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2623 lines (2152 loc) · 102 KB
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# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import deprecate
from .activations import FP32SiLU, get_activation
from .attention_processor import Attention
def get_timestep_embedding(
timesteps: torch.Tensor,
embedding_dim: int,
flip_sin_to_cos: bool = False,
downscale_freq_shift: float = 1,
scale: float = 1,
max_period: int = 10000,
):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
Args
timesteps (torch.Tensor):
a 1-D Tensor of N indices, one per batch element. These may be fractional.
embedding_dim (int):
the dimension of the output.
flip_sin_to_cos (bool):
Whether the embedding order should be `cos, sin` (if True) or `sin, cos` (if False)
downscale_freq_shift (float):
Controls the delta between frequencies between dimensions
scale (float):
Scaling factor applied to the embeddings.
max_period (int):
Controls the maximum frequency of the embeddings
Returns
torch.Tensor: an [N x dim] Tensor of positional embeddings.
"""
assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
half_dim = embedding_dim // 2
exponent = -math.log(max_period) * torch.arange(
start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
)
exponent = exponent / (half_dim - downscale_freq_shift)
emb = torch.exp(exponent)
emb = timesteps[:, None].float() * emb[None, :]
# scale embeddings
emb = scale * emb
# concat sine and cosine embeddings
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
# flip sine and cosine embeddings
if flip_sin_to_cos:
emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
# zero pad
if embedding_dim % 2 == 1:
emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
return emb
def get_3d_sincos_pos_embed(
embed_dim: int,
spatial_size: Union[int, Tuple[int, int]],
temporal_size: int,
spatial_interpolation_scale: float = 1.0,
temporal_interpolation_scale: float = 1.0,
device: Optional[torch.device] = None,
output_type: str = "np",
) -> torch.Tensor:
r"""
Creates 3D sinusoidal positional embeddings.
Args:
embed_dim (`int`):
The embedding dimension of inputs. It must be divisible by 16.
spatial_size (`int` or `Tuple[int, int]`):
The spatial dimension of positional embeddings. If an integer is provided, the same size is applied to both
spatial dimensions (height and width).
temporal_size (`int`):
The temporal dimension of postional embeddings (number of frames).
spatial_interpolation_scale (`float`, defaults to 1.0):
Scale factor for spatial grid interpolation.
temporal_interpolation_scale (`float`, defaults to 1.0):
Scale factor for temporal grid interpolation.
Returns:
`torch.Tensor`:
The 3D sinusoidal positional embeddings of shape `[temporal_size, spatial_size[0] * spatial_size[1],
embed_dim]`.
"""
if output_type == "np":
return _get_3d_sincos_pos_embed_np(
embed_dim=embed_dim,
spatial_size=spatial_size,
temporal_size=temporal_size,
spatial_interpolation_scale=spatial_interpolation_scale,
temporal_interpolation_scale=temporal_interpolation_scale,
)
if embed_dim % 4 != 0:
raise ValueError("`embed_dim` must be divisible by 4")
if isinstance(spatial_size, int):
spatial_size = (spatial_size, spatial_size)
embed_dim_spatial = 3 * embed_dim // 4
embed_dim_temporal = embed_dim // 4
# 1. Spatial
grid_h = torch.arange(spatial_size[1], device=device, dtype=torch.float32) / spatial_interpolation_scale
grid_w = torch.arange(spatial_size[0], device=device, dtype=torch.float32) / spatial_interpolation_scale
grid = torch.meshgrid(grid_w, grid_h, indexing="xy") # here w goes first
grid = torch.stack(grid, dim=0)
grid = grid.reshape([2, 1, spatial_size[1], spatial_size[0]])
pos_embed_spatial = get_2d_sincos_pos_embed_from_grid(embed_dim_spatial, grid, output_type="pt")
# 2. Temporal
grid_t = torch.arange(temporal_size, device=device, dtype=torch.float32) / temporal_interpolation_scale
pos_embed_temporal = get_1d_sincos_pos_embed_from_grid(embed_dim_temporal, grid_t, output_type="pt")
# 3. Concat
pos_embed_spatial = pos_embed_spatial[None, :, :]
pos_embed_spatial = pos_embed_spatial.repeat_interleave(
temporal_size, dim=0, output_size=pos_embed_spatial.shape[0] * temporal_size
) # [T, H*W, D // 4 * 3]
pos_embed_temporal = pos_embed_temporal[:, None, :]
pos_embed_temporal = pos_embed_temporal.repeat_interleave(
spatial_size[0] * spatial_size[1], dim=1
) # [T, H*W, D // 4]
pos_embed = torch.concat([pos_embed_temporal, pos_embed_spatial], dim=-1) # [T, H*W, D]
return pos_embed
def _get_3d_sincos_pos_embed_np(
embed_dim: int,
spatial_size: Union[int, Tuple[int, int]],
temporal_size: int,
spatial_interpolation_scale: float = 1.0,
temporal_interpolation_scale: float = 1.0,
) -> np.ndarray:
r"""
Creates 3D sinusoidal positional embeddings.
