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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 |