robomaster/models/transformer3d.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.

from typing import Any, Dict, Optional, Tuple, Union

import os
import json
import torch
import glob
import torch.nn.functional as F
from torch import nn

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.models.embeddings import CogVideoXPatchEmbed
from diffusers.utils import is_torch_version, logging
from diffusers.utils.torch_utils import maybe_allow_in_graph
from diffusers.models.attention import Attention, FeedForward
from diffusers.models.attention_processor import AttentionProcessor, CogVideoXAttnProcessor2_0, FusedCogVideoXAttnProcessor2_0
from diffusers.models.embeddings import TimestepEmbedding, Timesteps, get_3d_sincos_pos_embed, get_2d_sincos_pos_embed
from diffusers.models.modeling_outputs import Transformer2DModelOutput
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.normalization import AdaLayerNorm, CogVideoXLayerNormZero

from einops import rearrange

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

def zero_module(module):
    """
    Zero out the parameters of a module and return it.
    """
    for p in module.parameters():
        p.detach().zero_()
    return module

class FloatGroupNorm(nn.GroupNorm):
    def forward(self, x):
        return super().forward(x.to(self.bias.dtype)).type(x.dtype)
    
class CogVideoXPatchEmbed(nn.Module):
    def __init__(
        self,
        patch_size: int = 2,
        patch_size_t: Optional[int] = None,
        in_channels: int = 16,
        embed_dim: int = 1920,
        text_embed_dim: int = 4096,
        bias: bool = True,
        sample_width: int = 90,
        sample_height: int = 60,
        sample_frames: int = 49,
        temporal_compression_ratio: int = 4,
        max_text_seq_length: int = 226,
        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__()

        post_patch_height = sample_height // patch_size
        post_patch_width = sample_width // patch_size
        post_time_compression_frames = (sample_frames - 1) // temporal_compression_ratio + 1
        self.num_patches = post_patch_height * post_patch_width * post_time_compression_frames
        self.post_patch_height = post_patch_height
        self.post_patch_width = post_patch_width
        self.post_time_compression_frames = post_time_compression_frames
        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.max_text_seq_length = max_text_seq_length
        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)

        self.text_proj = nn.Linear(text_embed_dim, 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) -> 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,
        )
        pos_embedding = torch.from_numpy(pos_embedding).flatten(0, 1)
        joint_pos_embedding = torch.zeros(
            1, self.max_text_seq_length + num_patches, self.embed_dim, requires_grad=False
        )
        joint_pos_embedding.data[:, self.max_text_seq_length :].copy_(pos_embedding)

        return joint_pos_embedding

    def forward(self, text_embeds: torch.Tensor, image_embeds: torch.Tensor, flow_embeds: Optional[torch.Tensor] = None):
        r"""
        Args:
            text_embeds (`torch.Tensor`):
                Input text embeddings. Expected shape: (batch_size, seq_length, embedding_dim).
            image_embeds (`torch.Tensor`):
                Input image embeddings. Expected shape: (batch_size, num_frames, channels, height, width).
        """
        text_embeds = self.text_proj(text_embeds)

        text_batch_size, text_seq_length, text_channels = text_embeds.shape
        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)
            # b, f, h, w, c => b, f // 2, 2, h // 2, 2, w // 2, 2, c
            image_embeds = image_embeds.reshape(
                batch_size, num_frames // p_t, p_t, height // p, p, width // p, p, channels
            )
            # b, f // 2, 2, h // 2, 2, w // 2, 2, c => b, f // 2, h // 2, w // 2, c, 2, 2, 2
            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 = torch.cat(
            [text_embeds, image_embeds], dim=1
        ).contiguous()  # [batch, seq_length + num_frames x height x width, channels]