Args:
embed_dim (`int`):
The embedding dimension of inputs. It must be divisible by 16.
spatial_size (`int` or `Tuple[int, int]`):
The spatial dimension of positional embeddings. If an integer is provided, the same size is applied to both
spatial dimensions (height and width).
temporal_size (`int`):
The temporal dimension of postional embeddings (number of frames).
spatial_interpolation_scale (`float`, defaults to 1.0):
Scale factor for spatial grid interpolation.
temporal_interpolation_scale (`float`, defaults to 1.0):
Scale factor for temporal grid interpolation.
Returns:
`np.ndarray`:
The 3D sinusoidal positional embeddings of shape `[temporal_size, spatial_size[0] * spatial_size[1],
embed_dim]`.
"""
deprecation_message = (
"`get_3d_sincos_pos_embed` uses `torch` and supports `device`."
" `from_numpy` is no longer required."
" Pass `output_type='pt' to use the new version now."
)
deprecate("output_type=='np'", "0.33.0", deprecation_message, standard_warn=False)
if embed_dim % 4 != 0:
raise ValueError("`embed_dim` must be divisible by 4")
if isinstance(spatial_size, int):
spatial_size = (spatial_size, spatial_size)
embed_dim_spatial = 3 * embed_dim // 4
embed_dim_temporal = embed_dim // 4
# 1. Spatial
grid_h = np.arange(spatial_size[1], dtype=np.float32) / spatial_interpolation_scale
grid_w = np.arange(spatial_size[0], dtype=np.float32) / spatial_interpolation_scale
grid = np.meshgrid(grid_w, grid_h) # here w goes first
grid = np.stack(grid, axis=0)
grid = grid.reshape([2, 1, spatial_size[1], spatial_size[0]])
pos_embed_spatial = get_2d_sincos_pos_embed_from_grid(embed_dim_spatial, grid)
# 2. Temporal
grid_t = np.arange(temporal_size, dtype=np.float32) / temporal_interpolation_scale
pos_embed_temporal = get_1d_sincos_pos_embed_from_grid(embed_dim_temporal, grid_t)
# 3. Concat
pos_embed_spatial = pos_embed_spatial[np.newaxis, :, :]
pos_embed_spatial = np.repeat(pos_embed_spatial, temporal_size, axis=0) # [T, H*W, D // 4 * 3]
pos_embed_temporal = pos_embed_temporal[:, np.newaxis, :]
pos_embed_temporal = np.repeat(pos_embed_temporal, spatial_size[0] * spatial_size[1], axis=1) # [T, H*W, D // 4]
pos_embed = np.concatenate([pos_embed_temporal, pos_embed_spatial], axis=-1) # [T, H*W, D]
return pos_embed
def get_2d_sincos_pos_embed(
embed_dim,
grid_size,
cls_token=False,
extra_tokens=0,
interpolation_scale=1.0,
base_size=16,
device: Optional[torch.device] = None,
output_type: str = "np",
):
"""
Creates 2D sinusoidal positional embeddings.
Args:
embed_dim (`int`):
The embedding dimension.
grid_size (`int`):
The size of the grid height and width.
cls_token (`bool`, defaults to `False`):
Whether or not to add a classification token.
extra_tokens (`int`, defaults to `0`):
The number of extra tokens to add.
interpolation_scale (`float`, defaults to `1.0`):
The scale of the interpolation.
