src/models/unet_2d_decoder.py

from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.nn import functional as F

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.loaders import FromOriginalControlNetMixin
from diffusers.utils import BaseOutput, logging
from diffusers.models.embeddings import (
    TextImageProjection,
    TextImageTimeEmbedding,
    TextTimeEmbedding,
    TimestepEmbedding,
    Timesteps,
)
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.unet_2d_blocks import (
    CrossAttnDownBlock2D,
    DownBlock2D,
    UNetMidBlock2DCrossAttn,
    get_down_block,
)
from diffusers.models.unet_2d_condition import UNet2DConditionModel
from diffusers.models.controlnet import (
    ControlNetConditioningEmbedding,
    ControlNetOutput,
    ControlNetModel,
)

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


class UNetDec_ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlNetMixin):
    """
    A ControlNet model.

    Args:
        in_channels (`int`, defaults to 4):
            The number of channels in the input sample.
        flip_sin_to_cos (`bool`, defaults to `True`):
            Whether to flip the sin to cos in the time embedding.
        freq_shift (`int`, defaults to 0):
            The frequency shift to apply to the time embedding.
        down_block_types (`tuple[str]`, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
            The tuple of downsample blocks to use.
        only_cross_attention (`Union[bool, Tuple[bool]]`, defaults to `False`):
        block_out_channels (`tuple[int]`, defaults to `(320, 640, 1280, 1280)`):
            The tuple of output channels for each block.
        layers_per_block (`int`, defaults to 2):
            The number of layers per block.
        downsample_padding (`int`, defaults to 1):
            The padding to use for the downsampling convolution.
        mid_block_scale_factor (`float`, defaults to 1):
            The scale factor to use for the mid block.
        act_fn (`str`, 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`, defaults to 1e-5):
            The epsilon to use for the normalization.
        cross_attention_dim (`int`, 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 (`Union[int, Tuple[int]]`, defaults to 8):
            The dimension of the attention heads.
        use_linear_projection (`bool`, defaults to `False`):
        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.
        num_class_embeds (`int`, *optional*, defaults to 0):
            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`.
        upcast_attention (`bool`, defaults to `False`):
        resnet_time_scale_shift (`str`, defaults to `"default"`):
            Time scale shift config for ResNet blocks (see `ResnetBlock2D`). Choose from `default` or `scale_shift`.
        projection_class_embeddings_input_dim (`int`, *optional*, defaults to `None`):
            The dimension of the `class_labels` input when `class_embed_type="projection"`. Required when
            `class_embed_type="projection"`.
        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]`, *optional*, defaults to `(16, 32, 96, 256)`):
            The tuple of output channel for each block in the `conditioning_embedding` layer.
        global_pool_conditions (`bool`, defaults to `False`):
    """

    _supports_gradient_checkpointing = True

    @register_to_config
    def __init__(
        self,
        in_channels: int = 4,
        conditioning_channels: int = 3,
        flip_sin_to_cos: bool = True,
        freq_shift: int = 0,
        down_block_types: Tuple[str] = (
            "CrossAttnDownBlock2D",
            "CrossAttnDownBlock2D",
            "CrossAttnDownBlock2D",
            "DownBlock2D",
        ),
        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: 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: 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,
        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",
        projection_class_embeddings_input_dim: Optional[int] = None,
        controlnet_conditioning_channel_order: str = "rgb",
        conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256),
        global_pool_conditions: bool = False,
        addition_embed_type_num_heads=64,
    ):
        super().__init__()

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

        # # input
        # conv_in_kernel = 3
        # conv_in_padding = (conv_in_kernel - 1) // 2
        # self.conv_in = nn.Conv2d(
        #     in_channels,
        #     block_out_channels[0],
        #     kernel_size=conv_in_kernel,
        #     padding=conv_in_padding,
        # )
        self.conv_in = None

        # time
        time_embed_dim = 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]
        self.time_embedding = TimestepEmbedding(
            timestep_input_dim,
            time_embed_dim,
            act_fn=act_fn,
        )

        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 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)
        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
            )
        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 is not None:
            raise ValueError(
                f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'."
            )

