model.py

from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union
import os
import math
import json
from glob import glob
from functools import partial

import torch
import torch.nn as nn
import torch.utils.checkpoint
import torch.nn.functional as F
import numpy as np

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.loaders import UNet2DConditionLoadersMixin
from diffusers.utils import BaseOutput, logging, is_torch_version
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.unet_2d_blocks import (
    CrossAttnDownBlock2D,
    CrossAttnUpBlock2D,
    DownBlock2D,
    UNetMidBlock2DCrossAttn,
    UNetMidBlock2DSimpleCrossAttn,
    UpBlock2D,
    get_down_block as gdb,
    get_up_block as gub,
)
from diffusers.models.embeddings import (
    GaussianFourierProjection,
    ImageHintTimeEmbedding,
    ImageProjection,
    ImageTimeEmbedding,
    TextImageProjection,
    TextImageTimeEmbedding,
    TextTimeEmbedding,
    TimestepEmbedding,
    Timesteps,
)
from diffusers.models.activations import get_activation
from diffusers.models.attention_processor import AttentionProcessor, AttnProcessor, Attention, AttnAddedKVProcessor, AttnAddedKVProcessor2_0
from diffusers.models.resnet import Downsample2D, FirDownsample2D, FirUpsample2D, KDownsample2D, KUpsample2D, ResnetBlock2D, Upsample2D
from diffusers.models.transformer_2d import Transformer2DModel
from diffusers.models.dual_transformer_2d import DualTransformer2DModel


class CrossAttnDownBlock2DWithFlow(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        temb_channels: int,
        flow_channels: int,  # Added
        dropout: float = 0.0,
        num_layers: int = 1,
        transformer_layers_per_block: 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=1,
        cross_attention_dim=1280,
        output_scale_factor=1.0,
        downsample_padding=1,
        add_downsample=True,
        dual_cross_attention=False,
        use_linear_projection=False,
        only_cross_attention=False,
        upcast_attention=False,
    ):
        super().__init__()
        resnets = []
        attentions = []
        flow_convs = []

        self.has_cross_attention = True
        self.num_attention_heads = num_attention_heads

        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,
                        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,
                    )
                )
            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,
                    )
                )
            flow_convs.append(
                nn.Conv2d(
                    flow_channels, out_channels, kernel_size=3, padding=1, bias=False,
                )
            )
        self.attentions = nn.ModuleList(attentions)
        self.resnets = nn.ModuleList(resnets)
        self.flow_convs = nn.ModuleList(flow_convs)

        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=None,
        flow: Optional[torch.FloatTensor] = None,  # Added
    ):
        output_states = ()

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

        for i, (resnet, attn, flow_conv) 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 = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(attn, return_dict=False),
                    hidden_states,
                    encoder_hidden_states,
                    None,  # timestep
                    None,  # class_labels
                    cross_attention_kwargs,
                    attention_mask,
                    encoder_attention_mask,
                    **ckpt_kwargs,
                )[0]
            else:
                hidden_states = resnet(hidden_states, temb)
                if flow is not None:
                    hidden_states = hidden_states + flow_conv(flow)
                hidden_states = 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,
                )[0]

            # 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)

            output_states = output_states + (hidden_states,)

        return hidden_states, output_states


class UNetMidBlock2DCrossAttnWithFlow(nn.Module):
    def __init__(
        self,
        in_channels: int,
        temb_channels: int,
        flow_channels: int,  # Added
        dropout: float = 0.0,
        num_layers: int = 1,
        transformer_layers_per_block: 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=1,
        output_scale_factor=1.0,
        cross_attention_dim=1280,
        dual_cross_attention=False,
        use_linear_projection=False,
        upcast_attention=False,
    ):
        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)

        # 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,
            )
        ]
        flow_convs = [
            nn.Conv2d(
                flow_channels, in_channels, kernel_size=3, padding=1, bias=False,
            )
        ]
        attentions = []

        for _ 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,
                        cross_attention_dim=cross_attention_dim,
                        norm_num_groups=resnet_groups,
                        use_linear_projection=use_linear_projection,
                        upcast_attention=upcast_attention,
                    )
                )
            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,
                )
            )
            flow_convs.append(
                nn.Conv2d(
                    flow_channels, in_channels, kernel_size=3, padding=1, bias=False,
                )
            )

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

    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,
        flow: Optional[torch.FloatTensor] = None,  # Added
    ) -> torch.FloatTensor:
        hidden_states = self.resnets[0](hidden_states, temb)
        hidden_states = hidden_states + self.flow_convs[0](flow)
        for attn, resnet, flow_conv in zip(self.attentions, self.resnets[1:], self.flow_convs[1:]):
            hidden_states = 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,
            )[0]
            hidden_states = resnet(hidden_states, temb)
            hidden_states = hidden_states + flow_conv(flow)

        return hidden_states


class CrossAttnUpBlock2DWithFlow(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        prev_output_channel: int,
        temb_channels: int,
        flow_channels: int,  # Added
        dropout: float = 0.0,
        num_layers: int = 1,
        transformer_layers_per_block: 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=1,
        cross_attention_dim=1280,
        output_scale_factor=1.0,
        add_upsample=True,
        dual_cross_attention=False,
        use_linear_projection=False,
        only_cross_attention=False,
        upcast_attention=False,
    ):
        super().__init__()
        resnets = []
        attentions = []
        flow_convs = []

        self.has_cross_attention = True
        self.num_attention_heads = num_attention_heads

        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,
                        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,
                    )
                )
            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,
                    )
                )
            flow_convs.append(
                nn.Conv2d(
                    flow_channels, out_channels, kernel_size=3, padding=1, bias=False,
                )
            )
        self.attentions = nn.ModuleList(attentions)
        self.resnets = nn.ModuleList(resnets)
        self.flow_convs = nn.ModuleList(flow_convs)

        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

    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,
        flow: Optional[torch.FloatTensor] = None,  # Added
    ):
        for resnet, attn, flow_conv in zip(self.resnets, self.attentions, self.flow_convs):
            # pop res hidden states
            res_hidden_states = res_hidden_states_tuple[-1]
            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
            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 = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(attn, return_dict=False),
                    hidden_states,
                    encoder_hidden_states,
                    None,  # timestep
                    None,  # class_labels
                    cross_attention_kwargs,
                    attention_mask,
                    encoder_attention_mask,
                    **ckpt_kwargs,
                )[0]
            else:
                hidden_states = resnet(hidden_states, temb)
                hidden_states = hidden_states + flow_conv(flow)
                hidden_states = 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,
                )[0]