Returns:
pos_embed (`torch.Tensor`):
Shape is either `[grid_size * grid_size, embed_dim]` if not using cls_token, or `[1 + grid_size*grid_size,
embed_dim]` if using cls_token
"""
if output_type == "np":
deprecation_message = (
"`get_2d_sincos_pos_embed` uses `torch` and supports `device`."
" `from_numpy` is no longer required."
" Pass `output_type='pt' to use the new version now."
)
deprecate("output_type=='np'", "0.33.0", deprecation_message, standard_warn=False)
return get_2d_sincos_pos_embed_np(
embed_dim=embed_dim,
grid_size=grid_size,
cls_token=cls_token,
extra_tokens=extra_tokens,
interpolation_scale=interpolation_scale,
base_size=base_size,
)
if isinstance(grid_size, int):
grid_size = (grid_size, grid_size)
grid_h = (
torch.arange(grid_size[0], device=device, dtype=torch.float32)
/ (grid_size[0] / base_size)
/ interpolation_scale
)
grid_w = (
torch.arange(grid_size[1], device=device, dtype=torch.float32)
/ (grid_size[1] / base_size)
/ interpolation_scale
)
grid = torch.meshgrid(grid_w, grid_h, indexing="xy") # here w goes first
grid = torch.stack(grid, dim=0)
grid = grid.reshape([2, 1, grid_size[1], grid_size[0]])
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid, output_type=output_type)
if cls_token and extra_tokens > 0:
pos_embed = torch.concat([torch.zeros([extra_tokens, embed_dim]), pos_embed], dim=0)
return pos_embed
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid, output_type="np"):
r"""
This function generates 2D sinusoidal positional embeddings from a grid.
Args:
embed_dim (`int`): The embedding dimension.
grid (`torch.Tensor`): Grid of positions with shape `(H * W,)`.
Returns:
`torch.Tensor`: The 2D sinusoidal positional embeddings with shape `(H * W, embed_dim)`
"""
if output_type == "np":
deprecation_message = (
"`get_2d_sincos_pos_embed_from_grid` uses `torch` and supports `device`."
" `from_numpy` is no longer required."
" Pass `output_type='pt' to use the new version now."
)
deprecate("output_type=='np'", "0.33.0", deprecation_message, standard_warn=False)
return get_2d_sincos_pos_embed_from_grid_np(
embed_dim=embed_dim,
grid=grid,
)
if embed_dim % 2 != 0:
raise ValueError("embed_dim must be divisible by 2")
# use half of dimensions to encode grid_h
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0], output_type=output_type) # (H*W, D/2)
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1], output_type=output_type) # (H*W, D/2)
emb = torch.concat([emb_h, emb_w], dim=1) # (H*W, D)
return emb
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos, output_type="np"):
"""
This function generates 1D positional embeddings from a grid.
Args:
embed_dim (`int`): The embedding dimension `D`
pos (`torch.Tensor`): 1D tensor of positions with shape `(M,)`
Returns:
`torch.Tensor`: Sinusoidal positional embeddings of shape `(M, D)`.
"""
if output_type == "np":
deprecation_message = (
"`get_1d_sincos_pos_embed_from_grid` uses `torch` and supports `device`."
" `from_numpy` is no longer required."
" Pass `output_type='pt' to use the new version now."
)
deprecate("output_type=='np'", "0.33.0", deprecation_message, standard_warn=False)
return get_1d_sincos_pos_embed_from_grid_np(embed_dim=embed_dim, pos=pos)
if embed_dim % 2 != 0:
raise ValueError("embed_dim must be divisible by 2")
omega = torch.arange(embed_dim // 2, device=pos.device, dtype=torch.float64)
omega /= embed_dim / 2.0
omega = 1.0 / 10000**omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = torch.outer(pos, omega) # (M, D/2), outer product
emb_sin = torch.sin(out) # (M, D/2)
emb_cos = torch.cos(out) # (M, D/2)
emb = torch.concat([emb_sin, emb_cos], dim=1) # (M, D)
return emb
def get_2d_sincos_pos_embed_np(
embed_dim, grid_size, cls_token=False, extra_tokens=0, interpolation_scale=1.0, base_size=16
):
"""
Creates 2D sinusoidal positional embeddings.
