diff --git a/core/conversion/converters/impl/layer_norm.cpp b/core/conversion/converters/impl/layer_norm.cpp
index 0c00ee2c4d..f09fc84514 100644
--- a/core/conversion/converters/impl/layer_norm.cpp
+++ b/core/conversion/converters/impl/layer_norm.cpp
@@ -88,60 +88,25 @@ auto layer_norm_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns()
         return true;
       }
 
-      // Remove batch dimension from input shape for expand_size, which will
-      // be used to create weights for addScaleNd later.
-      auto expand_size = shape;
-      expand_size.erase(expand_size.begin(), expand_size.begin() + 1);
-
-      // Set up gamma_weights and beta_weights from gamma_expand and
-      // beta_expand.
-      auto gamma_weights = Weights(ctx, at::ones(expand_size));
-      auto beta_weights = Weights(ctx, at::zeros(expand_size));
+      auto normalized = div_out;
 
+      // gamma
       if (args[2].IValue()->isTensor()) {
-        torch::Tensor gamma;
-        gamma = args[2].unwrapToTensor();
-        auto gamma_expand = gamma.expand(expand_size);
-        gamma_weights = Weights(ctx, gamma_expand);
-      } else {
-        gamma_weights = Weights(ctx, at::ones(expand_size));
+        auto gamma = args[2].ITensorOrFreeze(ctx);
+        auto gamma_prod = add_elementwise(
+            ctx, nvinfer1::ElementWiseOperation::kPROD, normalized, gamma, (util::node_info(n) + "_gamma").c_str());
+        normalized = gamma_prod->getOutput(0);
       }
 
+      // beta
       if (args[3].IValue()->isTensor()) {
-        torch::Tensor beta;
-        beta = args[3].unwrapToTensor();
-        auto beta_expand = beta.expand(expand_size);
-        beta_weights = Weights(ctx, beta_expand);
-      } else {
-        beta_weights = Weights(ctx, at::zeros(expand_size));
+        auto beta = args[3].ITensorOrFreeze(ctx);
+        auto beta_sum = add_elementwise(
+            ctx, nvinfer1::ElementWiseOperation::kSUM, normalized, beta, (util::node_info(n) + "_beta").c_str());
+        normalized = beta_sum->getOutput(0);
       }
 
-      auto power = Weights(ctx, at::ones(expand_size));
-
-      auto gamma_tensor = ctx->net->addConstant(gamma_weights.shape, gamma_weights.data)->getOutput(0);
-      auto scale_l = add_elementwise(
-          ctx, nvinfer1::ElementWiseOperation::kPROD, div_out, gamma_tensor, (util::node_info(n) + "_scale").c_str());
-
-      auto beta_tensor = ctx->net->addConstant(beta_weights.shape, beta_weights.data)->getOutput(0);
-      auto shift_l = add_elementwise(
-          ctx,
-          nvinfer1::ElementWiseOperation::kSUM,
-          scale_l->getOutput(0),
-          beta_tensor,
-          (util::node_info(n) + "_shift").c_str());
-
-      auto power_tensor = ctx->net->addConstant(power.shape, power.data)->getOutput(0);
-      auto power_l = add_elementwise(
-          ctx,
-          nvinfer1::ElementWiseOperation::kPOW,
-          shift_l->getOutput(0),
-          power_tensor,
-          (util::node_info(n) + "_power").c_str());
-
-      power_l->setName((util::node_info(n) + "_scale_nd").c_str());
-      auto power_l_out = power_l->getOutput(0);
-
-      ctx->AssociateValueAndTensor(n->outputs()[0], power_l_out);
+      ctx->AssociateValueAndTensor(n->outputs()[0], normalized);
       return true;
     }});