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

        return hidden_states



def get_sin_cos_pos_embed(embed_dim: int, x: torch.Tensor):
    bsz, _ = x.shape
    x = x.reshape(-1)[:, None]
    div_term = torch.exp(torch.arange(0, embed_dim, 2) * (-math.log(10000.0) / embed_dim)).to(x.device)
    pos = x * div_term
    pos = torch.cat([torch.sin(pos), torch.cos(pos)], dim=-1).reshape(bsz, -1)
    return pos


def get_down_block(
    with_concatenated_flow: bool = False,
    *args,
    **kwargs,
):
    if not with_concatenated_flow or args[0] == "DownBlock2D":
        kwargs.pop("flow_channels", None)
        return gdb(*args, **kwargs)
    elif args[0] == "CrossAttnDownBlock2D":
        kwargs.pop("downsample_type", None)
        kwargs.pop("attention_head_dim", None)
        kwargs.pop("resnet_skip_time_act", None)
        kwargs.pop("resnet_out_scale_factor", None)
        kwargs.pop("cross_attention_norm", None)
        return CrossAttnDownBlock2DWithFlow(*args[1:], **kwargs)
    else:
        raise ValueError(f"Unknown down block type: {args[0]}")


def get_up_block(
    with_concatenated_flow: bool = False,
    *args,
    **kwargs,
):
    if not with_concatenated_flow or args[0] == "UpBlock2D":
        kwargs.pop("flow_channels", None)
        return gub(*args, **kwargs)
    elif args[0] == "CrossAttnUpBlock2D":
        kwargs.pop("upsample_type", None)
        kwargs.pop("attention_head_dim", None)
        kwargs.pop("resnet_skip_time_act", None)
        kwargs.pop("resnet_out_scale_factor", None)
        kwargs.pop("cross_attention_norm", None)
        return CrossAttnUpBlock2DWithFlow(*args[1:], **kwargs)
    else:
        raise ValueError(f"Unknown up block type: {args[0]}")


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


def avg_pool_nd(dims, *args, **kwargs):
    """
    Create a 1D, 2D, or 3D average pooling module.
    """
    if dims == 1:
        return nn.AvgPool1d(*args, **kwargs)
    elif dims == 2:
        return nn.AvgPool2d(*args, **kwargs)
    elif dims == 3:
        return nn.AvgPool3d(*args, **kwargs)
    raise ValueError(f"unsupported dimensions: {dims}")


def conv_nd(dims, *args, **kwargs):
    """
    Create a 1D, 2D, or 3D convolution module.
    """
    if dims == 1:
        return nn.Conv1d(*args, **kwargs)
    elif dims == 2:
        return nn.Conv2d(*args, **kwargs)
    elif dims == 3:
        return nn.Conv3d(*args, **kwargs)
    raise ValueError(f"unsupported dimensions: {dims}")


class Downsample(nn.Module):
    """
    A downsampling layer with an optional convolution.
    :param channels: channels in the inputs and outputs.
    :param use_conv: a bool determining if a convolution is applied.
    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
                 downsampling occurs in the inner-two dimensions.
    """

    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.dims = dims
        stride = 2 if dims != 3 else (1, 2, 2)
        if use_conv:
            self.op = conv_nd(
                dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
            )
        else:
            assert self.channels == self.out_channels
            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)

    def forward(self, x):
        assert x.shape[1] == self.channels
        return self.op(x)


class ResnetBlock(nn.Module):
    def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True):
        super().__init__()
        ps = ksize // 2
        if in_c != out_c or sk == False:
            self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps)
        else:
            # print('n_in')
            self.in_conv = None
        self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1)
        self.act = nn.ReLU()
        self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps)
        if sk == False:
            # self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps) # edit by zhouxiawang
            self.skep = nn.Conv2d(out_c, out_c, ksize, 1, ps)
        else:
            self.skep = None

        self.down = down
        if self.down == True:
            self.down_opt = Downsample(in_c, use_conv=use_conv)

    def forward(self, x):
        if self.down == True:
            x = self.down_opt(x)
        if self.in_conv is not None:  # edit
            x = self.in_conv(x)

        h = self.block1(x)
        h = self.act(h)
        h = self.block2(h)
        if self.skep is not None:
            return h + self.skep(x)
        else:
            return h + x


class Adapter(nn.Module):
    def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64, ksize=3, sk=False, use_conv=True):
        super(Adapter, self).__init__()
        self.unshuffle = nn.PixelUnshuffle(16)
        self.channels = channels
        self.nums_rb = nums_rb
        self.body = []
        for i in range(len(channels)):
            for j in range(nums_rb):
                if (i != 0) and (j == 0):
                    self.body.append(
                        ResnetBlock(channels[i - 1], channels[i], down=True, ksize=ksize, sk=sk, use_conv=use_conv))
                else:
                    self.body.append(
                        ResnetBlock(channels[i], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv))
        self.body = nn.ModuleList(self.body)
        self.conv_in = nn.Conv2d(cin * 16 * 16, channels[0], 3, 1, 1)

    def forward(self, x):
        # unshuffle
        x = self.unshuffle(x)
        # extract features
        features = []
        x = self.conv_in(x)
        for i in range(len(self.channels)):
            for j in range(self.nums_rb):
                idx = i * self.nums_rb + j
                x = self.body[idx](x)
            features.append(x)

        return features


class OneSidedAttnProcessor:
    r"""
    Processor for performing attention-related computations where the key and value are always from the upper half batch
    """

    def __call__(
        self,
        attn: Attention,
        hidden_states,
        encoder_hidden_states=None,
        attention_mask=None,
        temb=None,
    ):
        assert encoder_hidden_states is None
        residual = hidden_states

        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )

        assert batch_size % 2 == 0, "batch size must be even"
        half_batch_size = batch_size // 2
        hidden_states_1, hidden_states_2 = hidden_states.chunk(2, dim=0)

        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, half_batch_size)

        if attn.group_norm is not None:
            hidden_states_1 = attn.group_norm(hidden_states_1.transpose(1, 2)).transpose(1, 2)
            hidden_states_2 = attn.group_norm(hidden_states_2.transpose(1, 2)).transpose(1, 2)

        query_1 = attn.to_q(hidden_states_1)
        query_2 = attn.to_q(hidden_states_2)
        key = attn.to_k(hidden_states_1)
        value = attn.to_v(hidden_states_1)

        query = torch.cat([query_1, query_2], dim=0)
        key = torch.cat([key, key], dim=0)
        value = torch.cat([value, value], dim=0)

        query = attn.head_to_batch_dim(query)
        key = attn.head_to_batch_dim(key)
        value = attn.head_to_batch_dim(value)

        attention_probs = attn.get_attention_scores(query, key, attention_mask)
        hidden_states = torch.bmm(attention_probs, value)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


class UNet2DDragConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
    r"""
    A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
    shaped output.

    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
    for all models (such as downloading or saving).

