            )
            sample = torch.cat([sample[:, :, :1], fused_sample], dim=2)

        # support controlnet
        if mid_block_additional_residual is not None:
            if len(mid_block_additional_residual.shape) == 4:
                mid_block_additional_residual = mid_block_additional_residual.unsqueeze(2)
                # boardcast will solve this problemq
                # mid_block_additional_residual = repeat(mid_block_additional_residual, "b c f h w -> b c (f n) h w", n=video_length)

            sample = sample + mid_block_additional_residual

        # up
        for i, (upsample_block, motion_module_alpha) in enumerate(zip(self.up_blocks, motion_module_alphas[:-1][::-1])):
            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,
                    motion_module_alpha=motion_module_alpha,
                    cross_attention_kwargs=cross_attention_kwargs
                )
            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, motion_module_alpha=motion_module_alpha,
                    cross_attention_kwargs=cross_attention_kwargs
                )
        activations["upblocks_out"] = sample

        # post-process
        # frame-wise normalization
        sample = rearrange(sample, "b c f h w -> (b f) c h w")
        sample = self.conv_norm_out(sample)
        sample = rearrange(sample, "(b f) c h w -> b c f h w", f=video_length)

        sample = self.conv_act(sample)
        sample = self.conv_out(sample)

        if (not return_dict):
            return (sample,)
        elif debug:
            return UNet3DConditionOutput(sample=sample), activations
        else:
            return UNet3DConditionOutput(sample=sample)

    @classmethod
    def from_pretrained_2d(cls, pretrained_model_path, subfolder=None, unet_additional_kwargs=None, logger=None):
        if logger is not None:
            logger.info(f"Loading unet's pretrained weights from {pretrained_model_path} ...")

        if subfolder is not None:
            pretrained_model_path = os.path.join(pretrained_model_path, subfolder)

        config_file = os.path.join(pretrained_model_path, 'config.json')
        if not os.path.isfile(config_file):
            raise RuntimeError(f"{config_file} does not exist")

        with open(config_file, "r") as f:
            config = json.load(f)

        config["_class_name"] = cls.__name__
        config["down_block_types"] = [
            "CrossAttnDownBlock3D",
            "CrossAttnDownBlock3D",
            "CrossAttnDownBlock3D",
            "DownBlock3D"
        ]
        config["up_block_types"] = [
            "UpBlock3D",
            "CrossAttnUpBlock3D",
            "CrossAttnUpBlock3D",
            "CrossAttnUpBlock3D"
        ]



        model, unused_kwargs = cls.from_config(config, return_unused_kwargs=True, **unet_additional_kwargs)
        if logger is not None:
            logger.info(f"please check unused kwargs in 'unet_additional_kwargs' config:")
        for k, v in unused_kwargs.items():
            if logger is not None:
                logger.info(f"{k:50s}: {repr(v)}")

        model_file = os.path.join(pretrained_model_path, SAFETENSORS_WEIGHTS_NAME)
        if not os.path.isfile(model_file):
            raise RuntimeError(f"{model_file} does not exist")

        state_dict = safetensors.torch.load_file(model_file, device="cpu")
        missing, unexpected = model.load_state_dict(state_dict, strict=False)
        if logger is not None:
            logger.info(f"Missing keys: {len(missing)}; Unexpected keys: {len(unexpected)};")
        assert len(unexpected) == 0

        params = [p.numel() if "motion_modules." in n else 0 for n, p in model.named_parameters()]
        if logger is not None:
            logger.info(f"Motion module parameters: {sum(params) / 1e6} M")

        return model


class UNet3DConditionModelCameraCond(UNet3DConditionModel):
    _supports_gradient_checkpointing = True

    @classmethod
    def extract_init_dict(cls, config_dict, **kwargs):
        # Skip keys that were not present in the original config, so default __init__ values were used
        used_defaults = config_dict.get("_use_default_values", [])
        config_dict = {k: v for k, v in config_dict.items() if k not in used_defaults and k != "_use_default_values"}