Args:
embed_dim (`int`):
The embedding dimension.
grid_size (`int`):
The size of the grid height and width.
cls_token (`bool`, defaults to `False`):
Whether or not to add a classification token.
extra_tokens (`int`, defaults to `0`):
The number of extra tokens to add.
interpolation_scale (`float`, defaults to `1.0`):
The scale of the interpolation.
Returns:
pos_embed (`np.ndarray`):
Shape is either `[grid_size * grid_size, embed_dim]` if not using cls_token, or `[1 + grid_size*grid_size,
embed_dim]` if using cls_token
"""
if isinstance(grid_size, int):
grid_size = (grid_size, grid_size)
grid_h = np.arange(grid_size[0], dtype=np.float32) / (grid_size[0] / base_size) / interpolation_scale
grid_w = np.arange(grid_size[1], dtype=np.float32) / (grid_size[1] / base_size) / interpolation_scale
grid = np.meshgrid(grid_w, grid_h) # here w goes first
grid = np.stack(grid, axis=0)
grid = grid.reshape([2, 1, grid_size[1], grid_size[0]])
pos_embed = get_2d_sincos_pos_embed_from_grid_np(embed_dim, grid)
if cls_token and extra_tokens > 0:
pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
return pos_embed
def get_2d_sincos_pos_embed_from_grid_np(embed_dim, grid):
r"""
This function generates 2D sinusoidal positional embeddings from a grid.
Args:
embed_dim (`int`): The embedding dimension.
grid (`np.ndarray`): Grid of positions with shape `(H * W,)`.
Returns:
`np.ndarray`: The 2D sinusoidal positional embeddings with shape `(H * W, embed_dim)`
"""
if embed_dim % 2 != 0:
raise ValueError("embed_dim must be divisible by 2")
# use half of dimensions to encode grid_h
emb_h = get_1d_sincos_pos_embed_from_grid_np(embed_dim // 2, grid[0]) # (H*W, D/2)
emb_w = get_1d_sincos_pos_embed_from_grid_np(embed_dim // 2, grid[1]) # (H*W, D/2)
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
return emb
def get_1d_sincos_pos_embed_from_grid_np(embed_dim, pos):
"""
This function generates 1D positional embeddings from a grid.
Args:
embed_dim (`int`): The embedding dimension `D`
pos (`numpy.ndarray`): 1D tensor of positions with shape `(M,)`
Returns:
`numpy.ndarray`: Sinusoidal positional embeddings of shape `(M, D)`.
"""
if embed_dim % 2 != 0:
raise ValueError("embed_dim must be divisible by 2")
omega = np.arange(embed_dim // 2, dtype=np.float64)
omega /= embed_dim / 2.0
omega = 1.0 / 10000**omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product
emb_sin = np.sin(out) # (M, D/2)
emb_cos = np.cos(out) # (M, D/2)
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
return emb
class PatchEmbed(nn.Module):
"""
2D Image to Patch Embedding with support for SD3 cropping.
Args:
height (`int`, defaults to `224`): The height of the image.
width (`int`, defaults to `224`): The width of the image.
patch_size (`int`, defaults to `16`): The size of the patches.
in_channels (`int`, defaults to `3`): The number of input channels.
embed_dim (`int`, defaults to `768`): The output dimension of the embedding.
layer_norm (`bool`, defaults to `False`): Whether or not to use layer normalization.
flatten (`bool`, defaults to `True`): Whether or not to flatten the output.
bias (`bool`, defaults to `True`): Whether or not to use bias.
interpolation_scale (`float`, defaults to `1`): The scale of the interpolation.
pos_embed_type (`str`, defaults to `"sincos"`): The type of positional embedding.
pos_embed_max_size (`int`, defaults to `None`): The maximum size of the positional embedding.
