custom_cogvideox_transformer_3d.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 torch
from torch import nn
import torch.nn.functional as F

import numpy as np
from einops import rearrange

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils import 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
from diffusers.models.embeddings import CogVideoXPatchEmbed, TimestepEmbedding, Timesteps
from diffusers.models.modeling_outputs import Transformer2DModelOutput
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.normalization import AdaLayerNorm, CogVideoXLayerNormZero


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

try:
    from sageattention import sageattn
    SAGEATTN_IS_AVAILABLE = True
    logger.info("Using sageattn")
except:
    logger.info("sageattn not found, using sdpa")
    SAGEATTN_IS_AVAILABLE = False

def fft(tensor):
    tensor_fft = torch.fft.fft2(tensor)
    tensor_fft_shifted = torch.fft.fftshift(tensor_fft)
    B, C, H, W = tensor.size()
    radius = min(H, W) // 5
            
    Y, X = torch.meshgrid(torch.arange(H), torch.arange(W))
    center_x, center_y = W // 2, H // 2
    mask = (X - center_x) ** 2 + (Y - center_y) ** 2 <= radius ** 2
    low_freq_mask = mask.unsqueeze(0).unsqueeze(0).to(tensor.device)
    high_freq_mask = ~low_freq_mask
            
    low_freq_fft = tensor_fft_shifted * low_freq_mask
    high_freq_fft = tensor_fft_shifted * high_freq_mask

    return low_freq_fft, high_freq_fft

class CogVideoXAttnProcessor2_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.")
    @torch.compiler.disable()
    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.Tensor,
        encoder_hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        image_rotary_emb: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        text_seq_length = encoder_hidden_states.size(1)

        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_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)
        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[:, :, text_seq_length:] = apply_rotary_emb(query[:, :, text_seq_length:], image_rotary_emb)
            if not attn.is_cross_attention:
                key[:, :, text_seq_length:] = apply_rotary_emb(key[:, :, text_seq_length:], image_rotary_emb)

        if SAGEATTN_IS_AVAILABLE:
            hidden_states = sageattn(query, key, value, is_causal=False)
        else:
            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)

        encoder_hidden_states, hidden_states = hidden_states.split(
            [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1
        )
        return hidden_states, encoder_hidden_states


class FusedCogVideoXAttnProcessor2_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.")
    @torch.compiler.disable()
    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.Tensor,
        encoder_hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        image_rotary_emb: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        text_seq_length = encoder_hidden_states.size(1)

        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_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])

        qkv = attn.to_qkv(hidden_states)
        split_size = qkv.shape[-1] // 3
        query, key, value = torch.split(qkv, split_size, dim=-1)

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

        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        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[:, :, text_seq_length:] = apply_rotary_emb(query[:, :, text_seq_length:], image_rotary_emb)
            if not attn.is_cross_attention:
                key[:, :, text_seq_length:] = apply_rotary_emb(key[:, :, text_seq_length:], image_rotary_emb)

        if SAGEATTN_IS_AVAILABLE:
            hidden_states = sageattn(query, key, value, is_causal=False)
        else:
            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)

        encoder_hidden_states, hidden_states = hidden_states.split(
            [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1
        )
        return hidden_states, encoder_hidden_states
    
@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,
        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 = 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(),
        )

        # 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,
        )
        self.cached_hidden_states = []
        self.cached_encoder_hidden_states = []
        
    def forward(
        self,
        hidden_states: torch.Tensor,
        encoder_hidden_states: torch.Tensor,
        temb: torch.Tensor,
        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
        video_flow_feature: Optional[torch.Tensor] = None,
        fuser=None,
        fastercache_counter=0,
        fastercache_start_step=15,
        fastercache_device="cuda:0",
    ) -> 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
        )
        # Tora Motion-guidance Fuser
        if video_flow_feature is not None:
            H, W = video_flow_feature.shape[-2:]
            T = norm_hidden_states.shape[1] // H // W
            h = rearrange(norm_hidden_states, "B (T H W) C -> (B T) C H W", H=H, W=W)
            h = fuser(h, video_flow_feature.to(h), T=T)
            norm_hidden_states = rearrange(h, "(B T) C H W ->  B (T H W) C", T=T)
            del h, fuser        
        