        if self.use_positional_embeddings or self.use_learned_positional_embeddings:
            seq_length = height * width * num_frames // (self.patch_size**2)
            # pos_embeds = self.pos_embedding[:, : text_seq_length + seq_length]
            pos_embeds = self.pos_embedding
            emb_size = embeds.size()[-1]
            pos_embeds_without_text = pos_embeds[:, text_seq_length: ].view(1, self.post_time_compression_frames, self.post_patch_height, self.post_patch_width, emb_size)
            pos_embeds_without_text = pos_embeds_without_text.permute([0, 4, 1, 2, 3])
            pos_embeds_without_text = F.interpolate(pos_embeds_without_text,size=[self.post_time_compression_frames, height // self.patch_size, width // self.patch_size], mode='trilinear', align_corners=False)
            pos_embeds_without_text = pos_embeds_without_text.permute([0, 2, 3, 4, 1]).view(1, -1, emb_size)
            pos_embeds = torch.cat([pos_embeds[:, :text_seq_length], pos_embeds_without_text], dim = 1)
            pos_embeds = pos_embeds[:, : text_seq_length + seq_length]
            embeds = embeds + pos_embeds

        if flow_embeds is not None:
            
            if self.patch_size_t is not None:
                _, _, flow_channels, _, _ = flow_embeds.shape

                flow_embeds = flow_embeds.permute(0, 1, 3, 4, 2)
                # b, f, h, w, c => b, f // 2, 2, h // 2, 2, w // 2, 2, c
                flow_embeds = flow_embeds.reshape(
                    batch_size, num_frames // p_t, p_t, height // p, p, width // p, p, flow_channels
                )
                # b, f // 2, 2, h // 2, 2, w // 2, 2, c => b, f // 2, h // 2, w // 2, c, 2, 2, 2
                flow_embeds = flow_embeds.permute(0, 1, 3, 5, 7, 2, 4, 6).flatten(4, 7)

        return embeds, flow_embeds


@maybe_allow_in_graph
class CogVideoXBlock(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,
        block_idx: int,
        dim: int,
        num_attention_heads: int,
        attention_head_dim: int,
        time_embed_dim: int,
        block_interval: int = 1,
        flow_in_dim: int = 128,
        out_dim: int = 3072,
        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,
        finetune_init: bool = False,
    ):
        super().__init__()


        # 1. Self Attention
        self.norm1 = CogVideoXLayerNormZero(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=CogVideoXAttnProcessor2_0(),
        )
        
        if not finetune_init and (block_idx%block_interval==0):
            self.flow_spatial = nn.Conv2d(flow_in_dim, out_dim // 4, 3, padding=1)
            self.flow_temporal = zero_module(
                    nn.Conv1d(
                    out_dim // 4,
                    out_dim,
                    kernel_size=3,
                    stride=1,
                    padding=1,
                    padding_mode="replicate",
                )
            )
            self.flow_cond_norm = FloatGroupNorm(32, out_dim)

        # 2. Feed Forward
        self.norm2 = CogVideoXLayerNormZero(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,
        encoder_hidden_states: torch.Tensor,
        flow_states: torch.Tensor,
        temb: torch.Tensor,
        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
    ) -> torch.Tensor:
        text_seq_length = encoder_hidden_states.size(1)

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

        # attention
        attn_hidden_states, attn_encoder_hidden_states = self.attn1(
            hidden_states=norm_hidden_states,
            encoder_hidden_states=norm_encoder_hidden_states,
            image_rotary_emb=image_rotary_emb,
        )
        
        hidden_states = hidden_states + gate_msa * attn_hidden_states
        encoder_hidden_states = encoder_hidden_states + enc_gate_msa * attn_encoder_hidden_states

        if hasattr(self, "flow_spatial") and flow_states is not None:
            
            bz, f, h, w, c = flow_states.shape

            flow_states = rearrange(flow_states, "bz f h w c -> (bz f) c h w")
            flow_states = self.flow_spatial(flow_states)  # [5, 128, 30, 40] -> [5, 960, 30, 40]

            flow_states = rearrange(flow_states, "(bz f) c h w -> (bz h w) c f", f=f)
            flow_states = self.flow_temporal(flow_states)  # [1200, 960, 5] -> [1200, 1920, 5]
            flow_states = rearrange(flow_states, "(bz h w) c f -> bz (f h w) c", f=f, h=h, w=w)
            
