# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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import math
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import functional as F
from torch.nn.modules.normalization import GroupNorm

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.models.attention_processor import USE_PEFT_BACKEND, AttentionProcessor
from diffusers.models.autoencoders import AutoencoderKL
from diffusers.models.lora import LoRACompatibleConv
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.unet_2d_blocks import (
    CrossAttnDownBlock2D,
    CrossAttnUpBlock2D,
    DownBlock2D,
    Downsample2D,
    ResnetBlock2D,
    Transformer2DModel,
    UpBlock2D,
    Upsample2D,
)
from diffusers.models.unet_2d_condition import UNet2DConditionModel
from diffusers.utils import BaseOutput, logging


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


@dataclass
class ControlNetXSOutput(BaseOutput):
    """
    The output of [`ControlNetXSModel`].

    Args:
        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            The output of the `ControlNetXSModel`. Unlike `ControlNetOutput` this is NOT to be added to the base model
            output, but is already the final output.
    """

    sample: torch.FloatTensor = None


# copied from diffusers.models.controlnet.ControlNetConditioningEmbedding
class ControlNetConditioningEmbedding(nn.Module):
    """
    Quoting from https://arxiv.org/abs/2302.05543: "Stable Diffusion uses a pre-processing method similar to VQ-GAN
    [11] to convert the entire dataset of 512 × 512 images into smaller 64 × 64 “latent images” for stabilized
    training. This requires ControlNets to convert image-based conditions to 64 × 64 feature space to match the
    convolution size. We use a tiny network E(·) of four convolution layers with 4 × 4 kernels and 2 × 2 strides
    (activated by ReLU, channels are 16, 32, 64, 128, initialized with Gaussian weights, trained jointly with the full
    model) to encode image-space conditions ... into feature maps ..."
    """

    def __init__(
        self,
        conditioning_embedding_channels: int,
        conditioning_channels: int = 3,
        block_out_channels: Tuple[int, ...] = (16, 32, 96, 256),
    ):
        super().__init__()

        self.conv_in = nn.Conv2d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1)

        self.blocks = nn.ModuleList([])

        for i in range(len(block_out_channels) - 1):
            channel_in = block_out_channels[i]
            channel_out = block_out_channels[i + 1]
            self.blocks.append(nn.Conv2d(channel_in, channel_in, kernel_size=3, padding=1))
            self.blocks.append(nn.Conv2d(channel_in, channel_out, kernel_size=3, padding=1, stride=2))

        self.conv_out = zero_module(
            nn.Conv2d(block_out_channels[-1], conditioning_embedding_channels, kernel_size=3, padding=1)
        )

    def forward(self, conditioning):
        embedding = self.conv_in(conditioning)
        embedding = F.silu(embedding)

        for block in self.blocks:
            embedding = block(embedding)
            embedding = F.silu(embedding)

        embedding = self.conv_out(embedding)

        return embedding


class ControlNetXSModel(ModelMixin, ConfigMixin):
    r"""
    A ControlNet-XS model

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

    Most of parameters for this model are passed into the [`UNet2DConditionModel`] it creates. Check the documentation
    of [`UNet2DConditionModel`] for them.

    Parameters:
        conditioning_channels (`int`, defaults to 3):
            Number of channels of conditioning input (e.g. an image)
        controlnet_conditioning_channel_order (`str`, defaults to `"rgb"`):
            The channel order of conditional image. Will convert to `rgb` if it's `bgr`.
        conditioning_embedding_out_channels (`tuple[int]`, defaults to `(16, 32, 96, 256)`):
            The tuple of output channel for each block in the `controlnet_cond_embedding` layer.
        time_embedding_input_dim (`int`, defaults to 320):
            Dimension of input into time embedding. Needs to be same as in the base model.
        time_embedding_dim (`int`, defaults to 1280):
            Dimension of output from time embedding. Needs to be same as in the base model.
        learn_embedding (`bool`, defaults to `False`):
            Whether to use time embedding of the control model. If yes, the time embedding is a linear interpolation of
            the time embeddings of the control and base model with interpolation parameter `time_embedding_mix**3`.
        time_embedding_mix (`float`, defaults to 1.0):
            Linear interpolation parameter used if `learn_embedding` is `True`. A value of 1.0 means only the
            control model's time embedding will be used. A value of 0.0 means only the base model's time embedding will be used.
        base_model_channel_sizes (`Dict[str, List[Tuple[int]]]`):
            Channel sizes of each subblock of base model. Use `gather_subblock_sizes` on your base model to compute it.
    """

    @classmethod
    def init_original(cls, base_model: UNet2DConditionModel, is_sdxl=True):
        """
        Create a ControlNetXS model with the same parameters as in the original paper (https://github.com/vislearn/ControlNet-XS).

