src/models/unet_2d_blocks.py

# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py
from typing import Any, Dict, Optional, Tuple, Union

import numpy as np
import torch
import torch.nn.functional as F
from diffusers.models.activations import get_activation
from diffusers.models.attention_processor import Attention
from diffusers.models.dual_transformer_2d import DualTransformer2DModel
from diffusers.models.resnet import Downsample2D, ResnetBlock2D, Upsample2D
from diffusers.utils import is_torch_version, logging
from diffusers.utils.torch_utils import apply_freeu
from torch import nn

from .transformer_2d import Transformer2DModel

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


def get_down_block(
    down_block_type: str,
    num_layers: int,
    in_channels: int,
    out_channels: int,
    temb_channels: int,
    add_downsample: bool,
    resnet_eps: float,
    resnet_act_fn: str,
    transformer_layers_per_block: int = 1,
    num_attention_heads: Optional[int] = None,
    resnet_groups: Optional[int] = None,
    cross_attention_dim: Optional[int] = None,
    downsample_padding: Optional[int] = None,
    dual_cross_attention: bool = False,
    use_linear_projection: bool = False,
    only_cross_attention: bool = False,
    upcast_attention: bool = False,
    resnet_time_scale_shift: str = "default",
    attention_type: str = "default",
    resnet_skip_time_act: bool = False,
    resnet_out_scale_factor: float = 1.0,
    cross_attention_norm: Optional[str] = None,
    attention_head_dim: Optional[int] = None,
    downsample_type: Optional[str] = None,
    dropout: float = 0.0,
):
    # If attn head dim is not defined, we default it to the number of heads
    if attention_head_dim is None:
        logger.warn(
            f"It is recommended to provide `attention_head_dim` when calling `get_down_block`. Defaulting `attention_head_dim` to {num_attention_heads}."
        )
        attention_head_dim = num_attention_heads

    down_block_type = (
        down_block_type[7:]
        if down_block_type.startswith("UNetRes")
        else down_block_type
    )
    if down_block_type == "DownBlock2D":
        return DownBlock2D(
            num_layers=num_layers,
            in_channels=in_channels,
            out_channels=out_channels,
            temb_channels=temb_channels,
            dropout=dropout,
            add_downsample=add_downsample,
            resnet_eps=resnet_eps,
            resnet_act_fn=resnet_act_fn,
            resnet_groups=resnet_groups,
            downsample_padding=downsample_padding,
            resnet_time_scale_shift=resnet_time_scale_shift,
        )
    elif down_block_type == "CrossAttnDownBlock2D":
        if cross_attention_dim is None:
            raise ValueError(
                "cross_attention_dim must be specified for CrossAttnDownBlock2D"
            )
        return CrossAttnDownBlock2D(
            num_layers=num_layers,
            transformer_layers_per_block=transformer_layers_per_block,
            in_channels=in_channels,
            out_channels=out_channels,
            temb_channels=temb_channels,
            dropout=dropout,
            add_downsample=add_downsample,
            resnet_eps=resnet_eps,
            resnet_act_fn=resnet_act_fn,
            resnet_groups=resnet_groups,
            downsample_padding=downsample_padding,
            cross_attention_dim=cross_attention_dim,
            num_attention_heads=num_attention_heads,
            dual_cross_attention=dual_cross_attention,
            use_linear_projection=use_linear_projection,
            only_cross_attention=only_cross_attention,
            upcast_attention=upcast_attention,
            resnet_time_scale_shift=resnet_time_scale_shift,
            attention_type=attention_type,
        )
    raise ValueError(f"{down_block_type} does not exist.")