        # 0. Copy origin config dict
        original_dict = dict(config_dict.items())

        # 1. Retrieve expected config attributes from __init__ signature
        expected_keys = cls._get_init_keys(cls)
        expected_keys.remove("self")
        super_expected_keys = cls._get_init_keys(UNet3DConditionModel)
        super_expected_keys.remove("self")
        # remove general kwargs if present in dict
        if "kwargs" in expected_keys:
            expected_keys.remove("kwargs")
        if "kwargs" in super_expected_keys:
            super_expected_keys.remove("kwargs")
        # remove flax internal keys
        if hasattr(cls, "_flax_internal_args"):
            for arg in cls._flax_internal_args:
                expected_keys.remove(arg)
        expected_keys = expected_keys.union(super_expected_keys)

        # remove private attributes
        config_dict = {k: v for k, v in config_dict.items() if not k.startswith("_")}

        # 3. Create keyword arguments that will be passed to __init__ from expected keyword arguments
        init_dict = {}
        for key in expected_keys:
            # if config param is passed to kwarg and is present in config dict
            # it should overwrite existing config dict key
            if key in kwargs and key in config_dict:
                config_dict[key] = kwargs.pop(key)

            if key in kwargs:
                # overwrite key
                init_dict[key] = kwargs.pop(key)
            elif key in config_dict:
                # use value from config dict
                init_dict[key] = config_dict.pop(key)

        # 4. Give nice warning if unexpected values have been passed
        if len(config_dict) > 0:
            print(
                f"The config attributes {config_dict} were passed to {cls.__name__}, "
                "but are not expected and will be ignored. Please verify your "
                f"{cls.config_name} configuration file."
            )

        # 6. Define unused keyword arguments
        unused_kwargs = {**config_dict, **kwargs}

        # 7. Define "hidden" config parameters that were saved for compatible classes
        hidden_config_dict = {k: v for k, v in original_dict.items() if k not in init_dict}

        return init_dict, unused_kwargs, hidden_config_dict

    def __init__(self,
                 decoder_add_cameracond=True,
                 **kwargs):
        super(UNet3DConditionModelCameraCond, self).__init__(**kwargs)
        self.decoder_add_cameracond = decoder_add_cameracond

    def set_all_attn_processor(self,
                               add_spatial=False,
                               spatial_attn_names='attn1',
                               add_temporal=False,
                               add_spatial_lora=True,
                               add_motion_lora=False,
                               temporal_attn_names='0',
                               camera_feature_dimensions=[320, 640, 1280, 1280],
                               lora_kwargs={},
                               motion_lora_kwargs={},
                               **attention_processor_kwargs):
        lora_rank = lora_kwargs.pop('lora_rank')
        motion_lora_rank = motion_lora_kwargs.pop('lora_rank')
        spatial_attn_procs = {}
        if add_spatial:
            set_processor_names = spatial_attn_names.split(',')
            for name in self.attn_processors.keys():
                attention_name = name.split('.')[-2]
                cross_attention_dim = None if attention_name == 'attn1' else self.config.cross_attention_dim
                if name.startswith("mid_block"):
                    hidden_size = self.config.block_out_channels[-1]
                    block_id = -1
                    add_camera_adaptor = attention_name in set_processor_names
                    camera_feature_dim = camera_feature_dimensions[block_id] if add_camera_adaptor else None
                elif name.startswith("up_blocks"):
                    block_id = int(name[len("up_blocks.")])
                    hidden_size = list(reversed(self.config.block_out_channels))[block_id]
                    add_camera_adaptor = attention_name in set_processor_names
                    camera_feature_dim = list(reversed(camera_feature_dimensions))[block_id] if add_camera_adaptor else None
                else:
                    assert name.startswith("down_blocks")
                    block_id = int(name[len("down_blocks.")])
                    hidden_size = self.config.block_out_channels[block_id]
                    add_camera_adaptor = attention_name in set_processor_names
                    camera_feature_dim = camera_feature_dimensions[block_id] if add_camera_adaptor else None
                if add_camera_adaptor and add_spatial_lora:
                    spatial_attn_procs[name] = LORACameraAdaptorAttnProcessor(hidden_size=hidden_size,
                                                                            camera_feature_dim=camera_feature_dim,
                                                                            cross_attention_dim=cross_attention_dim,
                                                                            rank=lora_rank if lora_rank > 16 else hidden_size // lora_rank,
                                                                            **attention_processor_kwargs,
                                                                            **lora_kwargs)
                elif add_camera_adaptor:
                    spatial_attn_procs[name] = CameraAdaptorAttnProcessor(hidden_size=hidden_size,
                                                                        camera_feature_dim=camera_feature_dim,
                                                                        cross_attention_dim=cross_attention_dim,
                                                                        **attention_processor_kwargs)
                elif add_spatial_lora:
                    spatial_attn_procs[name] = CustomizedLoRAAttnProcessor(hidden_size=hidden_size,
                                                                           cross_attention_dim=cross_attention_dim,
                                                                           rank=lora_rank if lora_rank > 16 else hidden_size // lora_rank)
                else:
                    spatial_attn_procs[name] = CustomizedAttnProcessor()
        elif (not add_spatial) and add_spatial_lora:
            for name in self.attn_processors.keys():
                cross_attention_dim = None if name.endswith("attn1.processor") else self.config.cross_attention_dim
                if name.startswith("mid_block"):
                    hidden_size = self.config.block_out_channels[-1]
                elif name.startswith("up_blocks"):
                    block_id = int(name[len("up_blocks.")])
                    hidden_size = list(reversed(self.config.block_out_channels))[block_id]
                elif name.startswith("down_blocks"):
                    block_id = int(name[len("down_blocks.")])
                    hidden_size = self.config.block_out_channels[block_id]

