embeddings.py

# 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.
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# 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_2d_sincos_pos_embed(
    embed_dim, grid_size, cls_token=False, extra_tokens=0, interpolation_scale=1.0, base_size=16
):
    """
    grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or
    [1+grid_size*grid_size, embed_dim] (w/ or w/o 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(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(embed_dim, 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])  # (H*W, D/2)
    emb_w = get_1d_sincos_pos_embed_from_grid(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(embed_dim, pos):
    """
    embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (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."""

    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
            )
            persistent = True if pos_embed_max_size else False
            self.register_buffer("pos_embed", torch.from_numpy(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,
                )
                pos_embed = torch.from_numpy(pos_embed).float().unsqueeze(0).to(latent.device)
            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"""

    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),
        )


def get_2d_rotary_pos_embed(embed_dim, crops_coords, grid_size, use_real=True):
    """
    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.

    Returns:
        `torch.Tensor`: positional embdding with shape `( grid_size * grid_size, embed_dim/2)`.
    """
    start, stop = crops_coords
    grid_h = np.linspace(start[0], stop[0], grid_size[0], endpoint=False, dtype=np.float32)
    grid_w = np.linspace(start[1], stop[1], grid_size[1], endpoint=False, dtype=np.float32)
    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
    grid = np.stack(grid, axis=0)  # [2, W, H]

    grid = grid.reshape([2, 1, *grid.shape[1:]])
    pos_embed = get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=use_real)
    return pos_embed


def get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=False):
    assert embed_dim % 4 == 0

    # use half of dimensions to encode grid_h
    emb_h = get_1d_rotary_pos_embed(embed_dim // 2, grid[0].reshape(-1), use_real=use_real)  # (H*W, D/4)
    emb_w = get_1d_rotary_pos_embed(embed_dim // 2, grid[1].reshape(-1), use_real=use_real)  # (H*W, D/4)

    if use_real:
        cos = torch.cat([emb_h[0], emb_w[0]], dim=1)  # (H*W, D/2)
        sin = torch.cat([emb_h[1], emb_w[1]], dim=1)  # (H*W, D/2)
        return cos, sin
    else:
        emb = torch.cat([emb_h, emb_w], dim=1)  # (H*W, D/2)
        return emb


def get_2d_rotary_pos_embed_lumina(embed_dim, len_h, len_w, linear_factor=1.0, ntk_factor=1.0):
    assert embed_dim % 4 == 0

    emb_h = get_1d_rotary_pos_embed(
        embed_dim // 2, len_h, linear_factor=linear_factor, ntk_factor=ntk_factor
    )  # (H, D/4)
    emb_w = get_1d_rotary_pos_embed(
        embed_dim // 2, len_w, linear_factor=linear_factor, ntk_factor=ntk_factor
    )  # (W, D/4)
    emb_h = emb_h.view(len_h, 1, embed_dim // 4, 1).repeat(1, len_w, 1, 1)  # (H, W, D/4, 1)
    emb_w = emb_w.view(1, len_w, embed_dim // 4, 1).repeat(len_h, 1, 1, 1)  # (H, W, D/4, 1)

    emb = torch.cat([emb_h, emb_w], dim=-1).flatten(2)  # (H, W, D/2)
    return emb


def get_1d_rotary_pos_embed(
    dim: int, pos: Union[np.ndarray, int], theta: float = 10000.0, use_real=False, linear_factor=1.0, ntk_factor=1.0
):
    """
    Precompute the frequency tensor for complex exponentials (cis) with given dimensions.

    This function calculates a frequency tensor with complex exponentials using the given dimension 'dim' and the end
    index 'end'. The 'theta' parameter scales the frequencies. The returned tensor contains complex values in complex64
    data type.

    Args:
        dim (`int`): Dimension of the frequency tensor.
        pos (`np.ndarray` or `int`): Position indices for the frequency tensor. [S] or scalar
        theta (`float`, *optional*, defaults to 10000.0):
            Scaling factor for frequency computation. Defaults to 10000.0.
        use_real (`bool`, *optional*):
            If True, return real part and imaginary part separately. Otherwise, return complex numbers.
        linear_factor (`float`, *optional*, defaults to 1.0):
            Scaling factor for the context extrapolation. Defaults to 1.0.
        ntk_factor (`float`, *optional*, defaults to 1.0):
            Scaling factor for the NTK-Aware RoPE. Defaults to 1.0.
    Returns:
        `torch.Tensor`: Precomputed frequency tensor with complex exponentials. [S, D/2]
    """
    if isinstance(pos, int):
        pos = np.arange(pos)
    theta = theta * ntk_factor
    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) / linear_factor  # [D/2]
    t = torch.from_numpy(pos).to(freqs.device)  # type: ignore  # [S]
    freqs = torch.outer(t, freqs).float()  # type: ignore   # [S, D/2]
    if use_real:
        freqs_cos = freqs.cos().repeat_interleave(2, dim=1)  # [S, D]
        freqs_sin = freqs.sin().repeat_interleave(2, dim=1)  # [S, D]
        return freqs_cos, freqs_sin
    else:
        freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64     # [S, D/2]
        return freqs_cis


def apply_rotary_emb(
    x: torch.Tensor,
    freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]],
    use_real: bool = True,
) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings
    to the given query or key 'x' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are
    reshaped as complex numbers, and the frequency tensor is reshaped for broadcasting compatibility. The resulting
    tensors contain rotary embeddings and are returned as real tensors.

