diff --git a/ai_edge_quantizer/algorithms/nonlinear_quantize/float_casting.py b/ai_edge_quantizer/algorithms/nonlinear_quantize/float_casting.py
index 9ae7f50..f7b4107 100644
--- a/ai_edge_quantizer/algorithms/nonlinear_quantize/float_casting.py
+++ b/ai_edge_quantizer/algorithms/nonlinear_quantize/float_casting.py
@@ -141,23 +141,38 @@ def materialize_fc_conv(
       weight_tensor,
       graph_info.buffers,
   )
-  assert weight_content is not None
-  if not (
-      quant_params := tensor_quant_params_cache.lookup(
-          weight_tensor.buffer, _FP16_QUANT_CONFIG
+  if weight_content is None:
+    # If the weight data is not stored in the flatbuffer (e.g. dynamic weights),
+    # skip quantization for this tensor.
+    op2weight_params = qtyping.OpToTensorParams(
+        subgraph_op_id=op_info.subgraph_op_index,
+        transformations=[_QuantTransformation.NO_QUANTIZE],
+    )
+  else:
+    if not (
+        quant_params := tensor_quant_params_cache.lookup(
+            weight_tensor.buffer, _FP16_QUANT_CONFIG
+        )
+    ):
+      quant_params = qtyping.NonLinearQuantParams(
+          num_bits=16, quantized_data=weight_content.astype(np.float16)
       )
-  ):
+      tensor_quant_params_cache.insert(
+          weight_tensor.buffer, _FP16_QUANT_CONFIG, quant_params
+      )
+    op2weight_params = qtyping.OpToTensorParams(
+        subgraph_op_id=op_info.subgraph_op_index,
+        parameters=quant_params,
+        transformations=[_QuantTransformation.ADD_DEQUANTIZE],
+    )
     quant_params = qtyping.NonLinearQuantParams(
-        num_bits=16, quantized_data=weight_content.astype(np.float16)
+        num_bits=16, quantized_data=weight_content.astype(np.float16)  # pytype: disable=attribute-error
     )
-    tensor_quant_params_cache.insert(
-        weight_tensor.buffer, _FP16_QUANT_CONFIG, quant_params
+    op2weight_params = qtyping.OpToTensorParams(
+        subgraph_op_id=op_info.subgraph_op_index,
+        parameters=quant_params,
+        transformations=[_QuantTransformation.ADD_DEQUANTIZE],
     )
-  op2weight_params = qtyping.OpToTensorParams(
-      subgraph_op_id=op_info.subgraph_op_index,
-      parameters=quant_params,
-      transformations=[_QuantTransformation.ADD_DEQUANTIZE],
-  )
   op_tensor_params.append(
       qtyping.TensorTransformationParams(
           tensor_name=tfl_flatbuffer_utils.get_tensor_name(weight_tensor),
@@ -241,23 +256,30 @@ def materialize_conv2d_transpose(
       weight_tensor,
       graph_info.buffers,
   )
-  assert weight_content is not None
-  if not (
-      quant_params := tensor_quant_params_cache.lookup(
-          weight_tensor.buffer, _FP16_QUANT_CONFIG
-      )
-  ):
-    quant_params = qtyping.NonLinearQuantParams(
-        num_bits=16, quantized_data=weight_content.astype(np.float16)
+  if weight_content is None:
+    # If the weight data is not stored in the flatbuffer (e.g. dynamic weights),
+    # skip quantization for this tensor.
+    op2weight_params = qtyping.OpToTensorParams(
+        subgraph_op_id=op_info.subgraph_op_index,
+        transformations=[_QuantTransformation.NO_QUANTIZE],
     )
-    tensor_quant_params_cache.insert(
-        weight_tensor.buffer, _FP16_QUANT_CONFIG, quant_params
+  else:
+    if not (
+        quant_params := tensor_quant_params_cache.lookup(
+            weight_tensor.buffer, _FP16_QUANT_CONFIG
+        )
+    ):
+      quant_params = qtyping.NonLinearQuantParams(
+          num_bits=16, quantized_data=weight_content.astype(np.float16)
+      )
+      tensor_quant_params_cache.insert(
+          weight_tensor.buffer, _FP16_QUANT_CONFIG, quant_params
+      )
+    op2weight_params = qtyping.OpToTensorParams(
+        subgraph_op_id=op_info.subgraph_op_index,
+        parameters=quant_params,
+        transformations=[_QuantTransformation.ADD_DEQUANTIZE],
     )
-  op2weight_params = qtyping.OpToTensorParams(
-      subgraph_op_id=op_info.subgraph_op_index,
-      parameters=quant_params,
-      transformations=[_QuantTransformation.ADD_DEQUANTIZE],
-  )
   op_tensor_params.append(
       qtyping.TensorTransformationParams(
           tensor_name=tfl_flatbuffer_utils.get_tensor_name(weight_tensor),
diff --git a/ai_edge_quantizer/algorithms/nonlinear_quantize/float_casting_test.py b/ai_edge_quantizer/algorithms/nonlinear_quantize/float_casting_test.py
index 39deb0a..276c23d 100644
--- a/ai_edge_quantizer/algorithms/nonlinear_quantize/float_casting_test.py
+++ b/ai_edge_quantizer/algorithms/nonlinear_quantize/float_casting_test.py
@@ -14,6 +14,7 @@
 # ==============================================================================
 
 import pathlib
+from unittest import mock
 
 from absl.testing import absltest
 from absl.testing import parameterized
@@ -335,6 +336,50 @@ def test_conv2d_weight_only_succeeds(self):
         float_casting.materialize_fc_conv,
     )
 