def get_up_block(
    up_block_type: str,
    num_layers: int,
    in_channels: int,
    out_channels: int,
    prev_output_channel: int,
    temb_channels: int,
    add_upsample: bool,
    resnet_eps: float,
    resnet_act_fn: str,
    resolution_idx: Optional[int] = None,
    transformer_layers_per_block: int = 1,
    num_attention_heads: Optional[int] = None,
    resnet_groups: Optional[int] = None,
    cross_attention_dim: Optional[int] = None,
    dual_cross_attention: bool = False,
    use_linear_projection: bool = False,
    only_cross_attention: bool = False,
    upcast_attention: bool = False,
    resnet_time_scale_shift: str = "default",
    attention_type: str = "default",
    resnet_skip_time_act: bool = False,
    resnet_out_scale_factor: float = 1.0,
    cross_attention_norm: Optional[str] = None,
    attention_head_dim: Optional[int] = None,
    upsample_type: Optional[str] = None,
    dropout: float = 0.0,
) -> nn.Module:
    # If attn head dim is not defined, we default it to the number of heads
    if attention_head_dim is None:
        logger.warn(
            f"It is recommended to provide `attention_head_dim` when calling `get_up_block`. Defaulting `attention_head_dim` to {num_attention_heads}."
        )
        attention_head_dim = num_attention_heads

    up_block_type = (
        up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
    )
    if up_block_type == "UpBlock2D":
        return UpBlock2D(
            num_layers=num_layers,
            in_channels=in_channels,
            out_channels=out_channels,
            prev_output_channel=prev_output_channel,
            temb_channels=temb_channels,
            resolution_idx=resolution_idx,
            dropout=dropout,
            add_upsample=add_upsample,
            resnet_eps=resnet_eps,
            resnet_act_fn=resnet_act_fn,
            resnet_groups=resnet_groups,
            resnet_time_scale_shift=resnet_time_scale_shift,
        )
    elif up_block_type == "CrossAttnUpBlock2D":
        if cross_attention_dim is None:
            raise ValueError(
                "cross_attention_dim must be specified for CrossAttnUpBlock2D"
            )
        return CrossAttnUpBlock2D(
            num_layers=num_layers,
            transformer_layers_per_block=transformer_layers_per_block,
            in_channels=in_channels,
            out_channels=out_channels,
            prev_output_channel=prev_output_channel,
            temb_channels=temb_channels,
            resolution_idx=resolution_idx,
            dropout=dropout,
            add_upsample=add_upsample,
            resnet_eps=resnet_eps,
            resnet_act_fn=resnet_act_fn,
            resnet_groups=resnet_groups,
            cross_attention_dim=cross_attention_dim,
            num_attention_heads=num_attention_heads,
            dual_cross_attention=dual_cross_attention,
            use_linear_projection=use_linear_projection,
            only_cross_attention=only_cross_attention,
            upcast_attention=upcast_attention,
            resnet_time_scale_shift=resnet_time_scale_shift,
            attention_type=attention_type,
        )

    raise ValueError(f"{up_block_type} does not exist.")


class AutoencoderTinyBlock(nn.Module):
    """
    Tiny Autoencoder block used in [`AutoencoderTiny`]. It is a mini residual module consisting of plain conv + ReLU
    blocks.

    Args:
        in_channels (`int`): The number of input channels.
        out_channels (`int`): The number of output channels.
        act_fn (`str`):
            ` The activation function to use. Supported values are `"swish"`, `"mish"`, `"gelu"`, and `"relu"`.

    Returns:
        `torch.FloatTensor`: A tensor with the same shape as the input tensor, but with the number of channels equal to
        `out_channels`.
    """

    def __init__(self, in_channels: int, out_channels: int, act_fn: str):
        super().__init__()
        act_fn = get_activation(act_fn)
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
            act_fn,
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
            act_fn,
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
        )
        self.skip = (
            nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
            if in_channels != out_channels
            else nn.Identity()
        )
        self.fuse = nn.ReLU()

    def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:
        return self.fuse(self.conv(x) + self.skip(x))


class UNetMidBlock2D(nn.Module):
    """
    A 2D UNet mid-block [`UNetMidBlock2D`] with multiple residual blocks and optional attention blocks.