"""
def __init__(
self,
height=224,
width=224,
patch_size=16,
in_channels=3,
embed_dim=768,
layer_norm=False,
flatten=True,
bias=True,
interpolation_scale=1,
pos_embed_type="sincos",
pos_embed_max_size=None, # For SD3 cropping
):
super().__init__()
num_patches = (height // patch_size) * (width // patch_size)
self.flatten = flatten
self.layer_norm = layer_norm
self.pos_embed_max_size = pos_embed_max_size
self.proj = nn.Conv2d(
in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias
)
if layer_norm:
self.norm = nn.LayerNorm(embed_dim, elementwise_affine=False, eps=1e-6)
else:
self.norm = None
self.patch_size = patch_size
self.height, self.width = height // patch_size, width // patch_size
self.base_size = height // patch_size
self.interpolation_scale = interpolation_scale
# Calculate positional embeddings based on max size or default
if pos_embed_max_size:
grid_size = pos_embed_max_size
else:
grid_size = int(num_patches**0.5)
if pos_embed_type is None:
self.pos_embed = None
elif pos_embed_type == "sincos":
pos_embed = get_2d_sincos_pos_embed(
embed_dim,
grid_size,
base_size=self.base_size,
interpolation_scale=self.interpolation_scale,
output_type="pt",
)
persistent = True if pos_embed_max_size else False
self.register_buffer("pos_embed", pos_embed.float().unsqueeze(0), persistent=persistent)
else:
raise ValueError(f"Unsupported pos_embed_type: {pos_embed_type}")
def cropped_pos_embed(self, height, width):
"""Crops positional embeddings for SD3 compatibility."""
if self.pos_embed_max_size is None:
raise ValueError("`pos_embed_max_size` must be set for cropping.")
height = height // self.patch_size
width = width // self.patch_size
if height > self.pos_embed_max_size:
raise ValueError(
f"Height ({height}) cannot be greater than `pos_embed_max_size`: {self.pos_embed_max_size}."
)
if width > self.pos_embed_max_size:
raise ValueError(
f"Width ({width}) cannot be greater than `pos_embed_max_size`: {self.pos_embed_max_size}."
)
top = (self.pos_embed_max_size - height) // 2
left = (self.pos_embed_max_size - width) // 2
spatial_pos_embed = self.pos_embed.reshape(1, self.pos_embed_max_size, self.pos_embed_max_size, -1)
spatial_pos_embed = spatial_pos_embed[:, top : top + height, left : left + width, :]
spatial_pos_embed = spatial_pos_embed.reshape(1, -1, spatial_pos_embed.shape[-1])
return spatial_pos_embed
def forward(self, latent):
if self.pos_embed_max_size is not None:
height, width = latent.shape[-2:]
else:
height, width = latent.shape[-2] // self.patch_size, latent.shape[-1] // self.patch_size
latent = self.proj(latent)
if self.flatten:
latent = latent.flatten(2).transpose(1, 2) # BCHW -> BNC
if self.layer_norm:
latent = self.norm(latent)
if self.pos_embed is None:
return latent.to(latent.dtype)
# Interpolate or crop positional embeddings as needed
if self.pos_embed_max_size:
pos_embed = self.cropped_pos_embed(height, width)
else:
if self.height != height or self.width != width:
pos_embed = get_2d_sincos_pos_embed(
embed_dim=self.pos_embed.shape[-1],
grid_size=(height, width),
base_size=self.base_size,
interpolation_scale=self.interpolation_scale,
device=latent.device,
output_type="pt",
)
pos_embed = pos_embed.float().unsqueeze(0)
else:
pos_embed = self.pos_embed
return (latent + pos_embed).to(latent.dtype)
class LuminaPatchEmbed(nn.Module):
"""
2D Image to Patch Embedding with support for Lumina-T2X
Args:
patch_size (`int`, defaults to `2`): The size of the patches.
in_channels (`int`, defaults to `4`): The number of input channels.
embed_dim (`int`, defaults to `768`): The output dimension of the embedding.
bias (`bool`, defaults to `True`): Whether or not to use bias.
"""
def __init__(self, patch_size=2, in_channels=4, embed_dim=768, bias=True):
super().__init__()
self.patch_size = patch_size
self.proj = nn.Linear(
in_features=patch_size * patch_size * in_channels,
out_features=embed_dim,
bias=bias,
)
def forward(self, x, freqs_cis):
"""
Patchifies and embeds the input tensor(s).
Args:
x (List[torch.Tensor] | torch.Tensor): The input tensor(s) to be patchified and embedded.