                spatial_attn_procs[name] = CustomizedLoRAAttnProcessor(
                    hidden_size=hidden_size,
                    cross_attention_dim=cross_attention_dim,
                    rank=lora_rank if lora_rank > 16 else hidden_size // lora_rank,
                )
        else:
            for name in self.attn_processors.keys():
                spatial_attn_procs[name] = CustomizedAttnProcessor()
        self.set_attn_processor(spatial_attn_procs)

        mm_attn_procs = {}
        if add_temporal:
            set_processor_names = temporal_attn_names.split(',')
            cross_attention_dim = None
            for name in self.mm_attn_processors.keys():
                attention_name = name.split('.')[-2]
                if name.startswith("mid_block"):
                    hidden_size = self.config.block_out_channels[-1]
                    block_id = -1
                    add_camera_adaptor = attention_name in set_processor_names
                    camera_feature_dim = camera_feature_dimensions[block_id] if add_camera_adaptor else None
                elif name.startswith("up_blocks"):
                    block_id = int(name[len("up_blocks.")])
                    hidden_size = list(reversed(self.config.block_out_channels))[block_id]
                    add_camera_adaptor = (attention_name in set_processor_names) and self.decoder_add_cameracond
                    camera_feature_dim = list(reversed(camera_feature_dimensions))[block_id] if add_camera_adaptor else None
                elif name.startswith("down_blocks"):
                    block_id = int(name[len("down_blocks.")])
                    hidden_size = self.config.block_out_channels[block_id]
                    add_camera_adaptor = attention_name in set_processor_names
                    camera_feature_dim = camera_feature_dimensions[block_id] if add_camera_adaptor else None
                if add_camera_adaptor and add_motion_lora:
                    mm_attn_procs[name] = LORACameraAdaptorAttnProcessor(hidden_size=hidden_size,
                                                                       camera_feature_dim=camera_feature_dim,
                                                                       cross_attention_dim=cross_attention_dim,
                                                                       rank=motion_lora_rank if motion_lora_rank > 16 else hidden_size // motion_lora_rank,
                                                                       **attention_processor_kwargs,
                                                                       **motion_lora_kwargs)
                elif add_camera_adaptor:
                    mm_attn_procs[name] = CameraAdaptorAttnProcessor(hidden_size=hidden_size,
                                                                   camera_feature_dim=camera_feature_dim,
                                                                   cross_attention_dim=cross_attention_dim,
                                                                   **attention_processor_kwargs)
                elif add_motion_lora:
                    mm_attn_procs[name] = CustomizedLoRAAttnProcessor(hidden_size=hidden_size,
                                                                      cross_attention_dim=cross_attention_dim,
                                                                      rank=motion_lora_rank if motion_lora_rank > 16 else hidden_size // motion_lora_rank)
                else:
                    mm_attn_procs[name] = CustomizedAttnProcessor()
        elif (not add_temporal) and add_motion_lora:
            for name in self.mm_attn_processors.keys():
                cross_attention_dim = None
                if name.startswith("mid_block"):
                    hidden_size = self.config.block_out_channels[-1]
                elif name.startswith("up_blocks"):
                    block_id = int(name[len("up_blocks.")])
                    hidden_size = list(reversed(self.config.block_out_channels))[block_id]
                elif name.startswith("down_blocks"):
                    block_id = int(name[len("down_blocks.")])
                    hidden_size = self.config.block_out_channels[block_id]