    Args:
        in_channels (`int`): The number of input channels.
        temb_channels (`int`): The number of temporal embedding channels.
        dropout (`float`, *optional*, defaults to 0.0): The dropout rate.
        num_layers (`int`, *optional*, defaults to 1): The number of residual blocks.
        resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks.
        resnet_time_scale_shift (`str`, *optional*, defaults to `default`):
            The type of normalization to apply to the time embeddings. This can help to improve the performance of the
            model on tasks with long-range temporal dependencies.
        resnet_act_fn (`str`, *optional*, defaults to `swish`): The activation function for the resnet blocks.
        resnet_groups (`int`, *optional*, defaults to 32):
            The number of groups to use in the group normalization layers of the resnet blocks.
        attn_groups (`Optional[int]`, *optional*, defaults to None): The number of groups for the attention blocks.
        resnet_pre_norm (`bool`, *optional*, defaults to `True`):
            Whether to use pre-normalization for the resnet blocks.
        add_attention (`bool`, *optional*, defaults to `True`): Whether to add attention blocks.
        attention_head_dim (`int`, *optional*, defaults to 1):
            Dimension of a single attention head. The number of attention heads is determined based on this value and
            the number of input channels.
        output_scale_factor (`float`, *optional*, defaults to 1.0): The output scale factor.

    Returns:
        `torch.FloatTensor`: The output of the last residual block, which is a tensor of shape `(batch_size,
        in_channels, height, width)`.

    """

    def __init__(
        self,
        in_channels: int,
        temb_channels: int,
        dropout: float = 0.0,
        num_layers: int = 1,
        resnet_eps: float = 1e-6,
        resnet_time_scale_shift: str = "default",  # default, spatial
        resnet_act_fn: str = "swish",
        resnet_groups: int = 32,
        attn_groups: Optional[int] = None,
        resnet_pre_norm: bool = True,
        add_attention: bool = True,
        attention_head_dim: int = 1,
        output_scale_factor: float = 1.0,
    ):
        super().__init__()
        resnet_groups = (
            resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
        )
        self.add_attention = add_attention

        if attn_groups is None:
            attn_groups = (
                resnet_groups if resnet_time_scale_shift == "default" else None
            )

        # there is always at least one resnet
        resnets = [
            ResnetBlock2D(
                in_channels=in_channels,
                out_channels=in_channels,
                temb_channels=temb_channels,
                eps=resnet_eps,
                groups=resnet_groups,
                dropout=dropout,
                time_embedding_norm=resnet_time_scale_shift,
                non_linearity=resnet_act_fn,
                output_scale_factor=output_scale_factor,
                pre_norm=resnet_pre_norm,
            )
        ]
        attentions = []

        if attention_head_dim is None:
            logger.warn(
                f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {in_channels}."
            )
            attention_head_dim = in_channels

        for _ in range(num_layers):
            if self.add_attention:
                attentions.append(
                    Attention(
                        in_channels,
                        heads=in_channels // attention_head_dim,
                        dim_head=attention_head_dim,
                        rescale_output_factor=output_scale_factor,
                        eps=resnet_eps,
                        norm_num_groups=attn_groups,
                        spatial_norm_dim=temb_channels
                        if resnet_time_scale_shift == "spatial"
                        else None,
                        residual_connection=True,
                        bias=True,
                        upcast_softmax=True,
                        _from_deprecated_attn_block=True,
                    )
                )
            else:
                attentions.append(None)

            resnets.append(
                ResnetBlock2D(
                    in_channels=in_channels,
                    out_channels=in_channels,
                    temb_channels=temb_channels,
                    eps=resnet_eps,
                    groups=resnet_groups,
                    dropout=dropout,
                    time_embedding_norm=resnet_time_scale_shift,
                    non_linearity=resnet_act_fn,
                    output_scale_factor=output_scale_factor,
                    pre_norm=resnet_pre_norm,
                )
            )

        self.attentions = nn.ModuleList(attentions)
        self.resnets = nn.ModuleList(resnets)