        Parameters:
            base_model (`UNet2DConditionModel`):
                Base UNet model. Needs to be either StableDiffusion or StableDiffusion-XL.
            is_sdxl (`bool`, defaults to `True`):
                Whether passed `base_model` is a StableDiffusion-XL model.
        """

        def get_dim_attn_heads(base_model: UNet2DConditionModel, size_ratio: float, num_attn_heads: int):
            """
            Currently, diffusers can only set the dimension of attention heads (see https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131 for why).
            The original ControlNet-XS model, however, define the number of attention heads.
            That's why compute the dimensions needed to get the correct number of attention heads.
            """
            block_out_channels = [int(size_ratio * c) for c in base_model.config.block_out_channels]
            dim_attn_heads = [math.ceil(c / num_attn_heads) for c in block_out_channels]
            return dim_attn_heads

        if is_sdxl:
            return ControlNetXSModel.from_unet(
                base_model,
                time_embedding_mix=0.95,
                learn_embedding=True,
                size_ratio=0.1,
                conditioning_embedding_out_channels=(16, 32, 96, 256),
                num_attention_heads=get_dim_attn_heads(base_model, 0.1, 64),
            )
        else:
            return ControlNetXSModel.from_unet(
                base_model,
                time_embedding_mix=1.0,
                learn_embedding=True,
                size_ratio=0.0125,
                conditioning_embedding_out_channels=(16, 32, 96, 256),
                num_attention_heads=get_dim_attn_heads(base_model, 0.0125, 8),
            )

    @classmethod
    def _gather_subblock_sizes(cls, unet: UNet2DConditionModel, base_or_control: str):
        """To create correctly sized connections between base and control model, we need to know
        the input and output channels of each subblock.

        Parameters:
            unet (`UNet2DConditionModel`):
                Unet of which the subblock channels sizes are to be gathered.
            base_or_control (`str`):
                Needs to be either "base" or "control". If "base", decoder is also considered.
        """
        if base_or_control not in ["base", "control"]:
            raise ValueError("`base_or_control` needs to be either `base` or `control`")

        channel_sizes = {"down": [], "mid": [], "up": []}

        # input convolution
        channel_sizes["down"].append((unet.conv_in.in_channels, unet.conv_in.out_channels))

        # encoder blocks
        for module in unet.down_blocks:
            if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)):
                for r in module.resnets:
                    channel_sizes["down"].append((r.in_channels, r.out_channels))
                if module.downsamplers:
                    channel_sizes["down"].append(
                        (module.downsamplers[0].channels, module.downsamplers[0].out_channels)
                    )
            else:
                raise ValueError(f"Encountered unknown module of type {type(module)} while creating ControlNet-XS.")

        # middle block
        channel_sizes["mid"].append((unet.mid_block.resnets[0].in_channels, unet.mid_block.resnets[0].out_channels))

        # decoder blocks
        if base_or_control == "base":
            for module in unet.up_blocks:
                if isinstance(module, (CrossAttnUpBlock2D, UpBlock2D)):
                    for r in module.resnets:
                        channel_sizes["up"].append((r.in_channels, r.out_channels))
                else:
                    raise ValueError(
                        f"Encountered unknown module of type {type(module)} while creating ControlNet-XS."
                    )

        return channel_sizes

    @register_to_config
    def __init__(
        self,
        conditioning_channels: int = 3,
        conditioning_embedding_out_channels: Tuple[int] = (16, 32, 96, 256),
        controlnet_conditioning_channel_order: str = "rgb",
        time_embedding_input_dim: int = 320,
        time_embedding_dim: int = 1280,
        time_embedding_mix: float = 1.0,
        learn_embedding: bool = False,
        base_model_channel_sizes: Dict[str, List[Tuple[int]]] = {
            "down": [
                (4, 320),
                (320, 320),
                (320, 320),
                (320, 320),
                (320, 640),
                (640, 640),
                (640, 640),
                (640, 1280),
                (1280, 1280),
            ],
            "mid": [(1280, 1280)],
            "up": [
                (2560, 1280),
                (2560, 1280),
                (1920, 1280),
                (1920, 640),
                (1280, 640),
                (960, 640),
                (960, 320),
                (640, 320),
                (640, 320),
            ],
        },
        sample_size: Optional[int] = None,
        down_block_types: Tuple[str] = (
            "CrossAttnDownBlock2D",
            "CrossAttnDownBlock2D",
            "CrossAttnDownBlock2D",
            "DownBlock2D",
        ),
        up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
        norm_num_groups: Optional[int] = 32,
        cross_attention_dim: Union[int, Tuple[int]] = 1280,
        transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
        num_attention_heads: Optional[Union[int, Tuple[int]]] = 8,
        upcast_attention: bool = False,
    ):
        super().__init__()