                mm_attn_procs[name] = CustomizedLoRAAttnProcessor(
                    hidden_size=hidden_size,
                    cross_attention_dim=cross_attention_dim,
                    rank=motion_lora_rank if motion_lora_rank > 16 else hidden_size // motion_lora_rank,
                )
        else:
            for name in self.mm_attn_processors.keys():
                mm_attn_procs[name] = CustomizedAttnProcessor()
        self.set_mm_attn_processor(mm_attn_procs)

    def forward(
            self,
            sample: torch.FloatTensor,
            timestep: Union[torch.Tensor, float, int],
            encoder_hidden_states: Union[torch.Tensor, List[torch.Tensor]],
            class_labels: Optional[torch.Tensor] = None,
            attention_mask: Optional[torch.Tensor] = None,
            cross_attention_kwargs: Optional[Dict[str, Any]] = None,
            camera_embedding_features: List[torch.Tensor] = None,
            return_dict: bool = True,

            # support controlnet
            down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
            mid_block_additional_residual: Optional[torch.Tensor] = None,

            # other features
            motion_module_alphas: Union[tuple, float] = 1.0,
            debug: bool = False,
    ) -> Union[UNet3DConditionOutput, Tuple]:

        activations = {}

        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:]):
            self.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 necessary1
        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

        # extend encoder_hidden_states
        video_length = sample.shape[2]
        encoder_hidden_states = repeat(encoder_hidden_states, "b n c -> (b f) n c", f=video_length)

        # pre-process
        sample = self.conv_in(sample)           # b c f h w
        activations["conv_in_out"] = sample

        # to be fused
        if self.down_fusers[0] != None:
            # scale, shift   = self.down_fusers[0](sample[:,:,0].contiguous(), emb_single).unsqueeze(2).chunk(2, dim=1)
            # sample[:,:,1:] = (1 + scale) * sample[:,:,1:].contiguous() + shift
            fused_sample = self.down_fusers[0](
                init_hidden_state=sample[:, :, :1].contiguous(),
                post_hidden_states=sample[:, :, 1:].contiguous(),
                temb=emb_single,
            )
            sample = torch.cat([sample[:, :, :1], fused_sample], dim=2)

        activations["conv_in_fuse_out"] = sample

        # down
        down_block_res_samples = (sample,)

        # motion module alpha
        if isinstance(motion_module_alphas, float):
            motion_module_alphas = (motion_module_alphas,) * 5

        for downsample_block, camera_embedding_feature, down_fuser, motion_module_alpha in zip(self.down_blocks,
                                                                                             camera_embedding_features,
                                                                                             self.down_fusers[1:],
                                                                                             motion_module_alphas[:-1]):
            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,
                    motion_module_alpha=motion_module_alpha,
                    cross_attention_kwargs=cross_attention_kwargs.update({"camera_feature": camera_embedding_feature})
                    if cross_attention_kwargs is not None else {"camera_feature": camera_embedding_feature},
                    motion_cross_attention_kwargs={"camera_feature": camera_embedding_feature}
                )
            else:
                sample, res_samples = downsample_block(
                    hidden_states=sample,
                    temb=emb,
                    motion_module_alpha=motion_module_alpha,
                    cross_attention_kwargs=cross_attention_kwargs.update({"camera_feature": camera_embedding_feature})
                    if cross_attention_kwargs is not None else {"camera_feature": camera_embedding_feature},
                    motion_cross_attention_kwargs={"camera_feature": camera_embedding_feature}
                )