            norm_flow_states = self.flow_cond_norm(rearrange(flow_states, "bz (f h w) c -> (bz f) c h w", h=h, w=w))
            norm_flow_states = rearrange(norm_flow_states, "(bz f) c h w -> bz (f h w) c", f=f, h=h, w=w)

            hidden_states = hidden_states + norm_flow_states * flow_states

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

        # feed-forward
        norm_hidden_states = torch.cat([norm_encoder_hidden_states, norm_hidden_states], dim=1)
        ff_output = self.ff(norm_hidden_states)

        hidden_states = hidden_states + gate_ff * ff_output[:, text_seq_length:]
        encoder_hidden_states = encoder_hidden_states + enc_gate_ff * ff_output[:, :text_seq_length]

        return hidden_states, encoder_hidden_states


class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin):
    """
    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.
        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 or not 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 or not 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,
        out_channels: Optional[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,
        add_noise_in_inpaint_model: bool = False,
        finetune_init: bool = False,
    ):
        super().__init__()
        inner_dim = num_attention_heads * attention_head_dim
        self.patch_size_t = patch_size_t
        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 = CogVideoXPatchEmbed(
            patch_size=patch_size,
            patch_size_t=patch_size_t,
            in_channels=in_channels,
            embed_dim=inner_dim,
            text_embed_dim=text_embed_dim,
            bias=patch_bias,
            sample_width=sample_width,
            sample_height=sample_height,
            sample_frames=sample_frames,
            temporal_compression_ratio=temporal_compression_ratio,
            max_text_seq_length=max_text_seq_length,
            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
        self.time_proj = Timesteps(inner_dim, flip_sin_to_cos, freq_shift)
        self.time_embedding = TimestepEmbedding(inner_dim, time_embed_dim, timestep_activation_fn)

        # 3. Define spatio-temporal transformers blocks
        self.transformer_blocks = nn.ModuleList(
            [
                CogVideoXBlock(
                    block_idx=idx,
                    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,
                    finetune_init=finetune_init,
                )
                for idx in range(num_layers)
            ]
        )
        self.norm_final = nn.LayerNorm(inner_dim, norm_eps, norm_elementwise_affine)

        # 4. Output blocks
        self.norm_out = AdaLayerNorm(
            embedding_dim=time_embed_dim,
            output_dim=2 * inner_dim,
            norm_elementwise_affine=norm_elementwise_affine,
            norm_eps=norm_eps,
            chunk_dim=1,
        )

        if patch_size_t is None:
            # For CogVideox 1.0
            output_dim = patch_size * patch_size * out_channels
        else:
            # For CogVideoX 1.5
            output_dim = patch_size * patch_size * patch_size_t * out_channels

        self.proj_out = nn.Linear(inner_dim, output_dim)

        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,
        encoder_hidden_states: torch.Tensor,
        timestep: Union[int, float, torch.LongTensor],
        timestep_cond: Optional[torch.Tensor] = None,
        inpaint_latents: Optional[torch.Tensor] = None,
        flow_latents: Optional[torch.Tensor] = None,
        control_latents: Optional[torch.Tensor] = None,
        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
        return_dict: bool = True,
    ):

        batch_size, num_frames, channels, height, width = hidden_states.shape
        if num_frames == 1 and self.patch_size_t is not None:
            hidden_states = torch.cat([hidden_states, torch.zeros_like(hidden_states)], dim=1)
            if inpaint_latents is not None:
                inpaint_latents = torch.concat([inpaint_latents, torch.zeros_like(inpaint_latents)], dim=1)
            if control_latents is not None:
                control_latents = torch.concat([control_latents, torch.zeros_like(control_latents)], dim=1)
            local_num_frames = num_frames + 1
        else:
            local_num_frames = num_frames

