Source code for mmagic.models.editors.animatediff.unet_block

# Copyright (c) OpenMMLab. All rights reserved.
# Adapted from https://github.com/huggingface/diffusers/blob/
# main/src/diffusers/models/unet_2d_blocks.py

import torch
from torch import nn

from .attention_3d import Transformer3DModel
from .motion_module import get_motion_module
from .resnet_3d import Downsample3D, ResnetBlock3D, Upsample3D


def get_down_block(
    down_block_type,
    num_layers,
    in_channels,
    out_channels,
    temb_channels,
    add_downsample,
    resnet_eps,
    resnet_act_fn,
    attn_num_head_channels,
    resnet_groups=None,
    cross_attention_dim=None,
    downsample_padding=None,
    dual_cross_attention=False,
    use_linear_projection=False,
    only_cross_attention=False,
    upcast_attention=False,
    resnet_time_scale_shift='default',
    unet_use_cross_frame_attention=None,
    unet_use_temporal_attention=None,
    use_inflated_groupnorm=None,
    use_motion_module=None,
    motion_module_type=None,
    motion_module_kwargs=None,
):
    """get unet down path block."""

    down_block_type = down_block_type[7:] if down_block_type.startswith(
        'UNetRes') else down_block_type
    if down_block_type == 'DownBlock3D':
        return DownBlock3D(
            num_layers=num_layers,
            in_channels=in_channels,
            out_channels=out_channels,
            temb_channels=temb_channels,
            add_downsample=add_downsample,
            resnet_eps=resnet_eps,
            resnet_act_fn=resnet_act_fn,
            resnet_groups=resnet_groups,
            downsample_padding=downsample_padding,
            resnet_time_scale_shift=resnet_time_scale_shift,
            use_inflated_groupnorm=use_inflated_groupnorm,
            use_motion_module=use_motion_module,
            motion_module_type=motion_module_type,
            motion_module_kwargs=motion_module_kwargs,
        )
    elif down_block_type == 'CrossAttnDownBlock3D':
        if cross_attention_dim is None:
            raise ValueError('cross_attention_dim must be specified \
                            for CrossAttnDownBlock3D')
        return CrossAttnDownBlock3D(
            num_layers=num_layers,
            in_channels=in_channels,
            out_channels=out_channels,
            temb_channels=temb_channels,
            add_downsample=add_downsample,
            resnet_eps=resnet_eps,
            resnet_act_fn=resnet_act_fn,
            resnet_groups=resnet_groups,
            downsample_padding=downsample_padding,
            cross_attention_dim=cross_attention_dim,
            attn_num_head_channels=attn_num_head_channels,
            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,
            unet_use_cross_frame_attention=unet_use_cross_frame_attention,
            unet_use_temporal_attention=unet_use_temporal_attention,
            use_inflated_groupnorm=use_inflated_groupnorm,
            use_motion_module=use_motion_module,
            motion_module_type=motion_module_type,
            motion_module_kwargs=motion_module_kwargs,
        )
    raise ValueError(f'{down_block_type} does not exist.')


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,
    attn_num_head_channels,
    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',
    unet_use_cross_frame_attention=None,
    unet_use_temporal_attention=None,
    use_inflated_groupnorm=None,
    use_motion_module=None,
    motion_module_type=None,
    motion_module_kwargs=None,
):
    """get unet up path block."""

    up_block_type = up_block_type[7:] if up_block_type.startswith(
        'UNetRes') else up_block_type
    if up_block_type == 'UpBlock3D':
        return UpBlock3D(
            num_layers=num_layers,
            in_channels=in_channels,
            out_channels=out_channels,
            prev_output_channel=prev_output_channel,
            temb_channels=temb_channels,
            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,
            use_inflated_groupnorm=use_inflated_groupnorm,
            use_motion_module=use_motion_module,
            motion_module_type=motion_module_type,
            motion_module_kwargs=motion_module_kwargs,
        )
    elif up_block_type == 'CrossAttnUpBlock3D':
        if cross_attention_dim is None:
            raise ValueError(
                'cross_attention_dim must be specified for CrossAttnUpBlock3D')
        return CrossAttnUpBlock3D(
            num_layers=num_layers,
            in_channels=in_channels,
            out_channels=out_channels,
            prev_output_channel=prev_output_channel,
            temb_channels=temb_channels,
            add_upsample=add_upsample,
            resnet_eps=resnet_eps,
            resnet_act_fn=resnet_act_fn,
            resnet_groups=resnet_groups,
            cross_attention_dim=cross_attention_dim,
            attn_num_head_channels=attn_num_head_channels,
            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,
            unet_use_cross_frame_attention=unet_use_cross_frame_attention,
            unet_use_temporal_attention=unet_use_temporal_attention,
            use_inflated_groupnorm=use_inflated_groupnorm,
            use_motion_module=use_motion_module,
            motion_module_type=motion_module_type,
            motion_module_kwargs=motion_module_kwargs,
        )
    raise ValueError(f'{up_block_type} does not exist.')


