Method _downsample_2d

src/diffusers/models/downsampling.py:179–243

Fused `Conv2d()` followed by `downsample_2d()`. Padding is performed only once at the beginning, not between the operations. The fused op is considerably more efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of arbitrary orde

(
        self,
        hidden_states: torch.Tensor,
        weight: torch.Tensor | None = None,
        kernel: torch.Tensor | None = None,
        factor: int = 2,
        gain: float = 1,
    )

        self.out_channels = out_channels

    def _downsample_2d(
        self,
        hidden_states: torch.Tensor,
        weight: torch.Tensor | None = None,
        kernel: torch.Tensor | None = None,
        factor: int = 2,
        gain: float = 1,
    ) -> torch.Tensor:
        """Fused `Conv2d()` followed by `downsample_2d()`.
        Padding is performed only once at the beginning, not between the operations. The fused op is considerably more
        efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of
        arbitrary order.

        Args:
            hidden_states (`torch.Tensor`):
                Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
            weight (`torch.Tensor`, *optional*):
                Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`. Grouped convolution can be
                performed by `inChannels = x.shape[0] // numGroups`.
            kernel (`torch.Tensor`, *optional*):
                FIR filter of the shape `[firH, firW]` or `[firN]` (separable). The default is `[1] * factor`, which
                corresponds to average pooling.
            factor (`int`, *optional*, default to `2`):
                Integer downsampling factor.
            gain (`float`, *optional*, default to `1.0`):
                Scaling factor for signal magnitude.

        Returns:
            output (`torch.Tensor`):
                Tensor of the shape `[N, C, H // factor, W // factor]` or `[N, H // factor, W // factor, C]`, and same
                datatype as `x`.
        """

        assert isinstance(factor, int) and factor >= 1
        if kernel is None:
            kernel = [1] * factor

        # setup kernel
        kernel = torch.tensor(kernel, dtype=torch.float32)
        if kernel.ndim == 1:
            kernel = torch.outer(kernel, kernel)
        kernel /= torch.sum(kernel)

        kernel = kernel * gain

        if self.use_conv:
            _, _, convH, convW = weight.shape
            pad_value = (kernel.shape[0] - factor) + (convW - 1)
            stride_value = [factor, factor]
            upfirdn_input = upfirdn2d_native(
                hidden_states,
                torch.tensor(kernel, device=hidden_states.device),
                pad=((pad_value + 1) // 2, pad_value // 2),
            )
            output = F.conv2d(upfirdn_input, weight, stride=stride_value, padding=0)
        else:
            pad_value = kernel.shape[0] - factor
            output = upfirdn2d_native(