    Args:
        x (`torch.Tensor`):
            Query or key tensor to apply rotary embeddings. [B, H, S, D] xk (torch.Tensor): Key tensor to apply
        freqs_cis (`Tuple[torch.Tensor]`): Precomputed frequency tensor for complex exponentials. ([S, D], [S, D],)

    Returns:
        Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings.
    """
    if use_real:
        cos, sin = freqs_cis  # [S, D]
        cos = cos[None, None]
        sin = sin[None, None]
        cos, sin = cos.to(x.device), sin.to(x.device)

        x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1)  # [B, S, H, D//2]
        x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3)
        out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype)

        return out
    else:
        x_rotated = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
        freqs_cis = freqs_cis.unsqueeze(2)
        x_out = torch.view_as_real(x_rotated * freqs_cis).flatten(3)

        return x_out.type_as(x)


class TimestepEmbedding(nn.Module):
    def __init__(
        self,
        in_channels: int,
        time_embed_dim: int,
        act_fn: str = "silu",
        out_dim: int = None,
        post_act_fn: Optional[str] = None,
        cond_proj_dim=None,
        sample_proj_bias=True,
    ):
        super().__init__()

        self.linear_1 = nn.Linear(in_channels, time_embed_dim, sample_proj_bias)

        if cond_proj_dim is not None:
            self.cond_proj = nn.Linear(cond_proj_dim, in_channels, bias=False)
        else:
            self.cond_proj = None

        self.act = get_activation(act_fn)

        if out_dim is not None:
            time_embed_dim_out = out_dim
        else:
            time_embed_dim_out = time_embed_dim
        self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim_out, sample_proj_bias)

        if post_act_fn is None:
            self.post_act = None
        else:
            self.post_act = get_activation(post_act_fn)

    def forward(self, sample, condition=None):
        if condition is not None:
            sample = sample + self.cond_proj(condition)
        sample = self.linear_1(sample)

        if self.act is not None:
            sample = self.act(sample)

        sample = self.linear_2(sample)

        if self.post_act is not None:
            sample = self.post_act(sample)
        return sample


class Timesteps(nn.Module):
    def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, scale: int = 1):
        super().__init__()
        self.num_channels = num_channels
        self.flip_sin_to_cos = flip_sin_to_cos
        self.downscale_freq_shift = downscale_freq_shift
        self.scale = scale

    def forward(self, timesteps):
        t_emb = get_timestep_embedding(
            timesteps,
            self.num_channels,
            flip_sin_to_cos=self.flip_sin_to_cos,
            downscale_freq_shift=self.downscale_freq_shift,
            scale=self.scale,
        )
        return t_emb


class GaussianFourierProjection(nn.Module):
    """Gaussian Fourier embeddings for noise levels."""

    def __init__(
        self, embedding_size: int = 256, scale: float = 1.0, set_W_to_weight=True, log=True, flip_sin_to_cos=False
    ):
        super().__init__()
        self.weight = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False)
        self.log = log
        self.flip_sin_to_cos = flip_sin_to_cos

        if set_W_to_weight:
            # to delete later
            del self.weight
            self.W = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False)
            self.weight = self.W
            del self.W

    def forward(self, x):
        if self.log:
            x = torch.log(x)

        x_proj = x[:, None] * self.weight[None, :] * 2 * np.pi

        if self.flip_sin_to_cos:
            out = torch.cat([torch.cos(x_proj), torch.sin(x_proj)], dim=-1)
        else:
            out = torch.cat([torch.sin(x_proj), torch.cos(x_proj)], dim=-1)
        return out


class SinusoidalPositionalEmbedding(nn.Module):
    """Apply positional information to a sequence of embeddings.

    Takes in a sequence of embeddings with shape (batch_size, seq_length, embed_dim) and adds positional embeddings to
    them

    Args:
        embed_dim: (int): Dimension of the positional embedding.
        max_seq_length: Maximum sequence length to apply positional embeddings

    """

    def __init__(self, embed_dim: int, max_seq_length: int = 32):
        super().__init__()
        position = torch.arange(max_seq_length).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, embed_dim, 2) * (-math.log(10000.0) / embed_dim))
        pe = torch.zeros(1, max_seq_length, embed_dim)
        pe[0, :, 0::2] = torch.sin(position * div_term)
        pe[0, :, 1::2] = torch.cos(position * div_term)
        self.register_buffer("pe", pe)

    def forward(self, x):
        _, seq_length, _ = x.shape
        x = x + self.pe[:, :seq_length]
        return x


class ImagePositionalEmbeddings(nn.Module):
    """
    Converts latent image classes into vector embeddings. Sums the vector embeddings with positional embeddings for the
    height and width of the latent space.

    For more details, see figure 10 of the dall-e paper: https://arxiv.org/abs/2102.12092

    For VQ-diffusion:

    Output vector embeddings are used as input for the transformer.

    Note that the vector embeddings for the transformer are different than the vector embeddings from the VQVAE.