class UNetMidBlock3DCrossAttn(nn.Module):
    """3D unet mid block built by cross attention."""

    def __init__(
        self,
        in_channels: int,
        temb_channels: int,
        dropout: float = 0.0,
        num_layers: int = 1,
        resnet_eps: float = 1e-6,
        resnet_time_scale_shift: str = 'default',
        resnet_act_fn: str = 'swish',
        resnet_groups: int = 32,
        resnet_pre_norm: bool = True,
        attn_num_head_channels=1,
        output_scale_factor=1.0,
        cross_attention_dim=1280,
        dual_cross_attention=False,
        use_linear_projection=False,
        upcast_attention=False,
        unet_use_cross_frame_attention=None,
        unet_use_temporal_attention=None,
        use_inflated_groupnorm=None,
        use_motion_module=None,
        motion_module_type=None,
        motion_module_kwargs=None,
    ):
        super().__init__()

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

        # there is always at least one resnet
        resnets = [
            ResnetBlock3D(
                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,
                use_inflated_groupnorm=use_inflated_groupnorm,
            )
        ]
        attentions = []
        motion_modules = []

        for _ in range(num_layers):
            if dual_cross_attention:
                raise NotImplementedError
            cfa = unet_use_cross_frame_attention
            attentions.append(
                Transformer3DModel(
                    attn_num_head_channels,
                    in_channels // attn_num_head_channels,
                    in_channels=in_channels,
                    num_layers=1,
                    cross_attention_dim=cross_attention_dim,
                    norm_num_groups=resnet_groups,
                    use_linear_projection=use_linear_projection,
                    upcast_attention=upcast_attention,
                    unet_use_cross_frame_attention=cfa,
                    unet_use_temporal_attention=unet_use_temporal_attention,
                ))
            motion_modules.append(
                get_motion_module(
                    in_channels=in_channels,
                    motion_module_type=motion_module_type,
                    motion_module_kwargs=motion_module_kwargs,
                ) if use_motion_module else None)
            resnets.append(
                ResnetBlock3D(
                    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,
                    use_inflated_groupnorm=use_inflated_groupnorm,
                ))

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

    def forward(self,
                hidden_states,
                temb=None,
                encoder_hidden_states=None,
                attention_mask=None):
        """forward with hidden states."""

        hidden_states = self.resnets[0](hidden_states, temb)
        for attn, resnet, motion_module in zip(self.attentions,
                                               self.resnets[1:],
                                               self.motion_modules):
            hidden_states = attn(
                hidden_states,
                encoder_hidden_states=encoder_hidden_states).sample
            hidden_states = motion_module(
                hidden_states,
                temb,
                encoder_hidden_states=encoder_hidden_states
            ) if motion_module is not None else hidden_states
            hidden_states = resnet(hidden_states, temb)

        return hidden_states


class CrossAttnDownBlock3D(nn.Module):
    """Down block built by 3D cross attention."""

