finetune/models/camera_controller/transformer.py

# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI and The HuggingFace Team.
# All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pdb
from typing import Any, Dict, Optional, Tuple, Union

import torch
from torch import nn

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.loaders import PeftAdapterMixin
from diffusers.utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
from diffusers.utils.torch_utils import maybe_allow_in_graph
from diffusers.models.attention import Attention, FeedForward
from diffusers.models.attention_processor import AttentionProcessor, FusedCogVideoXAttnProcessor2_0

from diffusers.models.embeddings import CogVideoXPatchEmbed, get_3d_sincos_pos_embed, TimestepEmbedding, Timesteps
from diffusers.models.modeling_outputs import Transformer2DModelOutput
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.normalization import AdaLayerNorm, CogVideoXLayerNormZero

import inspect
import math
from typing import Callable, List, Optional, Tuple, Union
from diffusers.models.embeddings import apply_rotary_emb

import torch
import torch.nn.functional as F
from torch import nn

from diffusers.utils import deprecate, is_torch_xla_available, logging
from diffusers.utils.torch_utils import is_torch_version, maybe_allow_in_graph

try:
    from .flash_attn3 import IS_FLASH3_AVAILABLE
    from .flash_attn3 import flash_attn_interface
    from .flash_attn3 import CogVideoXFlashAttn3ControlnetXsProcessor, CogVideoXFlashAttn3Processor
except:
    pass

logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


class CogVideoXControlnetXsPatchEmbed(nn.Module):
    def __init__(
        self,
        patch_size: int = 2,
        patch_size_t: Optional[int] = None,
        in_channels: int = 16,
        embed_dim: int = 1920,
        bias: bool = True,
        sample_width: int = 90,
        sample_height: int = 60,
        sample_frames: int = 49,
        temporal_compression_ratio: int = 4,
        spatial_interpolation_scale: float = 1.875,
        temporal_interpolation_scale: float = 1.0,
        use_positional_embeddings: bool = True,
        use_learned_positional_embeddings: bool = True,
    ) -> None:
        super().__init__()

        self.patch_size = patch_size
        self.patch_size_t = patch_size_t
        self.embed_dim = embed_dim
        self.sample_height = sample_height
        self.sample_width = sample_width
        self.sample_frames = sample_frames
        self.temporal_compression_ratio = temporal_compression_ratio
        self.spatial_interpolation_scale = spatial_interpolation_scale
        self.temporal_interpolation_scale = temporal_interpolation_scale
        self.use_positional_embeddings = use_positional_embeddings
        self.use_learned_positional_embeddings = use_learned_positional_embeddings

        if patch_size_t is None:
            # CogVideoX 1.0 checkpoints
            self.proj = nn.Conv2d(
                in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias
            )
        else:
            # CogVideoX 1.5 checkpoints
            self.proj = nn.Linear(in_channels * patch_size * patch_size * patch_size_t, embed_dim)

        if use_positional_embeddings or use_learned_positional_embeddings:
            persistent = use_learned_positional_embeddings
            pos_embedding = self._get_positional_embeddings(sample_height, sample_width, sample_frames)
            self.register_buffer("pos_embedding", pos_embedding, persistent=persistent)

    def _get_positional_embeddings(
        self, sample_height: int, sample_width: int, sample_frames: int, device: Optional[torch.device] = None
    ) -> torch.Tensor:
        post_patch_height = sample_height // self.patch_size
        post_patch_width = sample_width // self.patch_size
        post_time_compression_frames = (sample_frames - 1) // self.temporal_compression_ratio + 1
        num_patches = post_patch_height * post_patch_width * post_time_compression_frames

        pos_embedding = get_3d_sincos_pos_embed(
            self.embed_dim,
            (post_patch_width, post_patch_height),
            post_time_compression_frames,
            self.spatial_interpolation_scale,
            self.temporal_interpolation_scale,
            device=device,
            output_type="pt",
        )
        pos_embedding = pos_embedding.flatten(0, 1)
        joint_pos_embedding = pos_embedding.new_zeros(
            1, num_patches, self.embed_dim, requires_grad=False
        )
        joint_pos_embedding.data[:, :].copy_(pos_embedding)

        return joint_pos_embedding

    def forward(self, image_embeds: torch.Tensor):
        r"""
        Args:
            image_embeds (`torch.Tensor`):
                Input image embeddings. Expected shape: (batch_size, num_frames, channels, height, width).
        """
        batch_size, num_frames, channels, height, width = image_embeds.shape