Returns:
Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], torch.Tensor]: A tuple containing the patchified
and embedded tensor(s), the mask indicating the valid patches, the original image size(s), and the
frequency tensor(s).
"""
freqs_cis = freqs_cis.to(x[0].device)
patch_height = patch_width = self.patch_size
batch_size, channel, height, width = x.size()
height_tokens, width_tokens = height // patch_height, width // patch_width
x = x.view(batch_size, channel, height_tokens, patch_height, width_tokens, patch_width).permute(
0, 2, 4, 1, 3, 5
)
x = x.flatten(3)
x = self.proj(x)
x = x.flatten(1, 2)
mask = torch.ones(x.shape[0], x.shape[1], dtype=torch.int32, device=x.device)
return (
x,
mask,
[(height, width)] * batch_size,
freqs_cis[:height_tokens, :width_tokens].flatten(0, 1).unsqueeze(0),
)
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, device: Optional[torch.device] = None
) -> torch.Tensor:
post_patch_height = sample_height // self.patch_size
post_patch_width = sample_width // self.patch_size
post_time_compression_frames = (sample_frames - 1) // self.temporal_compression_ratio + 1
num_patches = post_patch_height * post_patch_width * post_time_compression_frames
pos_embedding = get_3d_sincos_pos_embed(
self.embed_dim,
(post_patch_width, post_patch_height),
post_time_compression_frames,
self.spatial_interpolation_scale,
self.temporal_interpolation_scale,
device=device,
output_type="pt",
)
pos_embedding = pos_embedding.flatten(0, 1)
joint_pos_embedding = pos_embedding.new_zeros(
1, 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, device=embeds.device
)
else:
pos_embedding = self.pos_embedding
pos_embedding = pos_embedding.to(dtype=embeds.dtype)
embeds = embeds + pos_embedding
return embeds
class CogView3PlusPatchEmbed(nn.Module):
def __init__(
self,
in_channels: int = 16,
hidden_size: int = 2560,
patch_size: int = 2,
text_hidden_size: int = 4096,
pos_embed_max_size: int = 128,
):
super().__init__()
self.in_channels = in_channels
self.hidden_size = hidden_size
self.patch_size = patch_size
self.text_hidden_size = text_hidden_size
self.pos_embed_max_size = pos_embed_max_size
# Linear projection for image patches
self.proj = nn.Linear(in_channels * patch_size**2, hidden_size)
# Linear projection for text embeddings
self.text_proj = nn.Linear(text_hidden_size, hidden_size)
pos_embed = get_2d_sincos_pos_embed(
hidden_size, pos_embed_max_size, base_size=pos_embed_max_size, output_type="pt"
)
pos_embed = pos_embed.reshape(pos_embed_max_size, pos_embed_max_size, hidden_size)
self.register_buffer("pos_embed", pos_embed.float(), persistent=False)
def forward(self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor) -> torch.Tensor:
batch_size, channel, height, width = hidden_states.shape
if height % self.patch_size != 0 or width % self.patch_size != 0:
raise ValueError("Height and width must be divisible by patch size")
height = height // self.patch_size
width = width // self.patch_size
hidden_states = hidden_states.view(batch_size, channel, height, self.patch_size, width, self.patch_size)
hidden_states = hidden_states.permute(0, 2, 4, 1, 3, 5).contiguous()
hidden_states = hidden_states.view(batch_size, height * width, channel * self.patch_size * self.patch_size)
# Project the patches
hidden_states = self.proj(hidden_states)
encoder_hidden_states = self.text_proj(encoder_hidden_states)
hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
# Calculate text_length
text_length = encoder_hidden_states.shape[1]
image_pos_embed = self.pos_embed[:height, :width].reshape(height * width, -1)
text_pos_embed = torch.zeros(
(text_length, self.hidden_size), dtype=image_pos_embed.dtype, device=image_pos_embed.device
)
pos_embed = torch.cat([text_pos_embed, image_pos_embed], dim=0)[None, ...]
