diff --git a/compressai/layers/layers.py b/compressai/layers/layers.py
index 73fcbce1..9a75c0ca 100644
--- a/compressai/layers/layers.py
+++ b/compressai/layers/layers.py
@@ -27,10 +27,9 @@
 # OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
 # ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
+from typing import Any, Tuple
 import math
 
-from typing import Any
-
 import torch
 import torch.nn as nn
 
@@ -46,7 +45,8 @@
     "ResidualBlock",
     "ResidualBlockUpsample",
     "ResidualBlockWithStride",
-    "conv1x1",
+    "SpectralConv2d",
+    "SpectralConvTranspose2d",
     "conv3x3",
     "subpel_conv3x3",
     "QReLU",
@@ -54,6 +54,65 @@
 ]
 
 
+class _SpectralConvNdMixin:
+    def __init__(self, dim: Tuple[int, ...]):
+        self.dim = dim
+        self.weight_transformed = nn.Parameter(self._to_transform_domain(self.weight))
+        del self._parameters["weight"]  # Unregister weight, and fallback to property.
+
+    @property
+    def weight(self) -> Tensor:
+        return self._from_transform_domain(self.weight_transformed)
+
+    def _to_transform_domain(self, x: Tensor) -> Tensor:
+        return torch.fft.rfftn(x, s=self.kernel_size, dim=self.dim, norm="ortho")
+
+    def _from_transform_domain(self, x: Tensor) -> Tensor:
+        return torch.fft.irfftn(x, s=self.kernel_size, dim=self.dim, norm="ortho")
+
+
+class SpectralConv2d(nn.Conv2d, _SpectralConvNdMixin):
+    r"""Spectral 2D convolution.
+
+    Introduced in [Balle2018efficient].
+    Reparameterizes the weights to be derived from weights stored in the
+    frequency domain.
+    In the original paper, this is referred to as "spectral Adam" or
+    "Sadam" due to its effect on the Adam optimizer update rule.
+    The motivation behind representing the weights in the frequency
+    domain is that optimizer updates/steps may now affect all
+    frequencies to an equal amount.
+    This improves the gradient conditioning, thus leading to faster
+    convergence and increased stability at larger learning rates.
+
+    For comparison, see the TensorFlow Compression implementations of
+    `SignalConv2D
+    <https://github.com/tensorflow/compression/blob/v2.14.0/tensorflow_compression/python/layers/signal_conv.py#L61>`_
+    and
+    `RDFTParameter
+    <https://github.com/tensorflow/compression/blob/v2.14.0/tensorflow_compression/python/layers/parameters.py#L71>`_.
+
+    [Balle2018efficient]: `"Efficient Nonlinear Transforms for Lossy
+    Image Compression" <https://arxiv.org/abs/1802.00847>`_,
+    by Johannes Ballé, PCS 2018.
+    """
+
+    def __init__(self, *args: Any, **kwargs: Any):
+        super().__init__(*args, **kwargs)
+        _SpectralConvNdMixin.__init__(self, dim=(-2, -1))
+
+
+class SpectralConvTranspose2d(nn.ConvTranspose2d, _SpectralConvNdMixin):
+    r"""Spectral 2D transposed convolution.
+
+    Transposed version of :class:`SpectralConv2d`.
+    """
+
+    def __init__(self, *args: Any, **kwargs: Any):
+        super().__init__(*args, **kwargs)
+        _SpectralConvNdMixin.__init__(self, dim=(-2, -1))
+
+
 class MaskedConv2d(nn.Conv2d):
     r"""Masked 2D convolution implementation, mask future "unseen" pixels.
     Useful for building auto-regressive network components.