fix: unwrap scalar remote prompt inputs

README.md

@@ -79,7 +79,7 @@ Remote mode requires:
 
 The normal remote path is deployed-app lookup by name. On first remote use, the extension can auto-deploy the stable cloud entrypoint from [`comfyui_modal_sync_cloud.py`](comfyui_modal_sync_cloud.py) if the configured app does not exist yet.
 If a previous attempt imported that cloud entrypoint only partially before failing, the loader now discards the stale cached module and retries the import cleanly before auto-deploy.
-The local and remote subgraph runners also canonicalize malformed singleton-list socket indexes like `["node", [0]]` back to `["node", 0]` before executing a rewritten prompt.
+The local and remote subgraph runners also canonicalize malformed singleton-list wrappers in rewritten prompts, including socket indexes like `["node", [0]]`, scalar inputs like `[4]`, and wrapped execute node ids like `["12"]`, before handing the prompt to ComfyUI.
 
 ```bash
 export COMFY_MODAL_EXECUTION_MODE=remote

comfyui_modal_sync_cloud.py

@@ -1190,6 +1190,17 @@ def _normalize_link_output_index(value: Any) -> Any:
     return value
 
 
+def _normalize_prompt_input_value(value: Any) -> Any:
+    """Unwrap transport-added singleton lists around scalar prompt input values."""
+    if isinstance(value, list) and len(value) == 2 and isinstance(value[0], str):
+        return [value[0], _normalize_link_output_index(value[1])]
+    if isinstance(value, list) and len(value) == 1:
+        candidate = value[0]
+        if candidate is None or isinstance(candidate, bool | int | float | str):
+            return candidate
+    return value
+
+
 def _normalize_subgraph_payload(payload: dict[str, Any]) -> dict[str, Any]:
     """Return a subgraph payload with canonical prompt-link and output-index shapes."""
     normalized_payload = copy.deepcopy(payload)
@@ -1197,21 +1208,21 @@ def _normalize_subgraph_payload(payload: dict[str, Any]) -> dict[str, Any]:
     for node_info in normalized_payload.get("subgraph_prompt", {}).values():
         inputs = node_info.get("inputs") or {}
         for input_name, input_value in list(inputs.items()):
-            if not isinstance(input_value, list) or len(input_value) != 2:
-                continue
-            if not isinstance(input_value[0], str):
-                continue
-            inputs[input_name] = [
-                input_value[0],
-                _normalize_link_output_index(input_value[1]),
-            ]
+            inputs[input_name] = _normalize_prompt_input_value(input_value)
 
     for boundary_output in normalized_payload.get("boundary_outputs", []):
+        if "node_id" in boundary_output and isinstance(boundary_output["node_id"], list):
+            boundary_output["node_id"] = _normalize_prompt_input_value(boundary_output["node_id"])
         if "output_index" in boundary_output:
             boundary_output["output_index"] = _normalize_link_output_index(
                 boundary_output["output_index"]
             )
 
+    normalized_payload["execute_node_ids"] = [
+        _normalize_prompt_input_value(node_id)
+        for node_id in normalized_payload.get("execute_node_ids", [])
+    ]
+
     return normalized_payload
 
 

docs/LEARNINGS.md

@@ -82,7 +82,7 @@
 - Hybrid mapped components also need stream-level UI filtering. A warm PromptExecutor can still emit cached `executed` or preview UI events for static sibling nodes during per-item mapped runs, so the local relay has to drop any streamed UI event whose node id is outside the current mapped/static payload's execute and boundary-output node set.
 - Modal proxy nodes that sit on a mapped boundary must opt into ComfyUI `INPUT_IS_LIST` semantics themselves. Otherwise ComfyUI can auto-map the proxy once per item before Modal-Sync sees the batch, which makes the entire hybrid component rerun per item instead of letting the internal mapped scheduler split only the intended branch.
 - Once a proxy opts into `INPUT_IS_LIST`, ComfyUI also wraps the proxy's hidden payload input itself. The proxy has to unwrap singleton lists on `original_node_data` as well as ordinary kwargs, or it will reject valid rewritten payloads with a misleading "must be a mapping" error before remote execution even starts.
-- Rewritten remote prompts need a last-mile canonicalization pass before execution. If a socket index leaks through as `["node", [0]]` or a boundary output index arrives as `[0]`, ComfyUI's executor eventually crashes trying to treat the list like an integer; normalizing those singleton wrappers on both the local and Modal-side runners avoids that failure mode.
+- Rewritten remote prompts need a last-mile canonicalization pass before execution. If a socket index leaks through as `["node", [0]]`, a scalar input arrives as `[4]`, or an execute node id arrives as `["12"]`, ComfyUI's executor eventually crashes trying to treat the list like an integer or node id; normalizing those singleton wrappers on both the local and Modal-side runners avoids that failure mode.