    Args:
        num_embed (`int`):
            Number of embeddings for the latent pixels embeddings.
        height (`int`):
            Height of the latent image i.e. the number of height embeddings.
        width (`int`):
            Width of the latent image i.e. the number of width embeddings.
        embed_dim (`int`):
            Dimension of the produced vector embeddings. Used for the latent pixel, height, and width embeddings.
    """

    def __init__(
        self,
        num_embed: int,
        height: int,
        width: int,
        embed_dim: int,
    ):
        super().__init__()

        self.height = height
        self.width = width
        self.num_embed = num_embed
        self.embed_dim = embed_dim

        self.emb = nn.Embedding(self.num_embed, embed_dim)
        self.height_emb = nn.Embedding(self.height, embed_dim)
        self.width_emb = nn.Embedding(self.width, embed_dim)

    def forward(self, index):
        emb = self.emb(index)

        height_emb = self.height_emb(torch.arange(self.height, device=index.device).view(1, self.height))

        # 1 x H x D -> 1 x H x 1 x D
        height_emb = height_emb.unsqueeze(2)

        width_emb = self.width_emb(torch.arange(self.width, device=index.device).view(1, self.width))

        # 1 x W x D -> 1 x 1 x W x D
        width_emb = width_emb.unsqueeze(1)

        pos_emb = height_emb + width_emb

        # 1 x H x W x D -> 1 x L xD
        pos_emb = pos_emb.view(1, self.height * self.width, -1)

        emb = emb + pos_emb[:, : emb.shape[1], :]

        return emb


class LabelEmbedding(nn.Module):
    """
    Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.

    Args:
        num_classes (`int`): The number of classes.
        hidden_size (`int`): The size of the vector embeddings.
        dropout_prob (`float`): The probability of dropping a label.
    """

    def __init__(self, num_classes, hidden_size, dropout_prob):
        super().__init__()
        use_cfg_embedding = dropout_prob > 0
        self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)
        self.num_classes = num_classes
        self.dropout_prob = dropout_prob

    def token_drop(self, labels, force_drop_ids=None):
        """
        Drops labels to enable classifier-free guidance.
        """
        if force_drop_ids is None:
            drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
        else:
            drop_ids = torch.tensor(force_drop_ids == 1)
        labels = torch.where(drop_ids, self.num_classes, labels)
        return labels

    def forward(self, labels: torch.LongTensor, force_drop_ids=None):
        use_dropout = self.dropout_prob > 0
        if (self.training and use_dropout) or (force_drop_ids is not None):
            labels = self.token_drop(labels, force_drop_ids)
        embeddings = self.embedding_table(labels)
        return embeddings


class TextImageProjection(nn.Module):
    def __init__(
        self,
        text_embed_dim: int = 1024,
        image_embed_dim: int = 768,
        cross_attention_dim: int = 768,
        num_image_text_embeds: int = 10,
    ):
        super().__init__()

        self.num_image_text_embeds = num_image_text_embeds
        self.image_embeds = nn.Linear(image_embed_dim, self.num_image_text_embeds * cross_attention_dim)
        self.text_proj = nn.Linear(text_embed_dim, cross_attention_dim)

    def forward(self, text_embeds: torch.Tensor, image_embeds: torch.Tensor):
        batch_size = text_embeds.shape[0]

        # image
        image_text_embeds = self.image_embeds(image_embeds)
        image_text_embeds = image_text_embeds.reshape(batch_size, self.num_image_text_embeds, -1)

        # text
        text_embeds = self.text_proj(text_embeds)

        return torch.cat([image_text_embeds, text_embeds], dim=1)


class ImageProjection(nn.Module):
    def __init__(
        self,
        image_embed_dim: int = 768,
        cross_attention_dim: int = 768,
        num_image_text_embeds: int = 32,
    ):
        super().__init__()

        self.num_image_text_embeds = num_image_text_embeds
        self.image_embeds = nn.Linear(image_embed_dim, self.num_image_text_embeds * cross_attention_dim)
        self.norm = nn.LayerNorm(cross_attention_dim)

    def forward(self, image_embeds: torch.Tensor):
        batch_size = image_embeds.shape[0]

        # image
        image_embeds = self.image_embeds(image_embeds)
        image_embeds = image_embeds.reshape(batch_size, self.num_image_text_embeds, -1)
        image_embeds = self.norm(image_embeds)
        return image_embeds


class IPAdapterFullImageProjection(nn.Module):
    def __init__(self, image_embed_dim=1024, cross_attention_dim=1024):
        super().__init__()
        from .attention import FeedForward

        self.ff = FeedForward(image_embed_dim, cross_attention_dim, mult=1, activation_fn="gelu")
        self.norm = nn.LayerNorm(cross_attention_dim)

    def forward(self, image_embeds: torch.Tensor):
        return self.norm(self.ff(image_embeds))


class IPAdapterFaceIDImageProjection(nn.Module):
    def __init__(self, image_embed_dim=1024, cross_attention_dim=1024, mult=1, num_tokens=1):
        super().__init__()
        from .attention import FeedForward

        self.num_tokens = num_tokens
        self.cross_attention_dim = cross_attention_dim
        self.ff = FeedForward(image_embed_dim, cross_attention_dim * num_tokens, mult=mult, activation_fn="gelu")
        self.norm = nn.LayerNorm(cross_attention_dim)

    def forward(self, image_embeds: torch.Tensor):
        x = self.ff(image_embeds)
        x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)
        return self.norm(x)