        if self.patch_size_t is None:
            image_embeds = image_embeds.reshape(-1, channels, height, width)
            image_embeds = self.proj(image_embeds)
            image_embeds = image_embeds.view(batch_size, num_frames, *image_embeds.shape[1:])
            image_embeds = image_embeds.flatten(3).transpose(2, 3)  # [batch, num_frames, height x width, channels]
            image_embeds = image_embeds.flatten(1, 2)  # [batch, num_frames x height x width, channels]
        else:
            p = self.patch_size
            p_t = self.patch_size_t

            image_embeds = image_embeds.permute(0, 1, 3, 4, 2)
            image_embeds = image_embeds.reshape(
                batch_size, num_frames // p_t, p_t, height // p, p, width // p, p, channels
            )
            image_embeds = image_embeds.permute(0, 1, 3, 5, 7, 2, 4, 6).flatten(4, 7).flatten(1, 3)
            image_embeds = self.proj(image_embeds)

        embeds = image_embeds.contiguous()  # [batch, num_frames x height x width, channels]

        if self.use_positional_embeddings or self.use_learned_positional_embeddings:
            if self.use_learned_positional_embeddings and (self.sample_width != width or self.sample_height != height):
                raise ValueError(
                    "It is currently not possible to generate videos at a different resolution that the defaults. This should only be the case with 'THUDM/CogVideoX-5b-I2V'."
                    "If you think this is incorrect, please open an issue at https://github.com/huggingface/diffusers/issues."
                )

            pre_time_compression_frames = (num_frames - 1) * self.temporal_compression_ratio + 1

            if (
                self.sample_height != height
                or self.sample_width != width
                or self.sample_frames != pre_time_compression_frames
            ):
                pos_embedding = self._get_positional_embeddings(
                    height, width, pre_time_compression_frames, device=embeds.device
                )
            else:
                pos_embedding = self.pos_embedding

            pos_embedding = pos_embedding.to(dtype=embeds.dtype)
            embeds = embeds + pos_embedding

        return embeds

class CogVideoXControlnetXsLayerNormZero(nn.Module):
    def __init__(
        self,
        conditioning_dim: int,
        embedding_dim: int,
        elementwise_affine: bool = True,
        eps: float = 1e-5,
        bias: bool = True,
    ) -> None:
        super().__init__()

        self.silu = nn.SiLU()
        self.linear = nn.Linear(conditioning_dim, 3 * embedding_dim, bias=bias)
        self.norm = nn.LayerNorm(embedding_dim, eps=eps, elementwise_affine=elementwise_affine)

    def forward(
        self, hidden_states: torch.Tensor, temb: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        shift, scale, gate = self.linear(self.silu(temb)).chunk(3, dim=1)
        hidden_states = self.norm(hidden_states) * (1 + scale)[:, None, :] + shift[:, None, :]
        return hidden_states, gate[:, None, :]


class CogVideoXControlnetXsAttnProcessor2_0:
    r"""
    Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on
    query and key vectors, but does not include spatial normalization.
    """

    def __init__(self):
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError("CogVideoXAttnProcessor requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.Tensor,
        encoder_hidden_states = None,
        attention_mask: Optional[torch.Tensor] = None,
        image_rotary_emb: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:

        batch_size, sequence_length, _ = hidden_states.shape

        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

        query = attn.to_q(hidden_states)
        key = attn.to_k(hidden_states)
        value = attn.to_v(hidden_states)

        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads

        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)    # B,L,H,D --> B,H,L,D
        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        if attn.norm_q is not None:
            query = attn.norm_q(query)
        if attn.norm_k is not None:
            key = attn.norm_k(key)

        # Apply RoPE if needed
        if image_rotary_emb is not None:
            from diffusers.models.embeddings import apply_rotary_emb

            query = apply_rotary_emb(query, image_rotary_emb)
            if not attn.is_cross_attention:
                key = apply_rotary_emb(key, image_rotary_emb)

        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )

        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        return hidden_states

@maybe_allow_in_graph
class CogVideoXControlnetXsBlock(nn.Module):
    r"""
    Transformer block used in [CogVideoX](https://github.com/THUDM/CogVideo) model.

