diff --git a/test/cpp/test_aten_xla_tensor.cpp b/test/cpp/test_aten_xla_tensor.cpp
index 34a3da5e7ebb..54cbc12ae1e9 100644
--- a/test/cpp/test_aten_xla_tensor.cpp
+++ b/test/cpp/test_aten_xla_tensor.cpp
@@ -4311,6 +4311,47 @@ TEST_F(AtenXlaTensorTest, TestUpsampleBilinear2D) {
                        cpp_test::GetIgnoredCounters());
 }
 
+TEST_F(AtenXlaTensorTest, TestUpsampleBilinear2DWithScale) {
+  struct ImageInfo {
+    int batch_size;
+    int h;
+    int w;
+    int chans;
+    double scale_h;
+    double scale_w;
+  };
+
+  /* clang-format off */
+  std::vector<ImageInfo> inputs = {
+    {/*batch_size=*/2, /*h=*/5, /*w=*/5, /*chans=*/2, /*scale_h*/8.0/5, /*scale_w*/8.0/5},
+    {/*batch_size=*/2, /*h=*/1335, /*w=*/1335, /*chans=*/3, /*scale_h*/255.0/1335, /*scale_w*/255.0/1335},
+    {/*batch_size=*/2, /*h=*/255, /*w=*/255, /*chans=*/3, /*scale_h*/1335.0/255, /*scale_w*/1335.0/255},
+    {/*batch_size=*/2, /*h=*/254, /*w=*/243, /*chans=*/3, /*scale_h*/784.0/254, /*scale_w*/214.0/243}
+  };
+  /* clang-format on */
+
+  for (const auto& img_info : inputs) {
+    for (bool align_corners : {true, false}) {
+      torch::Tensor input = torch::rand(
+          {img_info.batch_size, img_info.chans, img_info.h, img_info.w},
+          torch::TensorOptions(torch::kFloat));
+      ForEachDevice([&](const torch::Device& device) {
+        torch::Tensor xla_input = CopyToDevice(input, device);
+        torch::Tensor result = torch::upsample_bilinear2d(
+            input, c10::nullopt, align_corners,
+            at::ArrayRef<double>{img_info.scale_h, img_info.scale_w});
+        torch::Tensor xla_result = torch::upsample_bilinear2d(
+            xla_input, c10::nullopt, align_corners,
+            at::ArrayRef<double>{img_info.scale_h, img_info.scale_w});
+        AllClose(result, xla_result, /*rtol=*/1e-4, /*atol=*/1e-4);
+      });
+    }
+  }
+  ExpectCounterNotChanged("aten::.*", cpp_test::GetIgnoredCounters());
+  ExpectCounterChanged("xla::upsample_bilinear2d",
+                       cpp_test::GetIgnoredCounters());
+}
+
 TEST_F(AtenXlaTensorTest, TestUpsampleBilinear2DBackward) {
   int batch_size = 2;
   int h = 5;
diff --git a/torch_xla/csrc/aten_xla_type.cpp b/torch_xla/csrc/aten_xla_type.cpp
index d12955e501af..906d4e0fae85 100644
--- a/torch_xla/csrc/aten_xla_type.cpp
+++ b/torch_xla/csrc/aten_xla_type.cpp
@@ -2904,16 +2904,21 @@ at::Tensor XLANativeFunctions::upsample_bilinear2d(
     c10::optional<double> scales_h, c10::optional<double> scales_w) {
   TORCH_LAZY_FN_COUNTER("xla::");
   XLATensorPtr self_tensor = bridge::GetXlaTensor(self);
+  absl::Span<const int64_t> input_dims =
+      self_tensor->shape().get().dimensions();
+  std::vector<int64_t> scaled_output_size =
+      torch::lazy::ToVector<int64_t>(output_size);
   if ((scales_h && *scales_h != 1.0) || (scales_w && *scales_w != 1.0)) {
-    return at::native::call_fallback_fn<
-        &xla_cpu_fallback, ATEN_OP(upsample_bilinear2d)>::call(self,
-                                                               output_size,
-                                                               align_corners,
-                                                               scales_h,
-                                                               scales_w);
+    scaled_output_size = GetOutputSizeWithScale(input_dims, scales_h, scales_w,
+                                                scaled_output_size);
+    if (!output_size.empty()) {
+      XLA_CHECK(scaled_output_size.at(0) == output_size.at(0) &&
+                scaled_output_size.at(1) == output_size.at(1))
+          << "Inferred output size and output_size from upstream are different";
+    }
   }
   return bridge::AtenFromXlaTensor(tensor_methods::upsample_bilinear2d(
-      self_tensor, torch::lazy::ToVector<int64_t>(output_size), align_corners));
+      self_tensor, scaled_output_size, align_corners));
 }
 
 at::Tensor XLANativeFunctions::upsample_bilinear2d_backward(