        # control net conditioning embedding
        self.controlnet_cond_embedding = ControlNetConditioningEmbedding(
            conditioning_embedding_channels=block_out_channels[0],
            block_out_channels=conditioning_embedding_out_channels,
            conditioning_channels=conditioning_channels,
        )

        self.down_blocks = nn.ModuleList([])

        if isinstance(only_cross_attention, bool):
            only_cross_attention = [only_cross_attention] * len(down_block_types)

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

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

        # # down
        # output_channel = block_out_channels[0]

        # controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
        # controlnet_block = zero_module(controlnet_block)
        # self.controlnet_down_blocks.append(controlnet_block)

        # 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

        #     for _ in range(layers_per_block):
        #         controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
        #         controlnet_block = zero_module(controlnet_block)
        #         self.controlnet_down_blocks.append(controlnet_block)

        #     if not is_final_block:
        #         controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
        #         controlnet_block = zero_module(controlnet_block)
        #         self.controlnet_down_blocks.append(controlnet_block)

        # mid
        mid_block_channel = block_out_channels[-1]

        controlnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1)
        controlnet_block = zero_module(controlnet_block)
        self.controlnet_mid_block = controlnet_block

        self.mid_block = UNetMidBlock2DCrossAttn(
            transformer_layers_per_block=transformer_layers_per_block[-1],
            in_channels=mid_block_channel,
            temb_channels=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,
            num_attention_heads=num_attention_heads[-1],
            resnet_groups=norm_num_groups,
            use_linear_projection=use_linear_projection,
            upcast_attention=upcast_attention,
        )

        # up
        self.controlnet_up_blocks = nn.ModuleList([])
        self.num_upsamplers = 0
        self.up_blocks = nn.ModuleList([])
        reversed_block_out_channels = list(reversed(block_out_channels))
        reversed_num_attention_heads = list(reversed(num_attention_heads))
        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(
                up_block_type,
                num_layers=layers_per_block + 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=time_embed_dim,
                add_upsample=add_upsample,
                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                resnet_groups=norm_num_groups,
                cross_attention_dim=cross_attention_dim,
                num_attention_heads=reversed_num_attention_heads[i],
                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,
                attention_head_dim=(
                    attention_head_dim[i]
                    if attention_head_dim[i] is not None
                    else output_channel
                ),
            )
            self.up_blocks.append(up_block)
            prev_output_channel = output_channel

            # if i>0:   # 因为我们只输出transformer相关的，而第一级的upblock是纯conv
            for _ in range(layers_per_block + 1):
                controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
                controlnet_block = zero_module(controlnet_block)
                self.controlnet_up_blocks.append(controlnet_block)

    @classmethod
    def from_unet(
        cls,
        unet: UNet2DConditionModel,
        controlnet_conditioning_channel_order: str = "rgb",
        conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256),
        load_weights_from_unet: bool = True,
    ):
        r"""
        Instantiate a [`ControlNetModel`] from [`UNet2DConditionModel`].

        Parameters:
            unet (`UNet2DConditionModel`):
                The UNet model weights to copy to the [`ControlNetModel`]. All configuration options are also copied
                where applicable.
        """
        transformer_layers_per_block = (
            unet.config.transformer_layers_per_block
            if "transformer_layers_per_block" in unet.config
            else 1
        )
        encoder_hid_dim = (
            unet.config.encoder_hid_dim if "encoder_hid_dim" in unet.config else None
        )
        encoder_hid_dim_type = (
            unet.config.encoder_hid_dim_type
            if "encoder_hid_dim_type" in unet.config
            else None
        )
        addition_embed_type = (
            unet.config.addition_embed_type
            if "addition_embed_type" in unet.config
            else None
        )
        addition_time_embed_dim = (
            unet.config.addition_time_embed_dim
            if "addition_time_embed_dim" in unet.config
            else None
        )