    Parameters:
        sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
            Height and width of input/output sample.
        in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.
        out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
        center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
        flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
            Whether to flip the sin to cos in the time embedding.
        freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
        down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
            The tuple of downsample blocks to use.
        mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`):
            Block type for middle of UNet, it can be either `UNetMidBlock2DCrossAttn` or
            `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped.
        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`):
            The tuple of upsample blocks to use.
        only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):
            Whether to include self-attention in the basic transformer blocks, see
            [`~models.attention.BasicTransformerBlock`].
        block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
            The tuple of output channels for each block.
        layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
        downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
        mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
        norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
            If `None`, normalization and activation layers is skipped in post-processing.
        norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
        cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
            The dimension of the cross attention features.
        transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1):
            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
            [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
            [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
        encoder_hid_dim (`int`, *optional*, defaults to None):
            If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
            dimension to `cross_attention_dim`.
        encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
            If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
            embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
        attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
        num_attention_heads (`int`, *optional*):
            The number of attention heads. If not defined, defaults to `attention_head_dim`
        resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
            for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
        class_embed_type (`str`, *optional*, defaults to `None`):
            The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
            `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
        addition_embed_type (`str`, *optional*, defaults to `None`):
            Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
            "text". "text" will use the `TextTimeEmbedding` layer.
        addition_time_embed_dim: (`int`, *optional*, defaults to `None`):
            Dimension for the timestep embeddings.
        num_class_embeds (`int`, *optional*, defaults to `None`):
            Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
            class conditioning with `class_embed_type` equal to `None`.
        time_embedding_type (`str`, *optional*, defaults to `positional`):
            The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
        time_embedding_dim (`int`, *optional*, defaults to `None`):
            An optional override for the dimension of the projected time embedding.
        time_embedding_act_fn (`str`, *optional*, defaults to `None`):
            Optional activation function to use only once on the time embeddings before they are passed to the rest of
            the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.
        timestep_post_act (`str`, *optional*, defaults to `None`):
            The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
        time_cond_proj_dim (`int`, *optional*, defaults to `None`):
            The dimension of `cond_proj` layer in the timestep embedding.
        conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer.
        conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer.
        projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when
            `class_embed_type="projection"`. Required when `class_embed_type="projection"`.
        class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time
            embeddings with the class embeddings.
        mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):
            Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If
            `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the
            `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`
            otherwise.
    """

    _supports_gradient_checkpointing = True

    @register_to_config
    def __init__(
        self,
        sample_size: Optional[int] = None,
        in_channels: int = 4,
        flow_channels: int = 3,
        out_channels: int = 4,
        center_input_sample: bool = False,
        flip_sin_to_cos: bool = True,
        freq_shift: int = 0,
        down_block_types: Tuple[str] = (
            "CrossAttnDownBlock2D",
            "CrossAttnDownBlock2D",
            "CrossAttnDownBlock2D",
            "DownBlock2D",
        ),
        mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
        up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
        only_cross_attention: Union[bool, Tuple[bool]] = False,
        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
        layers_per_block: Union[int, Tuple[int]] = 2,
        downsample_padding: int = 1,
        mid_block_scale_factor: float = 1,
        act_fn: str = "silu",
        norm_num_groups: Optional[int] = 32,
        norm_eps: float = 1e-5,
        cross_attention_dim: Union[int, Tuple[int]] = 1280,
        transformer_layers_per_block: Union[int, Tuple[int]] = 1,
        encoder_hid_dim: Optional[int] = None,
        encoder_hid_dim_type: Optional[str] = None,
        attention_head_dim: Union[int, Tuple[int]] = 8,
        num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
        dual_cross_attention: bool = False,
        use_linear_projection: bool = False,
        class_embed_type: Optional[str] = None,
        addition_embed_type: Optional[str] = None,
        addition_time_embed_dim: Optional[int] = None,
        num_class_embeds: Optional[int] = None,
        upcast_attention: bool = False,
        resnet_time_scale_shift: str = "default",
        resnet_skip_time_act: bool = False,
        resnet_out_scale_factor: int = 1.0,
        time_embedding_type: str = "positional",
        time_embedding_dim: Optional[int] = None,
        time_embedding_act_fn: Optional[str] = None,
        timestep_post_act: Optional[str] = None,
        time_cond_proj_dim: Optional[int] = None,
        conv_in_kernel: int = 3,
        conv_out_kernel: int = 3,
        projection_class_embeddings_input_dim: Optional[int] = None,
        class_embeddings_concat: bool = False,
        mid_block_only_cross_attention: Optional[bool] = None,
        cross_attention_norm: Optional[str] = None,
        addition_embed_type_num_heads=64,

        # Added
        clip_embedding_dim: int = 1024,
        num_clip_in: int = 25,
        dragging_embedding_dim: int = 256,
        use_drag_tokens: bool = True,
        single_drag_token: bool = False,
        num_drags: int = 10,

        class_dropout_prob: float = 0.1,
        
        flow_original_res: bool = False,
        flow_size: int = 512,

        input_concat_dragging: bool = True,
        attn_concat_dragging: bool = False,
        flow_multi_resolution_conv: bool = False,

        flow_in_old_version: bool = True,
    ):
        super().__init__()

        assert input_concat_dragging or attn_concat_dragging or flow_multi_resolution_conv
        if flow_multi_resolution_conv:
            assert not attn_concat_dragging and not input_concat_dragging

        self.sample_size = sample_size
        
        self.drag_dropout_prob = class_dropout_prob

        if num_attention_heads is not None:
            raise ValueError(
                "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."
            )

        # If `num_attention_heads` is not defined (which is the case for most models)
        # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
        # The reason for this behavior is to correct for incorrectly named variables that were introduced
        # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
        # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
        # which is why we correct for the naming here.
        num_attention_heads = num_attention_heads or attention_head_dim

        # Check inputs
        if len(down_block_types) != len(up_block_types):
            raise ValueError(
                f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
            )

        if len(block_out_channels) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
            )

        if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
            )

        if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
            )

        if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}."
            )

        if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
            )

        if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
            )

        # input
        conv_in_padding = (conv_in_kernel - 1) // 2

        self.num_drags = num_drags

        self.attn_concat_dragging = attn_concat_dragging
        if self.attn_concat_dragging:
            self.drag_extra_dim = 4 * self.num_drags

        self.flow_multi_resolution_conv = flow_multi_resolution_conv
        if self.flow_multi_resolution_conv:
            self.flow_adapter = Adapter(
                channels=block_out_channels[:1] + block_out_channels[:-1],
                nums_rb=2,
                cin=3,
                sk=True,
                use_conv=False,
            )

        self.input_concat_dragging = input_concat_dragging
        self.flow_in_old_version = flow_in_old_version
        if self.input_concat_dragging:
            if self.flow_in_old_version:
                self.conv_in_flow = nn.Conv2d(
                    in_channels + flow_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
                )
            else:
                self.conv_in = nn.Conv2d(
                    in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
                )
                self.conv_in_flow = nn.Conv2d(
                    flow_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding, bias=False
                )
        else:
            self.conv_in = nn.Conv2d(
                in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
            )

        self.flow_original_res = flow_original_res
        if flow_original_res and self.input_concat_dragging:
            self.num_flow_down_layers = 0
            cur_sample_size = sample_size
            while flow_size > cur_sample_size:
                assert flow_size % cur_sample_size == 0
                self.num_flow_down_layers += 1
                cur_sample_size *= 2
            
            self.flow_preprocess = nn.ModuleList([])
            for _ in range(self.num_flow_down_layers):
                self.flow_preprocess.append(nn.Conv2d(
                    flow_channels, flow_channels, kernel_size=3, padding=1
                ))
            self.flow_proj_act = get_activation(act_fn)

        self.num_clip_in = num_clip_in
        self.clip_proj = nn.ModuleList([])
        for i in range(num_clip_in):
            self.clip_proj.append(nn.Linear(clip_embedding_dim, clip_embedding_dim))
        self.clip_final = nn.Linear(clip_embedding_dim, cross_attention_dim)

        self.use_drag_tokens = use_drag_tokens
        self.single_drag_token = single_drag_token
        if use_drag_tokens:
            self.dragging_embedding_dim = dragging_embedding_dim
            self.drag_proj = nn.Linear(dragging_embedding_dim * 4, dragging_embedding_dim * 4)
            self.drag_final = nn.Linear(dragging_embedding_dim * 4, cross_attention_dim)
        self.proj_act = get_activation(act_fn)

