diff --git a/onnxruntime/contrib_ops/cuda/diffusion/bias_add.cc b/onnxruntime/contrib_ops/cuda/diffusion/bias_add.cc
index 6c3e8d7c321aa..5d5183221eda4 100644
--- a/onnxruntime/contrib_ops/cuda/diffusion/bias_add.cc
+++ b/onnxruntime/contrib_ops/cuda/diffusion/bias_add.cc
@@ -32,7 +32,7 @@ BiasAdd<T>::BiasAdd(const OpKernelInfo& op_info) : CudaKernel(op_info) {
 template <typename T>
 Status BiasAdd<T>::ComputeInternal(OpKernelContext* context) const {
   // Input:  [batch_size, height*width, channels]
-  // Bias:   [hidden_size]
+  // Bias:   [channels]
   // Skip:   [batch_size, height*width, channels]
   // Output: [batch_size, height*width, channels]
 
diff --git a/onnxruntime/contrib_ops/cuda/diffusion/bias_add_impl.cu b/onnxruntime/contrib_ops/cuda/diffusion/bias_add_impl.cu
index 0bc635dd85cbb..2983cc99e30b1 100644
--- a/onnxruntime/contrib_ops/cuda/diffusion/bias_add_impl.cu
+++ b/onnxruntime/contrib_ops/cuda/diffusion/bias_add_impl.cu
@@ -23,6 +23,8 @@
 #include "core/providers/cuda/cu_inc/common.cuh"
 #include "contrib_ops/cuda/diffusion/bias_add_impl.h"
 
+using namespace onnxruntime::cuda;
+
 namespace onnxruntime {
 namespace contrib {
 namespace cuda {
@@ -34,11 +36,7 @@ __global__ void BiasAddKernel(T const* input, T const* bias, T const* residual,
 
 #pragma unroll
   for (int32_t i = 0; i < C / TPB; ++i) {
-#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 530)
     output[base_offset] = input[base_offset] + bias[bias_offset] + residual[base_offset];
-#else
-    output[base_offset] = static_cast<T>(float(input[base_offset]) + float(bias[bias_offset]) + float(residual[base_offset]));
-#endif
     base_offset += TPB;
     bias_offset += TPB;
   }
diff --git a/onnxruntime/contrib_ops/cuda/diffusion/bias_split_gelu_impl.cu b/onnxruntime/contrib_ops/cuda/diffusion/bias_split_gelu_impl.cu
index 3cb95dad26b36..8069cbc0a1e0e 100644
--- a/onnxruntime/contrib_ops/cuda/diffusion/bias_split_gelu_impl.cu
+++ b/onnxruntime/contrib_ops/cuda/diffusion/bias_split_gelu_impl.cu
@@ -23,6 +23,8 @@
 #include "core/providers/cuda/cu_inc/common.cuh"
 #include "contrib_ops/cuda/diffusion/bias_split_gelu_impl.h"
 
+using namespace onnxruntime::cuda;
+
 namespace onnxruntime {
 namespace contrib {
 namespace cuda {
@@ -35,13 +37,9 @@ __global__ void biasSplitGeluKernel(T const* input, T const* bias, T* output) {
 
 #pragma unroll
   for (int32_t i = 0; i < HHS / TPB; ++i) {
-#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 530)
     auto value_left = (float)(input[index_input] + bias[index_bias]);
     auto value_right = (float)(input[index_input + HHS] + bias[index_bias + HHS]);
-#else
-    auto value_left = (float)(input[index_input]) + (float)(bias[index_bias]);
-    auto value_right = (float)(input[index_input + HHS]) + (float)(bias[index_bias + HHS]);
-#endif
+
     // Gelu is applied to right side only: Gelu(x) = x * 0.5 * (erf(x / sqrt(2)) + 1.0)
     float gelu_right = value_right * 0.5f * (erff(value_right / 1.41421356237f) + 1.0f);
     float result = value_left * gelu_right;
diff --git a/onnxruntime/python/tools/transformers/float16.py b/onnxruntime/python/tools/transformers/float16.py
index 1b057c0d4deb5..a7904c39f8491 100644
--- a/onnxruntime/python/tools/transformers/float16.py
+++ b/onnxruntime/python/tools/transformers/float16.py
@@ -202,8 +202,10 @@ def convert_float_to_float16(
     value_info_list = []
     node_list = []
 
-    # List of Resize or GroupNorm nodes that is not in block list
-    resize_node_list = []
+    # Some operators (Like Resize or GroupNorm) have data type fixed as float for some input.
+    # When it is converted to float16, there are mixed types: some inputs are float32 and some are float16.
+    # This list keeps track of such nodes that are not in block list.
+    mixed_float_type_node_list = []
 
     # type inference on input model
     if func_infer_shape is not None:
@@ -303,7 +305,7 @@ def convert_float_to_float16(
                             for attr in n.attribute:
                                 next_level.append(attr)
                         else:
-                            resize_node_list.append(n)
+                            mixed_float_type_node_list.append(n)
 
             # if q is model.graph.node.attribute, push q.g and q.graphs (GraphProto)
             # and process node.attribute.t and node.attribute.tensors (TensorProto)
@@ -344,8 +346,8 @@ def convert_float_to_float16(
                     )
                 )
 
-    # Some operators has data type fixed as float for some input. Add a float16 to float cast for those inputs.
-    for node in resize_node_list:
+    # Some operators have data type fixed as float for some input. Add a float16 to float cast for those inputs.
+    for node in mixed_float_type_node_list:
         for i, input_name in enumerate(node.input):
             if i not in ALWAYS_FLOAT_INPUTS[node.op_type]:
                 continue