class Decoder(nn.Module):
    r"""
    The `Decoder` layer of a variational autoencoder that decodes its latent representation into an output sample.

    Args:
        in_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        out_channels (`int`, *optional*, defaults to 3):
            The number of output channels.
        up_block_types (`Tuple[str, ...]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
            The types of up blocks to use. See `~diffusers.models.unet_2d_blocks.get_up_block` for available options.
        block_out_channels (`Tuple[int, ...]`, *optional*, defaults to `(64,)`):
            The number of output channels for each block.
        layers_per_block (`int`, *optional*, defaults to 2):
            The number of layers per block.
        norm_num_groups (`int`, *optional*, defaults to 32):
            The number of groups for normalization.
        act_fn (`str`, *optional*, defaults to `"silu"`):
            The activation function to use. See `~diffusers.models.activations.get_activation` for available options.
        norm_type (`str`, *optional*, defaults to `"group"`):
            The normalization type to use. Can be either `"group"` or `"spatial"`.
    """

    def __init__(
        self,
        in_channels: int = 3,
        out_channels: int = 3,
        up_block_types: Tuple[str, ...] = ("UpDecoderBlock2D",),
        block_out_channels: Tuple[int, ...] = (64,),
        layers_per_block: int = 2,
        norm_num_groups: int = 32,
        act_fn: str = "silu",
        norm_type: str = "group",  # group, spatial
        mid_block_add_attention=True,
    ):
        super().__init__()
        self.layers_per_block = layers_per_block

        self.conv_in = nn.Conv2d(
            in_channels,
            block_out_channels[-1],
            kernel_size=3,
            stride=1,
            padding=1,
        )

        self.up_blocks = nn.ModuleList([])

        temb_channels = in_channels if norm_type == "spatial" else None

        # mid
        self.mid_block = UNetMidBlock2D(
            in_channels=block_out_channels[-1],
            resnet_eps=1e-6,
            resnet_act_fn=act_fn,
            output_scale_factor=1,
            resnet_time_scale_shift="default" if norm_type == "group" else norm_type,
            attention_head_dim=block_out_channels[-1],
            resnet_groups=norm_num_groups,
            temb_channels=temb_channels,
            add_attention=mid_block_add_attention,
        )

        # up
        reversed_block_out_channels = list(reversed(block_out_channels))
        output_channel = reversed_block_out_channels[0]
        for i, up_block_type in enumerate(up_block_types):
            prev_output_channel = output_channel
            output_channel = reversed_block_out_channels[i]

            is_final_block = i == len(block_out_channels) - 1

            up_block = get_up_block(
                up_block_type,
                num_layers=self.layers_per_block + 1,
                in_channels=prev_output_channel,
                out_channels=output_channel,
                prev_output_channel=None,
                add_upsample=not is_final_block,
                resnet_eps=1e-6,
                resnet_act_fn=act_fn,
                resnet_groups=norm_num_groups,
                attention_head_dim=output_channel,
                temb_channels=temb_channels,
                resnet_time_scale_shift=norm_type,
            )
            self.up_blocks.append(up_block)
            prev_output_channel = output_channel

        # out
        if norm_type == "spatial":
            self.conv_norm_out = SpatialNorm(block_out_channels[0], temb_channels)
        else:
            self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-6)
        self.conv_act = nn.SiLU()
        self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1)

        self.gradient_checkpointing = False

    def forward(
        self,
        sample: torch.Tensor,
        latent_embeds: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        r"""The forward method of the `Decoder` class."""

        sample = self.conv_in(sample)

        upscale_dtype = next(iter(self.up_blocks.parameters())).dtype
        if torch.is_grad_enabled() 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"):
                # middle
                sample = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(self.mid_block),
                    sample,
                    latent_embeds,
                    use_reentrant=False,
                )
                sample = sample.to(upscale_dtype)

                # up
                for up_block in self.up_blocks:
                    sample = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(up_block),
                        sample,
                        latent_embeds,
                        use_reentrant=False,
                    )
            else:
                # middle
                sample = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(self.mid_block), sample, latent_embeds
                )
                sample = sample.to(upscale_dtype)

                # up
                for up_block in self.up_blocks:
                    sample = torch.utils.checkpoint.checkpoint(create_custom_forward(up_block), sample, latent_embeds)
        else:
            # middle
            sample = self.mid_block(sample, latent_embeds)
            sample = sample.to(upscale_dtype)

            # up
            for up_block in self.up_blocks:
                sample = up_block(sample, latent_embeds)

        # post-process
        if latent_embeds is None:
            sample = self.conv_norm_out(sample)
        else:
            sample = self.conv_norm_out(sample, latent_embeds)
        sample = self.conv_act(sample)
        sample = self.conv_out(sample)

        return sample
/Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py