    def forward(
        self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None
    ) -> torch.FloatTensor:
        hidden_states = self.resnets[0](hidden_states, temb)
        for attn, resnet in zip(self.attentions, self.resnets[1:]):
            if attn is not None:
                hidden_states = attn(hidden_states, temb=temb)
            hidden_states = resnet(hidden_states, temb)

        return hidden_states


class UNetMidBlock2DCrossAttn(nn.Module):
    def __init__(
        self,
        in_channels: int,
        temb_channels: int,
        dropout: float = 0.0,
        num_layers: int = 1,
        transformer_layers_per_block: Union[int, Tuple[int]] = 1,
        resnet_eps: float = 1e-6,
        resnet_time_scale_shift: str = "default",
        resnet_act_fn: str = "swish",
        resnet_groups: int = 32,
        resnet_pre_norm: bool = True,
        num_attention_heads: int = 1,
        output_scale_factor: float = 1.0,
        cross_attention_dim: int = 1280,
        dual_cross_attention: bool = False,
        use_linear_projection: bool = False,
        upcast_attention: bool = False,
        attention_type: str = "default",
    ):
        super().__init__()

        self.has_cross_attention = True
        self.num_attention_heads = num_attention_heads
        resnet_groups = (
            resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
        )

        # support for variable transformer layers per block
        if isinstance(transformer_layers_per_block, int):
            transformer_layers_per_block = [transformer_layers_per_block] * num_layers

        # there is always at least one resnet
        resnets = [
            ResnetBlock2D(
                in_channels=in_channels,
                out_channels=in_channels,
                temb_channels=temb_channels,
                eps=resnet_eps,
                groups=resnet_groups,
                dropout=dropout,
                time_embedding_norm=resnet_time_scale_shift,
                non_linearity=resnet_act_fn,
                output_scale_factor=output_scale_factor,
                pre_norm=resnet_pre_norm,
            )
        ]
        attentions = []

        for i in range(num_layers):
            if not dual_cross_attention:
                attentions.append(
                    Transformer2DModel(
                        num_attention_heads,
                        in_channels // num_attention_heads,
                        in_channels=in_channels,
                        num_layers=transformer_layers_per_block[i],
                        cross_attention_dim=cross_attention_dim,
                        norm_num_groups=resnet_groups,
                        use_linear_projection=use_linear_projection,
                        upcast_attention=upcast_attention,
                        attention_type=attention_type,
                    )
                )
            else:
                attentions.append(
                    DualTransformer2DModel(
                        num_attention_heads,
                        in_channels // num_attention_heads,
                        in_channels=in_channels,
                        num_layers=1,
                        cross_attention_dim=cross_attention_dim,
                        norm_num_groups=resnet_groups,
                    )
                )
            resnets.append(
                ResnetBlock2D(
                    in_channels=in_channels,
                    out_channels=in_channels,
                    temb_channels=temb_channels,
                    eps=resnet_eps,
                    groups=resnet_groups,
                    dropout=dropout,
                    time_embedding_norm=resnet_time_scale_shift,
                    non_linearity=resnet_act_fn,
                    output_scale_factor=output_scale_factor,
                    pre_norm=resnet_pre_norm,
                )
            )

        self.attentions = nn.ModuleList(attentions)
        self.resnets = nn.ModuleList(resnets)