class CombinedTimestepLabelEmbeddings(nn.Module):
    def __init__(self, num_classes, embedding_dim, class_dropout_prob=0.1):
        super().__init__()

        self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=1)
        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
        self.class_embedder = LabelEmbedding(num_classes, embedding_dim, class_dropout_prob)

    def forward(self, timestep, class_labels, hidden_dtype=None):
        timesteps_proj = self.time_proj(timestep)
        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype))  # (N, D)

        class_labels = self.class_embedder(class_labels)  # (N, D)

        conditioning = timesteps_emb + class_labels  # (N, D)

        return conditioning


class CombinedTimestepTextProjEmbeddings(nn.Module):
    def __init__(self, embedding_dim, pooled_projection_dim):
        super().__init__()

        self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
        self.text_embedder = PixArtAlphaTextProjection(pooled_projection_dim, embedding_dim, act_fn="silu")

    def forward(self, timestep, pooled_projection):
        timesteps_proj = self.time_proj(timestep)
        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=pooled_projection.dtype))  # (N, D)

        pooled_projections = self.text_embedder(pooled_projection)

        conditioning = timesteps_emb + pooled_projections

        return conditioning


class HunyuanDiTAttentionPool(nn.Module):
    # Copied from https://github.com/Tencent/HunyuanDiT/blob/cb709308d92e6c7e8d59d0dff41b74d35088db6a/hydit/modules/poolers.py#L6

    def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
        super().__init__()
        self.positional_embedding = nn.Parameter(torch.randn(spacial_dim + 1, embed_dim) / embed_dim**0.5)
        self.k_proj = nn.Linear(embed_dim, embed_dim)
        self.q_proj = nn.Linear(embed_dim, embed_dim)
        self.v_proj = nn.Linear(embed_dim, embed_dim)
        self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
        self.num_heads = num_heads

    def forward(self, x):
        x = x.permute(1, 0, 2)  # NLC -> LNC
        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (L+1)NC
        x = x + self.positional_embedding[:, None, :].to(x.dtype)  # (L+1)NC
        x, _ = F.multi_head_attention_forward(
            query=x[:1],
            key=x,
            value=x,
            embed_dim_to_check=x.shape[-1],
            num_heads=self.num_heads,
            q_proj_weight=self.q_proj.weight,
            k_proj_weight=self.k_proj.weight,
            v_proj_weight=self.v_proj.weight,
            in_proj_weight=None,
            in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
            bias_k=None,
            bias_v=None,
            add_zero_attn=False,
            dropout_p=0,
            out_proj_weight=self.c_proj.weight,
            out_proj_bias=self.c_proj.bias,
            use_separate_proj_weight=True,
            training=self.training,
            need_weights=False,
        )
        return x.squeeze(0)


class HunyuanCombinedTimestepTextSizeStyleEmbedding(nn.Module):
    def __init__(
        self,
        embedding_dim,
        pooled_projection_dim=1024,
        seq_len=256,
        cross_attention_dim=2048,
        use_style_cond_and_image_meta_size=True,
    ):
        super().__init__()

        self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)

        self.size_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)

        self.pooler = HunyuanDiTAttentionPool(
            seq_len, cross_attention_dim, num_heads=8, output_dim=pooled_projection_dim
        )

        # Here we use a default learned embedder layer for future extension.
        self.use_style_cond_and_image_meta_size = use_style_cond_and_image_meta_size
        if use_style_cond_and_image_meta_size:
            self.style_embedder = nn.Embedding(1, embedding_dim)
            extra_in_dim = 256 * 6 + embedding_dim + pooled_projection_dim
        else:
            extra_in_dim = pooled_projection_dim

        self.extra_embedder = PixArtAlphaTextProjection(
            in_features=extra_in_dim,
            hidden_size=embedding_dim * 4,
            out_features=embedding_dim,
            act_fn="silu_fp32",
        )

    def forward(self, timestep, encoder_hidden_states, image_meta_size, style, hidden_dtype=None):
        timesteps_proj = self.time_proj(timestep)
        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype))  # (N, 256)

        # extra condition1: text
        pooled_projections = self.pooler(encoder_hidden_states)  # (N, 1024)

        if self.use_style_cond_and_image_meta_size:
            # extra condition2: image meta size embdding
            image_meta_size = self.size_proj(image_meta_size.view(-1))
            image_meta_size = image_meta_size.to(dtype=hidden_dtype)
            image_meta_size = image_meta_size.view(-1, 6 * 256)  # (N, 1536)

            # extra condition3: style embedding
            style_embedding = self.style_embedder(style)  # (N, embedding_dim)

            # Concatenate all extra vectors
            extra_cond = torch.cat([pooled_projections, image_meta_size, style_embedding], dim=1)
        else:
            extra_cond = torch.cat([pooled_projections], dim=1)

        conditioning = timesteps_emb + self.extra_embedder(extra_cond)  # [B, D]

        return conditioning


class LuminaCombinedTimestepCaptionEmbedding(nn.Module):
    def __init__(self, hidden_size=4096, cross_attention_dim=2048, frequency_embedding_size=256):
        super().__init__()
        self.time_proj = Timesteps(
            num_channels=frequency_embedding_size, flip_sin_to_cos=True, downscale_freq_shift=0.0
        )

        self.timestep_embedder = TimestepEmbedding(in_channels=frequency_embedding_size, time_embed_dim=hidden_size)

        self.caption_embedder = nn.Sequential(
            nn.LayerNorm(cross_attention_dim),
            nn.Linear(
                cross_attention_dim,
                hidden_size,
                bias=True,
            ),
        )

    def forward(self, timestep, caption_feat, caption_mask):
        # timestep embedding:
        time_freq = self.time_proj(timestep)
        time_embed = self.timestep_embedder(time_freq.to(dtype=self.timestep_embedder.linear_1.weight.dtype))