 - Content-addressed sync should trust the deterministic payload path, not only the sidecar marker. If a crash happens after uploading a hash-named asset but before writing its `.done` marker, the next run should backfill the marker and reuse the existing payload instead of trying the same upload again and failing with `FileExistsError`.
 - The streamed Modal generator path needs the same tensor-aware serialization rules as ordinary node input/output transport. If a progress or result envelope leaks a raw `torch.Tensor`, Modal's JSON encoding fails before the local client ever sees it, so the stream layer should normalize event payloads through the shared serializer too.
 - Modal volume metadata calls can become a bottleneck on their own. Repeated `VolumeListFiles` checks for the same marker path are worth memoizing for the life of the queue request, and transient `ResourceExhaustedError` responses should be retried with backoff instead of surfacing immediately as hard prompt failures.

remote/modal_app.py

@@ -271,6 +271,17 @@ def _normalize_link_output_index(value: Any) -> Any:
     return value
 
 
+def _normalize_prompt_input_value(value: Any) -> Any:
+    """Unwrap transport-added singleton lists around scalar prompt input values."""
+    if isinstance(value, list) and len(value) == 2 and isinstance(value[0], str):
+        return [value[0], _normalize_link_output_index(value[1])]
+    if isinstance(value, list) and len(value) == 1:
+        candidate = value[0]
+        if candidate is None or isinstance(candidate, bool | int | float | str):
+            return candidate
+    return value
+
+
 def _normalize_subgraph_payload(payload: dict[str, Any]) -> dict[str, Any]:
     """Return a subgraph payload with canonical prompt-link and output-index shapes."""
     normalized_payload = copy.deepcopy(payload)
@@ -278,21 +289,21 @@ def _normalize_subgraph_payload(payload: dict[str, Any]) -> dict[str, Any]:
     for node_info in normalized_payload.get("subgraph_prompt", {}).values():
         inputs = node_info.get("inputs") or {}
         for input_name, input_value in list(inputs.items()):
-            if not isinstance(input_value, list) or len(input_value) != 2:
-                continue
-            if not isinstance(input_value[0], str):
-                continue
-            inputs[input_name] = [
-                input_value[0],
-                _normalize_link_output_index(input_value[1]),
-            ]
+            inputs[input_name] = _normalize_prompt_input_value(input_value)
 
     for boundary_output in normalized_payload.get("boundary_outputs", []):
+        if "node_id" in boundary_output and isinstance(boundary_output["node_id"], list):
+            boundary_output["node_id"] = _normalize_prompt_input_value(boundary_output["node_id"])
         if "output_index" in boundary_output:
             boundary_output["output_index"] = _normalize_link_output_index(
                 boundary_output["output_index"]
             )
 
+    normalized_payload["execute_node_ids"] = [
+        _normalize_prompt_input_value(node_id)
+        for node_id in normalized_payload.get("execute_node_ids", [])
+    ]
+
     return normalized_payload
 
 

tests/test_modal_executor_node.py

@@ -2529,3 +2529,42 @@ def test_local_remote_app_normalizes_wrapped_subgraph_link_indexes(
     )
     outputs = serialization_module.deserialize_node_outputs(payload)
     assert outputs == (10,)
+
+
+def test_local_remote_app_normalizes_wrapped_scalar_prompt_inputs(
+    remote_modal_app_module: Any,
+    serialization_module: Any,
+) -> None:
+    """The local fallback runner should unwrap singleton-list scalar prompt inputs."""
+    payload = remote_modal_app_module.execute_subgraph_locally(
+        payload={
+            "payload_kind": "subgraph",
+            "component_id": "component-1",
+            "subgraph_prompt": {
+                "remote_1": {
+                    "class_type": "BoundarySource",
+                    "inputs": {"value": [4]},
+                    "_meta": {},
+                }
+            },
+            "boundary_inputs": [],
+            "boundary_outputs": [
+                {
+                    "proxy_output_name": "remote_1_value",
+                    "node_id": "remote_1",
+                    "output_index": 0,
+                    "io_type": "INT",
+                    "is_list": False,
+                }
+            ],
+            "execute_node_ids": [["remote_1"]],
+            "extra_data": {},
+            "custom_nodes_bundle": None,
+        },
+        kwargs_payload="{}",
+        node_mapping={
+            "BoundarySource": _BoundarySourceNode,
+        },
+    )
+    outputs = serialization_module.deserialize_node_outputs(payload)
+    assert outputs == (5,)