        # time
        if time_embedding_type == "fourier":
            time_embed_dim = time_embedding_dim or block_out_channels[0] * 2
            if time_embed_dim % 2 != 0:
                raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.")
            self.time_proj = GaussianFourierProjection(
                time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
            )
            timestep_input_dim = time_embed_dim
        elif time_embedding_type == "positional":
            time_embed_dim = time_embedding_dim or block_out_channels[0] * 4

            self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
            timestep_input_dim = block_out_channels[0]
        else:
            raise ValueError(
                f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
            )

        self.time_embedding = TimestepEmbedding(
            timestep_input_dim,
            time_embed_dim,
            act_fn=act_fn,
            post_act_fn=timestep_post_act,
            cond_proj_dim=time_cond_proj_dim,
        )

        if encoder_hid_dim_type is None and encoder_hid_dim is not None:
            encoder_hid_dim_type = "text_proj"
            self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
            logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")

        if encoder_hid_dim is None and encoder_hid_dim_type is not None:
            raise ValueError(
                f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
            )

        if encoder_hid_dim_type == "text_proj":
            self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
        elif encoder_hid_dim_type == "text_image_proj":
            # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
            # case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)`
            self.encoder_hid_proj = TextImageProjection(
                text_embed_dim=encoder_hid_dim,
                image_embed_dim=cross_attention_dim,
                cross_attention_dim=cross_attention_dim,
            )
        elif encoder_hid_dim_type == "image_proj":
            # Kandinsky 2.2
            self.encoder_hid_proj = ImageProjection(
                image_embed_dim=encoder_hid_dim,
                cross_attention_dim=cross_attention_dim,
            )
        elif encoder_hid_dim_type is not None:
            raise ValueError(
                f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
            )
        else:
            self.encoder_hid_proj = None

        # class embedding
        if class_embed_type is None and num_class_embeds is not None:
            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
        elif class_embed_type == "timestep":
            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)
        elif class_embed_type == "identity":
            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
        elif class_embed_type == "projection":
            if projection_class_embeddings_input_dim is None:
                raise ValueError(
                    "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
                )
            # The projection `class_embed_type` is the same as the timestep `class_embed_type` except
            # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
            # 2. it projects from an arbitrary input dimension.
            #
            # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
            # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
            # As a result, `TimestepEmbedding` can be passed arbitrary vectors.
            self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
        elif class_embed_type == "simple_projection":
            if projection_class_embeddings_input_dim is None:
                raise ValueError(
                    "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
                )
            self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)
        else:
            self.class_embedding = None

        if addition_embed_type == "text":
            if encoder_hid_dim is not None:
                text_time_embedding_from_dim = encoder_hid_dim
            else:
                text_time_embedding_from_dim = cross_attention_dim

            self.add_embedding = TextTimeEmbedding(
                text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
            )
        elif addition_embed_type == "text_image":
            # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
            # case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`
            self.add_embedding = TextImageTimeEmbedding(
                text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
            )
        elif addition_embed_type == "text_time":
            self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
            self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
        elif addition_embed_type == "image":
            # Kandinsky 2.2
            self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
        elif addition_embed_type == "image_hint":
            # Kandinsky 2.2 ControlNet
            self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
        elif addition_embed_type is not None:
            raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")

        if time_embedding_act_fn is None:
            self.time_embed_act = None
        else:
            self.time_embed_act = get_activation(time_embedding_act_fn)

        self.down_blocks = nn.ModuleList([])
        self.up_blocks = nn.ModuleList([])

        if isinstance(only_cross_attention, bool):
            if mid_block_only_cross_attention is None:
                mid_block_only_cross_attention = only_cross_attention

            only_cross_attention = [only_cross_attention] * len(down_block_types)

        if mid_block_only_cross_attention is None:
            mid_block_only_cross_attention = False

        if isinstance(num_attention_heads, int):
            num_attention_heads = (num_attention_heads,) * len(down_block_types)

        if isinstance(attention_head_dim, int):
            attention_head_dim = (attention_head_dim,) * len(down_block_types)

        if isinstance(cross_attention_dim, int):
            cross_attention_dim = (cross_attention_dim,) * len(down_block_types)

        if isinstance(layers_per_block, int):
            layers_per_block = [layers_per_block] * len(down_block_types)

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

        if class_embeddings_concat:
            # The time embeddings are concatenated with the class embeddings. The dimension of the
            # time embeddings passed to the down, middle, and up blocks is twice the dimension of the
            # regular time embeddings
            blocks_time_embed_dim = time_embed_dim * 2
        else:
            blocks_time_embed_dim = time_embed_dim

        # down
        output_channel = block_out_channels[0]
        for i, down_block_type in enumerate(down_block_types):
            input_channel = output_channel
            output_channel = block_out_channels[i]
            is_final_block = i == len(block_out_channels) - 1

            down_block = get_down_block(
                self.attn_concat_dragging,
                down_block_type,
                num_layers=layers_per_block[i],
                transformer_layers_per_block=transformer_layers_per_block[i],
                in_channels=input_channel,
                out_channels=output_channel,
                temb_channels=blocks_time_embed_dim,
                add_downsample=not is_final_block,
                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                resnet_groups=norm_num_groups,
                cross_attention_dim=cross_attention_dim[i],
                num_attention_heads=num_attention_heads[i],
                downsample_padding=downsample_padding,
                dual_cross_attention=dual_cross_attention,
                use_linear_projection=use_linear_projection,
                only_cross_attention=only_cross_attention[i],
                upcast_attention=upcast_attention,
                resnet_time_scale_shift=resnet_time_scale_shift,
                resnet_skip_time_act=resnet_skip_time_act,
                resnet_out_scale_factor=resnet_out_scale_factor,
                cross_attention_norm=cross_attention_norm,
                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
                flow_channels=self.drag_extra_dim if self.attn_concat_dragging else None,
            )
            self.down_blocks.append(down_block)

        # mid
        if mid_block_type == "UNetMidBlock2DCrossAttn":
            mid_block_kwargs = dict(
                transformer_layers_per_block=transformer_layers_per_block[-1],
                in_channels=block_out_channels[-1],
                temb_channels=blocks_time_embed_dim,
                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                output_scale_factor=mid_block_scale_factor,
                resnet_time_scale_shift=resnet_time_scale_shift,
                cross_attention_dim=cross_attention_dim[-1],
                num_attention_heads=num_attention_heads[-1],
                resnet_groups=norm_num_groups,
                dual_cross_attention=dual_cross_attention,
                use_linear_projection=use_linear_projection,
                upcast_attention=upcast_attention,
            )

            if self.attn_concat_dragging:
                mid_block_kwargs["flow_channels"] = self.drag_extra_dim
                mid_block_type += "WithFlow"

            self.mid_block = eval(mid_block_type)(
                **mid_block_kwargs
            )
        elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn":
            raise NotImplementedError
            self.mid_block = UNetMidBlock2DSimpleCrossAttn(
                in_channels=block_out_channels[-1],
                temb_channels=blocks_time_embed_dim,
                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                output_scale_factor=mid_block_scale_factor,
                cross_attention_dim=cross_attention_dim[-1],
                attention_head_dim=attention_head_dim[-1],
                resnet_groups=norm_num_groups,
                resnet_time_scale_shift=resnet_time_scale_shift,
                skip_time_act=resnet_skip_time_act,
                only_cross_attention=mid_block_only_cross_attention,
                cross_attention_norm=cross_attention_norm,
            )
        elif mid_block_type is None:
            self.mid_block = None
        else:
            raise ValueError(f"unknown mid_block_type : {mid_block_type}")