        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        temb: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
    ) -> torch.FloatTensor:
        lora_scale = (
            cross_attention_kwargs.get("scale", 1.0)
            if cross_attention_kwargs is not None
            else 1.0
        )
        hidden_states = self.resnets[0](hidden_states, temb, scale=lora_scale)
        for attn, resnet in zip(self.attentions, self.resnets[1:]):
            if self.training and self.gradient_checkpointing:

                def create_custom_forward(module, return_dict=None):
                    def custom_forward(*inputs):
                        if return_dict is not None:
                            return module(*inputs, return_dict=return_dict)
                        else:
                            return module(*inputs)

                    return custom_forward

                ckpt_kwargs: Dict[str, Any] = (
                    {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
                )
                hidden_states, ref_feature = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    cross_attention_kwargs=cross_attention_kwargs,
                    attention_mask=attention_mask,
                    encoder_attention_mask=encoder_attention_mask,
                    return_dict=False,
                )
                hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(resnet),
                    hidden_states,
                    temb,
                    **ckpt_kwargs,
                )
            else:
                hidden_states, ref_feature = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    cross_attention_kwargs=cross_attention_kwargs,
                    attention_mask=attention_mask,
                    encoder_attention_mask=encoder_attention_mask,
                    return_dict=False,
                )
                hidden_states = resnet(hidden_states, temb, scale=lora_scale)

        return hidden_states


class CrossAttnDownBlock2D(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        temb_channels: int,
        dropout: float = 0.0,
        num_layers: int = 1,
        transformer_layers_per_block: Union[int, Tuple[int]] = 1,
        resnet_eps: float = 1e-6,
        resnet_time_scale_shift: str = "default",
        resnet_act_fn: str = "swish",
        resnet_groups: int = 32,
        resnet_pre_norm: bool = True,
        num_attention_heads: int = 1,
        cross_attention_dim: int = 1280,
        output_scale_factor: float = 1.0,
        downsample_padding: int = 1,
        add_downsample: bool = True,
        dual_cross_attention: bool = False,
        use_linear_projection: bool = False,
        only_cross_attention: bool = False,
        upcast_attention: bool = False,
        attention_type: str = "default",
    ):
        super().__init__()
        resnets = []
        attentions = []

        self.has_cross_attention = True
        self.num_attention_heads = num_attention_heads
        if isinstance(transformer_layers_per_block, int):
            transformer_layers_per_block = [transformer_layers_per_block] * num_layers

        for i in range(num_layers):
            in_channels = in_channels if i == 0 else out_channels
            resnets.append(
                ResnetBlock2D(
                    in_channels=in_channels,
                    out_channels=out_channels,
                    temb_channels=temb_channels,
                    eps=resnet_eps,
                    groups=resnet_groups,
                    dropout=dropout,
                    time_embedding_norm=resnet_time_scale_shift,
                    non_linearity=resnet_act_fn,
                    output_scale_factor=output_scale_factor,
                    pre_norm=resnet_pre_norm,
                )
            )
            if not dual_cross_attention:
                attentions.append(
                    Transformer2DModel(
                        num_attention_heads,
                        out_channels // num_attention_heads,
                        in_channels=out_channels,
                        num_layers=transformer_layers_per_block[i],
                        cross_attention_dim=cross_attention_dim,
                        norm_num_groups=resnet_groups,
                        use_linear_projection=use_linear_projection,
                        only_cross_attention=only_cross_attention,
                        upcast_attention=upcast_attention,
                        attention_type=attention_type,
                    )
                )
            else:
                attentions.append(
                    DualTransformer2DModel(
                        num_attention_heads,
                        out_channels // num_attention_heads,
                        in_channels=out_channels,
                        num_layers=1,
                        cross_attention_dim=cross_attention_dim,
                        norm_num_groups=resnet_groups,
                    )
                )
        self.attentions = nn.ModuleList(attentions)
        self.resnets = nn.ModuleList(resnets)

        if add_downsample:
            self.downsamplers = nn.ModuleList(
                [
                    Downsample2D(
                        out_channels,
                        use_conv=True,
                        out_channels=out_channels,
                        padding=downsample_padding,
                        name="op",
                    )
                ]
            )
        else:
            self.downsamplers = None