        # 1 - Create control unet
        self.control_model = UNet2DConditionModel(
            sample_size=sample_size,
            down_block_types=down_block_types,
            up_block_types=up_block_types,
            block_out_channels=block_out_channels,
            norm_num_groups=norm_num_groups,
            cross_attention_dim=cross_attention_dim,
            transformer_layers_per_block=transformer_layers_per_block,
            attention_head_dim=num_attention_heads,
            use_linear_projection=True,
            upcast_attention=upcast_attention,
            time_embedding_dim=time_embedding_dim,
        )

        # 2 - Do model surgery on control model
        # 2.1 - Allow to use the same time information as the base model
        adjust_time_dims(self.control_model, time_embedding_input_dim, time_embedding_dim)

        # 2.2 - Allow for information infusion from base model

        # We concat the output of each base encoder subblocks to the input of the next control encoder subblock
        # (We ignore the 1st element, as it represents the `conv_in`.)
        extra_input_channels = [input_channels for input_channels, _ in base_model_channel_sizes["down"][1:]]
        it_extra_input_channels = iter(extra_input_channels)

        for b, block in enumerate(self.control_model.down_blocks):
            for r in range(len(block.resnets)):
                increase_block_input_in_encoder_resnet(
                    self.control_model, block_no=b, resnet_idx=r, by=next(it_extra_input_channels)
                )

            if block.downsamplers:
                increase_block_input_in_encoder_downsampler(
                    self.control_model, block_no=b, by=next(it_extra_input_channels)
                )

        increase_block_input_in_mid_resnet(self.control_model, by=extra_input_channels[-1])

        # 2.3 - Make group norms work with modified channel sizes
        adjust_group_norms(self.control_model)

        # 3 - Gather Channel Sizes
        self.ch_inout_ctrl = ControlNetXSModel._gather_subblock_sizes(self.control_model, base_or_control="control")
        self.ch_inout_base = base_model_channel_sizes

        # 4 - Build connections between base and control model
        self.down_zero_convs_out = nn.ModuleList([])
        self.down_zero_convs_in = nn.ModuleList([])
        self.middle_block_out = nn.ModuleList([])
        self.middle_block_in = nn.ModuleList([])
        self.up_zero_convs_out = nn.ModuleList([])
        self.up_zero_convs_in = nn.ModuleList([])

        for ch_io_base in self.ch_inout_base["down"]:
            self.down_zero_convs_in.append(self._make_zero_conv(in_channels=ch_io_base[1], out_channels=ch_io_base[1]))
        for i in range(len(self.ch_inout_ctrl["down"])):
            self.down_zero_convs_out.append(
                self._make_zero_conv(self.ch_inout_ctrl["down"][i][1], self.ch_inout_base["down"][i][1])
            )

        self.middle_block_out = self._make_zero_conv(
            self.ch_inout_ctrl["mid"][-1][1], self.ch_inout_base["mid"][-1][1]
        )

        self.up_zero_convs_out.append(
            self._make_zero_conv(self.ch_inout_ctrl["down"][-1][1], self.ch_inout_base["mid"][-1][1])
        )
        for i in range(1, len(self.ch_inout_ctrl["down"])):
            self.up_zero_convs_out.append(
                self._make_zero_conv(self.ch_inout_ctrl["down"][-(i + 1)][1], self.ch_inout_base["up"][i - 1][1])
            )

        # 5 - Create conditioning hint embedding
        self.controlnet_cond_embedding = ControlNetConditioningEmbedding(
            conditioning_embedding_channels=block_out_channels[0],
            block_out_channels=conditioning_embedding_out_channels,
            conditioning_channels=conditioning_channels,
        )

        # In the mininal implementation setting, we only need the control model up to the mid block
        del self.control_model.up_blocks
        del self.control_model.conv_norm_out
        del self.control_model.conv_out

    @classmethod
    def from_unet(
        cls,
        unet: UNet2DConditionModel,
        conditioning_channels: int = 3,
        conditioning_embedding_out_channels: Tuple[int] = (16, 32, 96, 256),
        controlnet_conditioning_channel_order: str = "rgb",
        learn_embedding: bool = False,
        time_embedding_mix: float = 1.0,
        block_out_channels: Optional[Tuple[int]] = None,
        size_ratio: Optional[float] = None,
        num_attention_heads: Optional[Union[int, Tuple[int]]] = 8,
        norm_num_groups: Optional[int] = None,
    ):
        r"""
        Instantiate a [`ControlNetXSModel`] from [`UNet2DConditionModel`].