        # count how many layers upsample the images
        self.num_upsamplers = 0

        # up
        reversed_block_out_channels = list(reversed(block_out_channels))
        reversed_num_attention_heads = list(reversed(num_attention_heads))
        reversed_layers_per_block = list(reversed(layers_per_block))
        reversed_cross_attention_dim = list(reversed(cross_attention_dim))
        reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block))
        only_cross_attention = list(reversed(only_cross_attention))

        output_channel = reversed_block_out_channels[0]
        for i, up_block_type in enumerate(up_block_types):
            is_final_block = i == len(block_out_channels) - 1

            prev_output_channel = output_channel
            output_channel = reversed_block_out_channels[i]
            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]

            # add upsample block for all BUT final layer
            if not is_final_block:
                add_upsample = True
                self.num_upsamplers += 1
            else:
                add_upsample = False

            up_block = get_up_block(
                self.attn_concat_dragging,
                up_block_type,
                num_layers=reversed_layers_per_block[i] + 1,
                transformer_layers_per_block=reversed_transformer_layers_per_block[i],
                in_channels=input_channel,
                out_channels=output_channel,
                prev_output_channel=prev_output_channel,
                temb_channels=blocks_time_embed_dim,
                add_upsample=add_upsample,
                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                resnet_groups=norm_num_groups,
                cross_attention_dim=reversed_cross_attention_dim[i],
                num_attention_heads=reversed_num_attention_heads[i],
                dual_cross_attention=dual_cross_attention,
                use_linear_projection=use_linear_projection,
                only_cross_attention=only_cross_attention[i],
                upcast_attention=upcast_attention,
                resnet_time_scale_shift=resnet_time_scale_shift,
                resnet_skip_time_act=resnet_skip_time_act,
                resnet_out_scale_factor=resnet_out_scale_factor,
                cross_attention_norm=cross_attention_norm,
                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
                flow_channels=self.drag_extra_dim if self.attn_concat_dragging else None,
            )
            self.up_blocks.append(up_block)
            prev_output_channel = output_channel

        # out
        if norm_num_groups is not None:
            self.conv_norm_out = nn.GroupNorm(
                num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
            )

            self.conv_act = get_activation(act_fn)

        else:
            self.conv_norm_out = None
            self.conv_act = None

        conv_out_padding = (conv_out_kernel - 1) // 2
        self.conv_out = nn.Conv2d(
            block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding
        )

    @property
    def attn_processors(self) -> Dict[str, AttentionProcessor]:
        r"""
        Returns:
            `dict` of attention processors: A dictionary containing all attention processors used in the model with
            indexed by its weight name.
        """
        # set recursively
        processors = {}

        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
            if hasattr(module, "set_processor"):
                processors[f"{name}.processor"] = module.processor

            for sub_name, child in module.named_children():
                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)

            return processors

        for name, module in self.named_children():
            fn_recursive_add_processors(name, module, processors)

        return processors

    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
        r"""
        Sets the attention processor to use to compute attention.

        Parameters:
            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
                The instantiated processor class or a dictionary of processor classes that will be set as the processor
                for **all** `Attention` layers.

                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
                processor. This is strongly recommended when setting trainable attention processors.

        """
        count = len(self.attn_processors.keys())

        if isinstance(processor, dict) and len(processor) != count:
            raise ValueError(
                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
            )

        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
            if hasattr(module, "set_processor"):
                if not isinstance(processor, dict):
                    module.set_processor(processor)
                else:
                    module.set_processor(processor.pop(f"{name}.processor"))

            for sub_name, child in module.named_children():
                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)

        for name, module in self.named_children():
            fn_recursive_attn_processor(name, module, processor)

    def set_default_attn_processor(self):
        """
        Disables custom attention processors and sets the default attention implementation.
        """
        self.set_attn_processor(AttnProcessor())

    def set_attention_slice(self, slice_size):
        r"""
        Enable sliced attention computation.

        When this option is enabled, the attention module splits the input tensor in slices to compute attention in
        several steps. This is useful for saving some memory in exchange for a small decrease in speed.

        Args:
            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
                When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
                `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
                must be a multiple of `slice_size`.
        """
        sliceable_head_dims = []

        def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
            if hasattr(module, "set_attention_slice"):
                sliceable_head_dims.append(module.sliceable_head_dim)

            for child in module.children():
                fn_recursive_retrieve_sliceable_dims(child)

        # retrieve number of attention layers
        for module in self.children():
            fn_recursive_retrieve_sliceable_dims(module)

        num_sliceable_layers = len(sliceable_head_dims)

        if slice_size == "auto":
            # half the attention head size is usually a good trade-off between
            # speed and memory
            slice_size = [dim // 2 for dim in sliceable_head_dims]
        elif slice_size == "max":
            # make smallest slice possible
            slice_size = num_sliceable_layers * [1]

        slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size

        if len(slice_size) != len(sliceable_head_dims):
            raise ValueError(
                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
            )

        for i in range(len(slice_size)):
            size = slice_size[i]
            dim = sliceable_head_dims[i]
            if size is not None and size > dim:
                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")

        # Recursively walk through all the children.
        # Any children which exposes the set_attention_slice method
        # gets the message
        def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
            if hasattr(module, "set_attention_slice"):
                module.set_attention_slice(slice_size.pop())

            for child in module.children():
                fn_recursive_set_attention_slice(child, slice_size)

        reversed_slice_size = list(reversed(slice_size))
        for module in self.children():
            fn_recursive_set_attention_slice(module, reversed_slice_size)

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D, CrossAttnUpBlock2D, UpBlock2D)):
            module.gradient_checkpointing = value

    def _convert_drag_to_concatting_image(self, drag: torch.Tensor, current_resolution: int) -> torch.Tensor:
        assert self.drag_extra_dim == 4 * self.num_drags

        bsz = drag.shape[0]
        concatting_image = -torch.ones(bsz, self.drag_extra_dim, current_resolution, current_resolution)
        concatting_image = concatting_image.to(drag.device)

        not_all_zeros = drag.any(dim=-1).repeat_interleave(4, dim=1).unsqueeze(-1).unsqueeze(-1)

        y_grid, x_grid = torch.meshgrid(torch.arange(current_resolution), torch.arange(current_resolution), indexing="ij")
        y_grid = y_grid.to(drag.device).unsqueeze(0).unsqueeze(0)  # (1, 1, res, res)
        x_grid = x_grid.to(drag.device).unsqueeze(0).unsqueeze(0)