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        temb_channels: int,
        dropout: float = 0.0,
        num_layers: int = 1,
        resnet_eps: float = 1e-6,
        resnet_time_scale_shift: str = 'default',
        resnet_act_fn: str = 'swish',
        resnet_groups: int = 32,
        resnet_pre_norm: bool = True,
        attn_num_head_channels=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,
        unet_use_cross_frame_attention=None,
        unet_use_temporal_attention=None,
        use_inflated_groupnorm=None,
        use_motion_module=None,
        motion_module_type=None,
        motion_module_kwargs=None,
    ):
        super().__init__()
        resnets = []
        attentions = []
        motion_modules = []

        self.has_cross_attention = True
        self.attn_num_head_channels = attn_num_head_channels

        for i in range(num_layers):
            in_channels = in_channels if i == 0 else out_channels
            resnets.append(
                ResnetBlock3D(
                    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,
                    use_inflated_groupnorm=use_inflated_groupnorm,
                ))
            if dual_cross_attention:
                raise NotImplementedError
            cfa = unet_use_cross_frame_attention
            attentions.append(
                Transformer3DModel(
                    attn_num_head_channels,
                    out_channels // attn_num_head_channels,
                    in_channels=out_channels,
                    num_layers=1,
                    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,
                    unet_use_cross_frame_attention=cfa,
                    unet_use_temporal_attention=unet_use_temporal_attention,
                ))
            motion_modules.append(
                get_motion_module(
                    in_channels=out_channels,
                    motion_module_type=motion_module_type,
                    motion_module_kwargs=motion_module_kwargs,
                ) if use_motion_module else None)

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

        if add_downsample:
            self.downsamplers = nn.ModuleList([
                Downsample3D(
                    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,
                temb=None,
                encoder_hidden_states=None,
                attention_mask=None):
        """forward with hidden states."""

        output_states = ()

        for resnet, attn, motion_module in zip(self.resnets, self.attentions,
                                               self.motion_modules):
            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

                hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(resnet), hidden_states, temb)
                hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(attn, return_dict=False),
                    hidden_states,
                    encoder_hidden_states,
                )[0]
                if motion_module is not None:
                    hidden_states = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(motion_module),
                        hidden_states.requires_grad_(), temb,
                        encoder_hidden_states)

            else:
                hidden_states = resnet(hidden_states, temb)
                hidden_states = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states).sample

                # add motion module
                hidden_states = motion_module(
                    hidden_states,
                    temb,
                    encoder_hidden_states=encoder_hidden_states
                ) if motion_module is not None else hidden_states

            output_states += (hidden_states, )

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

            output_states += (hidden_states, )

        return hidden_states, output_states


class DownBlock3D(nn.Module):
    """Down block built by 3D resnet."""

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        temb_channels: int,
        dropout: float = 0.0,
        num_layers: int = 1,
        resnet_eps: float = 1e-6,
        resnet_time_scale_shift: str = 'default',
        resnet_act_fn: str = 'swish',
        resnet_groups: int = 32,
        resnet_pre_norm: bool = True,
        output_scale_factor=1.0,
        add_downsample=True,
        downsample_padding=1,
        use_inflated_groupnorm=None,
        use_motion_module=None,
        motion_module_type=None,
        motion_module_kwargs=None,
    ):
        super().__init__()
        resnets = []
        motion_modules = []

        for i in range(num_layers):
            in_channels = in_channels if i == 0 else out_channels
            resnets.append(
                ResnetBlock3D(
                    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,
                    use_inflated_groupnorm=use_inflated_groupnorm,
                ))
            motion_modules.append(
                get_motion_module(
                    in_channels=out_channels,
                    motion_module_type=motion_module_type,
                    motion_module_kwargs=motion_module_kwargs,
                ) if use_motion_module else None)

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

        if add_downsample:
            self.downsamplers = nn.ModuleList([
                Downsample3D(
                    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, temb=None, encoder_hidden_states=None):
        """forward with hidden states."""

        output_states = ()

        for resnet, motion_module in zip(self.resnets, self.motion_modules):
            if self.training and self.gradient_checkpointing:

                def create_custom_forward(module):

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

                    return custom_forward

                hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(resnet), hidden_states, temb)
                if motion_module is not None:
                    hidden_states = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(motion_module),
                        hidden_states.requires_grad_(), temb,
                        encoder_hidden_states)
            else:
                hidden_states = resnet(hidden_states, temb)

                # add motion module
                hidden_states = motion_module(
                    hidden_states,
                    temb,
                    encoder_hidden_states=encoder_hidden_states
                ) if motion_module is not None else hidden_states

            output_states += (hidden_states, )

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

            output_states += (hidden_states, )

        return hidden_states, output_states


class CrossAttnUpBlock3D(nn.Module):
    """Up block built by 3D cross attention."""