        Parameters:
            unet (`UNet2DConditionModel`):
                The UNet model we want to control. The dimensions of the ControlNetXSModel will be adapted to it.
            conditioning_channels (`int`, defaults to 3):
                Number of channels of conditioning input (e.g. an image)
            conditioning_embedding_out_channels (`tuple[int]`, defaults to `(16, 32, 96, 256)`):
                The tuple of output channel for each block in the `controlnet_cond_embedding` layer.
            controlnet_conditioning_channel_order (`str`, defaults to `"rgb"`):
                The channel order of conditional image. Will convert to `rgb` if it's `bgr`.
            learn_embedding (`bool`, defaults to `False`):
                Wether to use time embedding of the control model. If yes, the time embedding is a linear interpolation
                of the time embeddings of the control and base model with interpolation parameter
                `time_embedding_mix**3`.
            time_embedding_mix (`float`, defaults to 1.0):
                Linear interpolation parameter used if `learn_embedding` is `True`.
            block_out_channels (`Tuple[int]`, *optional*):
                Down blocks output channels in control model. Either this or `size_ratio` must be given.
            size_ratio (float, *optional*):
                When given, block_out_channels is set to a relative fraction of the base model's block_out_channels.
                Either this or `block_out_channels` must be given.
            num_attention_heads (`Union[int, Tuple[int]]`, *optional*):
                The dimension of the attention heads. The naming seems a bit confusing and it is, see https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131 for why.
            norm_num_groups (int, *optional*, defaults to `None`):
                The number of groups to use for the normalization of the control unet. If `None`,
                `int(unet.config.norm_num_groups * size_ratio)` is taken.
        """

        # Check input
        fixed_size = block_out_channels is not None
        relative_size = size_ratio is not None
        if not (fixed_size ^ relative_size):
            raise ValueError(
                "Pass exactly one of `block_out_channels` (for absolute sizing) or `control_model_ratio` (for relative sizing)."
            )

        # Create model
        if block_out_channels is None:
            block_out_channels = [int(size_ratio * c) for c in unet.config.block_out_channels]

        # Check that attention heads and group norms match channel sizes
        # - attention heads
        def attn_heads_match_channel_sizes(attn_heads, channel_sizes):
            if isinstance(attn_heads, (tuple, list)):
                return all(c % a == 0 for a, c in zip(attn_heads, channel_sizes))
            else:
                return all(c % attn_heads == 0 for c in channel_sizes)

        num_attention_heads = num_attention_heads or unet.config.attention_head_dim
        if not attn_heads_match_channel_sizes(num_attention_heads, block_out_channels):
            raise ValueError(
                f"The dimension of attention heads ({num_attention_heads}) must divide `block_out_channels` ({block_out_channels}). If you didn't set `num_attention_heads` the default settings don't match your model. Set `num_attention_heads` manually."
            )

        # - group norms
        def group_norms_match_channel_sizes(num_groups, channel_sizes):
            return all(c % num_groups == 0 for c in channel_sizes)

        if norm_num_groups is None:
            if group_norms_match_channel_sizes(unet.config.norm_num_groups, block_out_channels):
                norm_num_groups = unet.config.norm_num_groups
            else:
                norm_num_groups = min(block_out_channels)

                if group_norms_match_channel_sizes(norm_num_groups, block_out_channels):
                    print(
                        f"`norm_num_groups` was set to `min(block_out_channels)` (={norm_num_groups}) so it divides all block_out_channels` ({block_out_channels}). Set it explicitly to remove this information."
                    )
                else:
                    raise ValueError(
                        f"`block_out_channels` ({block_out_channels}) don't match the base models `norm_num_groups` ({unet.config.norm_num_groups}). Setting `norm_num_groups` to `min(block_out_channels)` ({norm_num_groups}) didn't fix this. Pass `norm_num_groups` explicitly so it divides all block_out_channels."
                    )

        def get_time_emb_input_dim(unet: UNet2DConditionModel):
            return unet.time_embedding.linear_1.in_features

        def get_time_emb_dim(unet: UNet2DConditionModel):
            return unet.time_embedding.linear_2.out_features

        # Clone params from base unet if
        #    (i)   it's required to build SD or SDXL, and
        #    (ii)  it's not used for the time embedding (as time embedding of control model is never used), and
        #    (iii) it's not set further below anyway
        to_keep = [
            "cross_attention_dim",
            "down_block_types",
            "sample_size",
            "transformer_layers_per_block",
            "up_block_types",
            "upcast_attention",
        ]
        kwargs = {k: v for k, v in dict(unet.config).items() if k in to_keep}
        kwargs.update(block_out_channels=block_out_channels)
        kwargs.update(num_attention_heads=num_attention_heads)
        kwargs.update(norm_num_groups=norm_num_groups)