        #fastercache
        B = norm_hidden_states.shape[0]
        if fastercache_counter >= fastercache_start_step + 3 and fastercache_counter%3!=0 and self.cached_hidden_states[-1].shape[0] >= B:
            attn_hidden_states = (
                self.cached_hidden_states[1][:B] + 
                (self.cached_hidden_states[1][:B] - self.cached_hidden_states[0][:B]) 
                * 0.3
                ).to(norm_hidden_states.device, non_blocking=True)
            attn_encoder_hidden_states = (
                self.cached_encoder_hidden_states[1][:B] + 
                (self.cached_encoder_hidden_states[1][:B] - self.cached_encoder_hidden_states[0][:B])
                 * 0.3
                ).to(norm_hidden_states.device, non_blocking=True)
        else:
            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,
            )
            if fastercache_counter == fastercache_start_step:
                self.cached_hidden_states = [attn_hidden_states.to(fastercache_device), attn_hidden_states.to(fastercache_device)]
                self.cached_encoder_hidden_states = [attn_encoder_hidden_states.to(fastercache_device), attn_encoder_hidden_states.to(fastercache_device)]
            elif fastercache_counter > fastercache_start_step:
                self.cached_hidden_states[-1].copy_(attn_hidden_states.to(fastercache_device))
                self.cached_encoder_hidden_states[-1].copy_(attn_encoder_hidden_states.to(fastercache_device))

        hidden_states = hidden_states + gate_msa * attn_hidden_states
        encoder_hidden_states = encoder_hidden_states + enc_gate_msa * attn_encoder_hidden_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,
        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,
    ):
        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 = CogVideoXPatchEmbed(
            patch_size=patch_size,
            in_channels=in_channels,
            embed_dim=inner_dim,
            text_embed_dim=text_embed_dim,
            bias=True,
            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(
                    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.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,
        )
        self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * out_channels)

        self.gradient_checkpointing = False

        self.fuser_list = None
        self.use_fastercache = False
        self.fastercache_counter = 0
        self.fastercache_start_step = 15
        self.fastercache_lf_step = 40
        self.fastercache_hf_step = 30
        self.fastercache_device = "cuda"

    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,
        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
        controlnet_states: torch.Tensor = None,
        controlnet_weights: Optional[Union[float, int, list, np.ndarray, torch.FloatTensor]] = 1.0,
        video_flow_features: Optional[torch.Tensor] = None,
        return_dict: bool = True,
    ):
        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)

        # 2. Patch embedding
        hidden_states = self.patch_embed(encoder_hidden_states, hidden_states)
        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:]
        if self.use_fastercache:
            self.fastercache_counter+=1
        if self.fastercache_counter >= self.fastercache_start_step + 3 and self.fastercache_counter % 5 !=0:
            # 3. Transformer blocks
            for i, block in enumerate(self.transformer_blocks):
                    hidden_states, encoder_hidden_states = block(
                        hidden_states=hidden_states[:1],
                        encoder_hidden_states=encoder_hidden_states[:1],
                        temb=emb[:1],
                        image_rotary_emb=image_rotary_emb,
                        video_flow_feature=video_flow_features[i][:1] if video_flow_features is not None else None,
                        fuser = self.fuser_list[i] if self.fuser_list is not None else None,
                        fastercache_counter = self.fastercache_counter,
                        fastercache_device = self.fastercache_device
                    )

                    if (controlnet_states is not None) and (i < len(controlnet_states)):
                        controlnet_states_block = controlnet_states[i]
                        controlnet_block_weight = 1.0
                        if isinstance(controlnet_weights, (list, np.ndarray)) or torch.is_tensor(controlnet_weights):
                            controlnet_block_weight = controlnet_weights[i]
                        elif isinstance(controlnet_weights, (float, int)):
                            controlnet_block_weight = controlnet_weights
                        