        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        temb: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        additional_residuals: Optional[torch.FloatTensor] = None,
    ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
        output_states = ()

        lora_scale = (
            cross_attention_kwargs.get("scale", 1.0)
            if cross_attention_kwargs is not None
            else 1.0
        )

        blocks = list(zip(self.resnets, self.attentions))

        for i, (resnet, attn) in enumerate(blocks):
            if self.training and self.gradient_checkpointing:

                def create_custom_forward(module, return_dict=None):
                    def custom_forward(*inputs):
                        if return_dict is not None:
                            return module(*inputs, return_dict=return_dict)
                        else:
                            return module(*inputs)

                    return custom_forward

                ckpt_kwargs: Dict[str, Any] = (
                    {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
                )
                hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(resnet),
                    hidden_states,
                    temb,
                    **ckpt_kwargs,
                )
                hidden_states, ref_feature = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    cross_attention_kwargs=cross_attention_kwargs,
                    attention_mask=attention_mask,
                    encoder_attention_mask=encoder_attention_mask,
                    return_dict=False,
                )
            else:
                hidden_states = resnet(hidden_states, temb, scale=lora_scale)
                hidden_states, ref_feature = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    cross_attention_kwargs=cross_attention_kwargs,
                    attention_mask=attention_mask,
                    encoder_attention_mask=encoder_attention_mask,
                    return_dict=False,
                )

            # apply additional residuals to the output of the last pair of resnet and attention blocks
            if i == len(blocks) - 1 and additional_residuals is not None:
                hidden_states = hidden_states + additional_residuals

            output_states = output_states + (hidden_states,)

        if self.downsamplers is not None:
            for downsampler in self.downsamplers:
                hidden_states = downsampler(hidden_states, scale=lora_scale)

            output_states = output_states + (hidden_states,)

        return hidden_states, output_states


class DownBlock2D(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        temb_channels: int,
        dropout: float = 0.0,
        num_layers: int = 1,
        resnet_eps: float = 1e-6,
        resnet_time_scale_shift: str = "default",
        resnet_act_fn: str = "swish",
        resnet_groups: int = 32,
        resnet_pre_norm: bool = True,
        output_scale_factor: float = 1.0,
        add_downsample: bool = True,
        downsample_padding: int = 1,
    ):
        super().__init__()
        resnets = []

        for i in range(num_layers):
            in_channels = in_channels if i == 0 else out_channels
            resnets.append(
                ResnetBlock2D(
                    in_channels=in_channels,
                    out_channels=out_channels,
                    temb_channels=temb_channels,
                    eps=resnet_eps,
                    groups=resnet_groups,
                    dropout=dropout,
                    time_embedding_norm=resnet_time_scale_shift,
                    non_linearity=resnet_act_fn,
                    output_scale_factor=output_scale_factor,
                    pre_norm=resnet_pre_norm,
                )
            )

        self.resnets = nn.ModuleList(resnets)

        if add_downsample:
            self.downsamplers = nn.ModuleList(
                [
                    Downsample2D(
                        out_channels,
                        use_conv=True,
                        out_channels=out_channels,
                        padding=downsample_padding,
                        name="op",
                    )
                ]
            )
        else:
            self.downsamplers = None

        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        temb: Optional[torch.FloatTensor] = None,
        scale: float = 1.0,
    ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
        output_states = ()

        for resnet in self.resnets:
            if self.training and self.gradient_checkpointing:

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return module(*inputs)

                    return custom_forward

                if is_torch_version(">=", "1.11.0"):
                    hidden_states = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(resnet),
                        hidden_states,
                        temb,
                        use_reentrant=False,
                    )
                else:
                    hidden_states = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(resnet), hidden_states, temb
                    )
            else:
                hidden_states = resnet(hidden_states, temb, scale=scale)

            output_states = output_states + (hidden_states,)

        if self.downsamplers is not None:
            for downsampler in self.downsamplers:
                hidden_states = downsampler(hidden_states, scale=scale)

            output_states = output_states + (hidden_states,)