        # Add controlnetxs-specific params
        kwargs.update(
            conditioning_channels=conditioning_channels,
            controlnet_conditioning_channel_order=controlnet_conditioning_channel_order,
            time_embedding_input_dim=get_time_emb_input_dim(unet),
            time_embedding_dim=get_time_emb_dim(unet),
            time_embedding_mix=time_embedding_mix,
            learn_embedding=learn_embedding,
            base_model_channel_sizes=ControlNetXSModel._gather_subblock_sizes(unet, base_or_control="base"),
            conditioning_embedding_out_channels=conditioning_embedding_out_channels,
        )

        return cls(**kwargs)

    @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.
        """
        return self.control_model.attn_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.

        """
        self.control_model.set_attn_processor(processor)

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

    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`.
        """
        self.control_model.set_attention_slice(slice_size)

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, (UNet2DConditionModel)):
            if value:
                module.enable_gradient_checkpointing()
            else:
                module.disable_gradient_checkpointing()

    def forward(
        self,
        base_model: UNet2DConditionModel,
        sample: torch.FloatTensor,
        timestep: Union[torch.Tensor, float, int],
        encoder_hidden_states: torch.Tensor,
        controlnet_cond: torch.Tensor,
        conditioning_scale: float = 1.0,
        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,
        return_dict: bool = True,
    ) -> Union[ControlNetXSOutput, Tuple]:
        """
        The [`ControlNetModel`] forward method.

        Args:
            base_model (`UNet2DConditionModel`):
                The base unet model we want to control.
            sample (`torch.FloatTensor`):
                The noisy input tensor.
            timestep (`Union[torch.Tensor, float, int]`):
                The number of timesteps to denoise an input.
            encoder_hidden_states (`torch.Tensor`):
                The encoder hidden states.
            controlnet_cond (`torch.FloatTensor`):
                The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
            conditioning_scale (`float`, defaults to `1.0`):
                How much the control model affects the base model outputs.
            class_labels (`torch.Tensor`, *optional*, defaults to `None`):
                Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
            timestep_cond (`torch.Tensor`, *optional*, defaults to `None`):
                Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the
                timestep_embedding passed through the `self.time_embedding` layer to obtain the final timestep
                embeddings.
            attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
                An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
                is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
                negative values to the attention scores corresponding to "discard" tokens.
            added_cond_kwargs (`dict`):
                Additional conditions for the Stable Diffusion XL UNet.
            cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`):
                A kwargs dictionary that if specified is passed along to the `AttnProcessor`.
            return_dict (`bool`, defaults to `True`):
                Whether or not to return a [`~models.controlnet.ControlNetOutput`] instead of a plain tuple.

        Returns:
            [`~models.controlnetxs.ControlNetXSOutput`] **or** `tuple`:
                If `return_dict` is `True`, a [`~models.controlnetxs.ControlNetXSOutput`] is returned, otherwise a
                tuple is returned where the first element is the sample tensor.
        """
        # check channel order
        channel_order = self.config.controlnet_conditioning_channel_order

        if channel_order == "rgb":
            # in rgb order by default
            ...
        elif channel_order == "bgr":
            controlnet_cond = torch.flip(controlnet_cond, dims=[1])
        else:
            raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}")

        # scale control strength
        n_connections = len(self.down_zero_convs_out) + 1 + len(self.up_zero_convs_out)
        scale_list = torch.full((n_connections,), conditioning_scale)

        # prepare attention_mask
        if attention_mask is not None:
            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
            attention_mask = attention_mask.unsqueeze(1)

        # 1. time
        timesteps = timestep
        if not torch.is_tensor(timesteps):
            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
            # This would be a good case for the `match` statement (Python 3.10+)
            is_mps = sample.device.type == "mps"
            if isinstance(timestep, float):
                dtype = torch.float32 if is_mps else torch.float64
            else:
                dtype = torch.int32 if is_mps else torch.int64
            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
        elif 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 = timesteps.expand(sample.shape[0])

        t_emb = base_model.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)

        if self.config.learn_embedding:
            ctrl_temb = self.control_model.time_embedding(t_emb, timestep_cond)
            base_temb = base_model.time_embedding(t_emb, timestep_cond)
            interpolation_param = self.config.time_embedding_mix**0.3

            temb = ctrl_temb * interpolation_param + base_temb * (1 - interpolation_param)
        else:
            temb = base_model.time_embedding(t_emb)