        # caption condition embedding:
        caption_mask_float = caption_mask.float().unsqueeze(-1)
        caption_feats_pool = (caption_feat * caption_mask_float).sum(dim=1) / caption_mask_float.sum(dim=1)
        caption_feats_pool = caption_feats_pool.to(caption_feat)
        caption_embed = self.caption_embedder(caption_feats_pool)

        conditioning = time_embed + caption_embed

        return conditioning


class TextTimeEmbedding(nn.Module):
    def __init__(self, encoder_dim: int, time_embed_dim: int, num_heads: int = 64):
        super().__init__()
        self.norm1 = nn.LayerNorm(encoder_dim)
        self.pool = AttentionPooling(num_heads, encoder_dim)
        self.proj = nn.Linear(encoder_dim, time_embed_dim)
        self.norm2 = nn.LayerNorm(time_embed_dim)

    def forward(self, hidden_states):
        hidden_states = self.norm1(hidden_states)
        hidden_states = self.pool(hidden_states)
        hidden_states = self.proj(hidden_states)
        hidden_states = self.norm2(hidden_states)
        return hidden_states


class TextImageTimeEmbedding(nn.Module):
    def __init__(self, text_embed_dim: int = 768, image_embed_dim: int = 768, time_embed_dim: int = 1536):
        super().__init__()
        self.text_proj = nn.Linear(text_embed_dim, time_embed_dim)
        self.text_norm = nn.LayerNorm(time_embed_dim)
        self.image_proj = nn.Linear(image_embed_dim, time_embed_dim)

    def forward(self, text_embeds: torch.Tensor, image_embeds: torch.Tensor):
        # text
        time_text_embeds = self.text_proj(text_embeds)
        time_text_embeds = self.text_norm(time_text_embeds)

        # image
        time_image_embeds = self.image_proj(image_embeds)

        return time_image_embeds + time_text_embeds


class ImageTimeEmbedding(nn.Module):
    def __init__(self, image_embed_dim: int = 768, time_embed_dim: int = 1536):
        super().__init__()
        self.image_proj = nn.Linear(image_embed_dim, time_embed_dim)
        self.image_norm = nn.LayerNorm(time_embed_dim)

    def forward(self, image_embeds: torch.Tensor):
        # image
        time_image_embeds = self.image_proj(image_embeds)
        time_image_embeds = self.image_norm(time_image_embeds)
        return time_image_embeds


class ImageHintTimeEmbedding(nn.Module):
    def __init__(self, image_embed_dim: int = 768, time_embed_dim: int = 1536):
        super().__init__()
        self.image_proj = nn.Linear(image_embed_dim, time_embed_dim)
        self.image_norm = nn.LayerNorm(time_embed_dim)
        self.input_hint_block = nn.Sequential(
            nn.Conv2d(3, 16, 3, padding=1),
            nn.SiLU(),
            nn.Conv2d(16, 16, 3, padding=1),
            nn.SiLU(),
            nn.Conv2d(16, 32, 3, padding=1, stride=2),
            nn.SiLU(),
            nn.Conv2d(32, 32, 3, padding=1),
            nn.SiLU(),
            nn.Conv2d(32, 96, 3, padding=1, stride=2),
            nn.SiLU(),
            nn.Conv2d(96, 96, 3, padding=1),
            nn.SiLU(),
            nn.Conv2d(96, 256, 3, padding=1, stride=2),
            nn.SiLU(),
            nn.Conv2d(256, 4, 3, padding=1),
        )

    def forward(self, image_embeds: torch.Tensor, hint: torch.Tensor):
        # image
        time_image_embeds = self.image_proj(image_embeds)
        time_image_embeds = self.image_norm(time_image_embeds)
        hint = self.input_hint_block(hint)
        return time_image_embeds, hint


class AttentionPooling(nn.Module):
    # Copied from https://github.com/deep-floyd/IF/blob/2f91391f27dd3c468bf174be5805b4cc92980c0b/deepfloyd_if/model/nn.py#L54

    def __init__(self, num_heads, embed_dim, dtype=None):
        super().__init__()
        self.dtype = dtype
        self.positional_embedding = nn.Parameter(torch.randn(1, embed_dim) / embed_dim**0.5)
        self.k_proj = nn.Linear(embed_dim, embed_dim, dtype=self.dtype)
        self.q_proj = nn.Linear(embed_dim, embed_dim, dtype=self.dtype)
        self.v_proj = nn.Linear(embed_dim, embed_dim, dtype=self.dtype)
        self.num_heads = num_heads
        self.dim_per_head = embed_dim // self.num_heads

    def forward(self, x):
        bs, length, width = x.size()

        def shape(x):
            # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
            x = x.view(bs, -1, self.num_heads, self.dim_per_head)
            # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
            x = x.transpose(1, 2)
            # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
            x = x.reshape(bs * self.num_heads, -1, self.dim_per_head)
            # (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)
            x = x.transpose(1, 2)
            return x

        class_token = x.mean(dim=1, keepdim=True) + self.positional_embedding.to(x.dtype)
        x = torch.cat([class_token, x], dim=1)  # (bs, length+1, width)