        x0 = (drag[..., 0] * current_resolution - 0.5).round().clip(0, current_resolution - 1)
        x_src = (drag[..., 0] * current_resolution - x0).unsqueeze(-1).unsqueeze(-1)  # (bsz, num_drags, 1, 1)
        x0 = x0.unsqueeze(-1).unsqueeze(-1)
        x0 = torch.stack([x0, x0, torch.zeros_like(x0) - 1, torch.zeros_like(x0) - 1], dim=2).view(bsz, 4 * self.num_drags, 1, 1)

        y0 = (drag[..., 1] * current_resolution - 0.5).round().clip(0, current_resolution - 1)
        y_src = (drag[..., 1] * current_resolution - y0).unsqueeze(-1).unsqueeze(-1)
        y0 = y0.unsqueeze(-1).unsqueeze(-1)
        y0 = torch.stack([y0, y0, torch.zeros_like(y0) - 1, torch.zeros_like(y0) - 1], dim=2).view(bsz, 4 * self.num_drags, 1, 1)

        x1 = (drag[..., 2] * current_resolution - 0.5).round().clip(0, current_resolution - 1)
        x_tgt = (drag[..., 2] * current_resolution - x1).unsqueeze(-1).unsqueeze(-1)
        x1 = x1.unsqueeze(-1).unsqueeze(-1)
        x1 = torch.stack([torch.zeros_like(x1) - 1, torch.zeros_like(x1) - 1, x1, x1], dim=2).view(bsz, 4 * self.num_drags, 1, 1)

        y1 = (drag[..., 3] * current_resolution - 0.5).round().clip(0, current_resolution - 1)
        y_tgt = (drag[..., 3] * current_resolution - y1).unsqueeze(-1).unsqueeze(-1)
        y1 = y1.unsqueeze(-1).unsqueeze(-1)
        y1 = torch.stack([torch.zeros_like(y1) - 1, torch.zeros_like(y1) - 1, y1, y1], dim=2).view(bsz, 4 * self.num_drags, 1, 1)

        # assert torch.all(x_src >= 0) and torch.all(x_src <= 1)
        # assert torch.all(y_src >= 0) and torch.all(y_src <= 1)
        # assert torch.all(x_tgt >= 0) and torch.all(x_tgt <= 1)
        # assert torch.all(y_tgt >= 0) and torch.all(y_tgt <= 1)

        value_image = torch.stack([x_src, y_src, x_tgt, y_tgt], dim=2).view(bsz, 4 * self.num_drags, 1, 1)
        value_image = value_image.expand(bsz, 4 * self.num_drags, current_resolution, current_resolution)

        concatting_image[(x_grid == x0) & (y_grid == y0) & not_all_zeros] = value_image[(x_grid == x0) & (y_grid == y0) & not_all_zeros]
        concatting_image[(x_grid == x1) & (y_grid == y1) & not_all_zeros] = value_image[(x_grid == x1) & (y_grid == y1) & not_all_zeros]

        return concatting_image

    def forward(
        self,
        # sample: torch.FloatTensor,
        # timestep: Union[torch.Tensor, float, int],
        # encoder_hidden_states: torch.Tensor,
        # class_labels: Optional[torch.Tensor] = None,
        # timestep_cond: Optional[torch.Tensor] = None,
        # attention_mask: Optional[torch.Tensor] = None,
        # cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        # added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
        # down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
        # mid_block_additional_residual: Optional[torch.Tensor] = None,
        # encoder_attention_mask: Optional[torch.Tensor] = None,
        # return_dict: bool = True,
        x: torch.FloatTensor,
        t: torch.Tensor,
        x_cond: torch.FloatTensor,
        x_cond_extra: Optional[torch.Tensor] = None,
        force_drop_ids: Optional[torch.Tensor] = None,
        hidden_cls: Optional[torch.Tensor] = None,
        drags: Optional[torch.Tensor] = None,
        save_features: bool = False,
    ) -> torch.Tensor:
        r"""
        The [`UNet2DConditionModel`] forward method.

        Args:
            sample (`torch.FloatTensor`):
                The noisy input tensor with the following shape `(batch, channel, height, width)`.
            timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
            encoder_hidden_states (`torch.FloatTensor`):
                The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
            encoder_attention_mask (`torch.Tensor`):
                A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
                `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
                which adds large negative values to the attention scores corresponding to "discard" tokens.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
                tuple.
            cross_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].
            added_cond_kwargs: (`dict`, *optional*):
                A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that
                are passed along to the UNet blocks.

        Returns:
            [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
                If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise
                a `tuple` is returned where the first element is the sample tensor.
        """
        # By default samples have to be AT least a multiple of the overall upsampling factor.
        # The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
        # However, the upsampling interpolation output size can be forced to fit any upsampling size
        # on the fly if necessary.
        default_overall_up_factor = 2 ** self.num_upsamplers

        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
        forward_upsample_size = False
        upsample_size = None

        if any(s % default_overall_up_factor != 0 for s in x.shape[-2:]):
            logger.info("Forward upsample size to force interpolation output size.")
            forward_upsample_size = True

        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension
        # expects mask of shape:
        #   [batch, key_tokens]
        # adds singleton query_tokens dimension:
        #   [batch,                    1, key_tokens]
        # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
        #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
        #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
        # if attention_mask is not None:
            # assume that mask is expressed as:
            #   (1 = keep,      0 = discard)
            # convert mask into a bias that can be added to attention scores:
            #       (keep = +0,     discard = -10000.0)
            # attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
            # attention_mask = attention_mask.unsqueeze(1)

        # convert encoder_attention_mask to a bias the same way we do for attention_mask
        # if encoder_attention_mask is not None:
        #     encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
        #     encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
        if self.flow_original_res and self.input_concat_dragging:
            for i in range(self.num_flow_down_layers):
                x_cond_extra = self.flow_preprocess[i](x_cond_extra)
                x_cond_extra = self.flow_proj_act(x_cond_extra)
                x_cond_extra = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)(x_cond_extra)
        if self.input_concat_dragging:
            assert x_cond_extra.shape[-1] == x.shape[-1], f"{x_cond_extra.shape} != {x.shape}"

        bsz, num_drags, drag_dim = drags.shape
        assert num_drags == self.num_drags
        if (self.train and self.drag_dropout_prob > 0) or force_drop_ids is not None:
            if force_drop_ids is None:
                drop_ids = torch.rand(bsz, device=x_cond_extra.device) < self.drag_dropout_prob
            else:
                drop_ids = force_drop_ids == 1
            x_cond_extra = torch.where(
                drop_ids[:, None, None, None].expand_as(x_cond_extra),
                torch.zeros_like(x_cond_extra),
                x_cond_extra,
            )
            drags = torch.where(
                drop_ids[:, None, None].expand_as(drags),
                torch.zeros_like(drags),
                drags,
            )

        if not self.input_concat_dragging:
            sample = torch.cat([x_cond, x], dim=0)
        else:
            sample_noised = torch.cat([x, x_cond_extra], dim=1)
            sample_input = torch.cat([x_cond, torch.zeros_like(x_cond_extra)], dim=1)
            sample = torch.cat([sample_input, sample_noised], dim=0)

        drags = torch.cat([torch.zeros_like(drags), drags], dim=0)

        if self.flow_multi_resolution_conv:
            x_cond_extra = torch.cat([torch.zeros_like(x_cond_extra), x_cond_extra], dim=0)
            flow_multi_resolution_features = self.flow_adapter(x_cond_extra)
        
