embeddings.py

import torch
import torch.nn as nn
import numpy as np
from typing import Tuple, Union, Optional
from diffusers.models.embeddings import get_3d_sincos_pos_embed, get_1d_rotary_pos_embed


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__()

        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):
        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)

        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 = 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:
            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)
                pos_embedding = pos_embedding.to(embeds.device, dtype=embeds.dtype)
            else:
                pos_embedding = self.pos_embedding

            embeds = embeds + pos_embedding

        return embeds

def get_3d_rotary_pos_embed(
    embed_dim,
    crops_coords,
    grid_size,
    temporal_size,
    theta: int = 10000,
    use_real: bool = True,
    grid_type: str = "linspace",
    max_size: Optional[Tuple[int, int]] = None,
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
    """
    RoPE for video tokens with 3D structure.

    Args:
    embed_dim: (`int`):
        The embedding dimension size, corresponding to hidden_size_head.
    crops_coords (`Tuple[int]`):
        The top-left and bottom-right coordinates of the crop.
    grid_size (`Tuple[int]`):
        The grid size of the spatial positional embedding (height, width).
    temporal_size (`int`):
        The size of the temporal dimension.
    theta (`float`):
        Scaling factor for frequency computation.
    grid_type (`str`):
        Whether to use "linspace" or "slice" to compute grids.

    Returns:
        `torch.Tensor`: positional embedding with shape `(temporal_size * grid_size[0] * grid_size[1], embed_dim/2)`.
    """
    if use_real is not True:
        raise ValueError(" `use_real = False` is not currently supported for get_3d_rotary_pos_embed")

    if grid_type == "linspace":
        start, stop = crops_coords
        grid_size_h, grid_size_w = grid_size
        grid_h = np.linspace(start[0], stop[0], grid_size_h, endpoint=False, dtype=np.float32)
        grid_w = np.linspace(start[1], stop[1], grid_size_w, endpoint=False, dtype=np.float32)
        grid_t = np.arange(temporal_size, dtype=np.float32)
        grid_t = np.linspace(0, temporal_size, temporal_size, endpoint=False, dtype=np.float32)
    elif grid_type == "slice":
        max_h, max_w = max_size
        grid_size_h, grid_size_w = grid_size
        grid_h = np.arange(max_h, dtype=np.float32)
        grid_w = np.arange(max_w, dtype=np.float32)
        grid_t = np.arange(temporal_size, dtype=np.float32)
    else:
        raise ValueError("Invalid value passed for `grid_type`.")

    # Compute dimensions for each axis
    dim_t = embed_dim // 4
    dim_h = embed_dim // 8 * 3
    dim_w = embed_dim // 8 * 3

    # Temporal frequencies
    freqs_t = get_1d_rotary_pos_embed(dim_t, grid_t, use_real=True)
    # Spatial frequencies for height and width
    freqs_h = get_1d_rotary_pos_embed(dim_h, grid_h, use_real=True)
    freqs_w = get_1d_rotary_pos_embed(dim_w, grid_w, use_real=True)

    # BroadCast and concatenate temporal and spaial frequencie (height and width) into a 3d tensor
    def combine_time_height_width(freqs_t, freqs_h, freqs_w):
        freqs_t = freqs_t[:, None, None, :].expand(
            -1, grid_size_h, grid_size_w, -1
        )  # temporal_size, grid_size_h, grid_size_w, dim_t
        freqs_h = freqs_h[None, :, None, :].expand(
            temporal_size, -1, grid_size_w, -1
        )  # temporal_size, grid_size_h, grid_size_2, dim_h
        freqs_w = freqs_w[None, None, :, :].expand(
            temporal_size, grid_size_h, -1, -1
        )  # temporal_size, grid_size_h, grid_size_2, dim_w

        freqs = torch.cat(
            [freqs_t, freqs_h, freqs_w], dim=-1
        )  # temporal_size, grid_size_h, grid_size_w, (dim_t + dim_h + dim_w)
        freqs = freqs.view(
            temporal_size * grid_size_h * grid_size_w, -1
        )  # (temporal_size * grid_size_h * grid_size_w), (dim_t + dim_h + dim_w)
        return freqs

    t_cos, t_sin = freqs_t  # both t_cos and t_sin has shape: temporal_size, dim_t
    h_cos, h_sin = freqs_h  # both h_cos and h_sin has shape: grid_size_h, dim_h
    w_cos, w_sin = freqs_w  # both w_cos and w_sin has shape: grid_size_w, dim_w

    if grid_type == "slice":
        t_cos, t_sin = t_cos[:temporal_size], t_sin[:temporal_size]
        h_cos, h_sin = h_cos[:grid_size_h], h_sin[:grid_size_h]
        w_cos, w_sin = w_cos[:grid_size_w], w_sin[:grid_size_w]

    cos = combine_time_height_width(t_cos, h_cos, w_cos)
    sin = combine_time_height_width(t_sin, h_sin, w_sin)
    return cos, sin