        # (bs*n_heads, class_token_length, dim_per_head)
        q = shape(self.q_proj(class_token))
        # (bs*n_heads, length+class_token_length, dim_per_head)
        k = shape(self.k_proj(x))
        v = shape(self.v_proj(x))

        # (bs*n_heads, class_token_length, length+class_token_length):
        scale = 1 / math.sqrt(math.sqrt(self.dim_per_head))
        weight = torch.einsum("bct,bcs->bts", q * scale, k * scale)  # More stable with f16 than dividing afterwards
        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)

        # (bs*n_heads, dim_per_head, class_token_length)
        a = torch.einsum("bts,bcs->bct", weight, v)

        # (bs, length+1, width)
        a = a.reshape(bs, -1, 1).transpose(1, 2)

        return a[:, 0, :]  # cls_token


def get_fourier_embeds_from_boundingbox(embed_dim, box):
    """
    Args:
        embed_dim: int
        box: a 3-D tensor [B x N x 4] representing the bounding boxes for GLIGEN pipeline
    Returns:
        [B x N x embed_dim] tensor of positional embeddings
    """

    batch_size, num_boxes = box.shape[:2]

    emb = 100 ** (torch.arange(embed_dim) / embed_dim)
    emb = emb[None, None, None].to(device=box.device, dtype=box.dtype)
    emb = emb * box.unsqueeze(-1)

    emb = torch.stack((emb.sin(), emb.cos()), dim=-1)
    emb = emb.permute(0, 1, 3, 4, 2).reshape(batch_size, num_boxes, embed_dim * 2 * 4)

    return emb


class GLIGENTextBoundingboxProjection(nn.Module):
    def __init__(self, positive_len, out_dim, feature_type="text-only", fourier_freqs=8):
        super().__init__()
        self.positive_len = positive_len
        self.out_dim = out_dim

        self.fourier_embedder_dim = fourier_freqs
        self.position_dim = fourier_freqs * 2 * 4  # 2: sin/cos, 4: xyxy

        if isinstance(out_dim, tuple):
            out_dim = out_dim[0]

        if feature_type == "text-only":
            self.linears = nn.Sequential(
                nn.Linear(self.positive_len + self.position_dim, 512),
                nn.SiLU(),
                nn.Linear(512, 512),
                nn.SiLU(),
                nn.Linear(512, out_dim),
            )
            self.null_positive_feature = torch.nn.Parameter(torch.zeros([self.positive_len]))

        elif feature_type == "text-image":
            self.linears_text = nn.Sequential(
                nn.Linear(self.positive_len + self.position_dim, 512),
                nn.SiLU(),
                nn.Linear(512, 512),
                nn.SiLU(),
                nn.Linear(512, out_dim),
            )
            self.linears_image = nn.Sequential(
                nn.Linear(self.positive_len + self.position_dim, 512),
                nn.SiLU(),
                nn.Linear(512, 512),
                nn.SiLU(),
                nn.Linear(512, out_dim),
            )
            self.null_text_feature = torch.nn.Parameter(torch.zeros([self.positive_len]))
            self.null_image_feature = torch.nn.Parameter(torch.zeros([self.positive_len]))

        self.null_position_feature = torch.nn.Parameter(torch.zeros([self.position_dim]))

    def forward(
        self,
        boxes,
        masks,
        positive_embeddings=None,
        phrases_masks=None,
        image_masks=None,
        phrases_embeddings=None,
        image_embeddings=None,
    ):
        masks = masks.unsqueeze(-1)

        # embedding position (it may includes padding as placeholder)
        xyxy_embedding = get_fourier_embeds_from_boundingbox(self.fourier_embedder_dim, boxes)  # B*N*4 -> B*N*C

        # learnable null embedding
        xyxy_null = self.null_position_feature.view(1, 1, -1)

        # replace padding with learnable null embedding
        xyxy_embedding = xyxy_embedding * masks + (1 - masks) * xyxy_null

        # positionet with text only information
        if positive_embeddings is not None:
            # learnable null embedding
            positive_null = self.null_positive_feature.view(1, 1, -1)

            # replace padding with learnable null embedding
            positive_embeddings = positive_embeddings * masks + (1 - masks) * positive_null

            objs = self.linears(torch.cat([positive_embeddings, xyxy_embedding], dim=-1))

        # positionet with text and image information
        else:
            phrases_masks = phrases_masks.unsqueeze(-1)
            image_masks = image_masks.unsqueeze(-1)

            # learnable null embedding
            text_null = self.null_text_feature.view(1, 1, -1)
            image_null = self.null_image_feature.view(1, 1, -1)

            # replace padding with learnable null embedding
            phrases_embeddings = phrases_embeddings * phrases_masks + (1 - phrases_masks) * text_null
            image_embeddings = image_embeddings * image_masks + (1 - image_masks) * image_null

            objs_text = self.linears_text(torch.cat([phrases_embeddings, xyxy_embedding], dim=-1))
            objs_image = self.linears_image(torch.cat([image_embeddings, xyxy_embedding], dim=-1))
            objs = torch.cat([objs_text, objs_image], dim=1)

        return objs


class PixArtAlphaCombinedTimestepSizeEmbeddings(nn.Module):
    """
    For PixArt-Alpha.

