Class Downsample2D

src/diffusers/models/downsampling.py:67–147

A 2D downsampling layer with an optional convolution. Parameters: channels (`int`): number of channels in the inputs and outputs. use_conv (`bool`, default `False`): option to use a convolution. out_channels (`int`, optional): number o

class Downsample2D(nn.Module):
    """A 2D downsampling layer with an optional convolution.

    Parameters:
        channels (`int`):
            number of channels in the inputs and outputs.
        use_conv (`bool`, default `False`):
            option to use a convolution.
        out_channels (`int`, optional):
            number of output channels. Defaults to `channels`.
        padding (`int`, default `1`):
            padding for the convolution.
        name (`str`, default `conv`):
            name of the downsampling 2D layer.
    """

    def __init__(
        self,
        channels: int,
        use_conv: bool = False,
        out_channels: int | None = None,
        padding: int = 1,
        name: str = "conv",
        kernel_size=3,
        norm_type=None,
        eps=None,
        elementwise_affine=None,
        bias=True,
    ):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.padding = padding
        stride = 2
        self.name = name

        if norm_type == "ln_norm":
            self.norm = nn.LayerNorm(channels, eps, elementwise_affine)
        elif norm_type == "rms_norm":
            self.norm = RMSNorm(channels, eps, elementwise_affine)
        elif norm_type is None:
            self.norm = None
        else:
            raise ValueError(f"unknown norm_type: {norm_type}")

        if use_conv:
            conv = nn.Conv2d(
                self.channels, self.out_channels, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias
            )
        else:
            assert self.channels == self.out_channels
            conv = nn.AvgPool2d(kernel_size=stride, stride=stride)

        # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
        if name == "conv":
            self.Conv2d_0 = conv
            self.conv = conv