diff --git a/examples/csmsc/vits/local/paddle2onnx.sh b/examples/csmsc/vits/local/paddle2onnx.sh
new file mode 120000
index 00000000..87c46634
--- /dev/null
+++ b/examples/csmsc/vits/local/paddle2onnx.sh
@@ -0,0 +1 @@
+../../tts3/local/paddle2onnx.sh
\ No newline at end of file
diff --git a/examples/csmsc/vits/run.sh b/examples/csmsc/vits/run.sh
index ac190bfa..f2c5d452 100755
--- a/examples/csmsc/vits/run.sh
+++ b/examples/csmsc/vits/run.sh
@@ -39,3 +39,34 @@ fi
 if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
     CUDA_VISIBLE_DEVICES=${gpus} ./local/inference.sh ${train_output_path} ${add_blank}|| exit -1
 fi
+
+# # not ready yet for operator missing in Paddle2ONNX
+# # paddle2onnx, please make sure the static models are in ${train_output_path}/inference first
+# # we have only tested the following models so far
+# if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
+#     # install paddle2onnx
+#     version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}')
+#     if [[ -z "$version" || ${version} != '1.0.0' ]]; then
+#         pip install paddle2onnx==1.0.0
+#     fi
+#     ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx vits_csmsc
+# fi
+
+# # inference with onnxruntime
+# if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
+#     ./local/ort_predict.sh ${train_output_path}
+# fi
+
+# # not ready yet for operator missing in Paddle-Lite
+# # must run after stage 3 (which stage generated static models)
+# if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then
+#     # NOTE by yuantian 2022.11.21: please compile develop version of Paddle-Lite to export and run TTS models,
+#     #                   cause TTS models are supported by https://github.com/PaddlePaddle/Paddle-Lite/pull/9587 
+#     #                   and https://github.com/PaddlePaddle/Paddle-Lite/pull/9706
+#     ./local/export2lite.sh ${train_output_path} inference pdlite vits_csmsc x86
+# fi
+
+# if [ ${stage} -le 8 ] && [ ${stop_stage} -ge 8 ]; then
+#     CUDA_VISIBLE_DEVICES=${gpus} ./local/lite_predict.sh ${train_output_path} || exit -1
+# fi
+
diff --git a/paddlespeech/t2s/models/vits/generator.py b/paddlespeech/t2s/models/vits/generator.py
index 7ecc5161..fbd2d665 100644
--- a/paddlespeech/t2s/models/vits/generator.py
+++ b/paddlespeech/t2s/models/vits/generator.py
@@ -279,6 +279,10 @@ class VITSGenerator(nn.Layer):
         from paddlespeech.t2s.models.vits.monotonic_align import maximum_path
 
         self.maximum_path = maximum_path
+        self.pad1d = nn.Pad1D(
+            padding=[1, 0],
+            mode='constant',
+            data_format='NLC', )
 
     def forward(
             self,
@@ -685,5 +689,6 @@ class VITSGenerator(nn.Layer):
         '''
 
         path = paddle.cast(path, dtype='float32')
-        path = path - F.pad(path, [0, 0, 1, 0, 0, 0])[:, :-1]
+        pad_tmp = self.pad1d(path)[:, :-1]
+        path = path - pad_tmp
         return path.unsqueeze(1).transpose([0, 1, 3, 2]) * mask
diff --git a/paddlespeech/t2s/models/vits/transform.py b/paddlespeech/t2s/models/vits/transform.py
index ea333dcf..61bd5ee2 100644
--- a/paddlespeech/t2s/models/vits/transform.py
+++ b/paddlespeech/t2s/models/vits/transform.py
@@ -18,6 +18,7 @@ This code is based on https://github.com/bayesiains/nflows.
 """
 import numpy as np
 import paddle
+from paddle import nn
 from paddle.nn import functional as F
 
 from paddlespeech.t2s.modules.nets_utils import paddle_gather
@@ -87,9 +88,9 @@ def unconstrained_rational_quadratic_spline(
     outputs = paddle.zeros(inputs.shape)
     logabsdet = paddle.zeros(inputs.shape)
     if tails == "linear":
-        unnormalized_derivatives = F.pad(
-            unnormalized_derivatives,
-            pad=[0] * (len(unnormalized_derivatives.shape) - 1) * 2 + [1, 1])
+        # 注意 padding 的参数顺序
+        pad2d = nn.Pad2D(padding=[1, 1, 0, 0], mode='constant')
+        unnormalized_derivatives = pad2d(unnormalized_derivatives)
         constant = np.log(np.exp(1 - min_derivative) - 1)
         unnormalized_derivatives[..., 0] = constant
         unnormalized_derivatives[..., -1] = constant
@@ -142,6 +143,10 @@ def rational_quadratic_spline(
     # for dygraph to static
     # if paddle.min(inputs) < left or paddle.max(inputs) > right:
     #     raise ValueError("Input to a transform is not within its domain")
+    pad1d = nn.Pad1D(
+        padding=[1, 0],
+        mode='constant',
+        data_format='NCL', )
 
     num_bins = unnormalized_widths.shape[-1]
     # for dygraph to static
@@ -153,11 +158,8 @@ def rational_quadratic_spline(
     widths = F.softmax(unnormalized_widths, axis=-1)
     widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
     cumwidths = paddle.cumsum(widths, axis=-1)
-    cumwidths = F.pad(
-        cumwidths,
-        pad=[0] * (len(cumwidths.shape) - 1) * 2 + [1, 0],
-        mode="constant",
-        value=0.0)
+
+    cumwidths = pad1d(cumwidths.unsqueeze(0)).squeeze()
     cumwidths = (right - left) * cumwidths + left
     cumwidths[..., 0] = left
     cumwidths[..., -1] = right
@@ -168,11 +170,7 @@ def rational_quadratic_spline(
     heights = F.softmax(unnormalized_heights, axis=-1)
     heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
     cumheights = paddle.cumsum(heights, axis=-1)
-    cumheights = F.pad(
-        cumheights,
-        pad=[0] * (len(cumheights.shape) - 1) * 2 + [1, 0],
-        mode="constant",
-        value=0.0)
+    cumheights = pad1d(cumheights.unsqueeze(0)).squeeze()
     cumheights = (top - bottom) * cumheights + bottom
     cumheights[..., 0] = bottom
     cumheights[..., -1] = top