    Reference:
    https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L164C9-L168C29
    """

    def __init__(self, embedding_dim, size_emb_dim, use_additional_conditions: bool = False):
        super().__init__()

        self.outdim = size_emb_dim
        self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)

        self.use_additional_conditions = use_additional_conditions
        if use_additional_conditions:
            self.additional_condition_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
            self.resolution_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=size_emb_dim)
            self.aspect_ratio_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=size_emb_dim)

    def forward(self, timestep, resolution, aspect_ratio, batch_size, hidden_dtype):
        timesteps_proj = self.time_proj(timestep)
        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype))  # (N, D)

        if self.use_additional_conditions:
            resolution_emb = self.additional_condition_proj(resolution.flatten()).to(hidden_dtype)
            resolution_emb = self.resolution_embedder(resolution_emb).reshape(batch_size, -1)
            aspect_ratio_emb = self.additional_condition_proj(aspect_ratio.flatten()).to(hidden_dtype)
            aspect_ratio_emb = self.aspect_ratio_embedder(aspect_ratio_emb).reshape(batch_size, -1)
            conditioning = timesteps_emb + torch.cat([resolution_emb, aspect_ratio_emb], dim=1)
        else:
            conditioning = timesteps_emb

        return conditioning


class PixArtAlphaTextProjection(nn.Module):
    """
    Projects caption embeddings. Also handles dropout for classifier-free guidance.

    Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
    """

    def __init__(self, in_features, hidden_size, out_features=None, act_fn="gelu_tanh"):
        super().__init__()
        if out_features is None:
            out_features = hidden_size
        self.linear_1 = nn.Linear(in_features=in_features, out_features=hidden_size, bias=True)
        if act_fn == "gelu_tanh":
            self.act_1 = nn.GELU(approximate="tanh")
        elif act_fn == "silu":
            self.act_1 = nn.SiLU()
        elif act_fn == "silu_fp32":
            self.act_1 = FP32SiLU()
        else:
            raise ValueError(f"Unknown activation function: {act_fn}")
        self.linear_2 = nn.Linear(in_features=hidden_size, out_features=out_features, bias=True)

    def forward(self, caption):
        hidden_states = self.linear_1(caption)
        hidden_states = self.act_1(hidden_states)
        hidden_states = self.linear_2(hidden_states)
        return hidden_states


class IPAdapterPlusImageProjectionBlock(nn.Module):
    def __init__(
        self,
        embed_dims: int = 768,
        dim_head: int = 64,
        heads: int = 16,
        ffn_ratio: float = 4,
    ) -> None:
        super().__init__()
        from .attention import FeedForward

        self.ln0 = nn.LayerNorm(embed_dims)
        self.ln1 = nn.LayerNorm(embed_dims)
        self.attn = Attention(
            query_dim=embed_dims,
            dim_head=dim_head,
            heads=heads,
            out_bias=False,
        )
        self.ff = nn.Sequential(
            nn.LayerNorm(embed_dims),
            FeedForward(embed_dims, embed_dims, activation_fn="gelu", mult=ffn_ratio, bias=False),
        )

    def forward(self, x, latents, residual):
        encoder_hidden_states = self.ln0(x)
        latents = self.ln1(latents)
        encoder_hidden_states = torch.cat([encoder_hidden_states, latents], dim=-2)
        latents = self.attn(latents, encoder_hidden_states) + residual
        latents = self.ff(latents) + latents
        return latents


class IPAdapterPlusImageProjection(nn.Module):
    """Resampler of IP-Adapter Plus.

    Args:
        embed_dims (int): The feature dimension. Defaults to 768. output_dims (int): The number of output channels,
        that is the same
            number of the channels in the `unet.config.cross_attention_dim`. Defaults to 1024.
        hidden_dims (int):
            The number of hidden channels. Defaults to 1280. depth (int): The number of blocks. Defaults
        to 8. dim_head (int): The number of head channels. Defaults to 64. heads (int): Parallel attention heads.
        Defaults to 16. num_queries (int):
            The number of queries. Defaults to 8. ffn_ratio (float): The expansion ratio
        of feedforward network hidden
            layer channels. Defaults to 4.
    """

    def __init__(
        self,
        embed_dims: int = 768,
        output_dims: int = 1024,
        hidden_dims: int = 1280,
        depth: int = 4,
        dim_head: int = 64,
        heads: int = 16,
        num_queries: int = 8,
        ffn_ratio: float = 4,
    ) -> None:
        super().__init__()
        self.latents = nn.Parameter(torch.randn(1, num_queries, hidden_dims) / hidden_dims**0.5)

        self.proj_in = nn.Linear(embed_dims, hidden_dims)

        self.proj_out = nn.Linear(hidden_dims, output_dims)
        self.norm_out = nn.LayerNorm(output_dims)

        self.layers = nn.ModuleList(
            [IPAdapterPlusImageProjectionBlock(hidden_dims, dim_head, heads, ffn_ratio) for _ in range(depth)]
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Forward pass.