    Parameters:
        dim (`int`):
            The number of channels in the input and output.
        num_attention_heads (`int`):
            The number of heads to use for multi-head attention.
        attention_head_dim (`int`):
            The number of channels in each head.
        time_embed_dim (`int`):
            The number of channels in timestep embedding.
        dropout (`float`, defaults to `0.0`):
            The dropout probability to use.
        activation_fn (`str`, defaults to `"gelu-approximate"`):
            Activation function to be used in feed-forward.
        attention_bias (`bool`, defaults to `False`):
            Whether or not to use bias in attention projection layers.
        qk_norm (`bool`, defaults to `True`):
            Whether or not to use normalization after query and key projections in Attention.
        norm_elementwise_affine (`bool`, defaults to `True`):
            Whether to use learnable elementwise affine parameters for normalization.
        norm_eps (`float`, defaults to `1e-5`):
            Epsilon value for normalization layers.
        final_dropout (`bool` defaults to `False`):
            Whether to apply a final dropout after the last feed-forward layer.
        ff_inner_dim (`int`, *optional*, defaults to `None`):
            Custom hidden dimension of Feed-forward layer. If not provided, `4 * dim` is used.
        ff_bias (`bool`, defaults to `True`):
            Whether or not to use bias in Feed-forward layer.
        attention_out_bias (`bool`, defaults to `True`):
            Whether or not to use bias in Attention output projection layer.
    """

    def __init__(
            self,
            dim: int,
            num_attention_heads: int,
            attention_head_dim: int,
            time_embed_dim: int,
            dropout: float = 0.0,
            activation_fn: str = "gelu-approximate",
            attention_bias: bool = False,
            qk_norm: bool = True,
            norm_elementwise_affine: bool = True,
            norm_eps: float = 1e-5,
            final_dropout: bool = True,
            ff_inner_dim: Optional[int] = None,
            ff_bias: bool = True,
            attention_out_bias: bool = True,
    ):
        super().__init__()

        # 1. Self Attention
        self.norm1 = CogVideoXControlnetXsLayerNormZero(time_embed_dim, dim, norm_elementwise_affine, norm_eps, bias=True)

        self.attn1 = Attention(
            query_dim=dim,
            dim_head=attention_head_dim,
            heads=num_attention_heads,
            qk_norm="layer_norm" if qk_norm else None,
            eps=1e-6,
            bias=attention_bias,
            out_bias=attention_out_bias,
            processor=CogVideoXControlnetXsAttnProcessor2_0(),
        )
        if IS_FLASH3_AVAILABLE:
            self.attn1.set_processor(CogVideoXFlashAttn3ControlnetXsProcessor())

        # 2. Feed Forward
        self.norm2 = CogVideoXControlnetXsLayerNormZero(time_embed_dim, dim, norm_elementwise_affine, norm_eps, bias=True)

        self.ff = FeedForward(
            dim,
            dropout=dropout,
            activation_fn=activation_fn,
            final_dropout=final_dropout,
            inner_dim=ff_inner_dim,
            bias=ff_bias,
        )

    def forward(
            self,
            hidden_states: torch.Tensor,
            temb: torch.Tensor,
            image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
    ) -> torch.Tensor:

        # norm & modulate
        norm_hidden_states, gate_msa = self.norm1(hidden_states, temb)

        # attention
        attn_hidden_states = self.attn1(
            hidden_states=norm_hidden_states,
            image_rotary_emb=image_rotary_emb,
        )

        hidden_states = hidden_states + gate_msa * attn_hidden_states

        # norm & modulate
        norm_hidden_states, gate_ff = self.norm2(hidden_states, temb)

        # feed-forward
        ff_output = self.ff(norm_hidden_states)

        hidden_states = hidden_states + gate_ff * ff_output

        return hidden_states


class CogVideoXTransformer3DControlnetXs(ModelMixin, ConfigMixin, PeftAdapterMixin):
    """
    A Transformer model for video-like data in [CogVideoX](https://github.com/THUDM/CogVideo).