        return hidden_states, output_states


class CrossAttnUpBlock2D(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        prev_output_channel: int,
        temb_channels: int,
        resolution_idx: Optional[int] = None,
        dropout: float = 0.0,
        num_layers: int = 1,
        transformer_layers_per_block: Union[int, Tuple[int]] = 1,
        resnet_eps: float = 1e-6,
        resnet_time_scale_shift: str = "default",
        resnet_act_fn: str = "swish",
        resnet_groups: int = 32,
        resnet_pre_norm: bool = True,
        num_attention_heads: int = 1,
        cross_attention_dim: int = 1280,
        output_scale_factor: float = 1.0,
        add_upsample: bool = True,
        dual_cross_attention: bool = False,
        use_linear_projection: bool = False,
        only_cross_attention: bool = False,
        upcast_attention: bool = False,
        attention_type: str = "default",
    ):
        super().__init__()
        resnets = []
        attentions = []

        self.has_cross_attention = True
        self.num_attention_heads = num_attention_heads

        if isinstance(transformer_layers_per_block, int):
            transformer_layers_per_block = [transformer_layers_per_block] * num_layers

        for i in range(num_layers):
            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
            resnet_in_channels = prev_output_channel if i == 0 else out_channels

            resnets.append(
                ResnetBlock2D(
                    in_channels=resnet_in_channels + res_skip_channels,
                    out_channels=out_channels,
                    temb_channels=temb_channels,
                    eps=resnet_eps,
                    groups=resnet_groups,
                    dropout=dropout,
                    time_embedding_norm=resnet_time_scale_shift,
                    non_linearity=resnet_act_fn,
                    output_scale_factor=output_scale_factor,
                    pre_norm=resnet_pre_norm,
                )
            )
            if not dual_cross_attention:
                attentions.append(
                    Transformer2DModel(
                        num_attention_heads,
                        out_channels // num_attention_heads,
                        in_channels=out_channels,
                        num_layers=transformer_layers_per_block[i],
                        cross_attention_dim=cross_attention_dim,
                        norm_num_groups=resnet_groups,
                        use_linear_projection=use_linear_projection,
                        only_cross_attention=only_cross_attention,
                        upcast_attention=upcast_attention,
                        attention_type=attention_type,
                    )
                )
            else:
                attentions.append(
                    DualTransformer2DModel(
                        num_attention_heads,
                        out_channels // num_attention_heads,
                        in_channels=out_channels,
                        num_layers=1,
                        cross_attention_dim=cross_attention_dim,
                        norm_num_groups=resnet_groups,
                    )
                )
        self.attentions = nn.ModuleList(attentions)
        self.resnets = nn.ModuleList(resnets)

        if add_upsample:
            self.upsamplers = nn.ModuleList(
                [Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]
            )
        else:
            self.upsamplers = None

        self.gradient_checkpointing = False
        self.resolution_idx = resolution_idx

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
        temb: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        upsample_size: Optional[int] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
    ) -> torch.FloatTensor:
        lora_scale = (
            cross_attention_kwargs.get("scale", 1.0)
            if cross_attention_kwargs is not None
            else 1.0
        )
        is_freeu_enabled = (
            getattr(self, "s1", None)
            and getattr(self, "s2", None)
            and getattr(self, "b1", None)
            and getattr(self, "b2", None)
        )

        for resnet, attn in zip(self.resnets, self.attentions):
            # pop res hidden states
            res_hidden_states = res_hidden_states_tuple[-1]
            res_hidden_states_tuple = res_hidden_states_tuple[:-1]

            # FreeU: Only operate on the first two stages
            if is_freeu_enabled:
                hidden_states, res_hidden_states = apply_freeu(
                    self.resolution_idx,
                    hidden_states,
                    res_hidden_states,
                    s1=self.s1,
                    s2=self.s2,
                    b1=self.b1,
                    b2=self.b2,
                )