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        prev_output_channel: 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,
        attn_num_head_channels=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,
        unet_use_cross_frame_attention=None,
        unet_use_temporal_attention=None,
        use_inflated_groupnorm=None,
        use_motion_module=None,
        motion_module_type=None,
        motion_module_kwargs=None,
    ):
        super().__init__()
        resnets = []
        attentions = []
        motion_modules = []

        self.has_cross_attention = True
        self.attn_num_head_channels = attn_num_head_channels

        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(
                ResnetBlock3D(
                    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,
                    use_inflated_groupnorm=use_inflated_groupnorm,
                ))
            if dual_cross_attention:
                raise NotImplementedError
            cfa = unet_use_cross_frame_attention
            attentions.append(
                Transformer3DModel(
                    attn_num_head_channels,
                    out_channels // attn_num_head_channels,
                    in_channels=out_channels,
                    num_layers=1,
                    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,
                    unet_use_cross_frame_attention=cfa,
                    unet_use_temporal_attention=unet_use_temporal_attention,
                ))
            motion_modules.append(
                get_motion_module(
                    in_channels=out_channels,
                    motion_module_type=motion_module_type,
                    motion_module_kwargs=motion_module_kwargs,
                ) if use_motion_module else None)

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

        if add_upsample:
            self.upsamplers = nn.ModuleList([
                Upsample3D(
                    out_channels, use_conv=True, out_channels=out_channels)
            ])
        else:
            self.upsamplers = None

        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states,
        res_hidden_states_tuple,
        temb=None,
        encoder_hidden_states=None,
        upsample_size=None,
        attention_mask=None,
    ):
        """forward with hidden states and res hidden states."""

        for resnet, attn, motion_module in zip(self.resnets, self.attentions,
                                               self.motion_modules):
            # 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

                hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(resnet), hidden_states, temb)
                hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(attn, return_dict=False),
                    hidden_states,
                    encoder_hidden_states,
                )[0]
                if motion_module is not None:
                    hidden_states = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(motion_module),
                        hidden_states.requires_grad_(), temb,
                        encoder_hidden_states)

            else:
                hidden_states = resnet(hidden_states, temb)
                hidden_states = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states).sample

                # add motion module
                hidden_states = motion_module(
                    hidden_states,
                    temb,
                    encoder_hidden_states=encoder_hidden_states
                ) if motion_module is not None else hidden_states

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

        return hidden_states


class UpBlock3D(nn.Module):
    """Up block built by 3D resnet."""

    def __init__(
        self,
        in_channels: int,
        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,
        use_motion_module=None,
        motion_module_type=None,
        motion_module_kwargs=None,
        use_inflated_groupnorm=None,
    ):
        super().__init__()
        resnets = []
        motion_modules = []

        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(
                ResnetBlock3D(
                    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,
                    use_inflated_groupnorm=use_inflated_groupnorm,
                ))
            motion_modules.append(
                get_motion_module(
                    in_channels=out_channels,
                    motion_module_type=motion_module_type,
                    motion_module_kwargs=motion_module_kwargs,
                ) if use_motion_module else None)

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

        if add_upsample:
            self.upsamplers = nn.ModuleList([
                Upsample3D(
                    out_channels, use_conv=True, out_channels=out_channels)
            ])
        else:
            self.upsamplers = None

        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states,
        res_hidden_states_tuple,
        temb=None,
        upsample_size=None,
        encoder_hidden_states=None,
    ):
        """forward with hidden states and res hidden states."""

        for resnet, motion_module in zip(self.resnets, self.motion_modules):
            # 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):

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

                    return custom_forward

                hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(resnet), hidden_states, temb)
                if motion_module is not None:
                    hidden_states = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(motion_module),
                        hidden_states.requires_grad_(), temb,
                        encoder_hidden_states)
            else:
                hidden_states = resnet(hidden_states, temb)
                hidden_states = motion_module(
                    hidden_states,
                    temb,
                    encoder_hidden_states=encoder_hidden_states
                ) if motion_module is not None else hidden_states

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

        return hidden_states