    Parameters:
        num_attention_heads (`int`, defaults to `30`):
            The number of heads to use for multi-head attention.
        attention_head_dim (`int`, defaults to `64`):
            The number of channels in each head.
        in_channels (`int`, defaults to `16`):
            The number of channels in the input.
        out_channels (`int`, *optional*, defaults to `16`):
            The number of channels in the output.
        flip_sin_to_cos (`bool`, defaults to `True`):
            Whether to flip the sin to cos in the time embedding.
        time_embed_dim (`int`, defaults to `512`):
            Output dimension of timestep embeddings.
        ofs_embed_dim (`int`, defaults to `512`):
            Output dimension of "ofs" embeddings used in CogVideoX-5b-I2B in version 1.5
        text_embed_dim (`int`, defaults to `4096`):
            Input dimension of text embeddings from the text encoder.
        num_layers (`int`, defaults to `30`):
            The number of layers of Transformer blocks to use.
        dropout (`float`, defaults to `0.0`):
            The dropout probability to use.
        attention_bias (`bool`, defaults to `True`):
            Whether to use bias in the attention projection layers.
        sample_width (`int`, defaults to `90`):
            The width of the input latents.
        sample_height (`int`, defaults to `60`):
            The height of the input latents.
        sample_frames (`int`, defaults to `49`):
            The number of frames in the input latents. Note that this parameter was incorrectly initialized to 49
            instead of 13 because CogVideoX processed 13 latent frames at once in its default and recommended settings,
            but cannot be changed to the correct value to ensure backwards compatibility. To create a transformer with
            K latent frames, the correct value to pass here would be: ((K - 1) * temporal_compression_ratio + 1).
        patch_size (`int`, defaults to `2`):
            The size of the patches to use in the patch embedding layer.
        temporal_compression_ratio (`int`, defaults to `4`):
            The compression ratio across the temporal dimension. See documentation for `sample_frames`.
        max_text_seq_length (`int`, defaults to `226`):
            The maximum sequence length of the input text embeddings.
        activation_fn (`str`, defaults to `"gelu-approximate"`):
            Activation function to use in feed-forward.
        timestep_activation_fn (`str`, defaults to `"silu"`):
            Activation function to use when generating the timestep embeddings.
        norm_elementwise_affine (`bool`, defaults to `True`):
            Whether to use elementwise affine in normalization layers.
        norm_eps (`float`, defaults to `1e-5`):
            The epsilon value to use in normalization layers.
        spatial_interpolation_scale (`float`, defaults to `1.875`):
            Scaling factor to apply in 3D positional embeddings across spatial dimensions.
        temporal_interpolation_scale (`float`, defaults to `1.0`):
            Scaling factor to apply in 3D positional embeddings across temporal dimensions.
    """

    _supports_gradient_checkpointing = True

    @register_to_config
    def __init__(
            self,
            num_attention_heads: int = 30,
            attention_head_dim: int = 64,
            in_channels: int = 16,
            flip_sin_to_cos: bool = True,
            freq_shift: int = 0,
            time_embed_dim: int = 512,
            text_embed_dim: int = 4096,
            num_layers: int = 30,
            dropout: float = 0.0,
            attention_bias: bool = True,
            sample_width: int = 90,
            sample_height: int = 60,
            sample_frames: int = 49,
            patch_size: int = 2,
            patch_size_t: Optional[int] = None,
            temporal_compression_ratio: int = 4,
            max_text_seq_length: int = 226,
            activation_fn: str = "gelu-approximate",
            timestep_activation_fn: str = "silu",
            norm_elementwise_affine: bool = True,
            norm_eps: float = 1e-5,
            spatial_interpolation_scale: float = 1.875,
            temporal_interpolation_scale: float = 1.0,
            use_rotary_positional_embeddings: bool = False,
            use_learned_positional_embeddings: bool = False,
            patch_bias: bool = True,
            **kwargs
    ):
        super().__init__()
        inner_dim = num_attention_heads * attention_head_dim

        if not use_rotary_positional_embeddings and use_learned_positional_embeddings:
            raise ValueError(
                "There are no CogVideoX checkpoints available with disable rotary embeddings and learned positional "
                "embeddings. If you're using a custom model and/or believe this should be supported, please open an "
                "issue at https://github.com/huggingface/diffusers/issues."
            )

        # 1. Patch embedding
        self.patch_embed = CogVideoXControlnetXsPatchEmbed(
            patch_size=patch_size,
            patch_size_t=patch_size_t,
            in_channels=in_channels,
            embed_dim=inner_dim,
            bias=patch_bias,
            sample_width=sample_width,
            sample_height=sample_height,
            sample_frames=sample_frames,
            temporal_compression_ratio=temporal_compression_ratio,
            spatial_interpolation_scale=spatial_interpolation_scale,
            temporal_interpolation_scale=temporal_interpolation_scale,
            use_positional_embeddings=not use_rotary_positional_embeddings,
            use_learned_positional_embeddings=use_learned_positional_embeddings,
        )
        self.embedding_dropout = nn.Dropout(dropout)

        # 2. Time embeddings and ofs embedding(Only CogVideoX1.5-5B I2V have)

        # self.time_proj = Timesteps(inner_dim, flip_sin_to_cos, freq_shift)
        # self.time_embedding = TimestepEmbedding(inner_dim, time_embed_dim, timestep_activation_fn)

        self.camera_condition_gft_beta_proj = Timesteps(time_embed_dim, flip_sin_to_cos, freq_shift)
        self.camera_condition_gft_beta_embedding = TimestepEmbedding(
            time_embed_dim, time_embed_dim, timestep_activation_fn
        )  # same as time embeddings, for camera_condition_gft_beta