        # 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)

        # 2. Patch embedding
        if inpaint_latents is not None:
            hidden_states = torch.concat([hidden_states, inpaint_latents], 2)
        if control_latents is not None:
            hidden_states = torch.concat([hidden_states, control_latents], 2)

        hidden_states, flow_states = self.patch_embed(encoder_hidden_states, hidden_states, flow_latents)
        hidden_states = self.embedding_dropout(hidden_states)

        text_seq_length = encoder_hidden_states.shape[1]
        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.training 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,
                    flow_states,
                    emb,
                    image_rotary_emb,
                    **ckpt_kwargs,
                )
            else:
                hidden_states, encoder_hidden_states = block(
                    hidden_states=hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    flow_states=flow_states,
                    temb=emb,
                    image_rotary_emb=image_rotary_emb,
                )

        if not self.config.use_rotary_positional_embeddings:
            # CogVideoX-2B
            hidden_states = self.norm_final(hidden_states)
        else:
            # CogVideoX-5B
            hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
            hidden_states = self.norm_final(hidden_states)
            hidden_states = hidden_states[:, text_seq_length:]

        # 4. Final block
        hidden_states = self.norm_out(hidden_states, temb=emb)
        hidden_states = self.proj_out(hidden_states)

        # 5. Unpatchify
        p = self.config.patch_size
        p_t = self.config.patch_size_t

        if p_t is None:
            output = hidden_states.reshape(batch_size, local_num_frames, height // p, width // p, -1, p, p)
            output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4)
        else:
            output = hidden_states.reshape(
                batch_size, (local_num_frames + p_t - 1) // p_t, height // p, width // p, -1, p_t, p, p
            )
            output = output.permute(0, 1, 5, 4, 2, 6, 3, 7).flatten(6, 7).flatten(4, 5).flatten(1, 2)
        
        if num_frames == 1:
            output = output[:, :num_frames, :]

        if not return_dict:
            return (output,)
        return Transformer2DModelOutput(sample=output)

    @classmethod
    def from_pretrained_2d(
        cls, pretrained_model_path, subfolder=None, transformer_additional_kwargs={},
        finetune_init=False, low_cpu_mem_usage=False, torch_dtype=torch.bfloat16
    ):
        if subfolder is not None:
            pretrained_model_path = os.path.join(pretrained_model_path, subfolder)
        print(f"loaded 3D transformer's pretrained weights from {pretrained_model_path} ...")

        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['finetune_init'] = finetune_init

        from diffusers.utils import WEIGHTS_NAME
        model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)
        model_file_safetensors = model_file.replace(".bin", ".safetensors")

        if low_cpu_mem_usage:
            try:
                import re
                from diffusers.utils import is_accelerate_available
                from diffusers.models.modeling_utils import load_model_dict_into_meta
                if is_accelerate_available():
                    import accelerate
                
                # Instantiate model with empty weights
                with accelerate.init_empty_weights():
                    model = cls.from_config(config, **transformer_additional_kwargs)

                param_device = "cpu"
                if os.path.exists(model_file):
                    state_dict = torch.load(model_file, map_location="cpu")
                elif os.path.exists(model_file_safetensors):
                    from safetensors.torch import load_file, safe_open
                    state_dict = load_file(model_file_safetensors)
                else:
                    from safetensors.torch import load_file, safe_open
                    model_files_safetensors = glob.glob(os.path.join(pretrained_model_path, "*.safetensors"))
                    state_dict = {}
                    for _model_file_safetensors in model_files_safetensors:
                        _state_dict = load_file(_model_file_safetensors)
                        for key in _state_dict:
                            state_dict[key] = _state_dict[key]
                model._convert_deprecated_attention_blocks(state_dict)
                # move the params from meta device to cpu
                missing_keys = set(model.state_dict().keys()) - set(state_dict.keys())
                if len(missing_keys) > 0:
                    raise ValueError(
                        f"Cannot load {cls} from {pretrained_model_path} because the following keys are"
                        f" missing: \n {', '.join(missing_keys)}. \n Please make sure to pass"
                        " `low_cpu_mem_usage=False` and `device_map=None` if you want to randomly initialize"
                        " those weights or else make sure your checkpoint file is correct."
                    )

                unexpected_keys = load_model_dict_into_meta(
                    model,
                    state_dict,
                    device=param_device,
                    dtype=torch_dtype,
                    model_name_or_path=pretrained_model_path,
                )