            # to be fused
            for sample_idx, fuser in enumerate(down_fuser):
                if fuser != None:
                    fused_sample = fuser(
                        init_hidden_state=res_samples[sample_idx][:, :, :1].contiguous(),
                        post_hidden_states=res_samples[sample_idx][:, :, 1:].contiguous(),
                        temb=emb_single,
                    )
                    res_samples = list(res_samples)
                    res_samples[sample_idx] = torch.cat([res_samples[sample_idx][:, :, :1], fused_sample], dim=2)
                    res_samples = tuple(res_samples)

            down_block_res_samples += res_samples

        # support controlnet
        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
            ):
                if len(down_block_additional_residual.shape) == 4:
                    # b c h w
                    # if input single condition, apply it to all frames
                    down_block_additional_residual = down_block_additional_residual.unsqueeze(2)
                    # boardcast will solve the problem
                    # down_block_additional_residual = repeat(down_block_additional_residual, "b c f h w -> b c (f n) h w", n=video_length)

                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,
            motion_module_alpha=motion_module_alphas[-1],
            cross_attention_kwargs=cross_attention_kwargs.update({"camera_feature": camera_embedding_features[-1]})
            if cross_attention_kwargs is not None else {"camera_feature": camera_embedding_features[-1]},
            motion_cross_attention_kwargs={"camera_feature": camera_embedding_features[-1]}
        )

        # mid block fuser
        if self.mid_fuser != None:
            fused_sample = self.mid_fuser(
                init_hidden_state=sample[:, :, :1],
                post_hidden_states=sample[:, :, 1:],
                temb=emb_single,
            )
            sample = torch.cat([sample[:, :, :1], fused_sample], dim=2)

        # support controlnet
        if mid_block_additional_residual is not None:
            if len(mid_block_additional_residual.shape) == 4:
                mid_block_additional_residual = mid_block_additional_residual.unsqueeze(2)
                # boardcast will solve this problemq
                # mid_block_additional_residual = repeat(mid_block_additional_residual, "b c f h w -> b c (f n) h w", n=video_length)

            sample = sample + mid_block_additional_residual

        # up
        for i, (upsample_block, motion_module_alpha) in enumerate(zip(self.up_blocks, motion_module_alphas[:-1][::-1])):
            is_final_block = i == len(self.up_blocks) - 1
            camera_embedding_feature = camera_embedding_features[-(i+1)] if self.decoder_add_cameracond else None

            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 self.decoder_add_cameracond:
                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,
                        motion_module_alpha=motion_module_alpha,
                        cross_attention_kwargs=cross_attention_kwargs.update({"camera_feature":camera_embedding_feature})
                        if cross_attention_kwargs is not None else {"camera_feature": camera_embedding_feature},
                        motion_cross_attention_kwargs={"camera_feature": camera_embedding_feature}
                    )
                else:
                    sample = upsample_block(
                        hidden_states=sample,
                        temb=emb,
                        res_hidden_states_tuple=res_samples,
                        upsample_size=upsample_size,
                        motion_module_alpha=motion_module_alpha,
                        cross_attention_kwargs=cross_attention_kwargs.update({"camera_feature": camera_embedding_feature})
                        if cross_attention_kwargs is not None else {"camera_feature": camera_embedding_feature},
                        motion_cross_attention_kwargs={"camera_feature": camera_embedding_feature}
                    )
            else:
                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,
                        motion_module_alpha=motion_module_alpha,
                        cross_attention_kwargs=cross_attention_kwargs,
                    )
                else:
                    sample = upsample_block(
                        hidden_states=sample,
                        temb=emb,
                        res_hidden_states_tuple=res_samples,
                        upsample_size=upsample_size,
                        motion_module_alpha=motion_module_alpha,
                        cross_attention_kwargs=cross_attention_kwargs
                    )

        activations["upblocks_out"] = sample

        # post-process
        # frame-wise normalization
        sample = rearrange(sample, "b c f h w -> (b f) c h w")
        sample = self.conv_norm_out(sample)
        sample = rearrange(sample, "(b f) c h w -> b c f h w", f=video_length)

        sample = self.conv_act(sample)
        sample = self.conv_out(sample)

        if (not return_dict):
            return (sample,)
        elif debug:
            return UNet3DConditionOutput(sample=sample), activations
        else:
            return UNet3DConditionOutput(sample=sample)