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

            if self.training and self.gradient_checkpointing:

                def create_custom_forward(module, return_dict=None):
                    def custom_forward(*inputs):
                        if return_dict is not None:
                            return module(*inputs, return_dict=return_dict)
                        else:
                            return module(*inputs)

                    return custom_forward

                ckpt_kwargs: Dict[str, Any] = (
                    {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
                )
                hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(resnet),
                    hidden_states,
                    temb,
                    **ckpt_kwargs,
                )
                hidden_states, ref_feature = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    cross_attention_kwargs=cross_attention_kwargs,
                    attention_mask=attention_mask,
                    encoder_attention_mask=encoder_attention_mask,
                    return_dict=False,
                )
            else:
                hidden_states = resnet(hidden_states, temb, scale=lora_scale)
                hidden_states, ref_feature = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    cross_attention_kwargs=cross_attention_kwargs,
                    attention_mask=attention_mask,
                    encoder_attention_mask=encoder_attention_mask,
                    return_dict=False,
                )

        if self.upsamplers is not None:
            for upsampler in self.upsamplers:
                hidden_states = upsampler(
                    hidden_states, upsample_size, scale=lora_scale
                )

        return hidden_states


class UpBlock2D(nn.Module):
    def __init__(
        self,
        in_channels: int,
        prev_output_channel: int,
        out_channels: int,
        temb_channels: int,
        resolution_idx: Optional[int] = None,
        dropout: float = 0.0,
        num_layers: int = 1,
        resnet_eps: float = 1e-6,
        resnet_time_scale_shift: str = "default",
        resnet_act_fn: str = "swish",
        resnet_groups: int = 32,
        resnet_pre_norm: bool = True,
        output_scale_factor: float = 1.0,
        add_upsample: bool = True,
    ):
        super().__init__()
        resnets = []

        for i in range(num_layers):
            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
            resnet_in_channels = prev_output_channel if i == 0 else out_channels

            resnets.append(
                ResnetBlock2D(
                    in_channels=resnet_in_channels + res_skip_channels,
                    out_channels=out_channels,
                    temb_channels=temb_channels,
                    eps=resnet_eps,
                    groups=resnet_groups,
                    dropout=dropout,
                    time_embedding_norm=resnet_time_scale_shift,
                    non_linearity=resnet_act_fn,
                    output_scale_factor=output_scale_factor,
                    pre_norm=resnet_pre_norm,
                )
            )

        self.resnets = nn.ModuleList(resnets)

        if add_upsample:
            self.upsamplers = nn.ModuleList(
                [Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]
            )
        else:
            self.upsamplers = None

        self.gradient_checkpointing = False
        self.resolution_idx = resolution_idx

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
        temb: Optional[torch.FloatTensor] = None,
        upsample_size: Optional[int] = None,
        scale: float = 1.0,
    ) -> torch.FloatTensor:
        is_freeu_enabled = (
            getattr(self, "s1", None)
            and getattr(self, "s2", None)
            and getattr(self, "b1", None)
            and getattr(self, "b2", None)
        )

        for resnet in self.resnets:
            # pop res hidden states
            res_hidden_states = res_hidden_states_tuple[-1]
            res_hidden_states_tuple = res_hidden_states_tuple[:-1]

            # FreeU: Only operate on the first two stages
            if is_freeu_enabled:
                hidden_states, res_hidden_states = apply_freeu(
                    self.resolution_idx,
                    hidden_states,
                    res_hidden_states,
                    s1=self.s1,
                    s2=self.s2,
                    b1=self.b1,
                    b2=self.b2,
                )

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

            if self.training and self.gradient_checkpointing:

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return module(*inputs)

                    return custom_forward

                if is_torch_version(">=", "1.11.0"):
                    hidden_states = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(resnet),
                        hidden_states,
                        temb,
                        use_reentrant=False,
                    )
                else:
                    hidden_states = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(resnet), hidden_states, temb
                    )
            else:
                hidden_states = resnet(hidden_states, temb, scale=scale)

        if self.upsamplers is not None:
            for upsampler in self.upsamplers:
                hidden_states = upsampler(hidden_states, upsample_size, scale=scale)

        return hidden_states