                        hidden_states = hidden_states + controlnet_states_block * controlnet_block_weight
                    
            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[:1])
            hidden_states = self.proj_out(hidden_states)

            # 5. Unpatchify
            # Note: we use `-1` instead of `channels`:
            #   - It is okay to `channels` use for CogVideoX-2b and CogVideoX-5b (number of input channels is equal to output channels)
            #   - However, for CogVideoX-5b-I2V also takes concatenated input image latents (number of input channels is twice the output channels)
            p = self.config.patch_size
            output = hidden_states.reshape(1, 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)
            
            (bb, tt, cc, hh, ww) = output.shape
            cond = rearrange(output, "B T C H W -> (B T) C H W", B=bb, C=cc, T=tt, H=hh, W=ww)
            lf_c, hf_c = fft(cond.float())
            #lf_step = 40
            #hf_step = 30
            if self.fastercache_counter <= self.fastercache_lf_step:
                self.delta_lf = self.delta_lf * 1.1
            if self.fastercache_counter >= self.fastercache_hf_step:
                self.delta_hf = self.delta_hf * 1.1
   
            new_hf_uc = self.delta_hf + hf_c
            new_lf_uc = self.delta_lf + lf_c

            combine_uc = new_lf_uc + new_hf_uc
            combined_fft = torch.fft.ifftshift(combine_uc)
            recovered_uncond = torch.fft.ifft2(combined_fft).real
            recovered_uncond = rearrange(recovered_uncond.to(output.dtype), "(B T) C H W -> B T C H W", B=bb, C=cc, T=tt, H=hh, W=ww)
            output = torch.cat([output, recovered_uncond])
        else:
            for i, block in enumerate(self.transformer_blocks):
                hidden_states, encoder_hidden_states = block(
                    hidden_states=hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    temb=emb,
                    image_rotary_emb=image_rotary_emb,
                    video_flow_feature=video_flow_features[i] if video_flow_features is not None else None,
                    fuser = self.fuser_list[i] if self.fuser_list is not None else None,
                    fastercache_counter = self.fastercache_counter,
                    fastercache_device = self.fastercache_device
                )

            if (controlnet_states is not None) and (i < len(controlnet_states)):
                controlnet_states_block = controlnet_states[i]
                controlnet_block_weight = 1.0
                if isinstance(controlnet_weights, (list, np.ndarray)) or torch.is_tensor(controlnet_weights):
                    controlnet_block_weight = controlnet_weights[i]
                elif isinstance(controlnet_weights, (float, int)):
                    controlnet_block_weight = controlnet_weights
                
                hidden_states = hidden_states + controlnet_states_block * controlnet_block_weight
                    
            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
            # Note: we use `-1` instead of `channels`:
            #   - It is okay to `channels` use for CogVideoX-2b and CogVideoX-5b (number of input channels is equal to output channels)
            #   - However, for CogVideoX-5b-I2V also takes concatenated input image latents (number of input channels is twice the output channels)
            p = self.config.patch_size
            output = hidden_states.reshape(batch_size, 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)

            if self.fastercache_counter >= self.fastercache_start_step + 1: 
                (bb, tt, cc, hh, ww) = output.shape
                cond = rearrange(output[0:1].float(), "B T C H W -> (B T) C H W", B=bb//2, C=cc, T=tt, H=hh, W=ww)
                uncond = rearrange(output[1:2].float(), "B T C H W -> (B T) C H W", B=bb//2, C=cc, T=tt, H=hh, W=ww)

                lf_c, hf_c = fft(cond)
                lf_uc, hf_uc = fft(uncond)

                self.delta_lf = lf_uc - lf_c
                self.delta_hf = hf_uc - hf_c

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