        controlnet = cls(
            encoder_hid_dim=encoder_hid_dim,
            encoder_hid_dim_type=encoder_hid_dim_type,
            addition_embed_type=addition_embed_type,
            addition_time_embed_dim=addition_time_embed_dim,
            transformer_layers_per_block=transformer_layers_per_block,
            in_channels=unet.config.in_channels,
            flip_sin_to_cos=unet.config.flip_sin_to_cos,
            freq_shift=unet.config.freq_shift,
            down_block_types=unet.config.down_block_types,
            only_cross_attention=unet.config.only_cross_attention,
            block_out_channels=unet.config.block_out_channels,
            layers_per_block=unet.config.layers_per_block,
            downsample_padding=unet.config.downsample_padding,
            mid_block_scale_factor=unet.config.mid_block_scale_factor,
            act_fn=unet.config.act_fn,
            norm_num_groups=unet.config.norm_num_groups,
            norm_eps=unet.config.norm_eps,
            cross_attention_dim=unet.config.cross_attention_dim,
            attention_head_dim=unet.config.attention_head_dim,
            num_attention_heads=unet.config.num_attention_heads,
            use_linear_projection=unet.config.use_linear_projection,
            class_embed_type=unet.config.class_embed_type,
            num_class_embeds=unet.config.num_class_embeds,
            upcast_attention=unet.config.upcast_attention,
            resnet_time_scale_shift=unet.config.resnet_time_scale_shift,
            projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim,
            controlnet_conditioning_channel_order=controlnet_conditioning_channel_order,
            conditioning_embedding_out_channels=conditioning_embedding_out_channels,
        ).requires_grad_(False)

        if load_weights_from_unet:
            # controlnet.conv_in.load_state_dict(unet.conv_in.state_dict())
            controlnet.time_proj.load_state_dict(unet.time_proj.state_dict())
            controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict())

            if controlnet.class_embedding:
                controlnet.class_embedding.load_state_dict(unet.class_embedding.state_dict())

            # controlnet.down_blocks.load_state_dict(unet.down_blocks.state_dict())
            controlnet.mid_block.load_state_dict(unet.mid_block.state_dict())
            # controlnet.up_blocks.load_state_dict(unet.up_blocks.state_dict(), strict=False) # TODO:将各个upblock第一层融合层使用weight进行部分初始化

            src_tensors = dict(named_params_and_buffers(unet.up_blocks))
            for name, tensor in named_params_and_buffers(controlnet.up_blocks):
                assert (name in src_tensors), name
                try:
                    # print('Successfully initializing ControlNet!', name, tensor.shape, src_tensors[name].shape)
                    tensor.copy_(src_tensors[name].detach())
                except:
                    # print('Mismatch occured in initializing ControlNet!', name, tensor.shape, src_tensors[name].shape)
                    # TODO: 确保所有upblock参数有初始化
                    if tensor.dim() == 1:
                        tensor.copy_(src_tensors[name].detach()[:tensor.shape[0]])
                    else:
                        tensor.copy_(src_tensors[name].detach()[:, :tensor.shape[1]])

        return controlnet

    def _set_gradient_checkpointing(self, module, value=False):
        if hasattr(module, "gradient_checkpointing"):
            module.gradient_checkpointing = value

    def forward(
        self,
        sample: torch.FloatTensor,
        timestep: Union[torch.Tensor, float, int],
        encoder_hidden_states: torch.Tensor,
        # controlnet_cond: torch.FloatTensor,
        conditioning_scale: float = 1.0,
        class_labels: Optional[torch.Tensor] = None,
        timestep_cond: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        guess_mode: bool = False,
        return_dict: bool = True,
        only_return_transformer_layers_out: bool = False,
    ) -> Union[ControlNetOutput, Tuple]:
        """
        The [`ControlNetModel`] forward method.

        Args:
            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`):
                The scale factor for ControlNet 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`):
            attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
            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`.
            guess_mode (`bool`, defaults to `False`):
                In this mode, the ControlNet encoder tries its best to recognize the input content of the input even if
                you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.
            return_dict (`bool`, defaults to `True`):
                Whether or not to return a [`~models.controlnet.ControlNetOutput`] instead of a plain tuple.