        # added time & text embeddings
        aug_emb = None

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

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

            class_emb = base_model.class_embedding(class_labels).to(dtype=self.dtype)
            temb = temb + class_emb

        if base_model.config.addition_embed_type is not None:
            if base_model.config.addition_embed_type == "text":
                aug_emb = base_model.add_embedding(encoder_hidden_states)
            elif base_model.config.addition_embed_type == "text_image":
                raise NotImplementedError()
            elif base_model.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 = base_model.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(temb.dtype)
                aug_emb = base_model.add_embedding(add_embeds)
            elif base_model.config.addition_embed_type == "image":
                raise NotImplementedError()
            elif base_model.config.addition_embed_type == "image_hint":
                raise NotImplementedError()

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

        # text embeddings
        cemb = encoder_hidden_states

        # Preparation
        guided_hint = self.controlnet_cond_embedding(controlnet_cond)

        h_ctrl = h_base = sample
        hs_base, hs_ctrl = [], []
        it_down_convs_in, it_down_convs_out, it_dec_convs_in, it_up_convs_out = map(
            iter, (self.down_zero_convs_in, self.down_zero_convs_out, self.up_zero_convs_in, self.up_zero_convs_out)
        )
        scales = iter(scale_list)

        base_down_subblocks = to_sub_blocks(base_model.down_blocks)
        ctrl_down_subblocks = to_sub_blocks(self.control_model.down_blocks)
        base_mid_subblocks = to_sub_blocks([base_model.mid_block])
        ctrl_mid_subblocks = to_sub_blocks([self.control_model.mid_block])
        base_up_subblocks = to_sub_blocks(base_model.up_blocks)

        # Cross Control
        # 0 - conv in
        h_base = base_model.conv_in(h_base)
        h_ctrl = self.control_model.conv_in(h_ctrl)
        if guided_hint is not None:
            h_ctrl += guided_hint
        h_base = h_base + next(it_down_convs_out)(h_ctrl) * next(scales)  # D - add ctrl -> base

        hs_base.append(h_base)
        hs_ctrl.append(h_ctrl)

        # 1 - down
        for m_base, m_ctrl in zip(base_down_subblocks, ctrl_down_subblocks):
            h_ctrl = torch.cat([h_ctrl, next(it_down_convs_in)(h_base)], dim=1)  # A - concat base -> ctrl
            h_base = m_base(h_base, temb, cemb, attention_mask, cross_attention_kwargs)  # B - apply base subblock
            h_ctrl = m_ctrl(h_ctrl, temb, cemb, attention_mask, cross_attention_kwargs)  # C - apply ctrl subblock
            h_base = h_base + next(it_down_convs_out)(h_ctrl) * next(scales)  # D - add ctrl -> base
            hs_base.append(h_base)
            hs_ctrl.append(h_ctrl)

        # 2 - mid
        h_ctrl = torch.cat([h_ctrl, next(it_down_convs_in)(h_base)], dim=1)  # A - concat base -> ctrl
        for m_base, m_ctrl in zip(base_mid_subblocks, ctrl_mid_subblocks):
            h_base = m_base(h_base, temb, cemb, attention_mask, cross_attention_kwargs)  # B - apply base subblock
            h_ctrl = m_ctrl(h_ctrl, temb, cemb, attention_mask, cross_attention_kwargs)  # C - apply ctrl subblock
        h_base = h_base + self.middle_block_out(h_ctrl) * next(scales)  # D - add ctrl -> base

        # 3 - up
        for i, m_base in enumerate(base_up_subblocks):
            h_base = h_base + next(it_up_convs_out)(hs_ctrl.pop()) * next(scales)  # add info from ctrl encoder
            h_base = torch.cat([h_base, hs_base.pop()], dim=1)  # concat info from base encoder+ctrl encoder
            h_base = m_base(h_base, temb, cemb, attention_mask, cross_attention_kwargs)

        h_base = base_model.conv_norm_out(h_base)
        h_base = base_model.conv_act(h_base)
        h_base = base_model.conv_out(h_base)

        if not return_dict:
            return h_base

        return ControlNetXSOutput(sample=h_base)

    def _make_zero_conv(self, in_channels, out_channels=None):
        # keep running track of channels sizes
        self.in_channels = in_channels
        self.out_channels = out_channels or in_channels

        return zero_module(nn.Conv2d(in_channels, out_channels, 1, padding=0))