                if cls._keys_to_ignore_on_load_unexpected is not None:
                    for pat in cls._keys_to_ignore_on_load_unexpected:
                        unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]

                if len(unexpected_keys) > 0:
                    print(
                        f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}"
                    )
                return model
            except Exception as e:
                print(
                    f"The low_cpu_mem_usage mode is not work because {e}. Use low_cpu_mem_usage=False instead."
                )

        model = cls.from_config(config, **transformer_additional_kwargs)
        if os.path.exists(model_file):
            state_dict = torch.load(model_file, map_location="cpu")
        elif os.path.exists(model_file_safetensors):
            from safetensors.torch import load_file, safe_open
            state_dict = load_file(model_file_safetensors)
        else:
            from safetensors.torch import load_file, safe_open
            model_files_safetensors = glob.glob(os.path.join(pretrained_model_path, "*.safetensors"))
            state_dict = {}
            for _model_file_safetensors in model_files_safetensors:
                _state_dict = load_file(_model_file_safetensors)
                for key in _state_dict:
                    state_dict[key] = _state_dict[key]
        
        if model.state_dict()['patch_embed.proj.weight'].size() != state_dict['patch_embed.proj.weight'].size():
            new_shape   = model.state_dict()['patch_embed.proj.weight'].size()
            if len(new_shape) == 5:
                state_dict['patch_embed.proj.weight'] = state_dict['patch_embed.proj.weight'].unsqueeze(2).expand(new_shape).clone()
                state_dict['patch_embed.proj.weight'][:, :, :-1] = 0
            elif len(new_shape) == 2:
                if model.state_dict()['patch_embed.proj.weight'].size()[1] > state_dict['patch_embed.proj.weight'].size()[1]:
                    model.state_dict()['patch_embed.proj.weight'][:, :state_dict['patch_embed.proj.weight'].size()[1]] = state_dict['patch_embed.proj.weight']
                    model.state_dict()['patch_embed.proj.weight'][:, state_dict['patch_embed.proj.weight'].size()[1]:] = 0
                    state_dict['patch_embed.proj.weight'] = model.state_dict()['patch_embed.proj.weight']
                else:
                    model.state_dict()['patch_embed.proj.weight'][:, :] = state_dict['patch_embed.proj.weight'][:, :model.state_dict()['patch_embed.proj.weight'].size()[1]]
                    state_dict['patch_embed.proj.weight'] = model.state_dict()['patch_embed.proj.weight']
            else:
                if model.state_dict()['patch_embed.proj.weight'].size()[1] > state_dict['patch_embed.proj.weight'].size()[1]:
                    model.state_dict()['patch_embed.proj.weight'][:, :state_dict['patch_embed.proj.weight'].size()[1], :, :] = state_dict['patch_embed.proj.weight']
                    model.state_dict()['patch_embed.proj.weight'][:, state_dict['patch_embed.proj.weight'].size()[1]:, :, :] = 0
                    state_dict['patch_embed.proj.weight'] = model.state_dict()['patch_embed.proj.weight']
                else:
                    model.state_dict()['patch_embed.proj.weight'][:, :, :, :] = state_dict['patch_embed.proj.weight'][:, :model.state_dict()['patch_embed.proj.weight'].size()[1], :, :]
                    state_dict['patch_embed.proj.weight'] = model.state_dict()['patch_embed.proj.weight']

        tmp_state_dict = {} 
        for key in state_dict:
            if key in model.state_dict().keys() and model.state_dict()[key].size() == state_dict[key].size():
                tmp_state_dict[key] = state_dict[key]
            else:
                import ipdb; ipdb.set_trace()
                print(key, "Size don't match, skip")
                
        state_dict = tmp_state_dict

        m, u = model.load_state_dict(state_dict, strict=False)
        print(f"### missing keys: {len(m)}; \n### unexpected keys: {len(u)};")
        print(m)
        
        params = [p.numel() if "." in n else 0 for n, p in model.named_parameters()]
        print(f"### All Parameters: {sum(params) / 1e6} M")
        
        model = model.to(torch_dtype)
        return model