        Returns:
            [`~models.controlnet.ControlNetOutput`] **or** `tuple`:
                If `return_dict` is `True`, a [`~models.controlnet.ControlNetOutput`] is returned, otherwise a tuple is
                returned where the first element is the sample tensor.
        """

        # 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 = 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, timestep_cond)
        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)

            class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
            emb = emb + class_emb

        if self.config.addition_embed_type is not None:
            if self.config.addition_embed_type == "text":
                aug_emb = self.add_embedding(encoder_hidden_states)

            elif self.config.addition_embed_type == "text_time":
                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)

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

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

        # 4. mid
        if self.mid_block is not None:
            sample = self.mid_block(
                sample,
                emb,
                encoder_hidden_states=encoder_hidden_states,
                attention_mask=attention_mask,
                cross_attention_kwargs=cross_attention_kwargs,
            )
        mid_block_res_sample = self.controlnet_mid_block(sample)

        # 5. up

        out_block_res_samples = []
        for i, upsample_block in enumerate(self.up_blocks):
            if (hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention):
                sample, res_samples = upsample_block(
                    hidden_states=sample,
                    temb=emb,
                    # res_hidden_states_tuple=res_samples,
                    encoder_hidden_states=encoder_hidden_states,
                    cross_attention_kwargs=cross_attention_kwargs,
                    attention_mask=attention_mask,
                )
            else:
                sample, res_samples = upsample_block(
                    hidden_states=sample,
                    temb=emb,
                    # res_hidden_states_tuple=res_samples,
                )
            # print('Out', i, [res_sample.shape for res_sample in res_samples])
            # if i in range(1, len(self.up_blocks)-1):
            #     res_samples[-1] = res_samples[-1][:, :sample.shape[1]//2]    #  为了适配controlNet输出的shape

            out_block_res_samples += res_samples

        # 5. Control net blocks

        controlnet_up_block_res_samples = ()
        assert len(out_block_res_samples) == len(self.controlnet_up_blocks), (len(out_block_res_samples), len(self.controlnet_up_blocks))
        for i, controlnet_block in enumerate(self.controlnet_up_blocks):    # zero proj out
            # if only_return_transformer_layers_out and i < 3:    # 第一个upblock是纯conv的
            #     continue
            up_block_res_sample = controlnet_block(out_block_res_samples[i])
            controlnet_up_block_res_samples = controlnet_up_block_res_samples + (up_block_res_sample,)

        up_block_res_samples = controlnet_up_block_res_samples

        # 6. scaling
        if guess_mode and not self.config.global_pool_conditions:
            raise NotImplementedError
            scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1, device=sample.device)  # 0.1 to 1.0

            scales = scales * conditioning_scale
            down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales)]
            mid_block_res_sample = mid_block_res_sample * scales[-1]  # last one
        else:
            up_block_res_samples = [sample * conditioning_scale for sample in up_block_res_samples]
            mid_block_res_sample = mid_block_res_sample * conditioning_scale

        if self.config.global_pool_conditions:
            up_block_res_samples = [torch.mean(sample, dim=(2, 3), keepdim=True) for sample in up_block_res_samples]
            mid_block_res_sample = torch.mean(mid_block_res_sample, dim=(2, 3), keepdim=True)

        if only_return_transformer_layers_out:
            down_block_res_samples = [sample for i, sample in enumerate(up_block_res_samples) if not i % 3== 2][::-1]
        else:
            down_block_res_samples = list(reversed(up_block_res_samples))

        if not return_dict:
            return (down_block_res_samples, mid_block_res_sample, up_block_res_samples)

        return ControlNetOutput(
            down_block_res_samples=down_block_res_samples,
            mid_block_res_sample=mid_block_res_sample,
            up_block_res_samples=up_block_res_samples,
        )


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


def named_params_and_buffers(module):
    assert isinstance(module, torch.nn.Module)
    return list(module.named_parameters()) + list(module.named_buffers())


from src.models.unet_2d_blocks import UpBlock2D, CrossAttnUpBlock2D
from diffusers.models.resnet import ResnetBlock2D

from diffusers.utils import is_torch_version
class UpBlock2D_woskip(UpBlock2D):
    def __init__(
        self,
        prev_output_channel: int,
        out_channels: int,
        temb_channels: int,
        dropout: float = 0.0,
        num_layers: int = 1,
        resnet_eps: float = 1e-6,
        resnet_time_scale_shift: str = "default",
        resnet_act_fn: str = "swish",
        resnet_groups: int = 32,
        resnet_pre_norm: bool = True,
        output_scale_factor=1.0,
        add_upsample=True,
    ):
        super(UpBlock2D_woskip, self).__init__(
            in_channels=0,
            prev_output_channel=prev_output_channel,
            out_channels=out_channels,
            temb_channels=temb_channels,
            dropout=dropout,
            num_layers=num_layers,
            resnet_eps=resnet_eps,
            resnet_time_scale_shift=resnet_time_scale_shift,
            resnet_act_fn=resnet_act_fn,
            resnet_groups=resnet_groups,
            resnet_pre_norm=resnet_pre_norm,
            output_scale_factor=output_scale_factor,
            add_upsample=add_upsample,
        )
        resnets = []

        for i in range(num_layers):
            resnet_in_channels = prev_output_channel if i == 0 else out_channels