    @torch.no_grad()
    def _check_if_vae_compatible(self, vae: AutoencoderKL):
        condition_downscale_factor = 2 ** (len(self.config.conditioning_embedding_out_channels) - 1)
        vae_downscale_factor = 2 ** (len(vae.config.block_out_channels) - 1)
        compatible = condition_downscale_factor == vae_downscale_factor
        return compatible, condition_downscale_factor, vae_downscale_factor


class SubBlock(nn.ModuleList):
    """A SubBlock is the largest piece of either base or control model, that is executed independently of the other model respectively.
    Before each subblock, information is concatted from base to control. And after each subblock, information is added from control to base.
    """

    def __init__(self, ms, *args, **kwargs):
        if not is_iterable(ms):
            ms = [ms]
        super().__init__(ms, *args, **kwargs)

    def forward(
        self,
        x: torch.Tensor,
        temb: torch.Tensor,
        cemb: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
    ):
        """Iterate through children and pass correct information to each."""
        for m in self:
            if isinstance(m, ResnetBlock2D):
                x = m(x, temb)
            elif isinstance(m, Transformer2DModel):
                x = m(x, cemb, attention_mask=attention_mask, cross_attention_kwargs=cross_attention_kwargs).sample
            elif isinstance(m, Downsample2D):
                x = m(x)
            elif isinstance(m, Upsample2D):
                x = m(x)
            else:
                raise ValueError(
                    f"Type of m is {type(m)} but should be `ResnetBlock2D`, `Transformer2DModel`,  `Downsample2D` or `Upsample2D`"
                )

        return x


def adjust_time_dims(unet: UNet2DConditionModel, in_dim: int, out_dim: int):
    unet.time_embedding.linear_1 = nn.Linear(in_dim, out_dim)


def increase_block_input_in_encoder_resnet(unet: UNet2DConditionModel, block_no, resnet_idx, by):
    """Increase channels sizes to allow for additional concatted information from base model"""
    r = unet.down_blocks[block_no].resnets[resnet_idx]
    old_norm1, old_conv1 = r.norm1, r.conv1
    # norm
    norm_args = "num_groups num_channels eps affine".split(" ")
    for a in norm_args:
        assert hasattr(old_norm1, a)
    norm_kwargs = {a: getattr(old_norm1, a) for a in norm_args}
    norm_kwargs["num_channels"] += by  # surgery done here
    # conv1
    conv1_args = [
        "in_channels",
        "out_channels",
        "kernel_size",
        "stride",
        "padding",
        "dilation",
        "groups",
        "bias",
        "padding_mode",
    ]
    if not USE_PEFT_BACKEND:
        conv1_args.append("lora_layer")

    for a in conv1_args:
        assert hasattr(old_conv1, a)

    conv1_kwargs = {a: getattr(old_conv1, a) for a in conv1_args}
    conv1_kwargs["bias"] = "bias" in conv1_kwargs  # as param, bias is a boolean, but as attr, it's a tensor.
    conv1_kwargs["in_channels"] += by  # surgery done here
    # conv_shortcut
    # as we changed the input size of the block, the input and output sizes are likely different,
    # therefore we need a conv_shortcut (simply adding won't work)
    conv_shortcut_args_kwargs = {
        "in_channels": conv1_kwargs["in_channels"],
        "out_channels": conv1_kwargs["out_channels"],
        # default arguments from resnet.__init__
        "kernel_size": 1,
        "stride": 1,
        "padding": 0,
        "bias": True,
    }
    # swap old with new modules
    unet.down_blocks[block_no].resnets[resnet_idx].norm1 = GroupNorm(**norm_kwargs)
    unet.down_blocks[block_no].resnets[resnet_idx].conv1 = (
        nn.Conv2d(**conv1_kwargs) if USE_PEFT_BACKEND else LoRACompatibleConv(**conv1_kwargs)
    )
    unet.down_blocks[block_no].resnets[resnet_idx].conv_shortcut = (
        nn.Conv2d(**conv_shortcut_args_kwargs) if USE_PEFT_BACKEND else LoRACompatibleConv(**conv_shortcut_args_kwargs)
    )
    unet.down_blocks[block_no].resnets[resnet_idx].in_channels += by  # surgery done here


def increase_block_input_in_encoder_downsampler(unet: UNet2DConditionModel, block_no, by):
    """Increase channels sizes to allow for additional concatted information from base model"""
    old_down = unet.down_blocks[block_no].downsamplers[0].conv

    args = [
        "in_channels",
        "out_channels",
        "kernel_size",
        "stride",
        "padding",
        "dilation",
        "groups",
        "bias",
        "padding_mode",
    ]
    if not USE_PEFT_BACKEND:
        args.append("lora_layer")

    for a in args:
        assert hasattr(old_down, a)
    kwargs = {a: getattr(old_down, a) for a in args}
    kwargs["bias"] = "bias" in kwargs  # as param, bias is a boolean, but as attr, it's a tensor.
    kwargs["in_channels"] += by  # surgery done here
    # swap old with new modules
    unet.down_blocks[block_no].downsamplers[0].conv = (
        nn.Conv2d(**kwargs) if USE_PEFT_BACKEND else LoRACompatibleConv(**kwargs)
    )
    unet.down_blocks[block_no].downsamplers[0].channels += by  # surgery done here