        # 3. Define spatio-temporal transformers blocks
        self.transformer_blocks = nn.ModuleList(
            [
                CogVideoXControlnetXsBlock(
                    dim=inner_dim,
                    num_attention_heads=num_attention_heads,
                    attention_head_dim=attention_head_dim,
                    time_embed_dim=time_embed_dim,
                    dropout=dropout,
                    activation_fn=activation_fn,
                    attention_bias=attention_bias,
                    norm_elementwise_affine=norm_elementwise_affine,
                    norm_eps=norm_eps,
                )
                for _ in range(num_layers)
            ]
        )

        self.gradient_checkpointing = False

    def _set_gradient_checkpointing(self, module, value=False):
        self.gradient_checkpointing = value

    @property
    # Copied from diffusers.models.unets.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, "get_processor"):
                processors[f"{name}.processor"] = module.get_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

    # Copied from diffusers.models.unets.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():
                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)

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

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedCogVideoXAttnProcessor2_0
    def fuse_qkv_projections(self):
        """
        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
        are fused. For cross-attention modules, key and value projection matrices are fused.

        <Tip warning={true}>

        This API is 🧪 experimental.

        </Tip>
        """
        self.original_attn_processors = None

        for _, attn_processor in self.attn_processors.items():
            if "Added" in str(attn_processor.__class__.__name__):
                raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")

        self.original_attn_processors = self.attn_processors

        for module in self.modules():
            if isinstance(module, Attention):
                module.fuse_projections(fuse=True)

        self.set_attn_processor(FusedCogVideoXAttnProcessor2_0())

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
    def unfuse_qkv_projections(self):
        """Disables the fused QKV projection if enabled.

        <Tip warning={true}>

        This API is 🧪 experimental.

        </Tip>

        """
        if self.original_attn_processors is not None:
            self.set_attn_processor(self.original_attn_processors)

    def forward(
            self,
            hidden_states: torch.Tensor,
            timestep: Union[int, float, torch.LongTensor],
            timestep_cond: Optional[torch.Tensor] = None,
            ofs: Optional[Union[int, float, torch.LongTensor]] = None,
            image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
            attention_kwargs: Optional[Dict[str, Any]] = None,
            return_dict: bool = True,
    ):
        pass
        # batch_size, num_frames, channels, height, width = hidden_states.shape
        #
        # # 1. Time embedding
        # timesteps = timestep
        # 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=hidden_states.dtype)
        # emb = self.time_embedding(t_emb, timestep_cond)
        #
        # if self.ofs_embedding is not None:
        #     ofs_emb = self.ofs_proj(ofs)
        #     ofs_emb = ofs_emb.to(dtype=hidden_states.dtype)
        #     ofs_emb = self.ofs_embedding(ofs_emb)
        #     emb = emb + ofs_emb
        #
        # # 2. Patch embedding
        # text_seq_length = encoder_hidden_states.shape[1]
        # hidden_states = self.patch_embed(encoder_hidden_states, hidden_states)
        # hidden_states = self.embedding_dropout(hidden_states)
        #
        # encoder_hidden_states = hidden_states[:, :text_seq_length]
        # hidden_states = hidden_states[:, text_seq_length:]
        #
        #
        # # 3. Transformer blocks
        # for i, block in enumerate(self.transformer_blocks):
        #     if self.camera_controller is not None and camera_embedding is not None and i == len(self.transformer_blocks)//2:
        #         hidden_states = self.camera_controller(
        #             camera_embedding, hidden_states, emb,
        #             gradient_checkpointing=self.gradient_checkpointing
        #         )
        #
        #     if torch.is_grad_enabled() and self.gradient_checkpointing:
        #
        #         def create_custom_forward(module):
        #             def custom_forward(*inputs):
        #                 return module(*inputs)
        #
        #             return custom_forward
        #
        #         ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
        #         hidden_states, encoder_hidden_states = torch.utils.checkpoint.checkpoint(
        #             create_custom_forward(block),
        #             hidden_states,
        #             encoder_hidden_states,
        #             emb,
        #             image_rotary_emb,
        #             **ckpt_kwargs,
        #         )
        #     else:
        #         hidden_states, encoder_hidden_states = block(
        #             hidden_states=hidden_states,
        #             encoder_hidden_states=encoder_hidden_states,
        #             temb=emb,
        #             image_rotary_emb=image_rotary_emb,
        #         )

        # return hidden_states