        # -1. (new) get encoder_hidden_states
        if self.use_drag_tokens:
            assert drag_dim == 4
            drags = drags.reshape(-1, 4)
            drags = get_sin_cos_pos_embed(embed_dim=self.dragging_embedding_dim, x=drags)
            drags = drags.reshape(-1, num_drags, self.dragging_embedding_dim * 4)
            drag_states = self.drag_proj(drags)
            drag_states = self.proj_act(drag_states)
            drag_states = self.drag_final(drag_states)

        assert hidden_cls.shape[1] >= self.num_clip_in
        cls_proj = 0
        for i in range(self.num_clip_in):
            current_cls = hidden_cls[:, -(i+1), :]
            cls_proj += self.clip_proj[i](current_cls)
        cls_proj = cls_proj / self.num_clip_in
        cls_proj = self.proj_act(cls_proj)
        cls_proj = self.clip_final(cls_proj)
        
        if self.use_drag_tokens:
            if not self.single_drag_token:
                encoder_hidden_states = torch.cat([drag_states, torch.concat([cls_proj[:, None, :], cls_proj[:, None, :]], dim=0)], dim=1)
                assert encoder_hidden_states.shape[1] == num_drags + 1
            else:
                encoder_hidden_states = torch.cat([torch.mean(drag_states, dim=1, keepdim=True), torch.concat([cls_proj[:, None, :], cls_proj[:, None, :]], dim=0)], dim=1)
                assert encoder_hidden_states.shape[1] == 2
        else:
            encoder_hidden_states = cls_proj[:, None, :]
            assert encoder_hidden_states.shape[1] == 1
            encoder_hidden_states = torch.cat([encoder_hidden_states, encoder_hidden_states], dim=0)

        # 0. center input if necessary
        assert not self.config.center_input_sample, "center_input_sample is not supported yet."
        if self.config.center_input_sample:
            sample = 2 * sample - 1.0

        # 1. time
        timesteps = t
        if len(timesteps.shape) == 0:
            timesteps = timesteps[None].to(sample.device)

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        timesteps = torch.cat([timesteps, timesteps], dim=0)

        t_emb = self.time_proj(timesteps)

        # `Timesteps` does not contain any weights and will always return f32 tensors
        # but time_embedding might actually be running in fp16. so we need to cast here.
        # there might be better ways to encapsulate this.
        t_emb = t_emb.to(dtype=sample.dtype)

        emb = self.time_embedding(t_emb, None)
        aug_emb = None

        if self.class_embedding is not None:
            if class_labels is None:
                raise ValueError("class_labels should be provided when num_class_embeds > 0")

            if self.config.class_embed_type == "timestep":
                class_labels = self.time_proj(class_labels)

                # `Timesteps` does not contain any weights and will always return f32 tensors
                # there might be better ways to encapsulate this.
                class_labels = class_labels.to(dtype=sample.dtype)

            class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)

            if self.config.class_embeddings_concat:
                emb = torch.cat([emb, class_emb], dim=-1)
            else:
                emb = emb + class_emb

        if self.config.addition_embed_type == "text":
            aug_emb = self.add_embedding(encoder_hidden_states)
        elif self.config.addition_embed_type == "text_image":
            # Kandinsky 2.1 - style
            if "image_embeds" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
                )

            image_embs = added_cond_kwargs.get("image_embeds")
            text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states)
            aug_emb = self.add_embedding(text_embs, image_embs)
        elif self.config.addition_embed_type == "text_time":
            # SDXL - style
            if "text_embeds" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
                )
            text_embeds = added_cond_kwargs.get("text_embeds")
            if "time_ids" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
                )
            time_ids = added_cond_kwargs.get("time_ids")
            time_embeds = self.add_time_proj(time_ids.flatten())
            time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))

            add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
            add_embeds = add_embeds.to(emb.dtype)
            aug_emb = self.add_embedding(add_embeds)
        elif self.config.addition_embed_type == "image":
            # Kandinsky 2.2 - style
            if "image_embeds" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
                )
            image_embs = added_cond_kwargs.get("image_embeds")
            aug_emb = self.add_embedding(image_embs)
        elif self.config.addition_embed_type == "image_hint":
            # Kandinsky 2.2 - style
            if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`"
                )
            image_embs = added_cond_kwargs.get("image_embeds")
            hint = added_cond_kwargs.get("hint")
            aug_emb, hint = self.add_embedding(image_embs, hint)
            sample = torch.cat([sample, hint], dim=1)

        emb = emb + aug_emb if aug_emb is not None else emb

        if self.time_embed_act is not None:
            emb = self.time_embed_act(emb)

        if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj":
            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)
        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj":
            # Kadinsky 2.1 - style
            if "image_embeds" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
                )

            image_embeds = added_cond_kwargs.get("image_embeds")
            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)
        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj":
            # Kandinsky 2.2 - style
            if "image_embeds" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
                )
            image_embeds = added_cond_kwargs.get("image_embeds")
            encoder_hidden_states = self.encoder_hid_proj(image_embeds)
        # 2. pre-process
        if self.input_concat_dragging:
            if self.flow_in_old_version:
                sample = self.conv_in_flow(sample)
            else:
                sample_x, sample_flow = torch.split(sample, 4, dim=1)
                sample_x = self.conv_in(sample_x)
                sample_flow = self.conv_in_flow(sample_flow)
                sample = sample_x + sample_flow
        else:
            sample = self.conv_in(sample)

        # 3. down
        down_block_res_samples = (sample,)
        for idx, downsample_block in enumerate(self.down_blocks):
            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
                # For t2i-adapter CrossAttnDownBlock2D
                additional_residuals = {}

                down_forward_kwargs = dict(
                    hidden_states=sample if not self.flow_multi_resolution_conv else (sample + flow_multi_resolution_features[idx]),
                    temb=emb,
                    encoder_hidden_states=encoder_hidden_states,
                    attention_mask=None,
                    cross_attention_kwargs=None,
                    encoder_attention_mask=None,
                    **additional_residuals,
                )

                if self.attn_concat_dragging:
                    down_forward_kwargs["flow"] = self._convert_drag_to_concatting_image(drags, sample.shape[-1])

                sample, res_samples = downsample_block(
                    **down_forward_kwargs
                )
            else:
                sample, res_samples = downsample_block(
                    hidden_states=sample if not self.flow_multi_resolution_conv else (sample + flow_multi_resolution_features[idx]),
                    temb=emb
                )

            down_block_res_samples += res_samples

        # 4. mid
        if self.mid_block is not None:
            if self.attn_concat_dragging:
                sample = self.mid_block(
                    sample,
                    emb,
                    encoder_hidden_states=encoder_hidden_states,
                    attention_mask=None,
                    cross_attention_kwargs=None,
                    encoder_attention_mask=None,
                    flow=self._convert_drag_to_concatting_image(drags, sample.shape[-1]),
                )
            else:
                sample = self.mid_block(
                    sample,
                    emb,
                    encoder_hidden_states=encoder_hidden_states,
                    attention_mask=None,
                    cross_attention_kwargs=None,
                    encoder_attention_mask=None,
                )

        # 5. up
        for i, upsample_block in enumerate(self.up_blocks):
            is_final_block = i == len(self.up_blocks) - 1