return (hidden_states + pos_embed).to(hidden_states.dtype)
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,
device: Optional[torch.device] = 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 = torch.linspace(
start[0], stop[0] * (grid_size_h - 1) / grid_size_h, grid_size_h, device=device, dtype=torch.float32
)
grid_w = torch.linspace(
start[1], stop[1] * (grid_size_w - 1) / grid_size_w, grid_size_w, device=device, dtype=torch.float32
)
grid_t = torch.arange(temporal_size, device=device, dtype=torch.float32)
grid_t = torch.linspace(
0, temporal_size * (temporal_size - 1) / temporal_size, temporal_size, device=device, dtype=torch.float32
)
elif grid_type == "slice":
max_h, max_w = max_size
grid_size_h, grid_size_w = grid_size
grid_h = torch.arange(max_h, device=device, dtype=torch.float32)
grid_w = torch.arange(max_w, device=device, dtype=torch.float32)
grid_t = torch.arange(temporal_size, device=device, dtype=torch.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, theta=theta, use_real=True)
# Spatial frequencies for height and width
freqs_h = get_1d_rotary_pos_embed(dim_h, grid_h, theta=theta, use_real=True)
freqs_w = get_1d_rotary_pos_embed(dim_w, grid_w, theta=theta, 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
def get_3d_rotary_pos_embed_allegro(
embed_dim,
crops_coords,
grid_size,
temporal_size,
interpolation_scale: Tuple[float, float, float] = (1.0, 1.0, 1.0),
theta: int = 10000,
device: Optional[torch.device] = None,
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
# TODO(aryan): docs
start, stop = crops_coords
grid_size_h, grid_size_w = grid_size
interpolation_scale_t, interpolation_scale_h, interpolation_scale_w = interpolation_scale
grid_t = torch.linspace(
0, temporal_size * (temporal_size - 1) / temporal_size, temporal_size, device=device, dtype=torch.float32
)
grid_h = torch.linspace(
start[0], stop[0] * (grid_size_h - 1) / grid_size_h, grid_size_h, device=device, dtype=torch.float32
)
grid_w = torch.linspace(
start[1], stop[1] * (grid_size_w - 1) / grid_size_w, grid_size_w, device=device, dtype=torch.float32
)
# Compute dimensions for each axis
dim_t = embed_dim // 3
dim_h = embed_dim // 3
dim_w = embed_dim // 3
# Temporal frequencies
freqs_t = get_1d_rotary_pos_embed(
dim_t, grid_t / interpolation_scale_t, theta=theta, use_real=True, repeat_interleave_real=False
)
# Spatial frequencies for height and width
freqs_h = get_1d_rotary_pos_embed(
dim_h, grid_h / interpolation_scale_h, theta=theta, use_real=True, repeat_interleave_real=False
)
freqs_w = get_1d_rotary_pos_embed(
dim_w, grid_w / interpolation_scale_w, theta=theta, use_real=True, repeat_interleave_real=False
)
return freqs_t, freqs_h, freqs_w, grid_t, grid_h, grid_w
def get_2d_rotary_pos_embed(
embed_dim, crops_coords, grid_size, use_real=True, device: Optional[torch.device] = None, output_type: str = "np"
):
"""
RoPE for image tokens with 2d structure.
Args:
embed_dim: (`int`):
The embedding dimension size
crops_coords (`Tuple[int]`)
The top-left and bottom-right coordinates of the crop.
grid_size (`Tuple[int]`):
The grid size of the positional embedding.
use_real (`bool`):
If True, return real part and imaginary part separately. Otherwise, return complex numbers.
device: (`torch.device`, **optional**):
The device used to create tensors.
Returns:
`torch.Tensor`: positional embedding with shape `( grid_size * grid_size, embed_dim/2)`.
"""
if output_type == "np":
deprecation_message = (
"`get_2d_sincos_pos_embed` uses `torch` and supports `device`."
" `from_numpy` is no longer required."
" Pass `output_type='pt' to use the new version now."
)
deprecate("output_type=='np'", "0.33.0", deprecation_message, standard_warn=False)
return _get_2d_rotary_pos_embed_np(
embed_dim=embed_dim,
crops_coords=crops_coords,
grid_size=grid_size,
use_real=use_real,
)
start, stop = crops_coords
# scale end by (steps−1)/steps matches np.linspace(..., endpoint=False)
grid_h = torch.linspace(
start[0], stop[0] * (grid_size[0] - 1) / grid_size[0], grid_size[0], device=device, dtype=torch.float32
)
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