def upsample_2d(hidden_states, kernel=None, factor=2, gain=1):
    r"""Upsample2D a batch of 2D images with the given filter.
    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and upsamples each image with the given
    filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the specified
    `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its shape is
    a: multiple of the upsampling factor.

    Args:
        hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
        kernel: FIR filter of the shape `[firH, firW]` or `[firN]`
          (separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling.
        factor: Integer upsampling factor (default: 2).
        gain: Scaling factor for signal magnitude (default: 1.0).

    Returns:
        output: Tensor of the shape `[N, C, H * factor, W * factor]`
    """
    assert isinstance(factor, int) and factor >= 1
    if kernel is None:
        kernel = [1] * factor

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

    kernel = kernel * (gain * (factor**2))
    pad_value = kernel.shape[0] - factor
    output = upfirdn2d_native(
        hidden_states,
        kernel.to(device=hidden_states.device),
        up=factor,
        pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2),
    )
    return output
Upsample2D a batch of 2D images with the given filter.
    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and upsamples each image with the given
    filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the specified
    `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its shape is
    a: multiple of the upsampling factor.

    Args:
        hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
        kernel: FIR filter of the shape `[firH, firW]` or `[firN]`
          (separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling.
        factor: Integer upsampling factor (default: 2).
        gain: Scaling factor for signal magnitude (default: 1.0).

    Returns:
        output: Tensor of the shape `[N, C, H * factor, W * factor]`
    
promptist/diffusers/src/diffusers/models/resnet.py