            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]

            # if we have not reached the final block and need to forward the
            # upsample size, we do it here
            if not is_final_block and forward_upsample_size:
                upsample_size = down_block_res_samples[-1].shape[2:]

            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
                up_block_forward_kwargs = dict(
                    hidden_states=sample,
                    temb=emb,
                    res_hidden_states_tuple=res_samples,
                    encoder_hidden_states=encoder_hidden_states,
                    attention_mask=None,
                    cross_attention_kwargs=None,
                    encoder_attention_mask=None,
                )

                if self.attn_concat_dragging:
                    up_block_forward_kwargs["flow"] = self._convert_drag_to_concatting_image(drags, sample.shape[-1])

                sample = upsample_block(
                    **up_block_forward_kwargs
                )
            else:
                sample = upsample_block(
                    hidden_states=sample,
                    temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
                )

        # 6. post-process
        if self.conv_norm_out:
            sample = self.conv_norm_out(sample)
            sample = self.conv_act(sample)
        sample = self.conv_out(sample)

        return sample[bsz:]
    
    def forward_with_cfg(
        self,
        x: torch.FloatTensor,
        t: torch.Tensor,
        x_cond: torch.FloatTensor,
        x_cond_extra: Optional[torch.Tensor] = None,
        hidden_cls: Optional[torch.Tensor] = None,
        drags: Optional[torch.Tensor] = None,
        cfg_scale: float = 1,
    ) -> torch.Tensor:
        half = x[: len(x) // 2]
        combined = torch.cat([half, half], dim=0)
        force_drop_ids = torch.arange(len(combined), device=combined.device) < len(half)
        model_out = self.forward(combined, t, x_cond, x_cond_extra, force_drop_ids=force_drop_ids, hidden_cls=hidden_cls, drags=drags)
        # For exact reproducibility reasons, we apply classifier-free guidance on only
        # three channels by default. The standard approach to cfg applies it to all channels.
        # This can be done by uncommenting the following line and commenting-out the line following that.
        # eps, rest = model_out[:, :3], model_out[:, 3:]
        eps, rest = model_out[:, :4], model_out[:, 4:]
        uncond_eps, cond_eps = torch.split(eps, len(eps) // 2, dim=0)
        half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
        eps = torch.cat([half_eps, half_eps], dim=0)
        return torch.cat([eps, rest], dim=1)

    @classmethod
    def from_pretrained_sd(cls, pretrained_model_path, subfolder="unet", unet_additional_kwargs=None, load=True):
        if subfolder is not None:
            pretrained_model_path = os.path.join(pretrained_model_path, subfolder)
        print(f"loading unet's pretrained weights from {pretrained_model_path} ...")

        config_file = os.path.join(pretrained_model_path, 'config.json')
        if not os.path.isfile(config_file):
            raise RuntimeError(f"{config_file} does not exist")
        with open(config_file, "r") as f:
            config = json.load(f)
        config["_class_name"] = cls.__name__

        from diffusers.utils import WEIGHTS_NAME
        one_sided_attn = unet_additional_kwargs.pop("one_sided_attn", True) if unet_additional_kwargs is not None else True
        model = cls.from_config(config, **unet_additional_kwargs) if unet_additional_kwargs is not None else cls.from_config(config)
        model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)
        if not os.path.isfile(model_file):
            raise RuntimeError(f"{model_file} does not exist")
        
        if load:
            state_dict = torch.load(model_file, map_location="cpu")
            m, u = model.load_state_dict(state_dict, strict=False)

        # Set the attention processor to always take k, v from the input (upper) branch
        if one_sided_attn:
            attn_processors_dict={
                "down_blocks.0.attentions.0.transformer_blocks.0.attn1.processor": OneSidedAttnProcessor(),
                "down_blocks.0.attentions.0.transformer_blocks.0.attn2.processor": AttnProcessor(),
                "down_blocks.0.attentions.1.transformer_blocks.0.attn1.processor": OneSidedAttnProcessor(),
                "down_blocks.0.attentions.1.transformer_blocks.0.attn2.processor": AttnProcessor(),
                "down_blocks.1.attentions.0.transformer_blocks.0.attn1.processor": OneSidedAttnProcessor(),
                "down_blocks.1.attentions.0.transformer_blocks.0.attn2.processor": AttnProcessor(),
                "down_blocks.1.attentions.1.transformer_blocks.0.attn1.processor": OneSidedAttnProcessor(),
                "down_blocks.1.attentions.1.transformer_blocks.0.attn2.processor": AttnProcessor(),
                "down_blocks.2.attentions.0.transformer_blocks.0.attn1.processor": OneSidedAttnProcessor(),
                "down_blocks.2.attentions.0.transformer_blocks.0.attn2.processor": AttnProcessor(),
                "down_blocks.2.attentions.1.transformer_blocks.0.attn1.processor": OneSidedAttnProcessor(),
                "down_blocks.2.attentions.1.transformer_blocks.0.attn2.processor": AttnProcessor(),

                "up_blocks.1.attentions.0.transformer_blocks.0.attn1.processor": OneSidedAttnProcessor(),
                "up_blocks.1.attentions.0.transformer_blocks.0.attn2.processor": AttnProcessor(),
                "up_blocks.1.attentions.1.transformer_blocks.0.attn1.processor": OneSidedAttnProcessor(),
                "up_blocks.1.attentions.1.transformer_blocks.0.attn2.processor": AttnProcessor(),
                "up_blocks.1.attentions.2.transformer_blocks.0.attn1.processor": OneSidedAttnProcessor(),
                "up_blocks.1.attentions.2.transformer_blocks.0.attn2.processor": AttnProcessor(),
                "up_blocks.2.attentions.0.transformer_blocks.0.attn1.processor": OneSidedAttnProcessor(),
                "up_blocks.2.attentions.0.transformer_blocks.0.attn2.processor": AttnProcessor(),
                "up_blocks.2.attentions.1.transformer_blocks.0.attn1.processor": OneSidedAttnProcessor(),
                "up_blocks.2.attentions.1.transformer_blocks.0.attn2.processor": AttnProcessor(),
                "up_blocks.2.attentions.2.transformer_blocks.0.attn1.processor": OneSidedAttnProcessor(),
                "up_blocks.2.attentions.2.transformer_blocks.0.attn2.processor": AttnProcessor(),
                "up_blocks.3.attentions.0.transformer_blocks.0.attn1.processor": OneSidedAttnProcessor(),
                "up_blocks.3.attentions.0.transformer_blocks.0.attn2.processor": AttnProcessor(),
                "up_blocks.3.attentions.1.transformer_blocks.0.attn1.processor": OneSidedAttnProcessor(),
                "up_blocks.3.attentions.1.transformer_blocks.0.attn2.processor": AttnProcessor(),
                "up_blocks.3.attentions.2.transformer_blocks.0.attn1.processor": OneSidedAttnProcessor(),
                "up_blocks.3.attentions.2.transformer_blocks.0.attn2.processor": AttnProcessor(),

                "mid_block.attentions.0.transformer_blocks.0.attn1.processor": OneSidedAttnProcessor(),
                "mid_block.attentions.0.transformer_blocks.0.attn2.processor": AttnProcessor(),
            }
            model.set_attn_processor(attn_processors_dict)

        return model