def increase_block_input_in_mid_resnet(unet: UNet2DConditionModel, by):
    """Increase channels sizes to allow for additional concatted information from base model"""
    m = unet.mid_block.resnets[0]
    old_norm1, old_conv1 = m.norm1, m.conv1
    # norm
    norm_args = "num_groups num_channels eps affine".split(" ")
    for a in norm_args:
        assert hasattr(old_norm1, a)
    norm_kwargs = {a: getattr(old_norm1, a) for a in norm_args}
    norm_kwargs["num_channels"] += by  # surgery done here
    conv1_args = [
        "in_channels",
        "out_channels",
        "kernel_size",
        "stride",
        "padding",
        "dilation",
        "groups",
        "bias",
        "padding_mode",
    ]
    if not USE_PEFT_BACKEND:
        conv1_args.append("lora_layer")

    conv1_kwargs = {a: getattr(old_conv1, a) for a in conv1_args}
    conv1_kwargs["bias"] = "bias" in conv1_kwargs  # as param, bias is a boolean, but as attr, it's a tensor.
    conv1_kwargs["in_channels"] += by  # surgery done here
    # conv_shortcut
    # as we changed the input size of the block, the input and output sizes are likely different,
    # therefore we need a conv_shortcut (simply adding won't work)
    conv_shortcut_args_kwargs = {
        "in_channels": conv1_kwargs["in_channels"],
        "out_channels": conv1_kwargs["out_channels"],
        # default arguments from resnet.__init__
        "kernel_size": 1,
        "stride": 1,
        "padding": 0,
        "bias": True,
    }
    # swap old with new modules
    unet.mid_block.resnets[0].norm1 = GroupNorm(**norm_kwargs)
    unet.mid_block.resnets[0].conv1 = (
        nn.Conv2d(**conv1_kwargs) if USE_PEFT_BACKEND else LoRACompatibleConv(**conv1_kwargs)
    )
    unet.mid_block.resnets[0].conv_shortcut = (
        nn.Conv2d(**conv_shortcut_args_kwargs) if USE_PEFT_BACKEND else LoRACompatibleConv(**conv_shortcut_args_kwargs)
    )
    unet.mid_block.resnets[0].in_channels += by  # surgery done here


def adjust_group_norms(unet: UNet2DConditionModel, max_num_group: int = 32):
    def find_denominator(number, start):
        if start >= number:
            return number
        while start != 0:
            residual = number % start
            if residual == 0:
                return start
            start -= 1

    for block in [*unet.down_blocks, unet.mid_block]:
        # resnets
        for r in block.resnets:
            if r.norm1.num_groups < max_num_group:
                r.norm1.num_groups = find_denominator(r.norm1.num_channels, start=max_num_group)

            if r.norm2.num_groups < max_num_group:
                r.norm2.num_groups = find_denominator(r.norm2.num_channels, start=max_num_group)

        # transformers
        if hasattr(block, "attentions"):
            for a in block.attentions:
                if a.norm.num_groups < max_num_group:
                    a.norm.num_groups = find_denominator(a.norm.num_channels, start=max_num_group)


def is_iterable(o):
    if isinstance(o, str):
        return False
    try:
        iter(o)
        return True
    except TypeError:
        return False


def to_sub_blocks(blocks):
    if not is_iterable(blocks):
        blocks = [blocks]

    sub_blocks = []

    for b in blocks:
        if hasattr(b, "resnets"):
            if hasattr(b, "attentions") and b.attentions is not None:
                for r, a in zip(b.resnets, b.attentions):
                    sub_blocks.append([r, a])

                num_resnets = len(b.resnets)
                num_attns = len(b.attentions)

                if num_resnets > num_attns:
                    # we can have more resnets than attentions, so add each resnet as separate subblock
                    for i in range(num_attns, num_resnets):
                        sub_blocks.append([b.resnets[i]])
            else:
                for r in b.resnets:
                    sub_blocks.append([r])

        # upsamplers are part of the same subblock
        if hasattr(b, "upsamplers") and b.upsamplers is not None:
            for u in b.upsamplers:
                sub_blocks[-1].extend([u])

        # downsamplers are own subblock
        if hasattr(b, "downsamplers") and b.downsamplers is not None:
            for d in b.downsamplers:
                sub_blocks.append([d])

    return list(map(SubBlock, sub_blocks))


def zero_module(module):
    for p in module.parameters():
        nn.init.zeros_(p)
    return module
