diff --git a/onnxruntime/core/session/inference_session.cc b/onnxruntime/core/session/inference_session.cc
index 123252badc7f0..d7188a60d376d 100644
--- a/onnxruntime/core/session/inference_session.cc
+++ b/onnxruntime/core/session/inference_session.cc
@@ -228,11 +228,17 @@ static bool AreAllComputeNodesAssignedToEpOrCpu(const Graph& graph, ProviderType
     }
   }
 
-  // Require at least one node on the target EP, and no Memcpy nodes.
-  // We allow CPU EPs to show up in the EP list as long as there is no Memcpy
-  // involved as shape subgraphs will be forced onto CPU and these will not have
-  // Memcpy nodes involved.
-  return has_node_on_provider && !HasMemcpyNodes(graph);
+  // Allow graph capture when there are no Memcpy nodes and either:
+  //   (a) at least one node is assigned to the target EP, or
+  //   (b) the graph has no nodes at all.
+  // Case (b) covers models that are fully consumed by the EP (e.g. runtime graph
+  // fusion) or trivially fold away to an empty graph (e.g. a lone Constant node,
+  // as used for allocator-initialization sessions). Such graphs have nothing that
+  // violates the "all compute on the EP or CPU" requirement, so they must not be
+  // rejected merely because no node remains assigned to the EP.
+  // CPU EPs are allowed to show up as long as there is no Memcpy involved, since
+  // shape subgraphs are forced onto CPU and do not introduce Memcpy nodes.
+  return (has_node_on_provider || graph.NumberOfNodes() == 0) && !HasMemcpyNodes(graph);
 }
 
 static bool AreAllNodesInMainGraphAssignedToOneEp(const Graph& graph, ProviderType provider) {
diff --git a/onnxruntime/test/shared_lib/test_inference.cc b/onnxruntime/test/shared_lib/test_inference.cc
index 809d1b903e8cd..e3227ec2e24b4 100644
--- a/onnxruntime/test/shared_lib/test_inference.cc
+++ b/onnxruntime/test/shared_lib/test_inference.cc
@@ -2506,7 +2506,8 @@ TEST(CApiTest, basic_cuda_graph) {
   Ort::ThrowOnError(api.GetExecutionProviderApi("DML", ORT_API_VERSION, reinterpret_cast<const void**>(&ort_dml_api)));
 
   auto dml_objects = CreateDmlObjects();
-  ort_dml_api->SessionOptionsAppendExecutionProvider_DML1(session_options, dml_objects.dml_device.Get(), dml_objects.command_queue.Get());
+  Ort::ThrowOnError(ort_dml_api->SessionOptionsAppendExecutionProvider_DML1(
+      session_options, dml_objects.dml_device.Get(), dml_objects.command_queue.Get()));
 #endif
 
   Ort::Session session(*ort_env, MODEL_URI, session_options);
@@ -2618,6 +2619,44 @@ TEST(CApiTest, basic_cuda_graph) {
   binding.ClearBoundOutputs();
 }
 
+#if defined(USE_DML)
+// Regression test for graph capture on a model that folds to an *empty* graph.
+// The model below is a single Constant node, which constant-folding removes,
+// leaving zero compute nodes. GenAI creates such an allocator-initialization
+// session with DML graph capture enabled. Previously the graph-capture selection
+// logic rejected an empty graph (because no node was assigned to the EP) and threw
+// "all compute graph nodes have not been partitioned to the DmlExecutionProvider".
+// An empty graph has nothing that violates the requirement, so it must succeed.
+TEST(CApiTest, DmlGraphCaptureEmptyGraph) {
+  // A minimal model consisting of a single Constant node producing "values".
+  // Constant folding removes the node, leaving an empty graph.
+  static const uint8_t constant_only_model[] = {
+      0x08, 0x0a, 0x3a, 0x5b, 0x0a, 0x31, 0x12, 0x06, 0x76, 0x61, 0x6c, 0x75,
+      0x65, 0x73, 0x22, 0x08, 0x43, 0x6f, 0x6e, 0x73, 0x74, 0x61, 0x6e, 0x74,
+      0x2a, 0x1d, 0x0a, 0x05, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2a, 0x11, 0x08,
+      0x01, 0x10, 0x01, 0x22, 0x04, 0x00, 0x00, 0x80, 0x3f, 0x42, 0x05, 0x76,
+      0x61, 0x6c, 0x75, 0x65, 0xa0, 0x01, 0x04, 0x12, 0x10, 0x74, 0x72, 0x69,
+      0x76, 0x69, 0x61, 0x6c, 0x5f, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x61, 0x6e,
+      0x74, 0x62, 0x14, 0x0a, 0x06, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x73, 0x12,
+      0x0a, 0x0a, 0x08, 0x08, 0x01, 0x12, 0x04, 0x0a, 0x02, 0x08, 0x01, 0x42,
+      0x04, 0x0a, 0x00, 0x10, 0x0d};
+
+  const auto& api = Ort::GetApi();
+  Ort::SessionOptions session_options;
+  session_options.AddConfigEntry("ep.dml.enable_graph_capture", "1");
+  const OrtDmlApi* ort_dml_api;
+  Ort::ThrowOnError(api.GetExecutionProviderApi("DML", ORT_API_VERSION, reinterpret_cast<const void**>(&ort_dml_api)));
+
+  auto dml_objects = CreateDmlObjects();
+  Ort::ThrowOnError(ort_dml_api->SessionOptionsAppendExecutionProvider_DML1(
+      session_options, dml_objects.dml_device.Get(), dml_objects.command_queue.Get()));
+
+  EXPECT_NO_THROW({
+    Ort::Session session(*ort_env, constant_only_model, sizeof(constant_only_model), session_options);
+  });
+}
+#endif  // defined(USE_DML)
+
 #if defined(USE_CUDA) || defined(USE_DML)
 struct CudaGraphInputOutputData_0 {
   const std::array<int64_t, 2> x_shape = {3, 2};
@@ -2786,7 +2825,8 @@ TEST(CApiTest, basic_cuda_graph_with_annotation) {
   const OrtDmlApi* ort_dml_api;
   Ort::ThrowOnError(api.GetExecutionProviderApi("DML", ORT_API_VERSION, reinterpret_cast<const void**>(&ort_dml_api)));
   auto dml_objects = CreateDmlObjects();
-  ort_dml_api->SessionOptionsAppendExecutionProvider_DML1(session_options, dml_objects.dml_device.Get(), dml_objects.command_queue.Get());
+  Ort::ThrowOnError(ort_dml_api->SessionOptionsAppendExecutionProvider_DML1(
+      session_options, dml_objects.dml_device.Get(), dml_objects.command_queue.Get()));
 
   Ort::MemoryInfo info_mem("DML", OrtAllocatorType::OrtDeviceAllocator, 0, OrtMemTypeDefault);
 #elif defined(USE_CUDA)