        Args:
            x (torch.Tensor): Input Tensor.
        Returns:
            torch.Tensor: Output Tensor.
        """
        latents = self.latents.repeat(x.size(0), 1, 1)

        x = self.proj_in(x)

        for block in self.layers:
            residual = latents
            latents = block(x, latents, residual)

        latents = self.proj_out(latents)
        return self.norm_out(latents)


class IPAdapterFaceIDPlusImageProjection(nn.Module):
    """FacePerceiverResampler of IP-Adapter Plus.

    Args:
        embed_dims (int): The feature dimension. Defaults to 768. output_dims (int): The number of output channels,
        that is the same
            number of the channels in the `unet.config.cross_attention_dim`. Defaults to 1024.
        hidden_dims (int):
            The number of hidden channels. Defaults to 1280. depth (int): The number of blocks. Defaults
        to 8. dim_head (int): The number of head channels. Defaults to 64. heads (int): Parallel attention heads.
        Defaults to 16. num_tokens (int): Number of tokens num_queries (int): The number of queries. Defaults to 8.
        ffn_ratio (float): The expansion ratio of feedforward network hidden
            layer channels. Defaults to 4.
        ffproj_ratio (float): The expansion ratio of feedforward network hidden
            layer channels (for ID embeddings). Defaults to 4.
    """

    def __init__(
        self,
        embed_dims: int = 768,
        output_dims: int = 768,
        hidden_dims: int = 1280,
        id_embeddings_dim: int = 512,
        depth: int = 4,
        dim_head: int = 64,
        heads: int = 16,
        num_tokens: int = 4,
        num_queries: int = 8,
        ffn_ratio: float = 4,
        ffproj_ratio: int = 2,
    ) -> None:
        super().__init__()
        from .attention import FeedForward

        self.num_tokens = num_tokens
        self.embed_dim = embed_dims
        self.clip_embeds = None
        self.shortcut = False
        self.shortcut_scale = 1.0

        self.proj = FeedForward(id_embeddings_dim, embed_dims * num_tokens, activation_fn="gelu", mult=ffproj_ratio)
        self.norm = nn.LayerNorm(embed_dims)

        self.proj_in = nn.Linear(hidden_dims, embed_dims)

        self.proj_out = nn.Linear(embed_dims, output_dims)
        self.norm_out = nn.LayerNorm(output_dims)

        self.layers = nn.ModuleList(
            [IPAdapterPlusImageProjectionBlock(embed_dims, dim_head, heads, ffn_ratio) for _ in range(depth)]
        )

    def forward(self, id_embeds: torch.Tensor) -> torch.Tensor:
        """Forward pass.

        Args:
            id_embeds (torch.Tensor): Input Tensor (ID embeds).
        Returns:
            torch.Tensor: Output Tensor.
        """
        id_embeds = id_embeds.to(self.clip_embeds.dtype)
        id_embeds = self.proj(id_embeds)
        id_embeds = id_embeds.reshape(-1, self.num_tokens, self.embed_dim)
        id_embeds = self.norm(id_embeds)
        latents = id_embeds

        clip_embeds = self.proj_in(self.clip_embeds)
        x = clip_embeds.reshape(-1, clip_embeds.shape[2], clip_embeds.shape[3])

        for block in self.layers:
            residual = latents
            latents = block(x, latents, residual)

        latents = self.proj_out(latents)
        out = self.norm_out(latents)
        if self.shortcut:
            out = id_embeds + self.shortcut_scale * out
        return out


class MultiIPAdapterImageProjection(nn.Module):
    def __init__(self, IPAdapterImageProjectionLayers: Union[List[nn.Module], Tuple[nn.Module]]):
        super().__init__()
        self.image_projection_layers = nn.ModuleList(IPAdapterImageProjectionLayers)

    def forward(self, image_embeds: List[torch.Tensor]):
        projected_image_embeds = []

        # currently, we accept `image_embeds` as
        #  1. a tensor (deprecated) with shape [batch_size, embed_dim] or [batch_size, sequence_length, embed_dim]
        #  2. list of `n` tensors where `n` is number of ip-adapters, each tensor can hae shape [batch_size, num_images, embed_dim] or [batch_size, num_images, sequence_length, embed_dim]
        if not isinstance(image_embeds, list):
            deprecation_message = (
                "You have passed a tensor as `image_embeds`.This is deprecated and will be removed in a future release."
                " Please make sure to update your script to pass `image_embeds` as a list of tensors to suppress this warning."
            )
            deprecate("image_embeds not a list", "1.0.0", deprecation_message, standard_warn=False)
            image_embeds = [image_embeds.unsqueeze(1)]

        if len(image_embeds) != len(self.image_projection_layers):
            raise ValueError(
                f"image_embeds must have the same length as image_projection_layers, got {len(image_embeds)} and {len(self.image_projection_layers)}"
            )

        for image_embed, image_projection_layer in zip(image_embeds, self.image_projection_layers):
            batch_size, num_images = image_embed.shape[0], image_embed.shape[1]
            image_embed = image_embed.reshape((batch_size * num_images,) + image_embed.shape[2:])
            image_embed = image_projection_layer(image_embed)
            image_embed = image_embed.reshape((batch_size, num_images) + image_embed.shape[1:])

            projected_image_embeds.append(image_embed)

        return projected_image_embeds