throw in something experimental based on @cfifty paper

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "vector-quantize-pytorch"
-version = "1.18.2"
+version = "1.18.3"
 description = "Vector Quantization - Pytorch"
 authors = [
     { name = "Phil Wang", email = "[email protected]" }

vector_quantize_pytorch/lookup_free_quantization.py

@@ -103,6 +103,7 @@ def __init__(
         commitment_loss_weight = 0.,
         diversity_gamma = 1.,
         straight_through_activation = nn.Identity(),
+        scale_trick = False,                        # @cfifty Fifty et al. https://arxiv.org/abs/2410.06424
         num_codebooks = 1,
         keep_num_codebooks_dim = None,
         codebook_scale = 1.,                        # for residual LFQ, codebook scaled down by 2x at each layer
@@ -160,6 +161,9 @@ def __init__(
 
         self.activation = straight_through_activation
 
+        assert not (scale_trick and spherical)
+        self.scale_trick = scale_trick
+
         # whether to use BSQ (binary spherical quantization)
 
         self.spherical = spherical
@@ -322,7 +326,12 @@ def forward(
 
             if self.training:
                 x = self.activation(x)
-                x = x + (quantized - x).detach()
+
+                if self.scale_trick:
+                    x = x * (quantized / x).detach()
+                else:
+                    x = x + (quantized - x).detach()
+
             else:
                 x = quantized