+  def test_conv2d_dynamic_weight_succeeds(self):
+    # Read from Model Explorer.
+    subgraph0 = self._test_model.subgraphs[0]
+    subgraph_op_id = 0
+    op = subgraph0.operators[subgraph_op_id]
+
+    op_info = qtyping.OpInfo(
+        op=op,
+        op_name=_TFLOpName.CONV_2D,
+        subgraph_op_index=subgraph_op_id,
+        op_quant_config=qtyping.OpQuantizationConfig(
+            weight_tensor_config=_TensorQuantConfig(
+                num_bits=16, dtype=qtyping.TensorDataType.FLOAT
+            ),
+            compute_precision=_ComputePrecision.FLOAT,  # WEIGHT_ONLY.
+            explicit_dequantize=True,
+        ),
+    )
+
+    with mock.patch.object(
+        tfl_flatbuffer_utils, "get_tensor_data", return_value=None
+    ):
+      tensor_quant_params = float_casting.materialize_fc_conv(
+          op_info,
+          self._graph_info,
+          self._tensor_name_to_qsv,
+          tensor_quant_params_cache=common_utils.TensorQuantParamsCache(),
+      )
+
+    # 4 tensors (input, weight, bias, output)
+    self.assertLen(tensor_quant_params, 4)
+    # The weight tensor is at index 1.
+    weight_params = tensor_quant_params[1]
+    self.assertEqual(weight_params.tensor_name, "sequential/conv2d/Conv2D")
+    self.assertLen(weight_params.consumers, 1)
+
+    # Transformation should be NO_QUANTIZE since we mocked get_tensor_data to
+    # None.
+    op_params = weight_params.consumers[0]
+    self.assertSequenceEqual(
+        op_params.transformations, [_QuantTransformation.NO_QUANTIZE]
+    )
+    self.assertIsNone(op_params.parameters)
+
   @parameterized.named_parameters(
       dict(
           testcase_name="invalid_fc",
@@ -468,6 +513,70 @@ def test_conv2d_transpose_weight_only_succeeds(self):
           is_inbounding_tensor=True,
       )
 
+  def test_conv2d_transpose_dynamic_weight_succeeds(self):
+    # Read from Model Explorer.
+    test_model_path = str(
+        pathlib.Path(_TEST_DATA_PREFIX_PATH)
+        / "single_conv2d_transpose_bias.tflite"
+    )
+
+    test_model = tfl_flatbuffer_utils.read_model(test_model_path)
+    # The test model has one subgraph for now.
+    graph_info = qtyping.GraphInfo(
+        subgraph_tensors=test_model.subgraphs[0].tensors,
+        buffers=test_model.buffers,
+    )
+
+    subgraph0 = test_model.subgraphs[0]
+    subgraph_op_id = 0
+    op = subgraph0.operators[subgraph_op_id]
+
+    op_info = qtyping.OpInfo(
+        op=op,
+        op_name=_TFLOpName.CONV_2D_TRANSPOSE,
+        subgraph_op_index=subgraph_op_id,
+        op_quant_config=qtyping.OpQuantizationConfig(
+            weight_tensor_config=_TensorQuantConfig(
+                num_bits=16, dtype=qtyping.TensorDataType.FLOAT
+            ),
+            compute_precision=_ComputePrecision.FLOAT,  # WEIGHT_ONLY.
+            explicit_dequantize=True,
+        ),
+    )
+
+    with mock.patch.object(
+        tfl_flatbuffer_utils, "get_tensor_data", return_value=None
+    ):
+      tensor_quant_params = float_casting.materialize_conv2d_transpose(
+          op_info,
+          graph_info,
+          self._tensor_name_to_qsv,
+          tensor_quant_params_cache=common_utils.TensorQuantParamsCache(),
+      )
+
+    _, _, bias_tensor, _ = tfl_flatbuffer_utils.parse_fc_bmm_conv_tensors(
+        op_info.op, graph_info.subgraph_tensors
+    )
+
+    num_configs = 4 if bias_tensor is not None else 3
+    self.assertLen(tensor_quant_params, num_configs)
+
+    # The weight tensor is at index 1.
+    weight_params = tensor_quant_params[1]
+    self.assertEqual(
+        weight_params.tensor_name,
+        "sequential_5/conv2d_transpose_3/conv2d_transpose",
+    )
+    self.assertLen(weight_params.consumers, 1)
+
+    # Transformation should be NO_QUANTIZE since we mocked get_tensor_data to
+    # None.
+    op_params = weight_params.consumers[0]
+    self.assertSequenceEqual(
+        op_params.transformations, [_QuantTransformation.NO_QUANTIZE]
+    )
+    self.assertIsNone(op_params.parameters)
+
   def test_depthwise_conv2d_weight_only_succeeds(self):
     # Read from Model Explorer.
     test_model_path = str(