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

        self.resnets = nn.ModuleList(resnets)

    def forward(self, hidden_states, temb=None, upsample_size=None, scale: float = 1.0):
        output_states = []
        for resnet in self.resnets:
            if self.training and self.gradient_checkpointing:

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

                    return custom_forward

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

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

        return hidden_states, output_states        


class CrossAttnUpBlock2D_woskip(CrossAttnUpBlock2D):
    def __init__(
        self,
        out_channels: int,
        prev_output_channel: int,
        temb_channels: int,
        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,
        attention_type="default",
    ):
        super(CrossAttnUpBlock2D_woskip, self).__init__(
            in_channels=0,
            out_channels=out_channels,
            prev_output_channel=prev_output_channel,
            temb_channels=temb_channels,
            dropout=dropout,
            num_layers=num_layers,
            transformer_layers_per_block=transformer_layers_per_block,
            resnet_eps=resnet_eps,
            resnet_time_scale_shift=resnet_time_scale_shift,
            resnet_act_fn=resnet_act_fn,
            resnet_groups=resnet_groups,
            resnet_pre_norm=resnet_pre_norm,
            num_attention_heads=num_attention_heads,
            cross_attention_dim=cross_attention_dim,
            output_scale_factor=output_scale_factor,
            add_upsample=add_upsample,
            dual_cross_attention=dual_cross_attention,
            use_linear_projection=use_linear_projection,
            only_cross_attention=only_cross_attention,
            upcast_attention=upcast_attention,
            attention_type=attention_type,
        )
        resnets = []

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

    def forward(
            self,
            hidden_states: 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,
        ):
        lora_scale = (
            cross_attention_kwargs.get("scale", 1.0)
            if cross_attention_kwargs is not None
            else 1.0
        )
        output_states = []
        for resnet, attn in zip(self.resnets, self.attentions):

            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 = 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]
            else:
                hidden_states = resnet(hidden_states, temb, scale=lora_scale)
                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]
            output_states = output_states + [hidden_states,]
        if self.upsamplers is not None:
            for upsampler in self.upsamplers:
                hidden_states = upsampler(hidden_states, upsample_size, scale=lora_scale)

        return hidden_states, output_states


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

    up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type

    if up_block_type == "UpBlock2D":
        return UpBlock2D_woskip(
            num_layers=num_layers,
            # in_channels=in_channels,
            out_channels=out_channels,
            prev_output_channel=prev_output_channel,
            temb_channels=temb_channels,
            dropout=dropout,
            add_upsample=add_upsample,
            resnet_eps=resnet_eps,
            resnet_act_fn=resnet_act_fn,
            resnet_groups=resnet_groups,
            resnet_time_scale_shift=resnet_time_scale_shift,
        )
    elif up_block_type == "CrossAttnUpBlock2D":
        if cross_attention_dim is None:
            raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock2D")
        return CrossAttnUpBlock2D_woskip(
            num_layers=num_layers,
            transformer_layers_per_block=transformer_layers_per_block,
            # in_channels=in_channels,
            out_channels=out_channels,
            prev_output_channel=prev_output_channel,
            temb_channels=temb_channels,
            dropout=dropout,
            add_upsample=add_upsample,
            resnet_eps=resnet_eps,
            resnet_act_fn=resnet_act_fn,
            resnet_groups=resnet_groups,
            cross_attention_dim=cross_attention_dim,
            num_attention_heads=num_attention_heads,
            dual_cross_attention=dual_cross_attention,
            use_linear_projection=use_linear_projection,
            only_cross_attention=only_cross_attention,
            upcast_attention=upcast_attention,
            resnet_time_scale_shift=resnet_time_scale_shift,
            attention_type=attention_type,
        )

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