diff --git a/lib/Conversion/TorchToLinalg/Linear.cpp b/lib/Conversion/TorchToLinalg/Linear.cpp
index a4962d12abdc0..9e74b66493ead 100644
--- a/lib/Conversion/TorchToLinalg/Linear.cpp
+++ b/lib/Conversion/TorchToLinalg/Linear.cpp
@@ -832,6 +832,12 @@ class ConvertAtenConvolutionOp : public OpConversionPattern<AtenConvolutionOp> {
           op, "only support padding from a list construct");
     paddingIntValues = getTypeConvertedValues(rewriter, loc, getTypeConverter(),
                                               paddingIntValues);
+    if (paddingIntValues.size() == 1) {
+      for (size_t iDim = 1; iDim < numSpatialDims; iDim++) {
+        paddingIntValues.push_back(paddingIntValues[0]);
+      }
+    }
+
     SmallVector<Value> outputPaddingIntValues;
     if (!getListConstructElements(op.getOutputPadding(),
                                   outputPaddingIntValues))
diff --git a/lib/Conversion/TorchToStablehlo/Linear.cpp b/lib/Conversion/TorchToStablehlo/Linear.cpp
index b42ed7cc77227..88617f139c96c 100644
--- a/lib/Conversion/TorchToStablehlo/Linear.cpp
+++ b/lib/Conversion/TorchToStablehlo/Linear.cpp
@@ -750,6 +750,11 @@ class ConvertAtenConvolutionOp : public ConvertAtenOp<AtenConvolutionOp> {
       return rewriter.notifyMatchFailure(op,
                                          "non-const padding list unsupported");
     }
+    if (padding.size() == 1) {
+      for (auto iDim = 1; iDim < inputTy.getRank() - 2; iDim++) {
+        padding.push_back(padding[0]);
+      }
+    }
     SmallVector<int64_t> dilation;
     if (!matchPattern(op.getDilation(), m_TorchListOfConstantInts(dilation))) {
       return rewriter.notifyMatchFailure(op,
diff --git a/lib/Conversion/TorchToTosa/TorchToTosa.cpp b/lib/Conversion/TorchToTosa/TorchToTosa.cpp
index e5f4fea4f46c5..7539bf49bf9dd 100644
--- a/lib/Conversion/TorchToTosa/TorchToTosa.cpp
+++ b/lib/Conversion/TorchToTosa/TorchToTosa.cpp
@@ -2163,6 +2163,9 @@ LogicalResult ConvertAtenOp<AtenConvolutionOp>::matchAndRewrite(
                     m_TorchListOfConstantInts(padding_2d)))
     return rewriter.notifyMatchFailure(op,
                                        "non-const padding list unsupported");
+  if (padding_2d.size() == 1) {
+    padding_2d.push_back(padding_2d[0]);
+  }
   // TOSA uses 4D padding {t, b, l, r} while Torch defines 2D padding {t, l}.
   // The Torch OFM computation uses 2*pad in each spatial direction, implying
   // the same t=b and l=r values for TOSA.
diff --git a/projects/pt1/e2e_testing/xfail_sets.py b/projects/pt1/e2e_testing/xfail_sets.py
index e7512fc89e982..179cd15e4c95c 100644
--- a/projects/pt1/e2e_testing/xfail_sets.py
+++ b/projects/pt1/e2e_testing/xfail_sets.py
@@ -29,6 +29,9 @@
     "DeformConv2D_basic",
     "ReduceAnyDimFloatModule_basic",
     "UnfoldModule_basic",
+    # TorchScript to the backend contract fails for conv.padding specified as str
+    "Conv2dWithValidPaddingModule_basic",
+    "Conv2dWithSamePaddingModule_basic",
 }
 
 if torch_version_for_comparison() < version.parse("2.5.0.dev"):
diff --git a/projects/pt1/python/torch_mlir_e2e_test/test_suite/conv.py b/projects/pt1/python/torch_mlir_e2e_test/test_suite/conv.py
index 3bc1760489460..25f4acddcd03d 100644
--- a/projects/pt1/python/torch_mlir_e2e_test/test_suite/conv.py
+++ b/projects/pt1/python/torch_mlir_e2e_test/test_suite/conv.py
@@ -191,6 +191,58 @@ def Conv2dWithPaddingDilationStrideStaticModule_grouped_multiplier(
     module.forward(tu.rand(5, 4, 10, 20))
 
 
+class Conv2dWithValidPaddingModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        torch.manual_seed(0)
+        self.conv = torch.nn.Conv2d(
+            1, 1, 1, stride=[1, 1], padding="valid", dilation=[1, 1], groups=1, bias=1
+        )
+        self.train(False)
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([1, 5, 6], torch.float32, True),
+        ]
+    )
+    def forward(self, x):
+        return self.conv(x)
+
+
+@register_test_case(module_factory=lambda: Conv2dWithValidPaddingModule())
+def Conv2dWithValidPaddingModule_basic(module, tu: TestUtils):
+    t = tu.rand(1, 5, 6)
+    module.forward(t)
+
+
+class Conv2dWithSamePaddingModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        torch.manual_seed(0)
+        self.conv = torch.nn.Conv2d(
+            1, 1, 1, stride=[1, 1], padding="same", dilation=[1, 1], groups=1, bias=1
+        )
+        self.train(False)
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([1, 5, 6], torch.float32, True),
+        ]
+    )
+    def forward(self, x):
+        return self.conv(x)
+
+
+@register_test_case(module_factory=lambda: Conv2dWithSamePaddingModule())
+def Conv2dWithSamePaddingModule_basic(module, tu: TestUtils):
+    t = tu.rand(1, 5, 6)
+    module.forward(t)
+
+
 # ==============================================================================